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Root Ecology for Sustainable Agroecosystems: Intraspecific Variation in a Pan-Tropical Tree Crop
by
Kira Alia Borden
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Department of Geography & Planning University of Toronto
© Copyright by Kira Alia Borden 2018
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Root Ecology for Sustainable Agroecosystems:
Intraspecific Variation in a Pan-Tropical Tree Crop
Kira Alia Borden
Doctor of Philosophy
Department of Geography & Planning
University of Toronto
2018
Abstract
Agroecosystems that rely on higher levels of biodiversity and fewer inputs to sustain both crop
production and ecosystem function require a dramatic shift away from a one-size-fits-all
approach to management. Notably, this means that phenotypic expression of plants on farms
cannot be assumed constant, as biotic and abiotic conditions in low-input, biodiverse
agroecosystems are complex. To accurately assess and predict plant and ecosystem function in
these agroecosystems, we require a more robust understanding of the drivers and consequences
of intraspecific variation in plants. This includes root systems, which are arguably understudied
but have a critical role in resource acquisition and use. To this end, I carried out research on the
root systems of an economically important and widely cultivated tree crop, Theobroma cacao L.,
in different species combinations: in monoculture or in mixture with other tree species (shade
trees). I used a trait-based approach to examine how intraspecific variation in root systems relate
to environment, such as the composition of species, soil resource availability, soil texture, and
climate. I found that 1) species combination can influence the allocation of biomass to T. cacao
root systems, with a tendency for higher root to shoot ratios when in mixture with shade trees; 2)
T. cacao demonstrate nutrient-specific foraging strategies through variation in root traits at fine-
scales within the root system, and these patterns can be influenced by shade trees; 3) T. cacao
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fine roots respond to fertilization by shifting towards more conservative resource acquisition
strategies, but this response is mediated by species combination and varies with soil depth; and
4) root resource acquisition strategies of T. cacao are more conservative in an optimal resource-
rich environment compared to suboptimal (drier) environments, but patterned root trait variation
is modified by species combination within edaphic-specific contexts. The observed intraspecific
root variation shown in my research is extensive and follows global patterns of root trait
relationships. This plasticity can influence carbon stocks and nutrient use efficiencies on farms,
as well as crop resiliency in a changing climate. I interpret these findings for informing
management and species diversification in agroecosystems.
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Acknowledgments
First, and foremost, I thank Professor Marney Isaac for her exceptional guidance and
supervision. I have been so fortunate to be able to draw from her expertise and enthusiasm
throughout my PhD. I also thank my supervisory committee members, Professor Sean Thomas
and Professor Jing Chen, who have been exceptional in both supporting and challenging me
during my PhD. I thank Professor Shibu Jose and Professor Peter Kotanen for contributing their
expertise and serving as external examiners. Professor Tat Smith and Professor Adam Martin
have also provided extremely helpful guidance during various stages of my research. Dr. Luke
Anglaaere at CSIR-Forestry Research Institute of Ghana (FORIG) was instrumental in helping
coordinate field research. None of this research would have been possible with the support,
knowledge, and field assistance from the cocoa farming communities in Ghana that I was
fortunate to work with. This includes community members and farmers of South Formangso,
Dedease, Amafie, and Mampong, including Sasou, Thomas Owusu, and Badu Yeboah. I had
much needed assistance, support, and friendship from William Amprofo, Agyeman Kofi, Manu
Kusi Martin, Kirstie Cadger, and Alfred Owusu who will be warmly remembered as a caring
friend to the Isaac research group. I’d also like to thank Dr. Stephen Adu Bredu, Sandra Owusu,
and Emmanual Asiedu-Opoku at FORIG, Emmanuel Asiedu-Opoku at University of Education
Winneba, Mampong Campus, Daniel Akoto at University of Energy and Natural Resources,
Sunyani, and Gregory Chermogoh and Justice Niyuo at the Forestry Commission of Ghana,
Wiawso. I thank Professor Evans Dawoe at Kwame Nkrumah University of Science &
Technology and Dr. Eric Adjei at CSIR-Soil Research Institute, Kumasi, Ghana for laboratory
facilities and soil analysis. I also appreciate the help I received in the laboratory at University of
Toronto Scarborough, specifically from Chai Chen, Tom Meulendyk, Tony Adamo, Stephanie
Gagliardi, Serra Buchanan, and Luzianne Reid, and the numerous work study students and
volunteers who assisted with plant and soil analysis. Jessica Finlayson, Julie Quenneville, and
Jennifer Caradonna in the Departments of Geography & Planning and Physical & Environmental
Sciences have been wonderful in guiding me through my PhD programming. I am grateful for
funding support from the Natural Sciences and Engineering Research Council of Canada, the
Department of Geography & Planning, the Centre for Global Change Science, and the School of
Graduate Studies. And finally, I owe many thanks to my family and friends for their support
throughout my graduate school years.
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Table of Contents
Acknowledgments.......................................................................................................................... iv
Table of Contents .............................................................................................................................v List of Tables ............................................................................................................................... viii List of Plates ....................................................................................................................................x List of Figures ................................................................................................................................ xi List of Appendices ....................................................................................................................... xiv
Chapter 1 Introduction and literature review ...................................................................................1 1.1 Introduction ..........................................................................................................................1
1.1.1 Theoretical approach ................................................................................................3 1.2 Literature review: Applied root ecology ..............................................................................4
1.2.1 Phenotypic plasticity of roots...................................................................................5
1.2.2 Root functional traits..............................................................................................10 1.2.3 Root research methodology ...................................................................................13
1.2.4 Roots in agroecosystems ........................................................................................16 1.3 Case study: Theobroma cacao L. in Ghana .......................................................................19
1.3.1 Cultivation of T. cacao ..........................................................................................19 1.3.2 Cocoa agroecosystems ...........................................................................................20
1.3.3 The root system of T. cacao ...................................................................................22 1.3.4 Cultivation of T. cacao in Ghana ...........................................................................23 1.3.5 Environmental and production concerns ...............................................................25
1.4 Research objectives ............................................................................................................26 1.4.1 Research sites and study systems ...........................................................................27
1.5 Thesis structure ..................................................................................................................34
Chapter 2 Root biomass variation in Theobroma cacao and implications for carbon stocks in
agroforestry systems..................................................................................................................36 2.1 Abstract ..............................................................................................................................36
2.2 Introduction ........................................................................................................................37 2.3 Methods..............................................................................................................................40
2.3.1 Study site and study plants .....................................................................................40
2.3.2 Coarse root biomass estimation using GPR ...........................................................41 2.3.3 Sampling of coarse roots and whole plant excavations .........................................42
2.3.4 Biomass allocation calculations .............................................................................43 2.3.5 Biomass carbon calculations ..................................................................................43 2.3.6 Statistical analysis ..................................................................................................44
2.4 Results ................................................................................................................................45 2.4.1 Coarse root biomass estimation .............................................................................45
2.4.2 Biomass allocation .................................................................................................45 2.4.3 Biomass carbon ......................................................................................................49
2.5 Discussion ..........................................................................................................................52 2.5.1 Biomass carbon stocks in cocoa agroecosystems ..................................................52 2.5.2 Toward accuracy of carbon accounting in agroforestry systems ...........................53
2.6 Conclusions ........................................................................................................................55 Chapter 3 Fine root distribution and morphology of Theobroma cacao reveals nutrient-
specific acquisition strategies in a multispecies agroecosystem ...............................................56 3.1 Abstract ..............................................................................................................................56
3.2 Introduction ........................................................................................................................57
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3.3 Methods..............................................................................................................................59
3.3.1 Study site and species combinations ......................................................................59
3.3.2 Soil interfaces between T. cacao and neighbour ...................................................60 3.3.3 Fine root analysis ...................................................................................................60 3.3.4 Soil chemical analysis ............................................................................................62 3.3.5 Statistical analysis ..................................................................................................62
3.4 Results ................................................................................................................................63
3.4.1 Soil nutrients: distribution and variation................................................................63 3.4.2 T. cacao fine root distribution and morphology ....................................................66 3.4.3 T. cacao fine root distribution and morphology in relation to soil nutrients and
heterospecific tree roots .........................................................................................69 3.5 Discussion ..........................................................................................................................71
3.5.1 Intra-root system foraging strategies for specific nutrients ...................................71
3.5.2 How is root foraging modified by neighbour trees? ..............................................72 3.6 Conclusions ........................................................................................................................73
Chapter 4 Shade trees regulate the fine root trait response of Theobroma cacao to fertilization ..74
4.1 Abstract ..............................................................................................................................74 4.2 Introduction ........................................................................................................................75 4.3 Methods..............................................................................................................................77
4.3.1 Site description.......................................................................................................77 4.3.2 Experimental design...............................................................................................77
4.3.3 Root ingrowth cores ...............................................................................................78 4.3.4 Fine root traits ........................................................................................................79 4.3.5 Statistical analysis ..................................................................................................79
4.4 Results and discussion .......................................................................................................80
4.4.1 Extent and direction of intraspecific root trait shifts following fertilization .........80 4.4.2 Coordinated resource acquisition strategies in a multispecies agroecosystem ......84
4.5 Conclusions ........................................................................................................................89
Chapter 5 Effects of interspecific interactions on Theobroma cacao root strategies across
optimal and suboptimal climates ...............................................................................................90
5.1 Abstract ..............................................................................................................................90 5.2 Introduction ........................................................................................................................91 5.3 Methods..............................................................................................................................93
5.3.1 Study sites ..............................................................................................................93 5.3.2 Fine root sampling and analysis .............................................................................96 5.3.3 Root growth ...........................................................................................................97
5.3.4 Soil sampling and analysis .....................................................................................97 5.3.5 Statistical analysis ..................................................................................................98
5.4 Results ................................................................................................................................99 5.4.1 Intraspecific root trait (co)variation in T. cacao ....................................................99 5.4.2 Abiotic effects on intraspecific trait variation in T. cacao.....................................99 5.4.3 Shade tree effects on intraspecific trait variation in T. cacao ..............................107
5.5 Discussion ........................................................................................................................107
5.5.1 How do root resource acquisition strategies of T. cacao vary across climatic
conditions? ...........................................................................................................107 5.5.2 How do shade trees modify resource acquisition strategies of T. cacao? ...........110
5.6 Conclusions ......................................................................................................................111 Chapter 6 Discussion ...................................................................................................................112
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6.1 Contributions and future research directions ...................................................................112
6.1.1 Chapter 2 ..............................................................................................................114
6.1.2 Chapter 3 ..............................................................................................................115 6.1.3 Chapter 4 ..............................................................................................................117 6.1.4 Chapter 5 ..............................................................................................................119
6.2 Recommendations for management .................................................................................120 6.2.1 How can root traits be used as indicators of plant and agroecosystem function
and ultimately inform management? ...................................................................121 6.2.2 What management strategies will maintain or improve plant and ecosystem
function in multispecies agroecosystems? ...........................................................123 6.2.3 How can ecological intensification be achieved and maintained in the cocoa
producing region in Ghana? .................................................................................124
6.3 Final remarks ...................................................................................................................126
References ....................................................................................................................................127 Appendix ......................................................................................................................................157
Copyright Acknowledgements.....................................................................................................164
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List of Tables
Table 1.1: Root traits that were investigated in my PhD research. Classification based on
McCormack et al. (2015) and Freschet and Roumet (2017).………..…………………..……….11
Table 2.1: Previously reported coarse root biomass (BGB) carbon in cocoa agroecosystems and
methods of estimation based on aboveground parameters………………………………...…38-39
Table 2.2: Root to shoot ratios (BGBGPR:AGBSA; mean ± SE) of 15-year-old T. cacao plants. T.
cacao coarse root biomass and carbon estimates are based on T. cacao aboveground biomassa
estimated for the site and tissue-specific carbon fractionb……………………………………….48
Table 3.1: Variation in soil nutrients within the lateral rooting zone (0 to 30 cm depth) of T.
cacao reported as minimum and maximum nutrient availability and the coefficient of
variation………………………………………………………………………………………….64
Table 3.2: Coefficients from LMMs of T. cacao fine root density (FRLD and FRBD) and
morphology (SRL, SRTA and D). Depth, soil nutrients, and roots of shade tree (FRBDshade) were
fixed effects and the sampled profile was assigned as a random effect. Significant (p < 0.05)
coefficients are in bold. Partial r2 are reported in parentheses. LMM results are reported in Table
A.3.………………………………………………………………………………………..…..….70
Table 4.1: Trait loadings on the first two axes of principal component analyses of T. cacao root
traits. Significant relationships between traits and axes are indicated in bold (p < 0.05)………..86
Table 4.2: Results of two-way ANOVA of species combination and fertilization level on
coordinated root strategies of T. cacao. Significant effects are indicated in bold (p < 0.05) and
marginally significant effects are indicated in italics (p < 0.1).……………………...……..……87
Table 5.1: Geographic, climatic, and biophysical conditions at the four sampling sites.…….....94
Table 5.2: Summary statistics and intraspecific trait variation (coefficient of variation; CV) of
fine root traits measured from 120 individual T. cacao………………………………………...100
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Table 5.3: Pearson correlations of fine root traits for surface roots of T. cacao among 120
individual trees. Top right of table reports the correlation coefficients (r) with significant values
in bold. Bottom left of table reports p-values…………………………………………………..101
Table 5.4: Pearson correlations of fine root traits of T. cacao with soil variables. Values in bold
indicate significant correlations (p < 0.05)……………………………………………………..103
Table 5.5: Sources of intraspecific trait variation (ITV) in fine roots of T. cacao. Variance
decomposition was based on a nested analysis of variance, which for each trait was based on 120
individual trees sampled in shallow soil (0 to 10 cm). Largest source of variation is in bold. Also
presented are the total explained variance associated when continuous soil variables (NO3-,
NH4+, PO4
-, soil moisture, sand content, and clay content) (“Fixed effects r2”) and the explained
variance associated with both the fixed effects and random effects……………………………106
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List of Plates
Plate 1.1: Images from a cocoa research station near South Formangso, Ashanti Region. Images
show top left: T. cacao in monoculture; top right: T. cacao in mixture with T. ivorensis and, also,
cocoa pods at different stages of decomposition after the beans were harvested; bottom left: GPR
being used in detection of coarse root biomass; and bottom right: destructive harvesting of T.
cacao for direct biomass measurements…………………………………………………………30
Plate 1.2: Images from a working farm near Wiawso, Western Region. Images show in the top
left: sampling of roots from T. cacao in mixture with T. ivorensis, which is off to the left of the
image; top right: a view of T. cacao in mixture with T. ivorensis from a nearby cleared plot;
bottom left: manual tracing and sampling of roots from T. cacao; and bottom right: sampling T.
cacao roots near the base of T. ivorensis………………………………………………………...31
Plate 1.3: Images from a cocoa research site near Mampong, Ashanti Region. Images show in
the top left: T. cacao in monoculture; top right: a view of the canopy of T. cacao, with canopy of
shade trees visible in the background; bottom left: the canopy of T. ivorensis above T. cacao; and
bottom right: manual tracing and sampling of roots from T. cacao……………………………..32
Plate 1.4: Images from a working farm near Dedease, Brong Ahafo Region. Images show in the
top left: T. cacao in monoculture; top right: T. cacao and T. superba in the foreground and
sampling of T. cacao in next to T. ivorensis in the background; bottom left: exposed large lateral
coarse root of T. cacao; and bottom right: manual tracing and sampling of roots from T. cacao in
monoculture…………………………………………………………………………………...…33
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List of Figures
Figure 1.1: Map of Ghana showing locations of study sites……………..……….….….………28
Figure 2.1: Relationship between T. cacao DBH (cm) and coarse root biomass (kg tree-1)
(BGBGPR = 0.50 + 1.37×DBH). Symbols represent T. cacao grown in different shade tree
treatments (■ = T. cacao in monoculture, ● = T. cacao in mixture with E. angolense, ▲= T.
cacao in mixture with T. ivorensis). Excavated amounts (BGBH) are indicated (×) but are not
included in the regressions……………………………………………………….…….….……..46
Figure 2.2: Relationship between BGB of individual T. cacao plants (kg tree-1) as estimated
from GPR and destructive sampling (BGBGPR) and estimated using a generalized allometric
equation (BGBGA). Linear regression is the solid line (BGBGA = 7.07 + 0.57×BGBGPR). Symbols
represent T. cacao grown in different shade tree treatments (■ = T. cacao in monoculture, ● = T.
cacao in mixture with E. angolense, ▲= T. cacao in mixture with T. ivorensis)………...……..47
Figure 2.3: Root to shoot (RS) ratios calculated for T. cacao plants across three different shade
tree treatments. Root estimates are from GPR and destructive sampling (BGBGPR) and estimated
using a generalized allometric equation (BGBGA). AGBSA was based on species-specific
allometric equation (Somarriba et al. 2013). The horizontal dashed line indicates a IPCC
recommended RS ratio of 0.20. The RS ratios measured from one complete harvested T. cacao
plant per treatment are also shown (×)…………………………………………………..……….50
Figure 2.4: Plot scale estimates of biomass carbon (Mg C ha-1). Carbon estimates are for T.
cacao plants and shade trees. Total biomass (T. cacao + shade) is indicated by horizontal
lines…………………………………………………………………………..…………………..51
Figure 3.1: Soil profiles (n = 9) used in this study. Left panel: Schematic showing the location of
a soil profile between a T. cacao tree and a shade/T. cacao tree. Right panel: An excavated soil
profile situated between a T. cacao tree (foreground) and a shade tree Entandrophragma
angolense (background)……………………...…………………………………………………..61
Figure 3.2: Vertical distribution of soil nutrients in soil interfaces (presented as least square
means ± SE, with soil interface as a random effect). When there was a significant effect from
species combination (p < 0.05), pairwise comparisons (Tukey) are shown with among group
xii
differences; letters indicate significant difference among species combination per sampling
depth……………………………………………………………………………………………...65
Figure 3.3: Vertical distribution of fine root density (FRLD and FRBD) and morphology (SRL,
SRTA, and D) of an individual T. cacao tree 1.5 m distance from stems. Values shown are the
least squares mean ± SE that were calculated using soil interface as a random effect. Also shown
is FRBDhetero of heterospecific neighbours. No significant differences were observed among T.
cacao roots in different neighbours for each depth interval……………………………………..67
Figure 3.4: Interpolated root maps depicting the distribution of soil nutrients (e.g., NH4+ and
Ca2+), shade tree fine roots (FRBDhetero), and T. cacao fine roots (e.g., FRLD and SRL) in three
soil interfaces between (on the left) two T. cacao, (in the middle) T. cacao and E. angolense, and
(on the right) T. cacao and T. ivorensis…………………………………………………………………68
Figure 4.1: Root trait response to nutrient influx (mean ± SE percent difference from roots in
native soil, i.e., control group) of T. cacao in three different species mixtures. Top row show data
of surface roots and bottom row shows data from subsurface roots. Zero on the x-axes signifies
no change in the root trait values. Note that scales on the x-axes vary to aid visual interpretation
of the data.……………………………..………………………………………...………………81
Figure 4.2: Ordination of fine root traits for T. cacao surface roots (top row) and subsurface
roots (bottom row) from principal component analysis (left panels) and resulting biplots of axes
scores grouped according to species composition (middle panels) and fertilization level (right
panels). Results from two-way ANOVA for each depth and each axis; there were no interactive
effects of species composition and fertilization (see Table 4.2).………………………….…….85
Figure 5.1: Principal component analysis of intraspecific root trait variation of T. cacao based
on 120 individuals sampled at 0 to 10 cm depth. Also shown are the 95% confidence ellipses
surrounding data from each sampled site……………………………………………………….102
Figure 5.2: Root trait values and PCA axis scores of T. cacao across four sites (Suboptimal-
Sandy (SS); Suboptimal-Loam (SL); Optimal-Sandy (OS); Optimal-Loam (OL)) and in
monoculture and in mixture with a shade tree T. ivorensis. Values present are least square means
± SE with block as a random effect. Same letters signify non-significant differences between
sites and asterisks show significant differences between management within sites……………104
xiii
Figure 6.1: Data on T. cacao roots reported in my PhD thesis, according to shade tree
management (i.e., T. cacao in different species compositions: T. cacao in monoculture (C-C), T.
cacao in mixture with E. angolense (C-E), and T. cacao in mixture with T. ivorensis (C-T)), in
comparison to all available ‘woody’ root trait data in the FRED 2.0 database: A: root to shoot
(RS) ratio using data of T. cacao n = 15 (Chp. 2) and FRED data n = 87; B: average fine root
diameter (D) within individual root systems of T. cacao n = 360 (Chp. 3) and FRED data n =
3891; C: relationships between fine root diameter (D) and specific root length (SRL) of T. cacao
n = 54 (Chp. 4) and FRED n = 1392; D: absorptive fine root RTDab and SRLab of T. cacao n =
120 (Chp. 5) and FRED n = 565………………………………………………………………113
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List of Appendices
Table A.1: Ground penetrating radar settings used during this study…………………………157
Table A.2: Details on the conditions of the site, plant, and radar signal response during GPR
survey…………………………………………………...………………………………………158
Table A.3: Results tables of linear mixed models with soil nutrients, root density of
heterospecific neighbour, and sampling depth as fixed variables. Soil interface (i.e., soil profile)
was a random factor. Significant coefficients are in bold (p < 0.05). Results are synthesized in
Table 3.2..…………………….……………………………………………………........…159-161
Table A.4: Trait loadings on the first two axes from principal component analyses of T. cacao
root traits. Significant correlations between traits and component coordinates are indicated in
bold with their significance level……………………………………………….……................162
Figure A.1: Calibration model used to estimate root biomass from GPR response (following
geo-image processing) (n = 30); y = 4.9 + 0.02x where x is the number of pixels above the image
intensity threshold of 202……………………………………………………………………….163
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Chapter 1 Introduction and literature review
1.1 Introduction
Agricultural landscapes can provide important ecosystem services related to plant productivity,
biogeochemical cycling, and habitat for non-cultivated species (Altieri 1999, Tscharntke et al.
2011, Zhang et al. 2007, Faucon et al. 2017). However, the widespread adoption of intensified
agriculture, particularly within the last century, has compromised critical ecosystem processes
(Lin et al. 2008, Philpott et al. 2008) and this has had startling repercussions on earth system
processes at a range of scales (Vitousek et al. 1997, Tilman 1999). Agricultural intensification,
often encouraged by economies of scale and incentives such as subsidies for inputs and seed,
preferentially favours concentrated and uniform crop production. Consequently, often a limited
number of crop species are cultivated over large areas (Tilman et al. 2011, Lin 2011). This active
control of species composition coinciding with expansion of land area under cultivation has
largely contributed to the current and unprecedented loss of biodiversity (Hooper et al. 2005,
Malézieux et al. 2009). Relatedly, maintaining high levels of productivity in these intensified
systems depends on externalization of ecological processes such as using continued irrigation
and shifting from nutrient cycling to reliance on fertilizer inputs, all of which have dramatically
altered local to global biogeochemical cycles (Vitousek et al. 1997, 2009, Tilman 1999, Tilman
et al. 2011, Tomich et al. 2011). There are also concerns around the sustainability of food
production itself as the capacity to maintain crop productivity is threatened by environmental
perturbations and general depletion in environmental quality, such as soil fertility, which can
lead to long-term declines in yield (Zhang et al. 2007).
Intentionally increasing planned species diversity in agricultural landscapes is an active
management strategy that can improve ecosystem function and is among the central tenants of
agroecology (Vandermeer et al. 1998, Hooper et al. 2005, Malézieux et al. 2009, Tilman et al.
2014). When diversity results in improved ecosystem function, it can be ascribed to a greater
diversity of functional attributes that lead to complementary filling of ecological niches and/or
the increased likelihood that a species present will have a large effect on a particular ecological
process (e.g., productivity, nutrient cycling, provide habitat) (Hooper et al. 2005, Cadotte et al.
2011). These complementary effects and selection probability effects (i.e., sampling effects) can
2
simultaneously lead to increased ecosystem function. Therefore, the relationship between
diversity and ecosystem function will largely depend on the combination of species and their
interactions. However, these relationships can be confounded when the functional realization of a
species is not constant. This is likely to happen across heterogeneous environments as plants can
express significant amounts of phenotypic plasticity in response to abiotic gradients and biotic
interactions (Callaway et al. 2003). Thus, assessments and predictions of ecological function in
agroecosystems necessitate inclusion of intraspecific variation in all dimensions of plants
including leaves, whole plants characteristics, and roots (Garnier and Navas 2012, Martin and
Isaac 2015, Barot et al. 2017, Damour et al. 2018).
Plant root systems have a decisive role in regulating ecological services that agroecosystems
both provide and rely on (Ong et al. 1991, Schroth 1999, Jose 2009). Roots regulate nutrient and
water cycling (Nepstad et al. 1994, Oliveira et al. 2005, Bardgett et al. 2014), and represent a
large proportion of total plant productivity and belowground inputs of organic carbon (C)
(Jackson et al. 1997, Mokany et al. 2006). Therefore, root ecology is a central, although often
overlooked, aspect of minimizing negative environmental impacts arising from agriculture, as
well as for reducing the potentially destabilizing effects of environmental change on agriculture.
Further consideration is needed regarding root phenotypic plasticity that permits plants to adjust
and acclimate to different environments and to different forms of management. For example, in
biodiverse agroecosystems, the effects of community neighbours are paramount (Callaway et al.
2003) and these interactions can affect crop growth towards or away from competitive (Miller
and Pallardy 2001), complementary (Mulia and Dupraz 2006, Zhang et al. 2014; Kumar and Jose
2018), or facilitative interactions (Li et al. 2006), with broad implications for soil resources (e.g.,
water, nutrients) and agroecosystem processes (e.g., nutrient cycling rates).
Agroecosystems that rely on higher levels of biodiversity and fewer inputs to sustain both crop
productivity and ecosystem function require a dramatic management shift away from a one-size-
fits-all approach to agriculture. To accurately assess and predict root, plant and ecosystem
function in these complex agroecosystems, we require a more robust understanding of the drivers
and consequences of intraspecific variation derived from interacting environmental effects. This
is of particular importance given the formidable role roots play in resource acquisition. My PhD
thesis aims to uncover how roots, from individual lateral roots to full root systems, of the same
species are affected by differences in environment – namely species composition, edaphic
3
conditions, and climate. I do this by focussing on a dominant pan-tropical tree-crop, Theobroma
cacao L. (Malvaceae). I present implications for key agroecological processes and services from
the plant to landscape scale.
1.1.1 Theoretical approach
I position my PhD research within an agroecological framework to examine the root ecology of a
cultivated plant grown in multispecies agriculture. To do so, I use a functional trait-based
approach to measure and quantify crop root interactions with environment.
Traditional agronomy is based on experimental studies and hypothesis testing of cropping
systems with limited plant diversity (i.e., monoculture), while diverse multispecies systems
studies commonly focus on observational work, which may be partially due to the challenges in
quantifying ecological complexity (Vandermeer et al. 1998, Tomich et al. 2011). Thus,
mechanistic understandings of plant function and interaction with agricultural environments are
largely based on research in monocultural systems. Furthermore, conventional measurement
methods in agroecosystems often chart and predict yield or productivity in relation to
environment and management (e.g., fertilizer application), which overlooks the vast and complex
suite of other ecological services. Within multispecies agroecosystems, combined yield-based
metrics can serve as indicators of interspecific interactions (e.g., the ‘land equivalent ratio’ can
indicate when competitive or complementary effects are likely) (Vandermeer 2011) but are
generally limited in providing a process-based understanding of what occurs in soil and plants.
Ultimately, there needs to be a shift away from a ‘black box’ approach, where inputs and outputs
to a system are quantified that more precisely describe plant resource acquisition strategies that
are related to a suite of ecological functions.
To systematically study agroecosystems, plant functional traits contextualized by environment –
i.e., trait-based ecology – is emerging as a powerful approach to study and predict the
morphological and physiological characteristics of plants given certain abiotic and biotic
conditions (Messier et al. 2010, Shipley et al. 2016). Trait-based approaches are often used in
comparative ecological studies that aim to capture differences across species, communities, and
biomes, although typically in non-cultivated systems (Garnier and Navas 2012, Damour et al.
2018). Using measurable attributes of a plant (trait) that relates to the plant and/or ecosystem
function (Faucon et al. 2017), these approaches are increasingly used to study intraspecific trait
4
variation (ITV) in relation to environmental gradients (Moran et al. 2016). While studying the
attributes of a single species in relation to environment (i.e., autecology) is a conventional and
widely used approach in agronomy (Bradshaw 1965), refinement and standardization in the units
of analysis has facilitated comparability among species and studies (Garnier et al. 2015),
including for roots (e.g., Pérez-Harguindeguy et al. (2013), Freschet & Roumet (2017), and
McCormack et al. (2017)). Root traits are now commonly empirically defined in relation to plant
function (e.g., resource acquisition) and their response and impact on the environment (e.g.,
nutrient cycling) (Weemstra et al. 2016, Freschet and Roumet 2017, Freschet et al. 2017). My
research is predicated on the argument that understanding crop root ITV can help to answer
questions such as: how can root traits be used as indicators of plant and agroecosystem function
and ultimately inform management? and: what management strategies will maintain or improve
plant and ecosystem function in multispecies agroecosystems?
In the following section 1.2, I review our understandings of root ecology, which is central to
developing and testing hypotheses on belowground processes in multispecies agroecosystems. I
then introduce my study system: cocoa agroforestry in section 1.3, and ultimately, in section 1.4,
I detail my research objectives and the four studies within my PhD thesis.
1.2 Literature review: Applied root ecology
As agricultural management and production increasingly intensified, ecologists recognized the
importance of plant root form and function for informing land-use and management decisions
This was notably articulated by Weaver almost a century ago in the seminal text The Ecological
Relations of Roots:
“A knowledge of root distribution and root competition under different natural
conditions is not only of much scientific value, but it also finds practical application
in a better understanding of the value of plants as indicators of distinguishing lands
of grazing value only from those with possibilities of crop production. It will result
in a more intelligent solution of the ecological problems of grazing and will
likewise be of great aid to the forester in selecting sites for afforestation.” (Weaver
1919)
In more recent times, the negative consequences of intensified agriculture have become more
apparent and the importance of roots and root interactions in managed environments is ever more
5
relevant. Accordingly, there is large interest in matching plant genotypes with specific rooting
patterns to optimize production in less intensely managed agroecosystems (Lynch 2007, White et
al. 2013). This approach, however, needs to also consider root phenotypic plasticity and
interactions with environment and management.
1.2.1 Phenotypic plasticity of roots
Phenotypic plasticity is the expression of multiple phenotypes within a single genotype
(Bradshaw 1965, Sultan 2000). Plasticity can confer some adaptive capacity in plants through
modification in architecture, morphology, and/or physiology in response to environmental
heterogeneity (Bradshaw 1965, Valladares et al. 2007). In my PhD, I investigate environmental
drivers of root variation in a cultivated tree and, thus, focus this literature review to
developmental forms of plasticity opposed to genetic or heritable (see sections 1.3.1 and 1.3.4 for
further details of genetic variability in the model species T. cacao).
Belowground, plants acquire resources that are heterogeneously distributed in time and space,
and within a diverse and complex soil matrix (Hutchings and de Kroon 1994, Hodge 2004, 2006,
Chen et al. 2016). In addition, roots must navigate obstructions to root growth and interact with
plant neighbours and soil biota. I summarize these modifications in the following subsections,
with emphasis on resource acquisition (opposed to stability requirements), and their relationships
to environment at three scales of analysis: total plant scale, root system scale, and individual root
scale. The soil and root processes occurring at these three scales are interrelated but
distinguishing them here does have plant functional and ecological significance, as well as
facilitates analysis of complex root system processes.
1.2.1.1 Plant-scale plasticity
Typically, between 20 and 40% of vegetative biomass is belowground (Deans et al. 1996,
Jackson et al. 1997) and this value varies widely among species (Borden et al. 2014). However,
within species there can also be variability in plant allocation to root organs and the growth of
the root system can diverge from growth of aboveground plant mass. Allocation between shoot
and root can vary within the same individual with age, and, thus, ontogeny is a primary driver
allocation patterns (Hutchings and John 2004), and size-dependent relationships are often used to
predict belowground biomass from aboveground plant mass (Cairns et al. 1997, Kuyah et al.
2012).
6
However, differential allocation patterns at the plant scale may also be in response to
environment, based on internal plant demands. According to optimal partitioning theory, plants
allocate proportionally more photosynthates towards root organs when soil resources are
limiting, while conversely more to the shoot if light is limiting (Chapin et al. 1987, Kozlowski
and Pallardy 1997, Koltai 2013). It is thought that when a plant is nutrient deficient, an increased
concentration of biosynthesized plant hormones (e.g., strigolactones) will have a positive effect
on root growth, altering the source-sink dynamics of photosynthates between above and
belowground organs (Koltai 2013). The resulting effect on plant allocation is observed across
gradients of nutrient availability, for example, in field studies of increasing N limitation across
Populus spp. plantations (Fortier et al. 2015) and in controlled experiments of P-deficient plants
(Koltai 2013). Relatively higher allocation to roots is also common in water-constrained
environments. In these cases, while overall plant growth is likely to decrease, the growth rate of
roots slows less than aboveground growth (Kozlowski and Pallardy 1997). Given the strong
control of resource availability over plant growth and relative allocation patterns, competition
plays an important role in these allocation patterns by regulating access to limiting resources
(Wilson 1988), while soil heterogeneity can also influence plant-scale allocation patterns
(Hutchings and John 2004, Hu et al. 2014).
1.2.1.2 Root system-scale plasticity
Relatively higher density of roots in a given soil volume is commonly understood as a plant’s
response to capitalize on localized, elevated soil resources (Hutchings and de Kroon 1994,
Pritchard 1998). In general, the vertical distribution of plant roots typically shows a higher
density of roots nearer the soil surface and decreases with depth, matching the general
distribution of soil nutrients (Jobbágy and Jackson 2004). However, root system architecture can
vary widely across species (Schenk and Jackson 2002, Borden et al. 2017b) and soil environment
and interspecific interactions can exert strong control of the expression of root architecture
(Callaway et al. 1991, 2003).
Root systems are considered modular in nature (Hodge 2006, de Kroon et al. 2009, McNickle et
al. 2009) and root system architecture is a realization of modifications that have occurred to
individual root systems in response to fine-scale variation in soil, the nutrient status of the plant,
and intrinsic constraints of the genotype. Meristematic cells in the root cap differentiate and
7
elongate driving root growth (Pritchard 1998) while new roots emerge and differentiate into
lateral roots, triggered by nutrient deficiency and localized high concentration of required
nutrients (Drew 1975, Robinson et al. 1999, Hodge 2004, Cahill et al. 2010). This process is
attributable to complex and integrated sensing and signalling mechanisms that respond to soil
environment given internal resource demands (Forde and Lorenzo 2001). Internal gradients of
phytohormones (e.g., auxin, cytokinin, and ethylene) interactively control growth rates and
lateral root initiation to acquire soil nutrients (López-Bucio et al. 2003, Jansen et al. 2013), while
root hydrotropism permits a root system to sense and grow in response to moisture gradients in
soil (Cassab et al. 2013, Ryan et al. 2016). Taken together, these highly integrated processes
allow individual plants, which are sessile in their environment, to actively ‘forage’ through soil
for soil nutrients and water.
Many greenhouse and controlled laboratory studies have observed striking patterns in root
system development of individual plants in relation to patches of nutrients (Drew 1975,
Robinson et al. 1999, Hodge 2004, Lambers et al. 2006, Cahill et al. 2010). This has been shown
for patches of nitrate (NO3-), ammonium (NH4
+), and phosphate (PO4-) (Drew 1975, Robinson et
al. 1999, Hodge 2004). These responses will depend on plant demand for the resources and
availability and mobility of those resources in soil (Hodge 2004). For example, due to the slow
rate of diffusion of PO4- in soil solution, acquisition of P from soil is increased through changes
in non-patterned (i.e., environmentally triggered) fine root branching and root hair growth (from
epidermal cells) (López-Bucio et al. 2003, Lambers et al. 2006, Koltai 2013, Wieckowski and
Schiefelbein 2013). Evidence of active foraging in root systems in field studies also shows
preferential rooting within soil that has elevated nutrients as opposed to soil that has lower
nutrient concentrations (McGrath et al. 2001, Eissenstat et al. 2015, Chen et al. 2018). However,
the form and delivery of nutrients can have a large effect on roots response. For example, organic
versus inorganic sources of nutrients can modulate root response (Hodge 2006), which is likely,
in part, due to the rate of change in nutrient availability; as plant roots have also been shown to
respond to temporal changes in nutrient availability and even have been shown to preferentially
grow where nutrients are increasing and avoid areas where concentrations of nutrients are
declining (Shemesh et al. 2010). In the presence of moisture gradients, evidence of root growth
towards higher moisture availability is reported but this response seems to be heavily affected by
spatial and temporal variation in water availability (Cassab et al. 2013).
8
Complex inter-plant interactions can influence the overall temporal and spatial distribution of
soil resources. In particular, root placement can be modified by the growth and activity of
neighbour roots in soil (Malamy 2005, Cahill et al. 2010). A neighbour can deplete resources in
the area immediately surrounding the root, which may discourage root growth and proliferation
by another plant root into that same area, but inhibitory or allelopathic chemicals can be released
from roots and also suppress root elongation or root initiation of neighbouring roots (Mahall and
Callaway 1992, Callaway et al. 2003). Lastly, there is emerging evidence of complex multi-
trophic interactions occurring in the rhizosphere that can mediate root-root interactions
(Mommer et al. 2016). Resource uptake and delivery of root-based organic inputs are occurring
throughout the root systems (Upson and Burgess 2013), which likely contributes to complex
interactions with soil microbial communities and nutrient cycles (Mommer et al. 2016).
1.2.1.3 Root-scale plasticity
At the scale of individual roots, a plant increases its capacity to absorb soil resources by
increasing the amount of absorptive root surface area and/or the uptake capacity of the surface
area; however, this occurs at cost to the plant, and thus root-scale plasticity is often standardized
by unit of investment from the plant (e.g., per unit biomass) (Guo et al. 2008, Freschet and
Roumet 2017). Therefore, morphological plasticity can be expressed as a change in length,
diameter, or frequency in root initiation per unit of biomass. In this thesis I refer to root
morphological variation as changes in the modular components of roots but acknowledge that
‘morphology’ of roots can be used in reference to the shape of the entire root system (i.e., root-
system morphology, such as rooting depth (Pérez-Harguindeguy et al. 2013)).
Root demographics (i.e., age and lifespan) and dynamics (i.e., growth rate and turnover) may
interactively affect root resource acquisition and use (Pregitzer et al. 1993, Adams et al. 2013,
Weemstra et al. 2017). There is much uncertainty on the mechanisms that trigger individual root
death, but there is evidence that this process is responsive to environment and plant conditions
(Raven and Pregitzer 2003). Changes in root dynamics may be reflected in the structural
composition of root tissue. Longer-lived roots require greater investment in the construction of
the root organs. This may include higher lignification in transport vessels that can withstand low
water potentials to maintain hydraulic conductivity and tissue that is more resistant to herbivory
9
and pathogens (Ryan et al. 2016), which likely also corresponds to slower root growth (Meyer
and Peterson 2013).
Abiotic stresses can trigger increased suberization in exodermal cells, which can reduce gas
exchange in saturated conditions or decrease moisture loss in droughty conditions (Meyer and
Peterson 2013). Conversely under more optimal conditions, a less developed exodermis permits
more rapid root growth, thus increasing the length of root with more cortex cells (i.e., more
absorptive surface area) (Meyer and Peterson 2013). The thickness of the root is also believed to
be important in determining the roots ability to transport water (Wang et al. 2017). Plasticity in
root diameter can occur through increased diameter in the stele (transport conduits) or through
increased cortex cells. Increased diameter is correlated to increased xylem diameter and
corresponding decrease in axial resistance (McCormack et al. 2015). Therefore, thicker roots
lead to greater water transport capacity, but under higher tension this may increase susceptibility
to cavitation and loss of water conductivity (Anderegg et al. 2012, Watt et al. 2013). Roots can
also reduce the number of living cortex cells (i.e., increase root cortex aerenchyma, or
intercellular air space) under resource-stressed conditions, which decreases metabolic costs with
fewer living cells (Postma and Lynch 2011).
While not the focus of my PhD research, other mechanisms of adaptation to environment via root
plasticity should be noted. Roots can alter the number of transporters at the cellular level to
increase uptake capacity of nutrients, as well as actively alter the chemistry in the rhizosphere to
increase nutrient concentration in soil; for example, exudation of H+/OH-, organic acids, or
enzymes like phosphatases to promote P availability in soil solution (Hinsinger 2001, Watt et al.
2013). In addition, symbiotic relationships with N2-fixing bacteria (Rhizobium spp. and Frankia
spp.) and fungi (e.g., arbuscular mycorrhizal fungi; AMF) can influence plant root structure and
function by increasing the amount of nutrients available to the plant in nutrient-limited
environments (Freschet et al. 2017, Kong et al. 2017). Plants can encourage relationships with
AMF by upregulating production and exudation of plant hormones (e.g., strigolactones) to signal
AMF establishment in roots (Koltai 2013). The metabolic costs of signalling for and maintaining
symbiotic relationships with AMF should ‘pay off’ for the plant as it reduces the need for the
plant roots to be as responsive to variation in soil resources by enhancing the plants ability to
access a larger volume of soil. There is also evidence that hyphae themselves are also responsive
to nutrient patches (Hutchings and de Kroon 1994, Chen et al. 2018). Roots can increase the
10
number of cortex cells (and consequently root diameter) to increase the ability for AMF hyphae
to infect the root tissue (Guo et al. 2008, Kong et al. 2014, Eissenstat et al. 2015). These
alternative but important pathways can enhance uptake of limiting nutrients N and P.
1.2.2 Root functional traits
Plant functional traits are the measurable attributes that relate to the growth, reproduction and
survival of a plant; but can also describe a plant’s response to environment (i.e., response traits)
and impact on the environment (i.e., effect traits) (Violle et al. 2007, Pérez-Harguindeguy et al.
2013, Garnier et al. 2015). Thus, trait-based study of roots is appealing as information on the
variation in plant function can be garnered from a more measurable feature of the root system.
For example, applying a trait-based framework to the study of roots within one species offers a
way to conceptualize and test hypotheses on species’ adaptation to environments, such as climate
and edaphic conditions, by studying the variation of root traits related to resource uptake across
environmental gradients (Ostonen et al. 2007a, Zadworny et al. 2016).
I summarize the roots traits measured in my PhD research in Table 1.1, along with their
functional significance as it relates to plant and ecosystem function. These root traits describe the
growth, construction, and maintenance of roots and their general role in resource acquisition. At
the scale of the plant and its root system, root traits describe resource acquisition through
variation in overall or localized distribution of roots in relation to plant size (e.g., RS ratio), or in
rooting locations (e.g., root distribution of fine root biomass density; FRBD), or in the relative
amount of absorptive roots in the root system (e.g., ratio of absorptive to transport fine roots
(A:T)). At the scale of individual roots, root traits must describe morphological and
physiological variation that relate to resource acquisition in relation to plant investment. For
example, increasing the length, area, and root branching per unit biomass (e.g., SRL, specific
root area (SRA), and specific root tip abundance (SRTA)), or metabolic processes that promote
ion exchange into the root, which has been associated with increased concentration of N in root
tissue (Nroot) (Roumet et al. 2016). Higher values in these traits characterizes an ‘acquisitive’
strategy (Mommer and Weemstra 2012, Fort et al. 2016). In contrast, more permanent root
organs can acquire soil resources for longer but require greater investment at the root scale.
Higher values in these traits characterizes a more ‘conservative’ strategy, given that resources
11
Table 1.1: Root traits that were investigated in my PhD research. Classification based on
McCormack et al. (2015) and Freschet and Roumet (2017).
Trait Abbreviation Units Description Functional significance
Overall plant allocation
Root to shoot
ratio
RS ratio unitless
Ratio of aboveground
biomass to belowground
biomass
Allocation
optimization
Growth
Root growth rate GRroot mg m-2 time-1
Fine root biomass growth
rate
Resource acquisition;
root productivity
Root
System/architecture
Fine root length
density
FRLD cm cm-3 Fine root length within
soil volume
Density of exploration;
resource acquisition
Fine root biomass
density
FRBD mg cm-3 Fine root biomass within
soil volume
Productivity; resource
acquisition
Absorptive to
transport length
ratio
A:T unitless
Ratio of fine root length
functionally classified as
absorptive compared to
transport*
Resource acquisition;
longevity
Morphology
Specific root tip
abundance SRTA tips mg-1
Number of tips per dry
weight biomass Resource acquisition
Average diameter D mm Average diameter of all
root length in sample
Resource acquisition;
soil penetration;
mycorrhizal
associations; longevity
Specific root
length SRL m g-1
Length of fine root per dry
weight biomass Resource acquisition
Specific root area SRA m2 kg-1
Total surface area of fine
roots per dry weight
biomass Resource acquisition
Root tissue
density RTD g cm-3
Dry weight biomass of
fine roots per total fine
root volume
Growth; longevity;
decomposition
Chemistry
Nitrogen content Nroot mg g-1 Concentration of N in fine
root biomass
Root respiration;
metabolic processes;
growth
Carbon to
nitrogen ratio C:Nroot unitless
Ratio of C to N in fine root
biomass
Longevity;
decomposition
12
invested to individual roots are less likely to be ‘lost’ to the surrounding environment through
root turnover and herbivory, and are characterized by thicker root diameter (D), denser roots
(e.g., root tissue density), and more recalcitrant stoichiometry (e.g., C to N ratio (C:Nroot)) (Prieto
et al. 2015).
Predictable variation in these root traits in relation to environment remains somewhat
contradictory (Weemstra et al. 2017) but some general patterns have emerged. For example,
within-species plasticity of root traits on a soil fertility gradient was reported, in particular root
diameter, with thicker roots in more fertile soils (Tobner et al. 2013) while specific root length
(SRL) is shown to increase with decreasing soil resource availability (Ostonen et al. 2007b,
Zadworny et al. 2016). In resource-constrained soil environments that experience transient
delivery of resources, it can be more economical for plants to grow roots that can rapidly access
and acquire transient resources but will also have high turnover (Fort et al. 2015). These roots are
more ephemeral and will be characterized higher acquisitive trait values.
The extent of variation and covariation in and among multiple root traits may collectively
represent inherent limits and trade-offs in the construction and growth of roots for a species or
genotype. While predictable variation and consistent trade-offs are widely documented in leaves
(e.g., the leaf economic spectrum) (Reich 2014), it is unclear if there are equivalent universal
trends in roots (Mommer and Weemstra 2012). It was originally postulated that fine root SRL, D,
and Nroot could be effective at describing resource acquisition versus conservation strategies
along with measurements of root respiration and longevity (Reich 2014). Seemingly
contradictory patterns in root traits may indicate specialized acquisition strategies among life
forms (e.g., grasses versus trees) but also for diverse soil-based resources with differing
mobilities in soil (Kramer-Walter et al. 2016, Weemstra et al. 2016). Likely as consequence,
more than one dimension of root trait spectra have been reported as being functionally
informative across species and communities (Weemstra et al. 2016, Liese et al. 2017). Therefore,
measurement of multiple root traits can be important to capture a more complete picture of
complex resource acquisition strategies belowground (Freschet and Roumet 2017). It should be
noted that there can be inherent autocorrelation among these traits, in particular D and SRL or
SRA, whereby as one increases, the other must decrease. Inclusion of traits associated with tissue
construction such as RTD can aid in teasing out variation in strategies outside of these inherent
13
geometric relationships. Dynamic and metabolic processes are challenging to quantify in field
studies, whereas static measurements on root morphology or chemical attributes can be collected
at a greater intensity. Therefore, in my PhD I rely primarily on root morphological and chemical
fine root traits to elucidate coordinated resource acquisition strategies in a tree crop.
1.2.3 Root research methodology
Research on plant root systems arguably lags behind research on areal components of plants.
While this is commonly claimed (e.g., Forde and Lorenzo (2001), Bardgett et al. (2014), and
Laliberté (2017)), this disparity can also be inferred, for example, in the dominance of
aboveground trait data versus root trait data (Pérez-Harguindeguy et al. 2013, Iversen et al. 2017)
and quantification efforts of aboveground biomass and productivity opposed to that of
belowground vegetative biomass (Chave et al. 2005). The difficulty in studying roots in soil is
presumably a major cause of this discrepancy. Indeed, researchers have acknowledged that roots
are “a royal pain to study” (Pregitzer 2002). For example, underestimation of root biomass is
likely when larger roots that are less frequent, or irregular, clumped roots are missed while
sampling an opaque medium (Taylor et al. 2013). It can also be difficult to accurately estimate
resource uptake rates of roots under field conditions, where resource uptake can vary at fine
scales within an extensive root system (Lucash et al. 2007). Furthermore, roots carry out multiple
and complex processes, including uptake of a suite of soil-based resources, stability and
carbohydrate storage, and direct and indirect interactions with soil biota and neighbours, all of
which contribute to the challenge in standardizing root units for measurement and analysis.
Therefore, well-designed and at times creative approaches are essential to study and document
roots in situ.
Measuring and sampling root systems at appropriate scales is important to accurately capture the
root process of interest. Even within an environment under uniform management and plant
species composition, soil physical and chemical properties can vary at multiple scales and, thus,
conventional sampling in block design to capture within-site heterogeneity are common in
agronomic studies, as in ecology. Individual plants respond to multiple soil environmental
gradients within the extent of their root system (Jackson and Caldwell 1993, Stoyan et al. 2000).
In this regard, non-pooled and continuous sampling from within the influence area of an
individual plant may be useful (e.g., Laclau et al. (2013)). This approach is more laborious,
14
destructive, and often limits the size of the study (e.g., number of plants studied), but more
detailed comparisons can be made, and the spatial relationships of fine roots and soil attributes
may be better understood.
Physically sampling coarse roots, particularly of mature trees, can be challenging and often
require large destructive efforts, especially if removing a taproot. Thus, indirect estimating via
allometric and RS ratios to estimate coarse roots structures are appealing and common (Chave et
al. 2005, Mokany et al. 2006). In the case of smaller, fine roots, more delicate work is needed to
avoid damaging and losing roots while extracting from soil. Commonly, soil cores or monoliths
are sampled from across a study plot (horizontal study) (e.g., Mora and Beer (2013)) or with
depth as incremental coring or from exposed soil trenches or profiles (vertical study) (e.g.,
Cardinael et al. (2015)). To remove root samples from a volume of soil, sequential sieving with
water can be used to collect increasingly smaller roots; although some roots are lost during this
process (Livesley et al. 1999).
Imaging software is an important tool in analyzing sampled roots and permits rapid
measurements on a large number of samples. These software programs typically take 2D images
of roots in a flatbed scanner, or from images collected from minirhizotrons, and measure the
length, thickness, colouration, and number of root ends. These measurements can in turn be used
to calculate root volume and surface based on cylindrical shape, and together can be combined
with measurements of root biomass to calculate other root traits.
When roots are removed directly from a plant in the field to be associated with a specific study
plant, there are few standardized sampling protocols. Compared to sampling of, for example,
stem tissue at a specified height, or leaves from standardized locations in the canopy, root
sampling for a target plant remains somewhat ambiguous (Pérez-Harguindeguy et al. 2013) and
context-dependent. However, roots should be sampled from a standardized depth due to large
influence of soil depth on the root (Freschet et al. 2017). Also, when interested in roots
responsible for resource acquisition, roots should be collected to distal root tips, avoiding those
roots with pioneering features (i.e., solely elongation with not lateral root initiation) to
standardize for functionally similar root organs (Freschet and Roumet 2017).
It can be challenging to accurately measure dynamic root traits, such as root growth and
turnover. Root growth can be measured using sequential coring, ingrowth cores, or by
15
monitoring growth on minirhizotrons (Vogt et al. 1998, Bengough et al. 2000, Taylor et al.
2013), but each method has its limitations that should be carefully considered. In particular,
sequential coring cannot control root demographics, while ingrowth cores and minirhizotrons
will likely have some affect the growth of roots as the soil environment is manipulated prior to
measurement (Vogt et al. 1998). Generally, however, ingrowth cores are effective for studies that
test for differences among sites or treatments (Vogt et al. 1998).
Non-destructive means of interpreting root structure and function are appealing. Imaging
technologies, such as computed tomography and magnetic resonance imaging, and modelling
techniques, such as topological models parameterized by root segments, can describe root system
architecture in situ (Nygren et al. 2013). Coarser resolution geo-imaging technologies, such as
ground-penetrating radar (GPR), have also proven effective at describing root architecture,
specifically the vertical distribution of coarse roots (Borden et al. 2017b) and coarse root
biomass (Butnor et al. 2001, Borden et al. 2014). These measurements can be linked to
measurements of root function, such as isotopic profiles to distinguish preferential water uptake
zones (Isaac et al. 2014), or with tissue analysis of coarse roots to account for carbon storage
potential (Borden et al. 2014). Thus, when field conditions permit radar signal detections of roots
(e.g., not too wet and not too clayey), this technology can be a useful tool in the study of tree root
systems. An advantage in using imaging technologies is in the ability to more completely sample
a root system, and, in particular, extensive root systems of mature perennial woody species, thus
overcoming the risk of underestimating biomass. However, some additional physical sampling
(Samuelson et al. 2010, Butnor et al. 2015) or modelling (Borden et al. 2017b) is likely required
to account for what is undetectable by radar signals.
Arguably, there has been increased focus on improving and standardizing collection protocol,
measurements, and categorization of roots (Pérez-Harguindeguy et al. 2013, McCormack et al.
2015, 2017, Freschet and Roumet 2017, Iversen et al. 2017). Part of these efforts include
specifying and parameterizing root measurements by their functional role and their ecological
significance (Mommer and Weemstra 2012, Bardgett et al. 2014, Faucon et al. 2017). For
example, resource uptake rate and mycorrhizal associations of roots can vary depending on
relative location with the root system (i.e., root order) (Rewald et al. 2011, Iversen 2014).
Increasingly the emphasis of measuring functionally-relevant root organs, instead of relying
solely on the conventional more arbitrary 2 mm cut off that divides coarse structural roots from
16
more ephemeral fine roots. This can be done by studying roots by root order, or more broadly
differentiating absorptive fine roots (approximately the first three orders, which are more
associated with resource uptake) from transport fine roots (higher order fine roots, which are
more associated with resource transport) (McCormack et al. 2015). Additionally, distinguishing
fine roots that are functionally distinct may relate to different temporal scales of resource
availability, for example, overall resource availability at a site versus variation in resource
availability within a growing season that requires roots to be more ‘responsive’.
1.2.4 Roots in agroecosystems
A dominant research theme in multispecies agroecosystems examines resource use efficiencies
gained in increasingly diverse cultivation systems (Schroth 1999, Isaac et al. 2007a, Bergeron et
al. 2011, Tully et al. 2012, Zhang et al. 2014, Brooker et al. 2015). Belowground, this is
commonly identified when roots differentiate spatially and temporally, contributing towards
competitive or complementary effects. These studies reveal that the spatial distribution of roots
can be modified when grown in mixture with other species (Mulia and Dupraz 2006, Freschet et
al. 2013, Isaac et al. 2014, Zhang et al. 2014, Cardinael et al. 2015; Kumar and Jose 2018). For
example, there is evidence of trees rooting more deeply when next to crops (Moreno et al. 2005)
or crop roots adjusting root distribution in response to neighbouring species (Isaac et al. 2014),
both of which can contribute to improved spatial differentiation of soil resources and contribute
to complementary effects, such as, on overall nutrient acquisition (Tully et al. 2012). However,
phenotypic plasticity in roots can be realized differently in different conditions (Freschet et al.
2013, Isaac et al. 2014). In a tropical agroforest, Theobroma cacao modified coarse root
distribution and water uptake depth in the presence of a shade tree but only on sandy soils and
not on loam soils (Isaac et al. 2014). These environment-specific processes in multispecies
agroecosystems can affect ecosystem function. To illustrate, reductions in NO3- leaching were
reported by Bergeron et al. (2011) in temperate agroforestry systems on sandy loam soils, but not
on a similarly-managed site on sandy soils. Thus, the interaction between root function and
environment, such as physical properties of the soil, can affect overall plant and ecosystem
function (Niether et al. 2017).
While niche separation in mixed species agriculture can improve overall resource acquisition
efficiencies, there will often be large spatial overlap of roots systems among species in mixtures.
17
Yet, in these cases, spatial and temporal overlap in roots may not signal only competition
between plants, as there are complex processes that occur in the rhizosphere and associated soil
biota; processes that are only now beginning to be understood (Mommer et al. 2016). For
example, roots can benefit from organic material such as exudates or other dead roots in soil
(Danjon et al. 2013), or root activity that promotes nutrients availability in soil for another plant,
such as H+ pumping into rhizosphere from leguminous plants that can increase availability of P
(Brooker et al. 2015). Furthermore, there are potential feedback effects with soil biota from
species composition. For example, in monocultures, species-specific soil organisms could be
more common and, thus, roots may have to adjust to increased risk of pathogens (de Kroon et al.
2012). While these multi-trophic dimensions are not examined in my PhD research, they should
be appreciated as their importance in root-soil processes, including in agroecosystems, is little
understood but likely immense.
Structural and functional complexity in multispecies agroecosystems can influence resource
availability. Influxes of organic inputs can originate from different sources (e.g., cover crops,
litter fall, crop residues, and organic amendments), which results in a complex delivery of
bioavailable nutrients in the soil (e.g., in quantity: total organic carbon and quality: humic acids
and low molecular organic acids) (Marinho et al. 2014), which can trigger microbial activity and
alter N availability at a range of scales (e.g., 2 m2) (Stoyan et al. 2000, Thevathasan et al. 2008).
In addition, the growth and activity of neighbouring and diverse root systems can directly and
indirectly effect the soil environment. Indeed, C flux from root litter can be more important than
leaf litter in some perennial cropping systems (Tscharntke et al. 2011). Due to complex sources
and delivery of organic matter to the soil, nutrient availability can be heterogeneous at multiple
scales (Jackson and Caldwell 1993, Stoyan et al. 2000, Mora and Beer 2013), and, thus, the
resource acquisition strategies of roots given heterogeneous soil environments is of particular
interest to understanding plant function in diverse agroecosystems.
Increasing and maintaining planned species diversity in agroecosystems is often touted as an
important strategy towards regulating atmospheric CO2. For example, through agroforestry
practices, Dixon (1995) estimated 12 to 228 Mg C ha-1 could be stored on productive lands,
which are significant gains over more intensified agricultural landscapes. Deeper rooted and
extensive perennial plants and trees are important components to potential C gains, both by
increasing C stocks in belowground biomass and also through increased inputs to soil via root
18
turnover, sloughing, and exudation (Paustian et al. 1997, Albrecht and Kandji 2003).
Overyielding in fine root biomass has been reported for plantations of mixtures of trees versus in
their monoculture counterparts (Laclau et al. 2013), but overall less is known on biomass
variation in agroecosystems belowground versus aboveground, or if phenotypic plasticity in root
systems is important in determining C stocks.
Crop plasticity and tolerance that permits plants to maintain physiological and metabolic
processes during extreme climatic events and long-term environmental change will be a large
determinant in the level of agroecosystem adaptability to climate change (Porter and Semenov
2005, Lin 2011, Garnier and Navas 2012, Mbow et al. 2014). Roots must maintain function
under soil environments that become wetter or drier, which has effects on nutrient availability
(Lambers et al. 2006, White et al. 2013). More complex vegetative structure and function can
modify microclimates and water and nutrient cycling that could in turn affect how crops can
grow on regional scale climatic gradients. Whether multispecies cropping systems mediates
environmental stressors is of critical importance for farmers. Therefore, an improved
understanding of how roots respond to environmental change in more biodiverse systems is
essential for adapting food production systems to climate change.
Just as Weaver advised a century ago, many continue to advocate the importance in matching
roots to environment (Lambers et al. 2006, White et al. 2013, Brooker et al. 2015, Rao et al.
2016). For example, a higher density of roots in shallow soils may permit better acquisition of
phosphate (PO4-) while deeper more expansive root system may be better at fully capturing
mobile nitrate (NO3-) (White et al. 2013). Alternatively, shallow roots may be best to respond to
sudden influxes of resources, whereas if there are deep roots that can access the water table, this
can be advantageous in drier environments (Callaway et al. 2003). However, if the functional
role of the same species systematically varies either spatially and/or temporally, there could be
implications on related ecosystem function, including farm and plant performance, while
systematic intraspecific trait variation (ITV) in relation to environmental gradients across sites
and regions may have consequences for landscape scale processes. Less understood, and more
challenging to predict, are the effects of management of multispecies agroecosystems on the
plastic responses to environment that could amplify or detract from improved plant and
ecosystem function. There are limits to accurately predicting and modelling more complex
cultivation systems that can have multiple genotype × environment interactions (Malézieux et al.
19
2009, Lovell et al. 2017). In this regard, there are useful applications in identifying root traits that
describe phenotypic plasticity and that are functionally relevant for agroecosystems (Garnier and
Navas 2012, Martin and Isaac 2015, Barot et al. 2017, Damour et al. 2018).
1.3 Case study: Theobroma cacao L. in Ghana
My PhD research focuses on the tropical tree crop Theobroma cacao L.. I herein refer to the tree
as T. cacao, while I use ‘cocoa’ in reference to the cultivated product or the cultivation system
(e.g., cocoa agroecosystem or cocoa agroforestry system). T. cacao is an important case study
species given its widespread cultivation in the tropics and its economic importance as the critical
component in a $98 billion USD chocolate industry (Schmitz and Shapiro 2015). Furthermore, as
T. cacao is a shade tolerant species, it is often grown below the canopy of thinned forest or co-
planted with shade trees (Tscharntke et al. 2011, Schroth and Ruf 2014). Therefore, as these
multi-strata agroecosystems intentionally feature a mixture of tree species, there is considerable
interest in studying the ecological services attributed to vegetative complexity, such as the
impacts on planned and associated biodiversity (e.g., Wade et al. (2010); Clough et al. (2011)),
pest regulation (e.g., Babin et al. (2010)), pollinators (e.g., Toledo-Hernández et al. (2017)),
nutrient and water cycling (e.g., Hartemink (2005); Isaac et al. (2014); Niether et al. (2017)), and
climate regulation through C dynamics (e.g., Somarriba et al. (2013)), of which the latter two
ecosystem services are heavily regulated by root system structure and function.
1.3.1 Cultivation of T. cacao
Theobroma cacao is believed to have evolved millions of years ago as a lower story rainforest
tree in South America, making it one of the oldest known cultivated species from the American
tropical rainforests. Cultivation of T. cacao was likely the result of human cross pollination for
the sweet pulp that envelops the seeds within the fruits (i.e., cocoa pods) (Young 2007). In the
wild, Theobroma species are known to grow in patchy, small clusters within the forest (Young
2007). Therefore, Theobroma species are presumably adapted to interact with both conspecifics
and heterospecifics within late-successional tropical humid forests with high rates of nutrient
cycling. T. cacao is a cauliflorous tree, whereby flowers are produced on the stems and branches.
Typically, less than 5% of flowers are pollinated and develop into the fruits (i.e., cocoa pods)
(Wood and Lass 2001) that contain the seeds (i.e., cocoa beans) which are the critical ingredient
of chocolate. Cultivation for the beans began approximately 3,000 years ago and spread north
20
through what is now Central America and into Mexico (Young 2007). This crop has a rich
history in the Americas, being fermented and roasted for drinking among the Mayan and Aztec
as well as having served as a form of currency (Cadbury 1896, Young 2007), and, thus, was an
important crop and commodity for thousands of years at a regional scale. Cocoa became popular
among European elites in the 1700s and by the late 1800s it was being consumed by middle-class
Europeans, solidifying its trading influence at the global scale (Cadbury 1896, Knapp 1920,
Wood and Lass 2001).
T. cacao is now grown throughout the humid tropics within approximately 20˚ of the equator.
Climatic requirements for optimal T. cacao growth are low lying humid regions characterized
with mean annual temperature (MAT) of 26 ˚C and mean annual precipitation (MAP) of 1250 to
2500 mm yr-1 (Wood and Lass 2001, Schroth et al. 2016b, van Vliet and Giller 2017). T. cacao is
grown on soils that range in texture and development, but the tree generally prefers well-drained
soils which do not dry out for long periods as the species is sensitive to drought (Wood and Lass
2001). Broadly, T. cacao has been associated under two varietal classifications: those considered
endemic to South America (Forestero), which are considered more robust and cultivated widely
in West Africa, and those endemic to Central America (Criollo), which are used to produce finer
chocolates. There are further subclasses of these varieties and recent genetic sequencing has
brought more detail to this crop species’ evolutionary history, but primarily for varieties found in
South and Central America where genetic diversity is high (Motamayor et al. 2008, Zhang and
Motilal 2016). Less is known on the genetic variability in West African T. cacao in Ghana, but it
is believed to be relatively low within the common hybrid varieties grown on established farms
(Asare et al. 2010). Cocoa pods begin forming after approximately 5 years of tree growth,
sometimes earlier. Some varieties produce cocoa for upwards of 40 years, while common hybrid
varieties typically are in production for approximately 20 years. T. cacao is traditionally and
conventionally grown under shade trees, although monocultural production is common in certain
regions, particularly in Southeast Asia (Schroth and Ruf 2014).
1.3.2 Cocoa agroecosystems
Tropical regions are plagued with the risk of boom-and-bust cycles in which previous forest soils
are at risk to degradation once forest vegetation is removed for cultivation purposes (Tscharntke
et al. 2011, Schroth and Ruf 2014). Thus, soils with initially high soil fertility, like those from
21
recently cleared forest, may provide sufficient organic matter and nutrients for several years of
cultivation, but eventually nutrient amendments are required given continued harvesting.
Nutrient retention within the system largely depends on the intensity of harvest (can be upwards
of 1000 kg cocoa beans ha-1 yr-1, but in Ghana closer to 400 kg ha-1 yr-1) or other removals. For
example, cocoa pods (or cocoa husks) are rich in nutrients but might be removed to prevent the
spread of pathogens, and can represent a substantial removal of nutrients, particularly for K
(Hartemink 2005, Fontes et al. 2014; van Vliet and Giller 2017). Overall, a large proportion of
total K and P stocks are in tree biomass, although P stocks in the soil can be built up using
fertilizers. N is required in the largest amount, and T. cacao growth responses to N additions are
commonly reported (van Vliet and Giller 2017). Leaching is typically not a large pathway for
nutrient losses from these systems (Wood and Lass 2001) given the extensive, mat-like root
system in surface soil. However, leaching can be problematic for young cocoa agroecosystems
before roots fill in as well as following application of NO3--N fertilizer when there is high
rainfall and other limiting nutrients (i.e., P and K) are not jointly applied to increase total nutrient
uptake (Hartemink 2005).
Nutrient cycling plays a large and important role in cocoa agroecosystems (Wood and Lass 2001,
Hartemink 2005, van Vliet and Giller 2017). There is often a thick layer of T. cacao leaves that
cover the soil of a cocoa agroecosystem. These leaves can rapidly decompose. In younger stands
leaves in T. cacao monoculture have been found to decompose more rapidly than leaves in
mixture (Isaac et al. 2007b), but as the stand ages, faster decomposition has been reported in
species mixture while in monoculture decomposition may slow (Hartemink 2005, Dawoe et al.
2010, van Vliet and Giller 2017). Belowground, fine root turnover can contribute 3 to 16% of
total nutrient inputs (N, P, K, Ca, Mg) (Muñoz and Beer 2001).
Water is most often the most limiting resource for cocoa production, and this is especially true in
cocoa growing regions that experience a dry season, such as West Africa. Cocoa agroecosystems
in West Africa are largely rain fed and production is controlled by water availability that varies
dramatically between the rainy and dry season. Flowering, production of cocoa pods, and the
dropping of leaves are all affected by patterns of precipitation over the year (Wood and Lass
2001). Thus, in West Africa, harvest typically occurs in two waves: in the middle of the rainy
season (July and August) and then again towards the end of the rainy season (October). Contrast
22
these dynamics to Southeast Asia where precipitation patterns are more constant through the year
and the production and harvesting of cocoa pods likewise occurs throughout the year.
Co-planting shade trees, or retention of forest canopy trees, is an important management strategy
employed by farmers. One benefit is that if the shade tree has more extensive root systems, this
can help alleviate the potential for leaching of nutrients during stand establishment. Inputs from
shade tree litter can contribute an additional source of organic material. Shade trees have shown
to mitigate soil fertility declines in cocoa agroecosystems (Isaac et al. 2005, Dawoe et al. 2014,
van Vliet and Giller 2017), although spacing and species composition may largely influence
these effects (Blaser et al. 2017, Wartenberg et al. 2017). These added nutrient deposits are
expected to benefit T. cacao, while the provision of shade provides a light environment more
suitable for the understory tree crop and has resulted in improved nutrient status of seedlings and
8-year-old T. cacao (Isaac et al. 2007a, 2007b).
1.3.3 The root system of T. cacao
T. cacao root system features a prominent tap root from which extensive lateral roots emerge. It
is unclear what extent the taproot contributes to soil resource acquisition for T. cacao, but it has
been associated with improved structural strength (Wood and Lass 2001) and there is some
evidence of water acquisition from deeper soils (30 to 100 cm) (Rajab et al. 2018). The lateral
root systems create a dense mat of roots predominantly in the top 10 cm of soil and nearly all
lateral roots are typically within top 30 cm of soil (Hartemink 2005, Nygren et al. 2013, Isaac et
al. 2014). These roots are largely responsible for nutrient and water uptake (Wood and Lass
2001, Lehmann 2003, Moser et al. 2010, Schwendenmann et al. 2010, Isaac et al. 2014).
Approximately 20% of biomass is allocated to roots for T. cacao, with RS ratios previously
reported for productive T. cacao ranging between 0.22 to 0.28 (Moser et al. 2010, Leuschner et
al. 2013, Rajab et al. 2016). There has been no explicit evaluation of variation in allocation
patterns to root biomass in response to environment or species composition, with the exception
of a seedling experiment where Isaac et al. (2007b) reported one-year-old T. cacao showed the
most allocation to roots with the highest RS ratio (0.31 to 0.37) when grown under artificial
shade with no belowground competition compared to when grown in monoculture or next to
shade trees Terminalia superba Engl. & Diels or Newbouldia laevis (P. Beauv.) Seem.
23
Many of the lateral roots of T. cacao and neighbouring heterospecific neighbours will likely
spatially co-exist in soil (e.g., T. cacao and N2-fixing Inga edulis Mart. in Costa Rica (Nygren et
al. 2013)). However, there is evidence that the root system architecture of T. cacao (vertical root
distribution) and physiology (water uptake) can be modified by a neighbour tree. In cocoa
agroforestry system in Ghana, Isaac et al. (2014) found evidence of more constrained root
distribution and water uptake when next to a competitor (Terminalia ivorensis A. Chev.) in
sandy soils. Differential depths of water uptake have been reported between T. cacao and
Gliricidia sepium (Jacq.) Kunth in Indonesia (Moser et al. 2010, Schwendenmann et al. 2010,
Rajab et al. 2018).
There is evidence that the root morphology (SRL, D) can also vary depending on species
combination. Rajab et al. (2018) found the morphology of fine roots to vary between T. cacao in
monoculture, in mixture with the N2-fixing G. sepium, and in mixture with multiple species (> 6
species). Specifically, these authors report T. cacao fine roots traits (SRL, D) were more
conservative when next to G. sepium, while in high-diversity mixture, fine root morphology of T.
cacao were more acquisitive and showed faster turnover rates (Rajab et al. 2018). To my
knowledge there are no reported experiments that test for the effects of fertilizer on T. cacao
roots. However, one field study examining another Theobroma species, did show increased root
proliferation into artificially elevated patches of nutrients in soil (specifically P), providing some
empirical evidence of nutrient foraging within this genus (McGrath et al. 2001). Additionally, T.
cacao can form associations with soil fungi (Iglesias et al. 2011), with hyphae that extend into
the dense litter layer found in cocoa agroecosystems. There is also evidence that T. cacao can
receive fixed N from N2-fixing species, either directly through mycelial networks or indirectly
from the N-rich root exudates and organic material from root turnover (Nygren and Leblanc
2015).
1.3.4 Cultivation of T. cacao in Ghana
Cultivation of T. cacao in Ghana is relatively new compared to the history of the crop, but the
current magnitude and importance of cocoa production in Ghana cannot be understated. Up until
the late 19th century, South and Central America and the Caribbean were the primary suppliers of
cocoa beans to a burgeoning global market (Cadbury 1896; Knapp 1920; Young 2007). The first
cultivation of cocoa in Ghana occurred sometime in the second half of the 19th century –
24
believed to be from seeds of Forestero varieties developed in Brazil and brought from plantations
on Boiko (then Fernando Po) and Sao Tomé, both islands off the coast of West Africa – and by
1925 Ghana was the largest producer in the world. The country now produces more than 700,000
tonnes of cocoa every year (ICCO 2014). Indeed, nearly three quarters of global cocoa
production occurs in West Africa, with Ghana currently the second largest producer in the world
after neighbouring Côte d’Ivoire (ICCO 2014).
National interest in the management of cocoa production is substantial. The cocoa industry
constitutes 60% of Ghana’s foreign earnings and it is estimated that close to a third of the
population relies directly or indirectly on the cocoa industry for their livelihoods (Fenger et al.
2017). The Ghana Cocoa Board (COCOBOD) regulates the farmgate price, which is paid out to
over 700,000 cocoa farmers, most smallholders with farms approximately 2 ha in size, who
cumulatively produce the most important export commodity for the country. Beans are
purchased directly from farmers by purchasers who then sell beans to primarily large
multinational agricultural companies at the major ports on the coast. The cocoa is mostly
destined for processing in Europe, although some processing plants have been established in
Ghana (Ryan 2011). The Cocoa Research Institute of Ghana (CRIG), which is within the
COCOBOD organization, is largely involved in varietal development, distributing planting
stock, and the study and development of fertilizer. Hybrid varieties were bred from the original
Amazon varieties to increase early vigor and early cocoa pod production as well as resistance to
pathogens. The resultant varieties are now known as Lower Amazon variety or West African
Amelando-hybrid, and these are widely cultivated throughout Ghana, and presumed to have low
genetic diversity (Asare et al. 2010, Zhang and Motilal 2016).
Large portions of Ghana meet the climatic requirements of T. cacao. Within the cocoa growing
region, areas that experience MAP of 1250 to 1500 mm are typical of more fertile sites, while
those with heavier rainfall (MAP > 2000 mm) typically have lower soil fertility (Wood and Lass
2001). Acrisols and Ferralsols are the soil orders that predominate (Krasilnikov et al. 2009).
Commonly, these soils are characterized by a deficiency of bioavailable P (primarily as available
as H2PO4- in acidic soils) (Hinsinger 2001, Lambers et al. 2006). These soils, particularly
Ferralsols, are susceptible to rapid decline in soil fertility with loss in vegetation. Additionally,
the higher concentrations of H+ and Al3+ in these soils can displace and inhibit other cations from
binding to organic material and clay exchange complex sites, resulting in increased leaching of
25
cations (K+, Ca2+, Mg2+). Currently, Asaase Wura (NPK 0-18-22 + Ca, S, and Mg) is one of the
main CRIG-promoted fertilizers for cocoa production in Ghana, although revised amendments
from CRIG now include N and T. cacao growth response has been found with additions of N
(Hartemink 2005, van Vliet and Giller 2017). Insecticides and fungicides are also recommended
to manage pests and fungal spread on farms. Farmers could, at times, acquire these inputs
without cost from bean purchasers and COCOBOD, but there was a recent announcement that
COCOBOD will now only subsidize a portion of the cost for these inputs.
1.3.5 Environmental and production concerns
The global demand for cocoa is projected to outstrip supply by the year 2020 (Schmitz and
Shapiro 2015). Aging cocoa farms, with trees beyond their peak productivity, and diseases are
the major constraints on cocoa production. To illustrate, yield losses from disease are
conservatively estimated at 20% (Ploetz 2016). In Ghana, black pod disease (Phytophthora
megakarya and Phytophthora palmivora) and cocoa swollen-shoot virus (CSSV) (genus
Badnavirus) are the main concerns (Ploetz 2016). Mirids (Sahlbergella singularis Hagl. and
Distantiella theobroma Distant) are also common pests that damage T. cacao in West Africa
(Babin et al. 2010).
Speculation on the future viability of cocoa production has amplified external and internal
drivers of agricultural intensification and expansion into forest. Drivers towards intensification
are manifested through promotion by cocoa industry and government for greater use of
fertilizers, fungicides, insecticides, and reduced shade trees, while at the same time farmers are
drawn to potential short-term yield gains (Ahenkorah et al. 1987, Schroth and Ruf 2014). New
farmers or current farmers may be encouraged to expand land under cultivation as soil fertility
decline on current farms, and/or due to the appeal of a steadily growing cocoa market (Ruf et al.
2015). While there is some evidence that the expansion into forest is slowing, there are still
concerns on biodiversity loss in the Guinea forest region (Wade et al. 2010, Norris et al. 2010).
There are also increasing concerns around the effects of climate change on cocoa production as it
relates to impacts on T. cacao physiology. In much of the growing region in Ghana, it is
expected that higher dry season temperatures will be compensated by a shorter dry season
resulting in limited change to total mean annual precipitation (Schroth et al. 2016b). There is
limited understanding of the tolerance of mature T. cacao to increased evapotranspiration or heat
26
load in different management conditions (Schwendenmann et al. 2010). In an experimental
drought in a perennial agroforestry system in Indonesia, T. cacao showed a decline in yield but
otherwise was unaffected, with roots able to uptake water at high tension (-1.5 MPa) (Moser et
al. 2010, Schwendenmann et al. 2010). The authors of these studies suggest the T. cacao might
have acclimated to drier conditions through reduction of transpiration and osmotic adjustments in
the roots. Additionally, however, microclimate regulation from shade trees coupled with vertical
differentiation of roots between T. cacao and shade trees reduced the impact of drying on T.
cacao (Moser et al. 2010, Schwendenmann et al. 2010) and leaf stomatal closure has been shown
to be a critical regulator of water loss for T. cacao with drying soils (de Almeida et al. 2016).
Conversely, large die-off of T. cacao was reported following extended drought, more so under
agroforestry compared to monoculture (Abdulai et al. 2018; but see Wanger et al. (2018)).
Therefore, it is essential to improve our understanding of the roles of intercropped shade trees in
mitigating environmental threats to cocoa production.
1.4 Research objectives
In my PhD research, I aim to quantify the extent and direction of variation in functional root
traits within tropical agroecosystems. I broadly ask: Does agroecosystem management affect
intraspecific variability of root traits, particularly those traits associated with critical ecological
function and services? This question is based on the following hypotheses: i) root traits vary
within a species (specifically T. cacao) and ii) this variation is largely explained by environment
(within and across resource-limited agroecosystems in Ghana), but which iii) management
practices can modify (specifically through integration of shade trees and use of fertilizer).
To answer my research question and to test my hypotheses, I carried out four studies with the
following objectives:
1) Quantify belowground biomass in coarse roots of T. cacao and determine the extent of
variation in total tree biomass allocation attributed to different shade management
scenarios (Chapter 2).
2) Document fine-scale nutrient-specific acquisition strategies within an individual T. cacao
root system and establish how these strategies are altered by neighbouring shade trees
(Chapter 3).
27
3) Measure the fine root trait response of T. cacao to fertilization and assess whether species
composition regulates this response (Chapter 4).
4) Compare the effects of shade trees on fine roots of individual T. cacao grown in optimal
and suboptimal climates (Chapter 5).
1.4.1 Research sites and study systems
I carried out my research on dedicated cocoa research sites and working farms, which are
summarized below and whose locations are shown in Figure 1.1. For Chapters 2, 3, and 4, data
were collected from a research station managed by the Forestry Research Institute of Ghana
(FORIG cocoa research station). For Chapter 5, I carried out a multi-site study that spanned a
rainfall gradient within the cocoa growing region of Ghana and thus data were collected from all
four sites. Climate information are from ~1 km resolution climate data from the WorldClim
database (Hijmans et al. 2005). At all sites, T. cacao of the common hybrid variety were between
12 and 15 years old.
The management systems present at these sites allowed me to compare T. cacao grown in
monoculture with T. cacao grown in mixture with shade trees. In chapters 2, 3, and 4, I study T.
cacao in monoculture and T. cacao with either two species of shade trees that farmers can also
harvest for timber: Entandrophragma angolense (Welw.) C. DC. (Meliaceae) and Terminalia
ivorensis A. Chev. (Combretaceae). In Chapter 5, I examine T. cacao grown in monoculture or
T. cacao grown with T. ivorensis.
E. angolense, locally named Edinam, is a late-successional deciduous hardwood species. T.
ivorensis, locally named Emire, is a fast growing early-successional deciduous species. E.
angolense is perceived by farmers to be deeper-rooted and thus less competitive tree with T.
cacao. Conversely, T. ivorensis has been found to dominate the top 10 cm of soil in a coffee
agroforestry system and thought to compete more strongly with the crop species (van Kanten et
al. 2005). However, farmers often select this tree species due to its tall and sparse shading,
timber, and perceived improvements to soil nutrient and water retention in soil (Graefe et al.
2017).
28
Figure 1.1: Maps of Ghana showing locations of study sites.
29
South Formangso, cocoa research station (Plate 1.1):
The primary research site was a dedicated cocoa research station managed by the Forestry
Research Institute of Ghana (CSIR-FORIG) (coordinates: N6° 36.6 W0° 58.3; elevation: 220 m).
It is accessible from the village of South Formangso in the Ashanti Region. It is in a moist semi-
deciduous forest zone. At the site, soils are sandy loam and Acrisols. Climatic conditions for T.
cacao are optimal. Mean annual rainfall (MAP) is 1528 mm with the wettest month receiving on
average 230 mm while the driest month receives 22 mm. Mean annual temperature is 26.2 °C.
Wiawso, working farm (Plate 1.2):
This cocoa farm near Wiaswo (Amafie) in the Western Region is in a wet evergreen forest zone
(coordinates: N6° 10.8 W2° 28.5; elevation: 235 m). This area is dominated by cocoa cultivation
and has optimal climate for cocoa cultivation. There is higher rainfall at MAP of 1546 mm with
the wettest month receiving approximately 260 mm and the driest month receiving 25 mm. MAT
is 26.1 °C. Soils are loam.
Mampong, crop research station (Plate 1.3):
This cocoa research station was established as part of a crop research facility at the University of
Education, Winneba, Mampong Campus in the Ashanti Region (coordinates: N7° 04.9 W1°
23.7; elevation: 365 m). The site is near the transition zone from semi-deciduous forest to
savanna and represents suboptimal climatic conditions for cocoa cultivation. There is lower
rainfall, with MAP of 1278 and the maximum rainfall month receiving 197 mm while the driest
month receives 12 mm. MAT is the lowest at this site at 23.6 °C. Soils are sandy loam.
Dedease, working farm (Plate 1.4):
This farm site, which is accessible from the village of Dedease in the Brong Ahafo Region
(coordinates: N7° 07.2 W2° 21.4; elevation: 255 m), is in semi-deciduous forest zone and while
situated in a cocoa growing region is also located near to the transition zone and experiences
suboptimal climatic conditions for cocoa cultivation. With lower rainfall of MAP 1216 mm. The
wettest month has 183 mm while the driest has 9 mm. MAT is 26.0 °C. Soils are clayey loam.
30
Plate 1.1: Images from a cocoa research station near South Formangso, Ashanti Region. Images
show top left: T. cacao in monoculture; top right: T. cacao in mixture with T. ivorensis and, also,
cocoa pods at different stages of decomposition after the beans were harvested; bottom left: GPR
being used in detection of coarse root biomass; and bottom right: destructive harvesting of T.
cacao for direct biomass measurements.
31
Plate 1.2: Images from a working farm near Wiawso, Western Region. Images show in the top
left: sampling of roots from T. cacao in mixture with T. ivorensis, which is off to the left of the
image; top right: a view of T. cacao in mixture with T. ivorensis from a nearby cleared plot;
bottom left: manual tracing and sampling of roots from T. cacao; and bottom right: sampling T.
cacao roots near the base of T. ivorensis.
32
Plate 1.3: Images from a cocoa research site near Mampong, Ashanti Region. Images show in
the top left: T. cacao in monoculture; top right: a view of the canopy of T. cacao, with canopy of
shade trees visible in the background; bottom left: the canopy of T. ivorensis above T. cacao; and
bottom right: manual tracing and sampling of roots from T. cacao.
33
Plate 1.4: Images from a working farm near Dedease, Brong Ahafo Region. Images show in the
top left: T. cacao in monoculture; top right: T. cacao and Terminalia superba in the foreground
and sampling of T. cacao in next to T. ivorensis in the background; bottom left: exposed large
lateral coarse root of T. cacao; and bottom right: manual tracing and sampling of roots from T.
cacao in monoculture.
34
1.5 Thesis structure
The thesis is comprised of four research chapters presented in manuscript format, preceded by an
introductory chapter and followed by a concluding chapter. Some modifications were made in
the chapters from the published article or from the manuscripts in preparation to maintain
consistency in the thesis.
In Chapter 2, I bridge conventional methods of quantifying coarse root biomass with non-
destructive application of GPR to estimate T. cacao belowground biomass (BGB) and C stocks
in an agroforestry system in Ghana. It was estimated that 15-year-old T. cacao have a RS ratio of
approximately 0.23. However, the results indicate that proportionally more biomass was
allocated to roots for T. cacao grown in mixture with shade trees. This variation has implications
for C inventories in cocoa agroecosystems. I presented this study in a talk at the Ecological
Society of America Annual Meeting, August 12, 2015, in Baltimore, USA. A modified version
of this chapter was published in Agroforestry Systems as “Root biomass variation of cocoa and
implications for carbon stocks in agroforestry systems” which was co-authored by Luke C.N,
Anglaaere, Stephen Adu-Bredu, and Marney E. Isaac. ‘Permission to reprint’ in Copyright
Acknowledgments section at end of thesis.
In Chapter 3, I carried out an intensive root and soil survey of the 2-dimenstional profiles of T.
cacao root systems. There was large variation in bulk soil nutrients within the rooting zone of T.
cacao. T. cacao was responsive to this variation: higher nutrient availability was associated with
higher fine rooting densities (fine root length and fine root biomass densities). This trend was
coupled with increased resource investment at the root scale, expressed as increased fine root
diameter and reduced specific root length and specific root tip abundance, but these trends were
nutrient-specific and varied among species combinations. These results suggest differential
resource requirements for T. cacao depending on species combination and that T. cacao roots are
responsive to those limitations through modular changes in the architecture and morphology
within the root system. A modified version of this chapter is under review as “Variation in fine
root traits reveals nutrient-specific acquisition strategies regulated by conspecific and
heterospecific neighbours” and was co-authored by Sean C. Thomas and Marney E. Isaac.
35
In Chapter 4, I carried out a manipulative fertilization field experiment that tested the direction
and magnitude of T. cacao root trait response to an influx of nutrients and species combination.
Generally, I found that traits shifted to be more conservative, although there was limited change
between two levels of fertilization demonstrating limitation to trait variation. I also investigated
if there was a local scale root economics spectrum (RES) among individual T. cacao and if the
management treatments were critical in driving the spread of individual trees on that spectrum.
Root proliferation and coordinated trait syndromes (e.g., RES) were better explained by species
combination rather than fertilization level. I presented this study in a talk at the Association of
Tropical Biology and Conservation Annual Meeting, July 10, 2017, in Merida, Mexico. A
modified version of this chapter is in preparation for submission as “Shade trees regulate the fine
root trait response of cocoa (Theobroma cacao L.) to fertilization” and was co-authored by
Marney E. Isaac.
In Chapter 5, I collected fine roots of T. cacao across optimal and suboptimal precipitation
regimes and in contrasting edaphic conditions (sandy vs. loam). I found a within-species RES
and T. cacao roots at a climatically optimal site with loam soils were more conservative on this
spectrum compared to T. cacao at the other sites. However, root resource acquisition strategies
of T. cacao were differentially responsive to a shade tree across different sites. Stronger
relationships to soil variables were observed for a secondary axis of coordinated root trait
variation that described fine root growth rate and a trade off in root diameter and root nitrogen
concentration in opposition to root tissue density, suggesting that multiple dimensions in root
trait covariation in T. cacao are important for resource acquisition strategies. A modified version
of this chapter is in preparation for submission as “Effects of interspecific interactions on T.
cacao root strategies across optimal and suboptimal climates”.
In Chapter 6, I summarize the major contributions from my PhD research, I highlight the
implications of my findings for both theoretical and applied purposes, and I present
recommendations for future research.
36
Chapter 2 Root biomass variation in Theobroma cacao and implications for
carbon stocks in agroforestry systems
2.1 Abstract
Theobroma cacao root systems are typically assumed to contribute a small portion of carbon (C)
to total C stocks in cocoa agroecosystems. Yet there are almost no direct measurements of T.
cacao coarse root biomass to support this assumption, presumably due to the difficulty of
measuring coarse roots in situ and the risk to farmers’ livelihoods. Instead, root biomass is
commonly estimated using allometry based on forest data, which might not be accurate for
perennial crops given their range of management conditions. In this study, I bridge conventional
methods of quantifying coarse root biomass with non-destructive application of ground
penetrating radar (GPR) to estimate T. cacao belowground biomass (BGB) and C stocks in an
agroforestry system in Ghana. BGB was measured for T. cacao grown with shade trees
(Entandrophragma angolense or Terminalia ivorensis) and in monoculture. BGB estimates
showed good accuracy, with a relative root mean square error of 7% from excavated plants. It
was estimated that 15-year-old T. cacao hold approximately 6.0 kg C plant-1 in coarse root
biomass and have a root to shoot ratio of approximately 0.23. However, the results indicate that
proportionally more biomass was allocated to roots for T. cacao grown in mixture with shade
trees. Plot-scale estimates show that T. cacao roots contributed 5.4 to 6.4 Mg C ha-1, representing
8 to 16% of C stocks in all live tree biomass (T. cacao + shade trees), depending on shade tree
management. My findings illustrate a promising approach for non-destructive BGB inventories
of perennial crops. It is highlighted that although commonly used pan-tropical allometric
equations may broadly function in estimating BGB for T. cacao, this approach assumes
proportional allocation between aboveground biomass and BGB, which may translate into
inaccuracies in C stock inventories across diverse cocoa agroecosystems.
37
2.2 Introduction
Cocoa cultivation occurs on 10 million ha of land globally (FAO 2013) and in regions where it is
produced can be a dominant form of land-use (Norris et al. 2010, Schroth et al. 2015), thus
deserving inclusion in national or sub-national carbon (C) inventories (Wade et al. 2010).
Dedicated attention towards the role of cocoa agroecosystems in the C cycle is reflected by many
recent C stock assessment studies in West Africa (Wade et al. 2010, Norgrove and Hauser 2013,
Saj et al. 2013), Central and South America (Somarriba et al. 2013, Jacobi et al. 2014, Schroth et
al. 2016a), and Southeast Asia (Smiley and Kroschel 2008, Leuschner et al. 2013, Rajab et al.
2016). As T. cacao is often cultivated under shade trees, cocoa agroforestry systems are
characterized by diverse vegetative structure, balancing the objectives of maintaining agricultural
productivity with increasing C stocks in tropical landscapes (Vaast and Somarriba 2014). Given
that tree species composition in agroforestry systems is a strong determinant of C stocks at the
farm or plot scale (Dixon 1995, Montagnini and Nair 2004, Kirby and Potvin 2007, Jose 2009,
Kessler et al. 2012), attention has been focused on contrasting the amount of C given a range of
on-farm tree diversity in cocoa agroecosystems (Wade et al. 2010, Somarriba et al. 2013, Saj et
al. 2013, Jacobi et al. 2014, Obeng and Aguilar 2015, Rajab et al. 2016).
The largest contributors to C stocks in cocoa agroforestry systems are typically shade tree
biomass and soil organic matter (Wade et al. 2010, Somarriba et al. 2013, Jacobi et al. 2014),
although the contribution from T. cacao becomes increasingly dominant with higher densities of
T. cacao and/or thinning of shade trees (Wade et al. 2010, Saj et al. 2013). To quantify the
biomass C in T. cacao, a number of species-specific allometric equations for aboveground
biomass (AGB) have been developed for T. cacao (e.g. Andrade et al. 2008; Smiley and
Kroschel 2008; Somarriba et al. 2013). However, studies that include estimates of belowground
biomass (BGB) of T. cacao almost entirely rely on allometric equations for tropical forests or a
set proportion of BGB in relation to AGB (i.e. root to shoot (RS) ratios) (Table 2.1). Given the
challenges of directly measuring root systems, these approaches are viable alternatives to
destructive excavation. However, equations derived from forest data arguably have reduced
accuracy in cultivated systems (Kuyah et al. 2012, Borden et al. 2014). Additionally, generalized
allometry might be limited in capturing variation of biomass allocation. This is of interest when
38
Tab
le 2
.1:
Pre
vio
usl
y r
eport
ed c
oar
se r
oot
bio
mas
s (B
GB
) ca
rbon i
n c
oco
a ag
roec
osy
stem
s an
d m
ethods
of
esti
mat
ion b
ased
on
aboveg
round p
aram
eter
s.
39
* c
alcu
late
d u
sin
g t
he
met
ho
ds
in c
ited
pap
er;
excl
ud
es f
ine
roo
t b
iom
ass
a ag
e cl
assi
fica
tio
n f
oll
ow
s S
aj e
t al
. (2
01
3):
im
mat
ure
pla
nta
tio
ns
= u
nd
er 8
yea
rs o
ld;
yo
un
g p
lan
tati
on
s =
9 t
o 2
0 y
ears
, m
atu
re p
lan
tati
on
s =
21
to
40 y
ears
;
sen
esce
nt
pla
nta
tio
ns
= 4
1 y
ears
an
d o
lder
b A
FS
1 =
sh
ade
tree
s fo
r ti
mber
or
fru
it p
rod
uct
ion
; A
FS
2 =
N2-f
ixin
g s
had
e tr
ees;
AF
S3
= m
ult
isp
ecie
s ag
rofo
rest
ry s
yst
ems
c G
A-G
A =
AG
B f
rom
gen
eral
ized
all
om
etri
c eq
uat
ion
use
d i
n a
gen
eral
ized
all
om
etri
c eq
uat
ion
for
BG
B;
SA
-GA
= A
GB
fro
m c
oco
a-sp
ecif
ic a
llo
met
ric
equ
atio
n u
sed
in
gen
eral
ized
all
om
etri
c eq
uat
ion f
or
BG
B;
SA
-SA
/S =
AG
B f
rom
co
coa-
spec
ific
all
om
etri
c eq
uat
ion
use
d i
n c
oco
a-s
pec
ific
RS
rat
io o
r w
ith
dir
ect
mea
sure
men
ts o
f ro
ots
§ A
GB
= e
xp
[-2.1
87
+ 0
.91
6 l
n(ρ
D2H
)] (
Ch
ave
et a
l. 2
005
)
ҍ A
GB
= e
xp
[-2
.97
7 +
0.9
4 l
n(ρ
D2H
)] (
Ch
ave
et a
l. 2
00
5)
† B
GB
= e
xp
[-1
.05
87 +
0.8
836
ln
(AG
B)]
(C
airn
s et
al.
199
7)
‡ B
GB
=
ex
p[-
1.0
85 +
0.9
26 l
n(A
GB
)] (
Cai
rns
et a
l. 1
997
)
40
management and/or environment exhibit strong control over resource availability, inducing
variable growing conditions for a cultivated species. The plasticity of T. cacao coarse and fine
root architecture and activity under different environmental and management conditions has been
documented via soil profiles (Moser et al. 2010, Schwendenmann et al. 2010), isotope signatures
(Schwendenmann et al. 2010, Isaac et al. 2014), and near-surface imaging using ground
penetrating radar (GPR) (Isaac et al. 2014). However, to my knowledge, there are no studies that
test for altered root biomass of productive T. cacao in response to effects from shade trees.
In this study, I used a combination of GPR geo-imagery with conventional sampling techniques
(soil cores and excavation) to quantify coarse root biomass for T. cacao in monoculture, T. cacao
in mixture with E. angolense, and T. cacao in mixture with T. ivorensis. The objectives of this
study were (i) to determine the contribution of T. cacao coarse roots to vegetative C stocks, (ii)
to distinguish intraspecific variation of T. cacao root biomass in three shade tree compositions
that are commonly practiced in Ghana (Anglaaere et al. 2011), and (iii) to evaluate the accuracy
of estimates, as this study represents a first-time application of GPR for BGB estimation within
cocoa agroecosystems.
2.3 Methods
2.3.1 Study site and study plants
The study was conducted at a cocoa research station, managed by the CSIR-Forestry Research
Institute of Ghana, located in South Formangso, Ashanti Region, Ghana (6˚36ˊ N and 0˚58ˊ W).
The site was previously secondary forest until it was cleared for food crop cultivation and left to
fallow until the research plot was established in 2001. It is located within a moist semi-deciduous
forest zone. The mean annual rainfall ranges from 1700 to 1850 mm with two maxima rainfall
seasons from March to July and September to November. Soils are predominantly loam or sandy
clay loam and are classified as Acrisols (Isaac et al. 2014).
Three shade tree treatments were used: 1) T. cacao in monoculture, 2) T. cacao in mixture with
E. angolense (DBH = 22.4 ± 3.6 cm; height = 14.1 ± 3.0; n = 5), and 3) T. cacao in mixture with
T. ivorensis (DBH = 52.8 ± 8.0 cm; height = 21.6 ± 2.0 m; n = 5). These shade tree species are
used for timber and are often selected by farmers in this region (Anglaaere et al. 2011). Fifteen-
year-old T. cacao at this site (DBH = 12.4 ± 2.8 cm; height = 6.1 ± 1.1 m; n = 45) are in regular
41
spacing of 3 m × 3 m and are all the same variety (hybrid T. cacao from the Cocoa Research
Institute of Ghana). In mixture, the shade trees were planted at the same time as T. cacao and in
replacement design of 12 m × 12 m spacing. Thus, T. cacao density in monoculture is 1111
plants ha-1 while in mixtures it is 1042 plants ha-1. Management was consistent across all
treatments and no fertilizer was applied to the site prior to this study. The coarse root systems of
15 individual T. cacao were surveyed, which included five T. cacao for each of the shade tree
treatments. T. cacao were chosen at random from pre-established study blocks (one per block),
although limited to where GPR survey was appropriate (e.g. relatively flat soil) and, when in
mixture, T. cacao that were 3 m from a shade tree.
2.3.2 Coarse root biomass estimation using GPR
After removal of leaf litter, grids 3.0 m × 3.0 m were centred at the base of each surveyed T.
cacao, with consistent orientation to plant spacing, and assumed to be the unit soil area of a
single T. cacao (Bengough et al. 2000). A GPR unit with a centre frequency of 1000 MHz with
an attached odometer (Sensors & Software Inc., Canada) (Table A.1) was used to collect geo-
imagery data in straight orthogonal lines with transect spacing of 0.10 m, which produced 62
geo-images for each surveyed root system. Potential sources of signal interference, such as
where understory crops were planted (e.g. cocoyam Xanthosoma sagittifolium (L.) Shott), were
marked in the data. These locations were later reviewed to omit any signal response that could be
falsely identified as T. cacao root biomass. All geo-image data were processed following a data
processing sequence that reduces background signal noise, compensates for signal attenuation
with depth, and delineates possible root reflections (Guo et al. 2013, Borden et al. 2014). Geo-
image processing was completed in EKKO software (Sensors & Software). Subsequently,
thresholding of processed images was completed in ImageJ 1.48v (US National Institutes of
Health, USA) to measure the number of pixels in a geo-image that were delineated as coarse root
biomass (Table A.2).
A calibration model was populated whereby root biomass was related to the corresponding radar
response following a user-guided approach that included a range of radar responses (Butnor et al.
2015). The corresponding roots at each identified location were excavated from the top 30 cm of
soil, cut to 10 cm lengths (matched to GPR transect spacing), washed, oven dried at 70 °C to
constant mass, and then weighed for root mass (n = 30). The minimum detectable root mass was
42
4.9 g, identified by the y-intercept of the calibration relationship (Table A.2 and Fig. A.1). This
calibration relationship was applied to all geo-imaged root responses to estimate biomass and
summed at the tree scale to estimate coarse root biomass. GPR signal attenuation occurred
between 30 and 40 cm soil depth and, thus, I limited lateral root biomass estimation to the top 30
cm of soil. T. cacao have shallow lateral root systems primarily in the top 30 cm of soil, but
some lateral root biomass might be located below 30 cm (Moser et al. 2010, Nygren et al. 2013,
Isaac et al. 2014).
2.3.3 Sampling of coarse roots and whole plant excavations
To quantify coarse root biomass too small for GPR signal detection, a 10-cm diameter auger was
used to extract soil and roots to 30 cm depth (soil core volume = 2356 cm3) at 5 random
locations within the GPR survey area of each study plant. Coarse roots (> 0.2 cm diameter) were
removed by hand from extracted soil cores. T. cacao roots, identified by their distinctly dark
reddish-brown colour, were separated from shade tree roots. The mean small coarse root (0.2 cm
to 1.3 cm diameter) biomass extracted from the soil cores was used to estimate the small root
biomass for each surveyed root system.
As GPR-based estimates cannot discern roots by species, using the same soil cores as above, I
calculated the biomass contribution of shade tree species to subtract from my estimates. To 30
cm sampling depth, T. ivorensis contributed 9% of the coarse root biomass in T. cacao-T.
ivorensis mixture and E. angolensis contributed less than 1% of the coarse root biomass in the T.
cacao-E. angolensis mixture and was assumed to be negligible.
A subsample of the study plants (n = 3) were destructively harvested to quantify both
aboveground biomass (ABGH) and belowground biomass (BGBH). T. cacao coarse root systems
were excavated for the purposes of (i) estimating taproot biomass that would be undetected by
GPR (i.e. below-stem biomass (Butnor et al. 2015)) and (ii) evaluating the accuracy of the
estimates. Square plots matching the area of the GPR survey grids were manually excavated to
30 cm. Tap roots were excavated completely (below 30 cm depth) and separated from lateral
roots. The destructively sampled T. cacao trees were stratified into stem, branch, and root
organs, and the fresh weight of the various organs determined with a weighing scale. Samples of
stem, branch, and root (n = 6) were weighed and oven dried to determine moisture contents,
43
which were used to calculate the dry weight biomass of each biomass organ for each harvested T.
cacao tree.
2.3.4 Biomass allocation calculations
AGB of T. cacao was estimated using a T. cacao-specific allometric equation (Somarriba et al.
2013):
Log(AGBSA) = (-1.684 + 2.158 × Log(D30) + 0.892 × Log(H)) (1)
where AGBSA is the species-specific allometric estimate of T. cacao aboveground biomass, D30 is
the diameter in cm of the stem at 30 cm above the ground and H is tree height in m, which was
measured using a clinometer. This equation was based on T. cacao in Central America with
DBH ranging between 1.3 to 26.8 cm and represents one of the only published species-specific
AGB equations for T. cacao.
BGB of T. cacao was estimated in two ways. One method was based on GPR data and calculated
as:
BGBGPR = BGBlateral + BGBtaproot. (2)
where BGBGPR is the sum of lateral root biomass (BGBlateral), which is the large coarse root
biomass estimated using GPR and the small coarse root biomass estimated from soil cores, and
BGBtaproot, which was calculated using the ratio of lateral roots to taproot biomass of the
harvested T. cacao. The second method involved application of the most commonly used
allometric equation (Table 1) reported in Cairns et al. (1997) as:
BGBGA = exp[-1.0587 + 0.8836 × ln(AGBSA)] (3)
where BGBGA is the BGB estimated using generalized allometric equation for tropical forests and
calculated using AGBSA from Eq. 1. RS ratios of T. cacao were calculated as the ratio of
BGBGPR to AGBSA.
2.3.5 Biomass carbon calculations
To determine the C fraction of T. cacao biomass, coarse roots were collected during excavation
and stem samples were collected at 1.3 m above the ground using an increment borer (n = 3).
44
Stem and root samples were stored in air-tight bags and frozen until they were later freeze-dried
to constant weight. Samples were ground in ball-mill and analyzed for total C using a CN
elemental analyzer (Thermo Flash 2000) at University of Toronto Scarborough, Canada. The C
fraction was determined using protocol found in Thomas and Martin (2012) that includes C
volatilized during oven drying.
An inventory of T. cacao tree metrics, D30 and H, was carried out to calculate AGBSA of T. cacao
at the study site (n = 45). Subsequently, BGB estimates of T. cacao across the site were
calculated using the RS ratios of surveyed T. cacao, as well as generalized allometry (Equation
3) for comparative purposes. C fractions from chemical analysis were applied to respective
above- and belowground biomass to calculate the C content of T. cacao. Plot scale estimates
were determined as the products of the T. cacao density and C contents of T. cacao, based on
shade tree treatment. As the effects of distance from shade tree on T. cacao BGB were not tested,
it was assumed that T. cacao 6 m from the shade trees (i.e., 50% of T. cacao plants in mixture)
were not affected by shade trees (Isaac et al. 2007a). Species-specific allometric equations, C
fractions, and a shade tree density of 68 trees ha-1 were used to estimate biomass (AGB + BGB)
C of the shade trees E. angolense and T. ivorensis (Deans et al. 1996, Henry et al. 2011, Yeboah
et al. 2014).
2.3.6 Statistical analysis
The relationship between coarse root biomass and radar signal response (i.e., calibration model)
was assessed with Pearson’s correlation coefficient and one-way analysis of variance (ANOVA)
(Fig. A.1). Relationships between aboveground plant metrics (DBH) and BGB estimates were
described using linear regression. Differences in BGB and RS ratios among shade tree treatments
were tested using ANOVA and, when significant, pairwise comparisons using Tukey’s HSD test.
Percent differences, root mean square error (RMSE), relative root mean square error (RRMSE),
and coefficient of determination (r2) were used to evaluate BGB estimator performance when
comparing methodologies. Prior to parametric tests, data were tested for equality of variance
using the Bartlett test and tested for normality of residuals using the Shapiro–Wilk test.
Statistical analyses were completed in R (R Foundation for Statistical Computing, Austria) with
the level of significance set at p < 0.05.
45
2.4 Results
2.4.1 Coarse root biomass estimation
Within this even-aged agroecosystem, aboveground tree size was related to amount of biomass
belowground, with BGBGPR positively correlating with the size of stem (r2 = 0.37; F1,13 = 7.54; p
= 0.02) (Fig. 2.1). This correlation between structural roots and stem size was driven by variation
in large coarse roots. GPR detected between 4.6 and 13.7 kg plant -1 in BGB and these values
were significantly and positively correlated with DBH (r2 = 0.36; F1,13 = 7.45; p = 0.02; data not
shown) unlike the smaller (0.2 to 1.3 cm diameter) coarse root biomass measured from soil cores
that did not correlate with DBH (F1,12 = 3.89; p > 0.05; data not shown). The ratio of excavated
lateral to excavated taproot biomass was 3.3 ± 0.2 (n = 3), which was used to estimate the taproot
biomass of each study tree, and thus taproots were found to comprise approximately 23% of
BGBGPR.
BGBGPR displayed good accuracy, with a mean percent difference of 12.0 ± 7.8% (n = 3), RMSE
of 3.0 kg tree-1, and RRMSE of 7.3% from BGBH of matched plants. BGBGA was less accurate,
indicated by a larger mean percent difference of 30.1 ± 1.6%, RMSE of 7.5 kg tree-1, and
RRMSE of 18.3%. While there was significant correlation between estimates from the two
approaches (r2 = 0.44; F1,13 = 10.10; p = 0.007) (Fig. 2.2), some inconsistencies were observed.
The RMSE between the two approaches was 5.6 kg tree-1 (RRMSE of 7.0%). There was a
tendency for BGBGA to be overestimated when there was less than 16.4 kg tree-1 and
underestimated when there was more (Fig. 2.2), or in more tangible terms, when T. cacao DBH
was less than or more than 12 cm.
2.4.2 Biomass allocation
While there was a preference for T. cacao to allocate proportionally more biomass belowground
when in mixture, there was no significant variation in RS detected among species combination
(F2,12 = 2.39; p > 0.05). T. cacao in mixture with T. ivorensis had a RS ratio of 0.28 ± 0.05 (±
SE; n = 5), T. cacao in mixture with E. angolense had a RS ratio of 0.23 ± 0.01 (n = 5), while T.
cacao in monoculture had a RS ratio of 0.19 ± 0.02 (n = 5) (Table 2.2). Applying treatment-
specific RS ratios to AGBSA across the study site (55.4 ± 5.2 kg tree-1; n =45), BGB for 15-year-
old T. cacao was estimated to be 10.4 ± 1.0 kg plant-1 in monoculture, 12.9 ± 1.2 kg tree-1 for
46
Figure 2.1: Relationship between T. cacao DBH (cm) and coarse root biomass (kg tree-1)
(BGBGPR = 0.50 + 1.37×DBH). Symbols represent T. cacao grown in different shade tree
treatments (■ = T. cacao in monoculture, ● = T. cacao in mixture with E. angolense, ▲= T.
cacao in mixture with T. ivorensis). Excavated amounts (BGBH) are indicated (×) but are not
included in the regressions.
47
Figure 2.2: Relationship between BGB of individual T. cacao (kg tree-1) as estimated from GPR
and destructive sampling (BGBGPR) and estimated using a generalized allometric equation
(BGBGA). Linear regression is the solid line (BGBGA = 7.07 + 0.57×BGBGPR). Symbols represent
T. cacao grown in different shade tree treatments (■ = T. cacao in monoculture, ● = T. cacao in
mixture with E. angolense, ▲= T. cacao in mixture with T. ivorensis).
48
Table 2.2: Root to shoot ratios (BGBGPR:AGBSA; mean ± SE) of 15-year-old T. cacao. T. cacao
coarse root biomass and C estimates are based on T. cacao aboveground biomassa estimated for
the site and tissue-specific C fractionb.
RS ratio
(BGBGPR:AGBSA)
Coarse root
biomass
(kg tree-1)
C in coarse
root
(kg C tree-1)
T. cacao average 0.23 ± 0.02 12.7 ± 1.2 6.0
By shade management
T. cacao in monoculture 0.19 ± 0.02 10.4 ± 1.0A 4.9
T. cacao in mixture with
Entandrophragma angolense 0.23 ± 0.01 12.9 ± 1.2AB 6.1
T. cacao in mixture with
Terminalia ivorensis 0.28 ± 0.05 15.6 ± 1.5B 7.3
Non-significant differences between treatments are indicated by same letters (Tukey HSD; p < 0.05)
There was no significant variation of RS ratios among shade treatments (ANOVA; p > 0.05) a 55.4 ± 5.2 kg tree-1 (mean ± SE; n = 45) calculated using T. cacao-specific allometric equation from
Somarriba et al. (2013) b 0.469 ± 0.005 (mean ± SE; n = 3) is the volatile-inclusive C fraction of T. cacao coarse roots on an oven
dry basis
49
T. cacao in mixture with E. angolense, and 15.6 ± 1.5 kg tree-1 for T. cacao in mixture with T.
ivorensis, in which T. cacao in monoculture was significantly less than T. cacao in mixture with
T. ivorensis (p = 0.01) (Table 2.2). Using generalized allometry, BGBGA of T. cacao was 11.8 ±
1.0 kg tree-1 in all shade tree treatments and the biomass allocation patterns were concentrated
around a mean RS ratio of 0.21 ± 0.00 (± SE), ranging between 0.19 and 0.22 (Fig. 2.3).
2.4.3 Biomass carbon
T. cacao coarse root C fraction was 0.469 ± 0.005 (mean ± SE; n = 3), with a volatile mass
fraction of 0.036 ± 0.005. The C fraction of T. cacao stems was 0.463 ± 0.002, with a volatile
mass fraction of 0.051 ± 0.005. BGBGPR C for T. cacao at this site amounted to 4.9, 6.1, and 7.3
kg C plant-1 for T. cacao in monoculture, T. cacao in mixture with E. angolense and T. cacao in
mixture T. ivorensis, respectively (Table 2.2). BGBGA C was consistently 5.6 kg C tree-1 in all
shade tree treatments.
Even though there was a higher density of T. cacao planted in monoculture (1111 trees ha-1)
compared to in mixture (1046 trees ha-1), plot scale estimates of T. cacao BGB C were relatively
similar for T. cacao in monoculture (5.4 Mg C ha-1) and T. cacao in mixture with E. angolense
(5.7 Mg C ha-1), while T. cacao BGB C in mixture with T. ivorensis (6.4 Mg C ha-1) was
approximately 15% higher than either system (Fig. 2.4). Using generalized allometry, T. cacao
BGBGA C at the plot scale in both mixtures was estimated to be 5.9 Mg C ha-1, which was
proximate to estimates based on BGBGPR for T. cacao in mixture with E. angolense but was an
underestimate of 8% for T. cacao in mixture with T. ivorensis. BGBGA plot scale estimate for T.
cacao in monoculture (6.3 Mg C ha-1) was 16% higher than the corresponding estimate based on
BGBGPR.
Total (AGB + BGB) live tree (T. cacao + shade) C stocks were estimated to be 33.9, 41.9, and
83.7 Mg C ha-1 for T. cacao monoculture, T. cacao-E. angolense mixture, and T. cacao-T.
ivorensis mixture, respectively (Fig. 2.4). Of these amounts, BGB (T. cacao + shade) C stock
amounted to 5.4, 7.2, and 17.8 Mg C ha-1 in T. cacao monoculture, T. cacao-E. angolense
mixture, and T. cacao-T. ivorensis mixture, respectively (Fig. 2.4).
50
Figure 2.3: Root:shoot ratios calculated for T. cacao across three different shade tree treatments.
Root estimates are from GPR and destructive sampling (BGBGPR) and estimated using a
generalized allometric equation (BGBGA). AGBSA was based on species-specific allometric
equation (Somarriba et al. 2013). The horizontal dashed line indicates the RS ratio of 0.20
recommended by the IPCC. The RS ratios measured from one complete harvested T. cacao per
treatment are also shown (×).
51
Figure 2.4: Plot scale estimates of biomass carbon (Mg C ha-1). Carbon estimates are for T.
cacao and shade trees. Total biomass (T. cacao + shade) is indicated by horizontal lines.
52
2.5 Discussion
2.5.1 Biomass carbon stocks in cocoa agroecosystems
T. cacao monoculture contained the least amount of biomass (AGB + BGB) C in plot scale
estimates, representing less than half (41%) of the biomass C in T. cacao-T. ivorensis mixture.
Furthermore, biomass C in T. cacao monoculture was 23% below current average biomass C
stocks on agricultural lands in Ghana (44 Mg C ha-1) (Zomer et al. 2016). T. cacao-T. ivorensis
mixture had the highest biomass C stocks (84 Mg C ha-1). This C estimate was generally higher
than reported biomass C in other cocoa agroforestry systems [Cameroon: 70 Mg C ha-1 (Saj et al.
2013), Bolivia: 69 Mg C ha-1 (Jacobi et al. 2014)] but was less than the 131 Mg C ha-1 reported
for traditional, high-shade, cocoa agroforestry systems in Ghana (Wade et al. 2010). T. ivorensis
contributed 60% of the biomass C to the plot scale estimate, demonstrating the high C storage
potential of this shade tree species. Biomass C in T. cacao-E. angolense mixture was the lowest
of the two mixtures. In this species combination, T. cacao plants contributed the majority (77%)
of biomass C. While individual E. angolense trees contained two to three times the amount of C
than individual T. cacao trees, more substantial C stocking advantages of T. cacao-E. angolense
mixtures over T. cacao monoculture might not be realized until at an older plantation age given
slower growth of late-successional E. angolense. It is unlikely that selection of shade tree species
would reflect C stocking objectives given the lack of C compensation schemes available to
farmers. Thus, while T. ivorensis shows a distinct advantage in C stocking potential, occurrence
of this shade tree in cocoa agroecosystems is attributed to Ghanaian farmers valuing its high
productivity (for timber) and the perceived improvements to soil conditions for T. cacao (Graefe
et al. 2017).
The relative contribution of T. cacao coarse roots to total biomass C stocks can largely depend
on the density that T. cacao is planted and the amount of BGB C of individual T. cacao. T. cacao
roots contributed 5.7 and 6.4 Mg C ha-1 to T. cacao-E. angolense and T. cacao-T. ivorensis
mixture, respectively, which was 13 and 8% of C in all live tree biomass (T. cacao + shade). My
estimates were proximate to root C contributions (11-13%) reported in other agroforestry
systems that had similar T. cacao density (> 1000 plants ha-1) (Leuschner et al. 2013) and root
system size (> 5.0 C plant-1) (Jacobi et al. 2014) (Table 2.1). In less intensively managed
agroforestry systems, with fewer T. cacao and greater density of shade trees, T. cacao BGB may
53
become less significant but perhaps not inconsequential for total C stocks. For example, in
agroforests across Central America, the densities of T. cacao were often half that reported in my
study while densities of shade trees were three to four times greater (Somarriba et al. 2013). Yet,
BGB C in T. cacao plants (~3.6 to 6.3 kg C plant-1) contributed to approximately 2 to 6% of total
biomass C in these agroforests (Table 1) (Somarriba et al. 2013). Differences reported among
studies and among T. cacao growing regions highlight the diversity in agroforestry systems and
the challenge and the need to improve context-specific estimates of C stocks.
2.5.2 Toward accuracy of C accounting in agroforestry systems
The use of site- and species-specific allometric equations, when available, is recommended over
generalized equations for higher accuracy in AGB estimates (Djomo et al. 2010). This
recommendation is seemingly valid but less tested for estimating BGB (Keller et al. 2001,
Mokany et al. 2006, Kuyah et al. 2012). Kuyah et al. (2012) reported that pan-tropical BGB
allometric equations underestimated root biomass in agroforestry systems by approximately 21%
and 35% (using equations from Mokany et al. (2006) and Cairns et al. (1997), respectively). This
study indicates that generalized allometric equations may be limited in tracking allocation
patterns of T. cacao. More empirical evidence beyond the size range of the same-aged T. cacao
plants used in my study are required to respond to this concern and parameterize size-dependent
allometric equations for T. cacao. In many cases, destructive harvesting to populate specialized
ratios and models might not be viable, particularly when complete harvesting can interfere with
farm productivity. As I show here, estimates from GPR data paired with small coarse root
sampling and taproot estimates (BGBGPR) were more proximate than allometric estimates
(BGBGA) to excavated amounts of coarse root biomass (BGBH). Non- and/or less-destructive
sampling of root systems can aid in testing for variation in RS ratios or allometry among
agroforestry management conditions. However, it is important to note that there are constraints
on the appropriateness of this technology across variable T. cacao growing sites, mainly when
soils have high moisture or clay content that attenuate radar signal and/or reduce dielectric
contrast at soil-root interfaces. Additional destructive sampling might also be required to
correctly compensate for variable contributions of coarse root biomass from non-target tree
species (e.g. shade trees).
54
The mean RS ratio of 0.23 was within the range of RS ratios previously reported for T. cacao
(0.22 to 0.28) (Moser et al. 2010, Leuschner et al. 2013, Rajab et al. 2016), but was greater than
the IPCC default RS ratio of 0.20 for this forest zone (International Panel for Climate Change
2006). Noteworthy, was the range of RS ratios (means 0.19 to 0.28) associated with different
forms of shade tree treatments, for same-aged T. cacao plants at the same site. Shade trees,
particularly large fast-growing shade trees such as T. ivorensis, can modify light, humidity, soil
moisture, and nutrient availability and cycling, all of which can, in turn, influence the nutrient
status of T. cacao plants (Isaac et al. 2007a, 2007b) and plant response to environment (e.g. in
root distribution) (Isaac et al. 2014), which may partially explain the allocation patterns observed
in this study. Agricultural interventions (e.g. pruning, harvesting, fertilizer application) may also
affect growth and stature of tree crops, resulting in altered allometric trajectories than forest trees
(Kuyah et al. 2012).
Whether intraspecific variation in root-based C stocks are deemed important for inclusion in C
inventories may depend on the scale of the study and/or ecosystem service compensation
program. In Ghana, average cocoa farm size is approximately 2 ha (Asare and Ræbild 2016) and
clear gains in biomass C stocks at this scale would mainly be credited to the presence of large
shade trees. However, variation reported in this study for T. cacao root biomass in agroforestry
compared to monocrop cultivation, a difference of 1 Mg C ha-1, represents a 12% improvement
on recent gains of biomass C on agricultural land in Ghana (average increase of 8.1 Mg C ha-1
between 2000 and 2010) (Zomer et al. 2016). Additional research is required to determine how
representative the results from this study are to regional estimates and if other management
factors, such as nutrient additions, are impactful on systematic variation of biomass allocation.
Nonetheless, generalized allometry should be used cautiously when estimating BGB, for
example, to avoid overestimating BGB of T. cacao in monoculture, which in this study was by
16% or approximately 1 Mg C ha-1. At regional or national scales, even small variation could
lead to substantial differences in C stock estimation considering that cocoa cultivation occurs on
1.6 million ha of land in Ghana (FAO 2013). Improvement in biomass and C estimation may
refine accounting of national or regional level C stocks but could also contribute to development
of compensation schemes for C sequestration that may help farmers maintain and/or convert to
pro-environmental agricultural practices such as agroforestry.
55
2.6 Conclusions
Belowground biomass should be included in C stock assessments of cocoa agroecosystems. In
agroforestry systems, the relative contribution of T. cacao BGB to total C stocks is particularly
important when T. cacao are planted in high densities. In this study, a widely-used pan-tropical
allometric equation was broadly functional in estimating BGB for the perennial crop species but
predicted near-static partitioning between above and belowground biomass and was subsequently
less accurate than methods using GPR. There was evidence of higher allocation to BGB of T.
cacao in agroforestry, particularly when grown in mixture with a fast-growing early successional
shade tree (Terminalia ivorensis). Conversely, T. cacao in monoculture may have lower
allocation to coarse roots and caution is suggested when estimating total C stocks to avoid
overestimation. More research is required to determine the magnitude of variability of biomass
allocation for this perennial crop at different ages and under different forms of management.
When present, these differences in root biomass may result in consequential differences for large
scale estimates of biomass C stocks.
56
Chapter 3 Fine root distribution and morphology of Theobroma cacao
reveals nutrient-specific acquisition strategies in a multispecies agroecosystem
3.1 Abstract
Fine root functional traits and trait spectra are gaining ground as a lens to assess plant response
to environment, with much work to date suggesting patterns of root acquisitive to conservative
strategies following soil nutrient conditions. Yet we lack simultaneous observations on how
specific soil cations and anions drive root trait expression. Potential for plastic fine-scale
responses of rooting systems is of particular relevance in agroecosystems designed in part to
enhance species complementarity and increase resource use efficiency. In the present study, I
examined nutrient acquisition strategies of an understory tree crop, Theobroma cacao L. Two-
dimensional vertical interfaces of T. cacao rooting density and morphology were compared to
availability of six soil nutrients in the neighbourhood of con- and hetero-specific roots. There
was dramatic fine-scale variation in soil nutrients within the range of T. cacao root systems.
Higher NH4+ and Ca2+ was associated with higher fine rooting densities, coupled with greater
investment to individual roots, expressed as increased fine root diameter and reduced specific
root length and specific root tip abundance. Conversely, NO3- had the opposite effects. Overall
root system strategies shifted toward higher acquisitive trait values when next to a heterospecific
neighbour. This study provides evidence that T. cacao are adapted to forage for nutrients in
heterogeneous soil environments, and that interactions with heterospecific neighbour trees result
in plastic responses in root architecture and morphology. Fine-scale developmental plasticity in
tree root systems suggests nutrient-specific and neighbour tree species effects on root trait
expression. Given these effects, simple geometric models will be inadequate to predict patterns
of nutrient acquisition in perennial multispecies ecosystems.
57
3.2 Introduction
Plant resource acquisition patterns are not static, and phenotypic plasticity in root systems can
modify belowground interactions with neighbour plants (Li et al. 2006; Cahill et al. 2010).
Although the extent of root foraging and related phenotypically plastic resource acquisition
strategies is highly species-specific (Bliss et al. 2002; Blair and Perfecto, 2004; Malamy, 2005;
Adams et al. 2013), belowground responses to both resource conditions and neighbours are
likely to be important for plant function and overall resource cycling. However, little is known
on how species interactions affect root system foraging in heterogeneous soils under field
conditions.
Plants can benefit from localized areas of high nutrient availability in soil by modifying their
root systems via signalling mechanisms in roots that encounter elevated concentrations of
nutrients (Forde and Lorenzo, 2001; Malamy, 2005). Although typically at the plant scale there
is relatively higher allocation of biomass to roots in nutrient-poor environments (Vitousek and
Sanford, 1986; Wright et al. 2011), in heterogeneous but nutrient-limited soil environments there
is generally greater allocation of root biomass to locations in soil where nutrients are more
abundant (Drew, 1975; Hutchings and de Kroon, 1994; Hodge, 2004). Additionally, studies on
root morphological traits across soil nutrient gradients indicate higher investment to individual
fine root organs with increased soil nutrients, with longer-lived roots characterized by thicker
diameter (D) and lower specific root length (SRL; m g-1) (Ostonen et al. 2007). Alternatively, the
reverse has been observed where roots grow more rapidly (with higher turnover) to exploit
resource-rich patches and, thus, show increased absorptive area per unit of biomass (e.g., higher
SRL and specific root tip abundance (SRTA; tips g-1)) while in resource-poor patches, roots
develop morphologies that limit nutrient losses (e.g., higher D) (Fort et al. 2016). In sum, there is
evidence that plants generally employ several concomitant and at times opposing strategies to
increase the nutrient acquisition in heterogeneous soils by altering root initiation and growth and
patterns of root morphology. These plastic responses can be nutrient specific, presumably
influenced by the mobility of the nutrient in the soil matrix, the signalling and uptake pathways
employed by roots, and the capacity to translocate the nutrient within the plant (Drew, 1975;
Mou et al. 1995; López-Bucio et al. 2003; Hodge, 2004), and are further contingent on the
overall nutrient status of the plant and localized distribution of resources within the range of the
root system (López-Bucio et al. 2003; de Kroon et al. 2009).
58
Within the scale of individual plants, the soil environment is heterogeneous and can be highly
modified by plants themselves. Organic deposits from aboveground sources (e.g., leaf litter)
(Scherer-Lorenzen et al. 2007; Xia et al. 2015) and belowground sources (e.g., root turnover and
exudation, and microbial activity) (Mommer et al. 2016) enhance variation in soil nutrients at a
range of scales (Jackson and Caldwell, 1993; Xia et al. 2015). At the same time, roots from
neighbouring plants generally deplete nutrients in localized areas, and root development patterns
are expected to reflect integrated responses to soil nutrient levels and competition with
neighbours (Cahill et al. 2010; Mommer et al. 2012). Numerous studies that manipulate soil
conditions and neighbour interactions under controlled conditions show dramatic plasticity of
roots in response to soil nutrients and competitors within localized patches (Mahall and
Callaway, 1992; Cahill et al. 2010; Semchenko et al. 2014). However, little is known on how
root traits vary in relation to multiple co-limiting nutrients, nor on how this variation is expressed
within a plant’s root system in naturally heterogeneous soil (Poorter and Ryser, 2015). Indeed,
there is a general lack of empirical evidence for modular plasticity within root systems of
individual plants in field conditions.
Plant nutrient demands and soil environments can be extremely complex in multispecies
agroecosystems in the humid tropics, such as agroforestry or intercropping systems. For
example, trees are retained from previous forest or are planted in agroecosystems to influence the
overall nutrient status of soil and crops, and species combinations are in principle chosen to
enhance niche complementarity and/or facilitation (Brooker et al. 2015; Cardinael et al. 2015). In
these conditions, plasticity of root systems can increase plant access to heterogeneous nutrient
availability in soil but can also mitigate competitive effects from other species through spatial
differentiation of rooting distribution and activity (McGrath et al. 2001; Li et al. 2006; Isaac et
al. 2014; Cardinael et al. 2015). This is generally the case for the tropical tree crop Theobroma
cacao L. – the study species in our study – which is commonly grown as an intercrop under the
canopy of larger heterospecific neighbour trees (i.e., shade trees) and typically receives few
external inputs. While shade trees with more complementary root distributions can be
preferentially planted with T. cacao (i.e., tree species with deeper rooting profiles), typically
there will be overlap of root systems in upper soil layers where nutrients are most abundant
(Isaac et al. 2014). To this end, studies that capture multi-dimensional distributions of root
59
systems (rather than vertical zonation only) can account for more nuanced root-soil patterns (e.g.,
Sudmeyer et al. (2004); Li et al. (2006); Laclau et al. (2013)).
In this study, we examined root system distribution and morphology of T. cacao in relation to
soil nutrients and neighbour roots on 2-dimensional vertical soil interfaces. Soil interfaces were
situated in three different species combinations: at the interface with conspecific neighbours and
with two heterospecific neighbouring trees of distinctive growth strategies (early vs. late
successional). We hypothesized that (i) root systems will exhibit higher investment to roots in
localized areas of higher nutrient availability (characterized by six major macro and micro soil
nutrients), and (ii) root trait values and patterned variation with soil nutrients will be moderated
by neighbour tree due to potential differences in resource dynamics among species combinations.
3.3 Methods
3.3.1 Study site and species combinations
The study was carried out in South Formangso, Ashanti Region, Ghana (6˚36ˊ N, 0˚58ˊ W) at a
cocoa research station managed by the Forestry Research Institute of Ghana. The 2-ha site is
situated on previously secondary forest that was cleared for cultivation and was left to fallow
until the cocoa agroforestry system was established in 2001. T. cacao hybrid planting stock from
the Cocoa Research Institute of Ghana was planted at a spacing of 3 × 3 m and, in agroforestry
treatments, shade trees were planted in replacement of T. cacao at 12 × 12 m spacing. No
fertilizer had been applied to the research site prior to the study. Soils are Acrisols with bulk
density of 1.22 ± 0.16 g cm-3 and soil pH ranging from 6.2 ± 0.1 near the soil surface to 4.9 ± 0.0
near 60 cm depth.
Study T. cacao trees (DBH = 14.6 ± 1.1 cm; mean ± SE) were selected from pre-established
blocks of species combinations at the site. The two shade tree species used in this study,
Terminalia ivorensis (DBH = 58.8 ± 3.8 cm) and Entandrophragma angolense (DBH = 19.9 ±
1.4 cm), are commonly used in this region to provide upper canopy shade (< 25% shade) for T.
cacao cultivation (Anglaaere et al. 2011). T. ivorensis is a fast-growing, early successional tree
species and was the largest of the two heterospecific neighbour species. This species is
characterized by many shallow lateral roots and has been shown to affect fine rooting densities
of Coffea arabica L. (van Kanten et al. 2005) and was assumed to have strong belowground
60
competitive effects due to high SRL (34.7 ± 9.3 m g-1; n = 30). Slower-growing, late-
successional E. angolense is perceived by farmers to be deeper rooted (Anglaaere et al. 2011)
and had a comparatively lower SRL (29.7 ± 6.2 m g-1; n = 30).
3.3.2 Soil interfaces between T. cacao and neighbour
Nine soil trenches 1 m wide and at least 60 cm deep were manually excavated. The exposed
‘interfaces’ in the trenches were perpendicular to transects connecting T. cacao with conspecific
neighbours or T. cacao with heterospecific neighbours and were located halfway between the
trees’ stems (i.e., 1.5 m from each stem) (Fig. 3.1). The location and size of the soil interfaces
were selected to represent an area occupied by an individual T. cacao root system and with
limited root system interactions from non-study T. cacao trees (Nygren et al. 2013, Borden et al.
2017a), while sampling scale and intensity was first assessed from preliminary soil profiles that
were tested for soil nutrients (Soils Institute of Ghana, Kumasi, Ghana). In each of the present
study’s soil interfaces, 40 soil cores (5 cm diameter; 100 cm3 volume) were taken horizontally
and in a stratified random sampling scheme. Samples were taken at centred 2.5, 7.5, 15, 27.5 cm
depth (i.e., to 30 cm) to capture the dominant rooting zone of T. cacao and centred at 57.5 cm
depth to capture root strategies in deeper soils. This vertical sampling scheme was repeated every
20 cm intervals across the x direction (centred 0, 20, 40, 60, 80, 100 cm across) followed by 10
additional samples taken at random, non-sampled locations in the soil interface, identified using
an x, y coordinate systems. In the lab, samples were gently homogenized by hand and then
divided into two approximately equal volumes of soil, with half of each sample (~50 cm3) used
for fine root analysis and the other half used for soil chemical analysis. Samples were stored in
polyethylene bags and frozen until further processing.
3.3.3 Fine root analysis
Roots were removed using forceps from soil samples passed through sequential sieving with
water. Collected roots were then placed in petri dish of RO water to further loosen and remove
soil from roots. Any roots greater than 2 mm, measured with digital calipers, were removed. Fine
roots were separated by species through visual inspection using a stereoscopic microscope. T.
cacao fine roots are distinctly reddish-brown, whereas the heterospecific neighbour tree roots
were lighter in colour. We removed dead roots, characterized by their lack of turgor, black
colouring, and easy separation of stele from cortex. Fine roots were then scanned using a flatbed
61
Figure 3.1: Soil profiles (n = 9) used in this study. Left panel: Schematic showing the location of
a soil profile between a T. cacao tree and a shade/T. cacao tree. Right panel: An excavated soil
profile situated between a T. cacao tree (foreground) and a shade tree Entandrophragma
angolense (background).
62
scanner (STD4800; Regent Instruments Inc., Canada) at 600 dpi. From these images, average
fine root diameter, number of root tips, and total fine root length from each core sample were
measured using WinRhizo (2009; Regent Instruments, Canada). Fine root dry weights were
measured after 48 hours of drying at 65°C. These data were used to calculate five root traits that
characterized either the density of roots in each sample: fine root length density (FRLD; cm cm-
3) and fine root biomass density (FRBD; mg cm-3), or the morphology of the roots in the sample:
specific root length (SRL; m g-1), specific root tip abundance (SRTA; tips mg-1), and average
root diameter (D; mm). A correction factor of 0.5 was used to account for rooting densities with
conspecific neighbours to adjust for assumed presence of two T. cacao root systems. Dry weight
biomass of heterospecific fine roots was used to calculate fine root biomass density of
heterospecific neighbours (FRBDhetero; mg cm-3) in each sample.
3.3.4 Soil chemical analysis
From each soil sample, NO3- an NH4
+ were extracted from field moist soils in KCl solution,
filtered through Fisher P8 filter paper, and measured using a spectrophotometer with flow
injection analyzer (QuikChem 8500, Lachat Instruments, USA). The remaining soils from each
sample were air-dried for 2 weeks and sieved through 2 mm mesh. From these soils, PO4- was
extracted in a 1:10 soil to Bray’s 1 solution, filtered through Fisher P5 filter paper, and measured
using the spectrophotometer. Air-dried soil was further ground in a ball mill (Retsch Ltd.,
Germany) and from these soils, exchangeable K+, Mg2+, and Ca2+ were extracted with
ammonium acetate (NH4OAc), filtered through Fisher P8 filter paper, and analyzed using an
atomic absorption spectrometer (AAnalyst 200, PerkinElmer, USA). Soil chemical analyses were
carried out at the University of Toronto Scarborough, Toronto, Canada.
3.3.5 Statistical analysis
All statistical analyses were completed in R (version 3.2.4). We quantified and compared the in
situ nutrient conditions within the scale of individual T. cacao root systems. First, soil nutrient
levels for each species combination was described using least squares means (calculated within
10 cm depth intervals to interpret on a vertical profile), with soil interface assigned as a random
factor, and differences of soil nutrient levels among treatments were tested using the ‘lsmeans’
package. The amount of variation in soil nutrients encountered by individual T. cacao root
systems was assessed by the range and coefficient of variation (CV) of each soil nutrient. Next,
63
we quantified and compared intra-root system variation of T. cacao with different neighbour
species. Systematic variation in the vertical distribution, with data pooled into 10-cm intervals, of
T. cacao fine root densities and morphology by species combination were assessed using least
mean squares, with soil interface as a random effect. Two-dimensional interpretations of root and
soil variables in each 100-cm wide × 60-cm deep soil interface were produced using inverse
distance weighting on a grid with cells of 5 × 5 cm (approximating the soil core diameter) using
the ‘gstat’ package and visualized using the ‘rasterVis’ package.
I examined the directional relationships between T. cacao rooting densities and morphological
traits with localized soil nutrient availability, focusing on data within the dominant rooting zone
of T. cacao (0 to 30 cm depth). To do so, LMMs for each root trait in each species combination
were fit with sampling depth assigned as fixed variable and soil interface assigned as a random
factor. All measured soil nutrients were included in the LMMs as fixed variables to evaluate how
a change in availability of each nutrient is related to variation in root traits while accounting for
variation of the other measured soil nutrients under field conditions. To determine if roots from a
neighbouring tree are related to the development of T. cacao roots while controlling for variation
in nutrient availability, the effects of localized rooting density of heterospecific neighbours on T.
cacao root traits were analyzed by further including a fixed variable of FRBDhetero. The ‘fixed
effects r2’ was calculated using the ‘r2beta’ function (with method ‘nsj’) in the ‘r2glmm’
package to estimate the amount of variation in T. cacao root traits explained by all fixed
variables (Nakagawa and Schielzeth 2013). This procedure also allowed us to estimate partial r2
of each fixed variable. For parametric analyses, residuals were tested for normality using the
Shapiro-Wilk test. To meet parametric assumptions, root and soil data were log10 transformed.
The level of significance was at p < 0.05.
3.4 Results
3.4.1 Soil nutrients: distribution and variation
Within the dominant lateral rooting zone of an individual T. cacao root system (i.e., 100-cm
wide × 30-cm deep soil interface) there was large variation in soil nutrients (Table 3.1). Soil
NO3- and K+ could vary by two orders of magnitude, showing a large range and large CV, except
for soil K+ in T. cacao-E. angolense mixture. There were particularly high concentrations of soil
NO3- in monoculture (max: 82.0 mg g-1), which was concentrated in surface soils (Fig. 3.2). Soil
64
Table 3.1: Variation in soil nutrients within the lateral rooting zone (0 to 30 cm depth) of T.
cacao reported as minimum and maximum nutrient availability and the coefficient of variation.
NO3-
mg g-1
NH4+
mg g-1
PO4-
mg g-1
K+
cmol(+) kg-1
Ca2+
cmol(+) kg-1
Mg2+
cmol(+) kg-1
T. cacao
monoculture
0.6 – 82.0
(118%)
2.7 – 133.4
(104%)
7.4 – 31.3
(26%)
0.01 – 1.79
(92%)
0.84 – 22.3
(99%)
0.3 – 5.2
(78%)
T. cacao-
E. angolense
mixture
0.1 – 19.5
(150%)
2.6 – 148.2
(72%)
12.0 –
28.1
(15%)
0.05 – 0.45
(48%)
1.1 – 34.2
(116%)
0.4 – 5.5
(80%)
T. cacao-
T. ivorensis
mixture
0.2 – 53.4
(151%)
7.2 – 178.5
(83%)
6.4 – 47.4
(35%)
0.01 – 1.91
(186%)
0.7 – 14.5
(86%)
0.3 – 8.2
(99%)
65
Figure 3.2: Vertical distribution of soil nutrients in soil interfaces (presented as least square
means ± SE, with soil interface as a random effect). When there was a significant effect from
species combination (p < 0.05), pairwise comparisons (Tukey) are shown with among group
differences; letters indicate significant difference among species combination per sampling
depth.
66
K+ was highest in monoculture, particularly when compared to the T. cacao-T. ivorensis mixture
(Table 3.1; Fig. 3.2). Soil NH4+, Ca2+, and Mg2+ also showed high variability, and soil PO4
- was
the least variable with the lowest CV (15 to 35%) (Table 3.1). Overall, both mixtures had higher
soil NH4+ than monoculture (Fig. 3.2). Soil nutrients generally decreased with depth, although
soil K+ was more evenly distributed vertically in the soil profiles (Fig. 3.2).
3.4.2 T. cacao fine root distribution and morphology
As with soil nutrients, T. cacao vertical distributions of fine roots were concentrated near the soil
surface and decreased with depth (Fig. 3.3; Fig. 3.4). Over 90% of T. cacao fine roots (to a depth
of 60 cm) were in the top 30 cm of soil regardless of neighbour species. T. cacao roots next to E.
angolense tended to be concentrated in shallow soils with 71% of both fine root length and
biomass located in the top 10 cm of soil (Fig. 3.4). For T. cacao with conspecifics, 67% of fine
root length and 64% of fine root biomass were in the same depth of soil. When next to T.
ivorensis, there was 70% of fine root length but only 59% of fine root biomass in the top 10 cm,
with more evenly distributed fine root biomass between 10 and 30 cm, indicating that T. cacao
fine root biomass was more vertically dispersed with this heterospecific neighbour. Vertically,
the two heterospecific neighbour species also showed decreasing densities of roots with depth,
but T. ivorensis showed a higher concentration of fine root biomass in shallow (0 – 10 cm) soil
compared to E. angolense that had more evenly distributed fine root biomass within the soil
interfaces (Fig. 3.3; Fig. 3.4).
Individual T. cacao had higher FRLD when next to a heterospecific neighbour than when next to
a conspecific neighbour, particularly in the top 20 cm of soil. Near the soil surface (0 to 10 cm)
mean FRLD of T. cacao was 1.85 ± 0.28 and 1.90 ± 0.29 cm cm-3 when next to E. angolense and
T. ivorensis, respectively, nearly double the value of 1.01 ± 0.28 cm cm-3 for T. cacao next to
conspecifics. FRBD was also higher for T. cacao next to heterospecifics (1.30 ± 0.17 and 1.23 ±
0.18 mg cm-3 with E. angolense and T. ivorensis, respectively) than when next to conspecifics
(1.09 ± 0.17 mg cm-3) (Fig. 3.4). The morphology of T. cacao fine roots next to conspecifics
were generally more conservative than when next to heterospecifics, with consistently lower
SRL and SRTA and higher D in sole-cropping (Fig. 3.4). Overall, there was considerable
variation in lateral root distribution observed within individual root systems of T. cacao (Fig.
3.4).
67
Fig
ure
3.3
: V
erti
cal
dis
trib
uti
on o
f fi
ne
root
den
sity
(F
RL
D a
nd F
RB
D)
and m
orp
holo
gy (
SR
L, S
RT
A, an
d D
) of
an
indiv
idual
T. ca
cao
tre
e 1.5
m d
ista
nce
fro
m s
tem
s. V
alues
show
n a
re t
he
leas
t sq
uar
es m
ean ±
SE
that
wer
e ca
lcula
ted
usi
ng
soil
inte
rfac
e as
a r
andom
eff
ect.
Als
o s
how
n a
re F
RB
Dh
eter
o o
f het
erosp
ecif
ic n
eighbours
. N
o s
ignif
ican
t dif
fere
nce
s
wer
e obse
rved
am
ong T
. ca
cao
roots
in d
iffe
rent
nei
ghbours
for
each
dep
th i
nte
rval
68
Figure 3.4: Interpolated root maps depicting the distribution of soil nutrients (e.g., NH4+ and
Ca2+), shade tree fine roots (FRBDhetero), and T. cacao fine roots (e.g., FRLD and SRL) in three
soil interfaces between (on the left) two T. cacao, (in the middle) T. cacao and E. angolense, and
(on the right) T. cacao and T. ivorensis.
69
3.4.3 T. cacao fine root distribution and morphology in relation to soil nutrients and heterospecific tree roots
Significant directional effects of each nutrient on root traits were consistent regardless of species
combination (Table 3.2). Soil NH4+ and Ca2+ had a generally positive effect on T. cacao fine root
densities (FRLD and FRBD) and investment at the root scale, expressed as positive coefficients
for D and negative coefficients for SRL and SRTA in LMMs (Table 3.2; Table A.3). However,
opposite trends were observed for soil NO3- and K+, particularly for T. cacao next to conspecifics
and T. cacao next to T. ivorensis. Soil PO4- was limited as a predictor variable in root trait
variation. Mg2+ generally had negative effect on localized investment to roots for T. cacao next
to T. ivorensis, with a significant negative D coefficient (p = 0.04) and a marginally significant
positive SRTA coefficient (p = 0.05) (Table 3.2; Table A.3).
Depth, soil nutrients, and FRBDhetero together explained similar proportion of variation in FRLD
and FRBD of T. cacao next to conspecifics and T. cacao next to E. angolense (‘fixed effects r2’
= 0.52 to 0.65) as well as FRLD for T. cacao next to T. ivorensis (r2 = 0.61) (Table 3.3).
However, these same variables were less effective in explaining variation in FRBD of T. cacao
next to T. ivorensis (r2 = 0.29). In most cases, variation in rooting densities (FRLD and FRBD)
was better explained by the fixed variables (depth, nutrients, FRBDhetero) than was the variation
in root morphology (SRL, SRTA, and D) (r2 = 0.09 to 0.22), except for a notably high ‘fixed
effects r2’ found for SRTA of T. cacao next to conspecifics (0.43). For T. cacao next to E.
angolense, variation in root traits was mainly explained by differences in depth (partial r2 = 0.08
to 0.21) while the effects of localized nutrient variation at similar depths were weakly related to
variation in root traits. In contrast, variation in nutrients were just as, or more important than
differences with depth in explaining variation in root traits of T. cacao next to conspecifics and
T. cacao next to T. ivorensis (Table 3.2). There was a negative effect on FRBD from FRBDhetero,
but this was a non-significant (p = 0.17). There was a marginally significant positive effect was
observed for SRL (p = 0.07) (Table 3.2; Table A.3). Conversely, for T. cacao next to E.
angolense, there was little to no evidence of localized response to FRBDhetero, with non-
significant directional effects corresponding to more conservative resource strategy of individual
roots (i.e., lower SRL) (Table 3.2).
70
Table 3.2: Coefficients from LMMs of T. cacao fine root density (FRLD and FRBD) and
morphology (SRL, SRTA and D). Depth, soil nutrients, and roots of shade tree (FRBDshade) were
fixed effects and the sampled profile was assigned as a random effect. Significant (p < 0.05)
coefficients are in bold. Partial r2 are reported in parentheses. LMM results are reported in Table
A.3.
root trait intercept depth (cm) logNO3- logNH4
+ logPO4- logK+ logCa2+ logMg2+ FRBDhetero
‘fixed
effects r2’
T. cacao in monoculture
logFRLD -0.43 -0.02
(0.06)
-0.16
(0.01)
0.16
(0.01)
-0.26
(0.00)
-0.21
(0.03)
0.75
(0.08)
0.24
(0.00)
-- 0.55
logFRBD -1.41 -0.03
(0.05)
-0.46
(0.05)
0.48
(0.03)
0.44
(0.01)
-0.22
(0.02)
0.68
(0.05)
0.54
(0.01)
-- 0.52
logSRL 1.82 0.00
(0.00)
0.32
(0.05)
-0.39
(0.05)
-0.32
(0.01)
0.10
(0.01)
-0.17
(0.01)
-0.29
(0.01)
-- 0.22
logSRTA 1.13 0.00
(0.00)
0.57
(0.10)
-0.44
(0.04)
0.03
(0.00)
0.12
(0.01)
-0.88
(0.12)
-0.18
(0.00)
-- 0.43
logD -0.45 0.00
(0.00)
-0.04
(0.00)
0.14
(0.03)
-0.09
(0.00)
0.00
(0.00)
0.11
(0.01)
-0.02
(0.00)
-- 0.09
T. cacao in mixture with E. angolense
logFRLD -2.12 -0.03
(0.21)
-0.06
(0.01)
0.06
(0.00)
1.64
(0.03)
0.03
(0.00)
0.38
(0.04)
0.07
(0.00)
-0.09
(0.00) 0.65
logFRBD -1.62 -0.05
(0.21)
0.07
(0.00)
0.13
(0.01)
0.73
(0.00)
-0.39
(0.01)
0.42
(0.03)
0.01
(0.00)
0.05
(0.00) 0.58
logSRL 1.03 0.02
(0.08)
-0.07
(0.01)
-0.16
(0.02)
0.26
(0.00)
-0.02
(0.00)
-0.09
(0.00)
0.44
(0.02)
-0.15
(0.01) 0.17
logSRTA 1.75 0.02
(0.09)
0.01
(0.00)
-0.12
(0.01)
-0.40
(0.00)
0.30
(0.01)
-0.22
(0.01)
0.46
(0.01)
-0.06
(0.00) 0.20
logD -0.14 -0.01
(0.19)
0.00
(0.00)
0.01
(0.00)
-0.07
(0.00)
0.06
(0.00)
-0.03
(0.00)
-0.21
(0.02)
0.02
(0.00) 0.21
T. cacao in mixture with T. ivorensis
logFRLD -0.97 -0.02
(0.08)
0.04
(0.00)
0.61
(0.05)
-0.24
(0.01)
-0.10
(0.00)
0.71
(0.03)
-0.32
(0.01)
-0.03
(0.00) 0.61
logFRBD -2.50 -0.01
(0.00)
-0.02
(0.00)
1.00
(0.05)
0.13
(0.00)
-0.10
(0.00)
0.78
(0.02)
-0.62
(0.02)
-0.20
(0.02) 0.29
logSRL 2.56 -0.01
(0.00)
0.07
(0.01)
-0.68
(0.04)
-0.23
(0.01)
0.05
(0.00)
0.30
(0.04)
0.30
(0.01)
0.19
(0.03) 0.10
logSRTA 2.71 -0.01
(0.00)
0.02
(0.00)
-0.80
(0.04)
-0.17
(0.00)
0.06
(0.00)
-0.31
(0.00)
0.84
(0.04)
0.17
(0.02) 0.09
logD -0.84 0.00
(0.00)
-0.04
(0.01)
0.22
(0.04)
0.09
(0.01)
0.01
(0.00)
-0.07
(0.00)
-0.25
(0.04)
-0.05
(0.02) 0.12
71
3.5 Discussion
3.5.1 Intra-root system foraging strategies for specific nutrients
Most roots in tropical ecosystems are concentrated in the top 30 cm of soil (Schenk and Jackson
2002), reflecting rapid uptake of nutrients and deposition by leaf litter. The present study
confirmed the high densities of T. cacao fine roots in the uppermost mineral soil, which mirrored
the vertical patterns in nutrient availability. However, we also found large variation in soil
nutrient availability that occurred laterally within the scale of individual root systems. Within the
dominant rooting zone, fine roots of T. cacao were spatially coupled to heterogeneously
distributed nutrients that would suggest active modular root development in the foraging of soil
resources for this species.
Foraging strategies realized through root architectural and morphological plasticity can be
nutrient-specific. For soil NH4+ and Ca2+, my first hypothesis was consistently supported:
locations with higher soil nutrient availability were associated with higher investment of roots in
a soil volume (i.e., higher FRLD and FRBD), coupled with greater investment of biomass at the
root scale (expressed as lower SRL and SRTA and higher D). Inconsistent and/or opposite
effects were found for soil NO3-, K+, and Mg2+: patterns were generally neutral or, in some cases,
higher local concentrations of these nutrients in soil were associated with reduced density of
roots (lower FRLD and FRBD) and ‘less expensive’ roots (higher SRL and SRTA, lower D). In
the case of the more mobile soil nutrients: NO3- and Mg2+ (Gransee and Führs 2013), it may be
more economical for plants to increase uptake with short-lived, younger roots (Blair and
Perfecto, 2004). Additionally, however, these negative associations between rooting densities
and nutrients were found when there was distinctly higher availability of the nutrient compared
to other species combinations. Thus, I speculate over-supply in nutrients favours reduced root
allocation (Vitousek and Sanford, 1986; Wright et al. 2011); this explanation seems likely for
soil K+ in T. cacao monocultures.
T. cacao root trait variation was generally unrelated to localized variation in soil PO4-. McGrath
et al. (2001) reported increased fine root proliferation of fine roots of Theobroma grandifolium
into soil cores that were artificially enriched with PO4-. However, in natural conditions, PO4
-
gradients may occur predominantly at smaller scales (e.g., gradients of 1 mm or less within the
rhizosphere) (Hinsinger, 2001). As rhizosphere soil was mixed in with bulk soil within 5 cm soil
72
cores, our sampling design likely limited our ability to detect foraging for this nutrient, a
conclusion also supported by the relatively limited variation in this nutrient in this present study.
Additionally, other root traits which were not measured in our study may better capture foraging
strategies for specific nutrients, such as root hair abundance or mycorrhizal associations
(Hinsinger, 2001; Hodge, 2004).
3.5.2 How is root foraging modified by neighbour trees?
Relationships between T. cacao fine rooting densities and soil nutrients differed among species
combinations, supporting my second hypothesis. The combination of species likewise resulted in
altered relationships between root morphology and soil nutrients. Predictions of fine-scale root
system development often rely on geometric models based on modular construction of individual
roots to describe root system architecture, but are largely parameterized from controlled
conditions (Lynch, 1995; de Kroon et al. 2009). The present study shows that neighbour tree
identity and root distribution can control developmental plasticity and that this may be needed to
be accounted for when describing belowground plant processes in species-diverse environments.
Root responses to localized sources of nutrients are expected to be driven by differential nutrient
demands of the plant (Forde and Lorenzo, 2001), which can be modified by neighbours (Isaac et
al. 2007). Root trait-soil nutrient trends were most pronounced for T. cacao next to conspecifics,
and specifically for N and Ca, suggesting these may be co-limiting nutrients in the sole-cropping
system. Patterns differed markedly in root profiles near heterospecific neighbours. Nitrogen best
explained patterns for T. cacao next to T. ivorensis, while no dominant nutrient emerged in
variation in root traits of T. cacao next to E. angolense. In these low-input agroecosystems,
nutrient cycling is a significant component of nutrient delivery and shade tree leaves can
constitute a substantial proportion of litterfall in shaded cocoa agroecosystems (perhaps a third to
a half of total litter inputs (van Vliet and Giller 2017). In the present study, litter from the
expansive T. ivorensis canopy is likely an important determinant of nutrient dynamics and
distribution. Litter from fast-growing species such as T. ivorensis, is commonly associated with
higher rates of decomposition (Cornwell et al. 2008), which may drive subsequent plant-soil
feedbacks. In the present study, there was a general shift in overall root morphological traits
towards higher SRL, SRTA, and lower D, when in mixture compared to fine root traits observed
in monoculture. More acquisitive root traits in mixture compared to monoculture have been
73
reported in other treed ecosystems (e.g., Bolte and Villanueva (2006) and Duan et al. (2017)) and
in T. cacao specifically (Rajab et al. 2018). Higher SRL has been associated with shorter root
lifespan and faster decomposition (Cornwell et al. 2008; Weemstra et al. 2016; Freschet and
Roumet 2017); thus, higher SRL in species mixture may be associated with increased nutrient
cycling rates. Ecosystem-scale impacts of plastic responses of root morphology to heterospecific
neighbours deserve further research attention.
With regard to applications, the present study provides insights into belowground plant processes
among intercropped species, which is essential for developing ecologically-informed agricultural
practices (Brooker et al. 2015). Differential root distribution and activity can contribute to
belowground complementarity in agroecosystems (Brooker et al. 2015; Borden et al. 2017b).
However, phenotypic plasticity may also contribute to, or detract from, complementary
interactions. I found that T. cacao next to T. ivorensis had more evenly distributed fine roots in
the upper 30 cm of soil suggesting greater complementarity belowground, while T. cacao roots
next to E. angolense were more concentrated near the surface. Observed patterns are also
consistent with shifts in limiting nutrients depending on the identity of neighbours. My results
support the general conclusion that root-root interactions between neighbouring plants depend on
both local resources and species identity (Mommer et al. 2016).
3.6 Conclusions
Results support the conclusion that soil nutrient heterogeneity occurs at scales relevant to
individual plants in agroecosystems. Root system architectural and root morphological variation
of T. cacao trees was partially explained by variation in nutrient availability and there was
evidence of altered root distribution of T. cacao depending on the neighbour tree species. By
relating root traits to soil nutrient availability on 2-dimensional soil interfaces, I found that fine
root trait expression had nutrient-specific relationships at localized scales. At the plant scale,
intraspecific root traits shifted towards more overall resource-acquiring morphology when next
to a heterospecific neighbour. These results provide a clear demonstration that simple geometric
or architectural models will not suffice to explain rooting behaviour necessary to model species
interactions in real multispecies managed ecosystems.
74
Chapter 4 Shade trees regulate the fine root trait response of Theobroma
cacao to fertilization
4.1 Abstract
Sustainable fertilizer practices in multispecies agroecosystems need to be context specific and
account for variation in the capacity of crop plants to acquire mineral nutrients within different
species combinations and across diverse growing environments. In this study, I used a novel
trait-based approach to measure the fine root phenotypic response of a tropical understory tree
crop, Theobroma cacao, to two levels of NPK fertilizer and assessed whether these responses
were regulated by neighbouring shade trees (Entandrophragma angolense or Terminalia
ivorensis) in comparison to the response of cocoa in monoculture. Following fertilization, T.
cacao fine roots were extracted from ingrowth cores and analyzed for a suite of traits positively
associated with resource acquisition: fine root growth rate, ratio of absorptive to transport roots,
specific root length, specific root area, specific root tip abundance, and root nitrogen content, as
well as traits positively associated with resource conservation and longer-lived root organs: root
tissue density, average diameter, and carbon to nitrogen ratio. Fertilization generally shifted T.
cacao roots in surface soils towards conservative resource acquisitions strategies compared to the
control group (upwards of 70% mean percent difference) and there were no observable
differences between the two levels of fertilizer used in this study. However, these patterns were
inconsistent in subsurface roots and patterns varied with neighbour species identity, which
suggests complex interactions with shade trees. Coordinated root trait syndromes (e.g., root
economics spectrum) were detected among individual T. cacao at the same site and explained 28
to 45% of total trait variation, but the resource acquisition strategy of individual trees (i.e.,
position on coordinated trait axes) were more affected by species combination rather than
fertilization level. This study provides first insights into root functional trait variation, spectra,
and response to fertilization of an economically important tree crop as mediated by species
combination. I propose that a trait-based approach can be used to improve diagnostics of nutrient
amendments in multispecies agriculture.
75
4.2 Introduction
In tropical agroecosystems, nutrient amendments are often necessary for sustaining crop
production, but nutrients from fertilizers that are not acquired by plants can be a source of non-
point pollution as well as an inefficiency in resource use for a farmer (Vitousek et al. 1997).
Therefore, it is imperative that nutrient management strategies simultaneously support crop
productivity and limit nutrient losses to the wider environment. At the same time, increasing or
maintaining higher levels of biodiversity on farms is a critical management strategy employed by
farmers to, in part, improve overall nutrient levels and nutrient cycling efficiencies on farms
(Malézieux et al. 2009; Barot et al. 2017). However, even in biodiverse agroecosystems, nutrient
amendments can be needed to offset the losses from harvested material. In multispecies
agroecosystems in the tropics – such as in cocoa agroforests, which is the system in the present
study – environmental heterogeneity can be high and, thus, resource acquisition strategies among
individual plants of the same species at the same site can be expected to vary, which poses a
challenge for appropriate nutrient prescriptions.
Assessing root functional trait response to fertilization may serve as important proxy to plants’
capacity to adjust to management interventions, such as fertilization or planned species diversity.
Indeed, systematic intraspecific trait variation in roots has been observed with artificially
manipulated nutrient gradients (Mou et al. 2013; Eissenstat et al. 2015; Wang et al. 2016, 2017;
Chen et al. 2017). Plants construct longer-lived root organs in soil enriched with nutrients
(Ostonen et al. 2007). This may be empirically assessed using functional root traits that are
known to be positively related to root longevity: diameter (D; mm) (Eissenstat et al. 2015; Yan et
al. 2017) and the ratio of carbon to nitrogen (C:Nroot) (Chen et al. 2017), which are root traits
typically defined as resource conserving. At the same time, greater investment to fine root tissues
also results in a decrease in root traits typically defined as resource acquiring as these traits are
generally positively related with nutrient uptake rates and root turnover: specific root length
(SRL; m g-1), specific root tip abundance (SRTA; tips g-1 or comparable trait describing
branching intensity) (Ostonen et al. 2007; Eissenstat et al. 2015; Liu et al. 2015; Chen et al.
2017), as well as the nitrogen content of roots (Nroot), which is associated metabolic processes in
the root (i.e., nutrient uptake) (Mommer and Weemstra 2012). However, inherent genotypic
constraints in root phenotypic plasticity will limit root response to nutrients and consequently the
extent and direction of root trait response to increased nutrient availability (Eissenstat et al.
76
2015). At these phenotypic limits, the capacity for plants to match uptake of nutrients with
increasingly elevated nutrients in soil is expected to be constrained.
Furthermore, it is unlikely that these root trait responses occur independently from one another
and, thus, understanding the covariation among multiple traits may describe critical trade-offs in
plant growth and construction. An illustrative example is the ‘root economics spectrum’, which
places individuals that express more resource-conserving strategies opposite to individuals that
express more resource-acquiring strategies (Mommer and Weemstra 2012; Isaac et al. 2017).
Although additional, multidimensional spectra of coordinated trait variation in roots have also
been detected (Kong et al. 2014; Prieto et al. 2015; Weemstra et al. 2016; Liese et al. 2017)
likely due to the complex role of roots, the physical properties of soil (Freschet et al. 2017),
differences in soil conditions with depth (Fort et al. 2016; Yan et al. 2017), competitive effects
from neighbouring plants (Valverde-Barrantes et al. 2013), and associations with mycorrhizal
fungi (Eissenstat et al. 2015; Liu et al. 2015). Analyses of root traits and coordinated root trait
variation have characterized resource acquisition strategies across soil nutrient availability
gradients (Weemstra et al. 2017) and, importantly, trade-offs in resource acquiring versus
conserving strategies have been reported among individuals of the same species (e.g., in
Cunninghamia lanceolata (Wang et al. 2016) and in a crop species Coffea arabica (Isaac et al.
2017)).
Farm-scale heterogeneity may be largely controlled by species composition, which in turn can
affect how roots respond to fertilization. For example, McGrath et al. (2001) reported that roots
of Theobroma grandiflorium cultivated on P-limited soils in the Amazon preferentially grew in
P-fertilized soil but that this growth response was affected by heterospecific neighbouring trees.
Wang et al. (2016) reported that coordinated root strategies in C. lanceolata were modified by
nutrient amendments as well as thinning and pruning practices. However, other than root
placement and growth, it is unknown how combined management effects of fertilization and
species composition can modify root intraspecific trait variation in root morphology and
chemical traits, nor how these effects are reflected in coordinated resource acquisition strategies.
This study has the following objectives: (1) to determine the extent and direction of intraspecific
root trait shifts in response to fertilization, and (2) to assess if interspecific interactions (i.e.,
species combination) are important in modifying intraspecific trait response. I hypothesize that
77
the expression of acquisitive roots traits will be suppressed following fertilization, concurrently
with an increase in conservative trait values, but the extent of trait responses will be modified in
species mixture due to interspecific interactions and control over resource cycling and
availability. I also wanted to determine (3) the overall resource acquisition strategies among
individual T. cacao at the same site assessed by coordinated root trait spectra and (4) the
importance of management (species combination and fertilization) in controlling the expression
of these strategies. I carried out a manipulative fertilization experiment to measure root traits on
similar-aged roots in a tropical agroforest featuring same-age and same-genotype stand of T.
cacao. The roots collected in this study environment were assumed to be responding directly to
fertilization, but within limits of the studied genotype and environmental conditions invoked by
species combination.
4.3 Methods
4.3.1 Site description
This study was conducted on a 15-year-old research site consisting of even-aged T. cacao (DBH
= 12.4 ± 2.8 cm; height = 6.1 ± 1.1 m; n = 45) at a density of 1,111 trees ha-1 in monoculture or
interspersed with shade trees that are commonly selected by farmers in the region: T. ivorensis
(as fast-growing pioneer species) (DBH = 58.8 ± 3.8 cm; mean ± SE) and E. angolense (a slower
growing hardwood species) (DBH = 19.9 ± 1.4 cm) that were planted at a density of 68 trees ha-
1. The site consists of Acrisol soils with a sandy clay loam texture and a bulk density of 1.22 ±
0.16 g cm-3 (± SD; n = 9) between 0 and 10 cm and 1.47 ± 0.14 g cm-3 between 10 and 20 cm soil
depth, determined using a metal corer of known volume and calculating the soil moisture content
of soils after oven drying for 48 hours at 105°C. The experiment occurred over the latter half of
the rainy season (July through October), which coincided with the second peak of cocoa pod
production, and roots were collected prior to the dry season. No fertilizer had been applied at the
site prior to the experiment.
4.3.2 Experimental design
The experiment was established as a split-plot design with triplicate replication for each
treatment combination. The main plot factor was T. cacao in three species combinations: T.
cacao in monoculture, T. cacao in mixture with E. angolense, and T. cacao in mixture with T.
78
ivorensis. Within each species combination, two levels of fertilizer were added plus a control.
Thus, in total, twenty-seven T. cacao trees were selected with the requirement that trees appeared
healthy and structurally representative for trees at the site. Fertilization sub-plots of dimensions 2
× 1 m were established between the selected T. cacao and neighbour tree, with centre of plot
located 1.5 m from the study tree and neighbour tree and oriented perpendicularly to the transect
between the stems. After installing root ingrowth cores (see section 4.3.3), multi-nutrient
fertilizer (15-15-15 NPK granular fertilizer; 15% N (6.5% NO3--N and 8.5% NH4
--N) + 15%
P2O5 + 15% K2O + 2% MgO + 0.1% Zn) was broadcast applied in two levels: i) moderate
fertilization (187.5 kg ha-1) or ii) high fertilization (375 kg ha-1). Fertilizer levels were
characterized by recommended fertilizer dosages for this region (Isaac et al. 2007b, van Vliet and
Giller 2017). High fertilizer delivered an influx of nutrients representing a 49, 51, 14, and 2%
increase above native soil available N (NO3- + NH4
+), available P, exchangeable K, and
exchangeable Mg, respectively (based on soil nutrient availability measured near the time of
fertilization; Chapter 3). Control plots were established to measure fine roots that grew in native
soil (i.e., for trees with no fertilizer added). Surface roots (0 to 10 cm depth) and subsurface roots
(10 to 20 cm depth) were analyzed separately due to expected differences in surface applied-
fertilizer with depth, the importance of depth in driving root trait variation (Freschet et al. 2017),
and differential rooting patterns at this site among species combination (Isaac et al. 2014).
4.3.3 Root ingrowth cores
In each fertilization plot, two ingrowth cores (mesh size 2 mm) were deployed prior to
application of fertilizer. Soil and roots were removed using a 7-cm diameter auger, incrementally
to avoid compaction of soil, to 20 cm depth. Roots were removed, and then root-free soil was
replaced into ingrowth bags that lined the soil cores, soil was replaced by depth increments to
emulate soil conditions. Leaf litter was removed for fertilizer application but replaced for the
duration of the experiment. After four months, ingrowth cores were removed by digging into soil
around the bags and cutting roots at the mesh interface. Samples were stored in polyethylene
bags and frozen until processing. Samples were divided into depth intervals of 0 to 10 cm and 10
to 20 cm.
79
4.3.4 Fine root traits
Fine roots (< 2 mm diameter) were removed from soil samples using forceps and placed in petri
dish of RO water to loosen soil particles from roots. Fine roots of T. cacao were visually
identified by colour, texture, and morphology using a stereoscopic microscope. Cleaned roots
were scanned using a flatbed scanner (STD4800; Regent Instruments Inc., Canada) at 800 dpi.
Images were analyzed in WinRhizo (2009; Regent Instruments, Canada) to quantify fine root
length, average diameter, and number of tips, which were subsequently used – along with the dry
weight biomass determined after 48 hours of drying at 65 °C – to calculate several root traits. In
monoculture, I assumed that two neighbouring and equally distanced T. cacao plants contributed
equally to fine roots in soil ingrowth cores. Thus, for T. cacao in monoculture, a correction factor
of 0.5 was applied to all measures of fine root biomass. Fine root growth rate (GRroot; mg cm-3 4-
mo-1), specific root length (SRL; m g-1), specific root area (cm2 g-1), specific root tip abundance
(STRA; tips g-1), which are all associated with resource acquisition. Another resource acquisition
trait is the ratio of absorptive root length to transport root length (A:T) (i.e., relative amount of
ephemeral roots that are predominantly responsible for nutrient uptake) (McCormack et al.
2015). To calculate A:T, I used a diameter cut off that captured most of the first three orders
(Withington et al. 2006, Roumet et al. 2016) using T. cacao root data from this site: fine roots of
T. cacao below a cut-off of 0.50 mm generally did not exhibit secondary growth and represented
85.2 ± 0.07% (± SD; n = 30) of absorptive (root orders 1 to 3) length. Root tissue density (RTD;
g cm-3) and average fine root diameter (D; mm) are associated with resource conserving
strategies. Root C and N, and subsequently the ratio of C to N (C:Nroot), were determined using
combustion analysis on a CN analyzer (C:N 628, LECO Instruments, Canada). Nroot is associated
with being an acquisitive trait as it is related to increased metabolic processes in the root (i.e.,
nutrient uptake), while C:Nroot is a conservative trait associated with construction of longer-live
root tissues. Traits were selected a priori based on previously described patterns in root resource
acquisition strategies (Freschet and Roumet 2017).
4.3.5 Statistical analysis
All statistical analyses were performed in R v. 3.2.4 (R Foundation for Statistical Computing,
Austria). To examine the response of fine roots to nutrient additions, I calculated the percent
difference in root trait values between two levels of fertilization from the mean trait values of
non-fertilized control group (similar to the response ratio calculated in Ostonen et al. (2007b)).
80
The mean trait values calculated from ingrowth cores within each fertilization plot were used as
the unit of analysis: i.e., a value for each individual T. cacao. I ran unpaired two-sample t-tests
on fine root trait values between T. cacao in fertilized sub-plots from those from those
established as controls. To describe coordinated root trait variation (i.e., resource acquisition
strategies), principal component analysis (PCA) in the ‘ade4’ package was used to identify axes
that best represent the combined variance of all root traits through unconstrained ordination of all
the measured root traits of individual T. cacao. All variables were centred and scaled to unit
variance prior to analysis. Principal component scores were used to evaluate the extent to which
management (species combinations and fertilization) influenced the relative position of
individual T. cacao on the dominant axes. Analyses were completed separately for roots sampled
from the two depth intervals (surface and subsurface roots). Two-way ANOVA was used to test
if there are differences in the overall position of T. cacao on these axes among species
combination, fertilization level, or if there are interactive effects. When significant, Tukey’s
HSD was used to test for between group differences. Prior to parametric tests, data were tested
for normality of residuals using the Shapiro-Wilk test and equality of variance among groups
was tested using the Bartlett test. Root trait data were log transformed when required to improve
residual normality and reduce heteroscedasticity. The level of significance was p < 0.05.
4.4 Results and discussion
4.4.1 Extent and direction of intraspecific root trait shifts following
fertilization
More T. cacao fine roots grew into fertilized sub-plots within a resource constrained
agroecosystem: GRroot was highly variable but overall there were large increases in GRroot
observed in comparison to the control group (Fig. 4.1). At the same time, newly grown surface
roots were more resource conservative. Specifically, fertilization generally lowered acquisitive
trait values: A:T, SRTA, SRL, and SRA, and stimulated conservative trait values: D and C:Nroot
in surface roots (Fig. 4.1). Directional trends in root morphological variation in this experiment
are similar to those reported in long-term field fertilization and controlled laboratory experiments
in which there was a decrease in acquisitive root trait values (Ostonen et al. 2007; Kramer-
Walter and Laughlin 2017). These findings support my first hypothesis and suggests that if soil
81
Fig
ure
4.1
: R
oot
trai
t re
sponse
to n
utr
ient
infl
ux (
mea
n ±
SE
per
cent
dif
fere
nce
fro
m r
oots
in n
ativ
e so
il, i.
e., co
ntr
ol
gro
up)
of
T. ca
cao i
n t
hre
e dif
fere
nt
spec
ies
mix
ture
s. T
op r
ow
show
dat
a of
surf
ace
roots
and b
ott
om
row
show
s dat
a fr
om
subsu
rfac
e
roots
: dia
met
er (
D),
C:N
in r
oot
tiss
ue
(C:N
root)
, ro
ot
tiss
ue
den
sity
(R
TD
), N
conte
nt
of
roots
(N
roo
t), sp
ecif
ic r
oot
- ar
ea (
SR
A),
length
(S
RL
), t
ip a
bundan
ce (
SR
TA
), a
bso
rpti
ve:
tran
sport
root
length
(A
:T),
fin
e ro
ot
gro
wth
rat
e (G
Rro
ot)
. Z
ero o
n t
he
x a
xes
signif
ies
no c
han
ge
in t
he
root
trai
t val
ues
. S
ignif
ican
t re
sponse
s (t
-tes
t) s
ho
wn w
ith a
ster
isks
(p:
∙ <
0.1
; *<
0.0
5;
**<
0.0
1)
82
resources become more abundant, plants will invest in longer-lived root tissue. Overall, trait
shifts in C:Nroot were minimal but positive (0 to 10% increase) as hypothesized. There were also
small but inconsistent trait shifts in Nroot and RTD (Fig. 4.1). Effects of fertilization were most
pronounced in GRroot, A:T, and SRTA, which parallel results from other studies that show
architectural traits and root branching to be highly responsive to soil resources (Kong et al. 2014;
Liese et al. 2017).
Observed trait shifts in surface roots within each species combination were also generally
consistent regardless of fertilization level. Root trait shifts in response to increased soil nutrients
are presumably nonlinear and limited in extent. Wang et al. (2017) reported a hump-shaped
curve of root morphological and hydraulic traits in Pinus tabuliformus seedlings in response to a
gradient of N levels. Similarly, Einsmann et al. (1999) found rooting densities across multiple
species to peak at intermediate fertilization levels and be suppressed at high levels. Therefore,
the fertilizer dosages used in this study may characterize a limit of root trait response for cocoa
in this environment. With a larger pulse of nutrients, there could be diminished advantage in
phenotypic plasticity in acquiring elevated nutrients and variation in physiology such as
increased ion transport capacity could be more economical for the plant (Wang et al. 2017).
Structural and functional complexity in multispecies agroecosystems can influence resource
availability. I found that T. cacao fine root response to fertilization was mediated by neighbour
species identity. T. cacao in mixture with E. angolense showed a more pronounced and
consistent response in surface roots compared to T. cacao in mixture with T. ivorensis. For root
traits of T. cacao in mixture with E. angolense there was a 25% or greater difference in mean
trait values of A:T, SRTA, SRL, and D (except D at half fertilization, which increased by 14%)
and there was also high GRroot in response to fertilizer (62 and 157% increases after moderate
and high fertilization, respectively). On the other hand, surface roots of T. cacao in mixture with
T. ivorensis were the least affected by fertilization. The largest trait shift for T. cacao in mixture
with T. ivorensis was an increase in GRroot (30% increase at both fertilization levels), a decrease
in A:T (26 and 13% decrease following moderate and high fertilization, respectively), and an
increase in D (21% decreased following under moderate fertilization). However, while
architectural and morphological traits for T. cacao in mixture with T. ivorensis were highly
variable, there were significant chemical trait shifts (p < 0.05) (Fig. 4.1). Tree species
83
composition will affect the quantity and quality of inputs (e.g., litter fall, cocoa husks residues,
root inputs), which results in a complex delivery of bioavailable nutrients in the soil, resulting in
altered nutrient status in plants (Isaac et al. 2007) and soil (Borden et al. under review) and in
turn, the root response to nutrient influx is expected to vary.
More consistent trait shifts occurred in surface roots compared to subsurface roots, which
presumably was, in part, due to more direct nutrient interception by shallower roots. Given that
fertilizers are typically applied to the surface of soils, a pronounced effect in roots nearest the
surface is expected, especially with shallow rooted species such as T. cacao. However, the
response pattern in subsurface roots is intriguing as trait shifts were directionally inconsistent
between fertilization levels as well as being largely dependent on neighbour species. In
subsurface roots, the expected trait shifts were observed for T. cacao in mixture with E.
angolense but only following high fertilization (Fig. 4.1). A similar pattern emerged for T. cacao
in monoculture but only at moderate fertilization, while after high fertilization, directional shifts
were reversed. The largest trait shift was in the substantial increase in GRroot of subsurface roots
of T. cacao in mixture with T. ivorensis after moderate fertilization (242% increase). Following
high fertilization, there was also large positive shifts in acquisition traits for T. cacao subsurface
roots when in mixture with T. ivorensis, where SRL increased by 42%, SRTA increased by
105%, and A:T by over 214% (Fig. 4.1). Findings point to differential interactions with
neighbouring trees and nutrient availability with depth. Notably, acquisition traits of subsurface
roots of T. cacao in mixture with T. ivorensis increased dramatically following fertilization,
which suggests that root trait modifications with depth may be advantageous for T. cacao when
next to a shallow-rooted pioneer species. Indeed, previous studies have shown T. cacao to have a
plastic response to T. ivorensis within the top 30 cm of soil (Isaac et al. 2014; Borden et al. under
review). Some large trait shifts in subsurface roots countered the hypothesized response. In these
cases, roots may be demonstrating a nutrient-specific response to more mobile nutrients such as
NO3- (presumably from increased nutrient leaching) through preference for root traits that lead to
faster nutrient uptake (i.e., acquisitive root traits) (Borden et al. under review).
84
4.4.2 Coordinated resource acquisition strategies in a multispecies
agroecosystem
In an economically important tree crop, T. cacao, at the same site, I found the expected root trait
trade-off, known as the root economics spectrum. The dominant coordinate trait axis (PC1) that
captures this trade-off, T. cacao with root traits associated with greater resource uptake given
lower biomass investment (i.e., acquisitive traits) were aligned in opposition to T. cacao
expression root traits that are associated with higher investment into longer-lived root tissues
(i.e., conservative traits). Specifically, this trade-off shows individual cocoa with higher specific
root length, specific root tip abundance, and the ratio of absorptive to transport roots aligning in
opposition to T. cacao with thicker diameter; and this was observed in roots at both depths (Fig.
4.2); although specific root area was uncoordinated with PC1 in surface roots (ANOVA; p =
0.54; Table 4.1) it was aligned with acquisitive traits on the same axis in subsurface roots (p <
0.01). The first axis explained over 40% of fine root trait variation among individuals of T. cacao
regardless of depth interval. Recent research of Coffea arabica suggests that coordinated leaf
trait variability may be weakened by fertilization (Martin et al. 2016), but this has not been tested
for roots and very little is known on coordinated root traits among individuals of the same
species at local scales (Isaac et al. 2017). In the present study, T. cacao were grown in tropical
nutrient-limited soils that had not been previously fertilized and, thus, local nutrient
heterogeneity and subsequent variation in resource acquisition strategies were expected.
Neighbour species controlled coordinated root trait syndromes in T. cacao, suggesting that the
abiotic and biotic conditions invoked by different species of trees can lead to variation in the
overall resource acquisition strategies in this species. Overall, species composition, as compared
to fertilization, more strongly controlled coordinated trait variation for T. cacao and there were
no significant interactive effects of species combination × fertilization level on coordinated root
trait syndromes at either depth (Table 4.2). In surface roots, T. cacao in species mixture tended
towards higher PC1 values (more conservative on the root economics spectrum) compared to T.
cacao in monoculture, but this was non-significant (p = 0.286) (Table 4.2; Fig. 4.2). In surface
roots, T. cacao fine roots from fertilized plots tended to express more conservative strategies
than fine roots from the control but as non-significantly higher PC1 axis scores (p = 0.153).
85
Fig
ure
4.2
: O
rdin
atio
n o
f T
. ca
cao s
urf
ace
roots
(to
p r
ow
) an
d s
ubsu
rfac
e ro
ots
(bott
om
ro
w)
from
pri
nci
pal
com
ponen
t
anal
ysi
s (l
eft
pan
els)
and r
esult
ing b
iplo
ts o
f ax
es s
core
s gro
up
ed a
ccord
ing t
o s
pec
ies
com
posi
tion (
mid
dle
pan
els:
T. ca
cao i
n
monocu
lture
(C
-C),
in m
ixtu
re w
ith E
. angole
nse
(C
-E),
or
in m
ixtu
re w
ith T
. iv
ore
nsi
s (C
-T))
and f
erti
liza
tion l
evel
(ri
ght
pan
els)
. R
esult
s fr
om
tw
o-w
ay A
NO
VA
; no i
nte
ract
ive
effe
cts
of
spec
ies
com
posi
tion a
nd
fer
tili
zati
on (
see
Tab
le 4
.2).
86
Table 4.1: Trait loadings on the first two axes of principal component analyses of T. cacao root
traits: diameter (D), C:N in root tissue (C:Nroot), root tissue density (RTD), N content of roots
(Nroot), specific root - area (SRA), length (SRL), tip abundance (SRTA), absorptive:transport root
length (A:T), fine root growth rate (GRroot). Significant relationships between traits and axes are
indicated in bold (p < 0.05).
PC1 PC2
Surface roots
D 0.46*** 0.02
RTD -0.28** -0.40***
C:Nroot 0.32*** 0.36**
Nroot -0.32*** -0.39***
SRA -0.06 0.57***
SRL -0.42*** 0.29*
SRTA -0.42*** 0.23
A:T -0.37*** 0.24*
GRroot 0.09 -0.21
Subsurface roots
D 0.45*** 0.19
RTD -0.04 -0.48***
C:Nroot 0.07 0.54***
Nroot -0.04 -0.51***
SRA -0.36** 0.34**
SRL -0.48*** 0.09
SRTA -0.47*** -0.12
A:T -0.39*** 0.20
GRroot 0.23* 0.13 *** p < 0.001; ** p <0.01; * p < 0.05
87
Table 4.2: Results of two-way ANOVA of species combination and fertilization level on
coordinated root strategies of T. cacao. Significant effects are indicated in bold (p < 0.05).
Source df S.S. F-value p-value
Surface roots
PC1
Species combination 2 11.24 1.349 0.286
Fertilization level 2 17.49 2.100 0.153
Sp. comb. × Fert. 4 4.85 0.291 0.880
Residuals 17 70.82
PC2
Species combination 2 21.93 4.70 0.024
Fertilization level 2 6.10 1.31 0.296
Sp. comb. × Fert. 4 3.48 0.37 0.825
Residuals 17 39.63
Subsurface roots
PC1
Species combination 2 22.36 3.51 0.053
Fertilization level 2 2.83 0.44 0.649
Sp. comb. × Fert. 4 22.38 1.75 0.185
Residuals 17 54.22
PC2
Species combination 2 39.17 22.40 <0.001
Fertilization level 2 6.29 3.60 0.049
Sp. comb. × Fert. 4 4.60 1.32 0.304
Residuals 17 14.86
88
In subsurface roots, there was a marginally significant species combination effect on PC1 (p =
0.053) (Table 4.2), with subsurface roots of T. cacao in mixture with T. ivorensis significantly
higher than T. cacao subsurface roots in mixture with E. angolense (p = 0.044). Similarly,
location of individual C. arabica along a root economics spectrum was reported to be partially
explained by shade tree management practices (Isaac et al. 2017).
Unlike in leaf traits that often coordinate on a dominate leaf economics spectrum (including in
crop plants (Martin et al. 2016; Isaac et al. 2017)), it is increasingly recognized that root resource
acquisition strategies are likely to by multidimensional (Weemstra et al. 2016). In the present
study, a second axis (PC2) showed a trade-off of T. cacao with dense, N-rich roots in opposition
to T. cacao with less dense roots of higher C:Nroot. This secondary axis explained close to 30% of
total variation (Fig. 4.2; Table 4.1). Interestingly, coordinated root trait syndromes described by
PC2 were more influenced by management than the root economics spectrum described by PC1,
which suggests an important trade-off in the chemical and structural construction of fine roots in
T. cacao’s overall acquisition strategy and response to management. Species composition had a
significant effect on PC2 scores at both depth intervals (surface roots: p = 0.014; subsurface
roots: p < 0.001) (Table 4.2; Fig. 4.2). T. cacao in monoculture tended to have denser, N-rich
roots (higher RTD and Nroot) than when in mixture with E. angolense at both depths, shown by
the significantly lower PC2 axis scores (surface: p = 0.020; subsurface: p < 0.001), and also
lower PC2 scores than T. cacao roots in mixture with T. ivorensis, but significantly so only in
subsurface roots (p < 0.001) and not in surface roots (p = 0.469). Interestingly, fertilization level
had an effect in determining the coordinated resource acquisition strategies in subsurface roots (p
= 0.049), which it did not in surface roots. There was significantly lower PC2 axis scores for
subsurface roots in high fertilization compared to moderate fertilization (p = 0.049). Notably, for
surface roots, GRroot was uncoordinated with PC1 (p = 0.380) and or PC2 (p = 0.076), which
higher values were characterized by denser, N-rich fine roots. Conversely, in subsurface roots,
GRroot was positively associated with PC1, showing alignment with conservative root traits (p <
0.01) (Table 4.1). Broadly, these root trait syndromes characterize different strategies between
root growth and placement versus morphological and physiological adjustments in relation to the
soil environment (Kong et al. 2014).
89
4.5 Conclusions
This study characterizes fine root response of T. cacao under active management strategies
employed by farmers in low-input tropical agroecosystems, providing some of the first insights
into root functional intraspecific trait variation and covariation (trait spectra) while controlling
for these management regimes. Notably, I show that the presence and activity of shade trees are
critical in determining crop response to fertilization as well as crops’ overall resource acquisition
strategies. This is important for understanding crop function as well as interspecific interactions
in multispecies agroecosystems. Systematic and patterned variation in plant functional traits are
known to relate with abiotic and biotic conditions, but much less is known on the direct and
indirect effects of management practices on the expression of crop traits (Martin and Isaac 2015;
Barot et al. 2017; Damour et al. 2018). Broadly, these understandings can improve our ability to
predict ecosystem function across managed environments. Additionally, from an applied sense,
results from this study indicate that shifts in traits and trait syndromes can be used as indicators
of plant response to fertilizer application. For example, if traits can be used to estimate the point
at which crops reach luxury consumption of resources, or the maximum extent to which roots
can respond to nutrient influx, farmers can more accurately apply fertilizer dosages. For T. cacao
specifically, fertilizer amendments are being increasingly catered to soil conditions across the
cocoa growing region of Ghana (Snoeck et al. 2006). Similarly, nutrient amendments could also
be detailed to species combination. These more refined nutrient diagnostics and prescriptions are
essential to maintain high nutrient use efficiency in the agroecosystem and minimize nutrient
losses. Future research is needed to chart these trends and to develop extension efforts that can
translate trait-based understandings of plant and ecosystem function with traits on plants that
could be interpreted by farmers (Isaac et al. 2018).
90
Chapter 5 Effects of interspecific interactions on Theobroma cacao root
strategies across optimal and suboptimal climates
5.1 Abstract
Crop adaptations to suboptimal climatic conditions are critical for sustaining yield and other
agroecological functions, particularly in low-input cultivated systems in the tropics. However,
crops may respond differently to climate across soil types and management. Quantifying how
crop root form and function vary in relation to multiple environmental factors may provide new
insight into crop resiliency to climate change. I collected fine roots of Theobroma cacao in
surface soils under two management scenarios: monoculture or mixture with the shade tree
Terminalia ivorensis, across optimal and suboptimal precipitation regimes and in contrasting
edaphic conditions (sandy vs. loam) in Ghana. Fine roots were analyzed for a suite of
morphological and chemical traits and fine root growth rates were measured using ingrowth
cores. Results show that multiple dimensions of root trait covariation in T. cacao are important
for resource acquisition strategies. T. cacao roots at a climatically optimal site with fine-textured
soils expressed a resource conserving strategy on a primary trait spectrum (i.e., the root
economics spectrum) compared to T. cacao at the other sites. However, T. cacao was
differentially responsive to the presence of the shade tree across the sites of contrasting climatic
and edaphic conditions. A secondary axis of coordinated root trait variation that described a
trade-off in fine root growth rate, root diameter and root nitrogen concentration in opposition to
root tissue density was more strongly correlated with soil variables and thus may serve as proxy
to plant response to environment across the region. From these data, the effects of suboptimal
climate were less influential on T. cacao roots in sandy versus loam soil; however, in more fine-
textured soils, the effects from shade trees may be more consequential for T. cacao root function.
This study demonstrates potential in using trait-based approach to diagnose crop function across
environmental gradients through quantification of intraspecific root trait variation and
covariation within a tree crop.
91
5.2 Introduction
Sustaining agricultural productivity will be increasingly challenging in the coming decades
(Parry et al. 2005, Lin 2011). Changes in temperature and precipitation, both inter- and intra-
annually, means that crop species are at increasing likelihood of being cultivated outside of their
optimal environmental conditions (Porter and Semenov 2005). However, as phenotypic plasticity
is a key mechanism for plants to survive across large environmental gradients, crop species that
demonstrate variation in form and function in relation to environmental change could be more
resilient to climate change (Ryan et al. 2016). Understanding the patterns and extent to which
crops can adapt to variable, often suboptimal conditions is particularly important for
understanding long-term crop growth in low-input cultivated systems in the tropics, where
environmental perturbations and their impact on plant performance can be severe (Parry et al.
2005, Rao et al. 2016).
T. cacao is receiving considerable attention in this regard as a tropical tree crop mostly grown on
smallholder farms with few external inputs or irrigation (Clough et al. 2009, Schroth and Ruf
2014). In West Africa for example, where 70% of the world’s cocoa is produced, the land area
that is climatically optimal for T. cacao cultivation is projected to decrease by approximately
half by the year 2050 (Schroth et al. 2016b). But across the range of cocoa production, the degree
to which shifts in environmental conditions might influence T. cacao growth and yield are
expected to differ widely. In Ghana specifically – the second largest cocoa producing country
and the location of this study – certain areas within or near the forest-savanna transition zone are
under threat of increased drought stress while, conversely, in the southwest, precipitation may be
in excess of T. cacao requirements (Schroth et al. 2016b), leading to major questions
surrounding how this crop will grow over the following decades. Between these two extremes, T.
cacao is cultivated on soils that vary in texture and development (Wood and Lass 2001), which
may be critical in imparting different hydraulic properties and resource availability (Fernandez-
Illescas et al. 2001, Chapman et al. 2012) and, consequently, moderating T. cacao response to
changes in climate (Niether et al. 2017). Given the important regulating nature of roots in the
acquisition of soil moisture in diverse soil environments, studying variation in T. cacao root
92
traits may provide important insight into understanding and predicting the tree crop’s growth
across diverse environments in order to design more resilient agroecosystems.
Although considerable research has focused on how aboveground plasticity mediates plant
environmental responses, recent research indicates that community-level or interspecific
differences in root traits are key in determining plant growth, survival, and reproduction across
environmental gradients (Prieto et al. 2015, Bergmann et al. 2017, Freschet et al. 2017). At the
same time, intraspecific trait variation (ITV) in roots is also likely to moderate relationships
between plant function and environmental variability among plants of the same species (Hajek et
al. 2013, Isaac et al. 2017). Coordinated trait variation within a species can describe the
fundamental constraints on the construction and function of roots, notably by positioning
individuals of the same species on a ‘root economics spectrum’ (RES) (Hajek et al. 2013, Isaac
et al. 2017) or, potentially, within multidimensional trait spectra that reflect the complexity of
resource acquisition strategies, particularly in fine root organs (Kramer-Walter et al. 2016,
Weemstra et al. 2016). However, for T. cacao, and more generally for crop plants, explicit
evaluations of resource acquisition strategies revealed by crop root ITV and covariation across
environmental gradients remain limited (Isaac et al. 2017).
Confounding the response of T. cacao to environmental change are management strategies
employed by farmers (Damour et al. 2018). In particular, farmers often intercrop shade trees with
T. cacao. On one hand, agroforestry practices can regulate micro-climates towards improved
growing conditions compared to monocultures by mitigating heat stress effects on shade-adapted
understory tree crops (Tscharntke et al. 2011, Schroth and Ruf 2014, Schroth et al. 2016a,
Niether et al. 2017). On the other hand, higher overall water demand in certain combinations of
species compared to monoculture may increase negative effects on T. cacao physiology and
survival when water is limited (Abdulai et al. (2018) but see Norgrove (2018) and Wanger et al.
(2018)). Research into the effects of interspecific interactions on T. cacao root systems has
largely focused on root architecture (e.g., depth, distribution) or allocation to root mass (e.g., root
to shoot ratio) in different species combinations (Moser et al. 2010, Schwendenmann et al. 2010,
Isaac et al. 2014, Rajab et al. 2016, 2018, Borden et al. 2017b). Less studied are the effects at the
root scale (e.g., root morphology (Rajab et al. 2018)) and coordinated trait spectra. For other tree
species, some studies report a systematic shift in root ITV values towards resource acquiring
93
strategies (i.e., higher acquisitive and lower conservative trait values) when in mixture with
another tree or crop species (e.g., increase in specific root length (SRL) (Bolte and Villanueva
2006, Duan et al. 2017) and decrease in average root diameter (D) (Duan et al. 2017)). This
tendency towards acquisitive morphological root traits may reflect higher turnover rates in
mixture, which Rajab et al. (2018) observed in T. cacao grown in multispecies mixture, although
an opposite trend was observed for T. cacao in mixture with only G. sepium. This force on
phenotypic plasticity on root organs may be important in determining community level processes
and adaptability to environmental change (Moran et al. 2016). However, few studies have
integrated soil physical properties and interspecific effects from neighbouring trees (Isaac et al.
2014), and none, to my knowledge, have studied T. cacao fine roots over multiple sites and
climatic conditions.
The objective of this study was to assess the effects of soil environment and shade trees on fine
root ITV of individual T. cacao grown in optimal and suboptimal climates. I focus on surface
roots (in top 0 to 10 cm of soil), where the majority of fine roots are located for this species and
where the presence and activity of T. cacao roots are important for accessing soil moisture and
nutrients that can be spatially and temporally transient (da Silva and Kummerow 1998,
Schwendenmann et al. 2010, Isaac et al. 2014, Niether et al. 2017). I hypothesized that 1) root
traits of T. cacao would coordinate and align onto the defined RES; 2) with T. cacao exhibiting a
resource conserving strategy in higher resource environments (i.e., wetter environments with
loam soils) and resource acquiring strategy in more resource constrained environments (i.e., drier
environments with sandy soils); but that 3) a fast-growing shade tree (Terminalia ivorensis L.)
will affect the rooting strategies, specifically the position of individual T. cacao on the RES, as
fine roots of T. cacao would be more acquisitive in species mixture versus monoculture due to
shade tree effects on resource availability and cycling.
5.3 Methods
5.3.1 Study sites
Four sites were selected within the cocoa growing region in Ghana (Table 5.1; Fig. 1.1), which
were qualitatively characterized based on broad differences in precipitation and soil texture.
Mean annual precipitation (MAP) for each site was determined from ~1 km resolution climate
94
Table 5.1: Geographic, climatic, and biophysical conditions at the sampling sites within the
cocoa growing region of Ghana.
Optimal climate
Suboptimal climate
Optimal-
Loam (OL)
Optimal-
Sandy (OS)
Suboptimal-
Loam (SL)
Suboptimal-
Sandy (SS)
Coordinates
N6° 10.8
W2° 28.5
N6° 36.6
W0° 58.3
N7° 07.2
W2° 21.4
N7° 04.9
W1° 23.7
Elevation (m)
235 220
255 365
Mean Annual
Precipitation
(MAP; mm)
1546 1528
1216 1278
Mean Annual
Temperature (MAT;
°C)
26.1 26.2
26.0 23.6
Basal area T. cacao
(m2 ha-1)β
m: 20.8 ± 2.1
s: 21.9 ± 1.5
m: 11.3 ± 3.2
s: 11.6 ± 1.0
m: 13.1 ± 1.9
s: 11.8 ± 2.8
m: 17.0 ± 1.0
s: 10.1 ± 2.7
Density T. cacao
(stems ha-1)β
m: 2399 ± 522
s: 3255 ± 808
m: 1111
s: 1042
m: 1304 ± 73
s: 2000 ± 78
m: 1998 ± 452
s: 1559 ± 185
Sand (%)
34.1 ± 1.5 59.7 ± 0.8
44.9 ± 1.8 75.1 ± 0.6
Clay (%)
18.3 ± 1.3 18.3 ± 0.3
25.3 ± 0.4 17.0 ± 0.3
Silt (%)
43.4 ± 1.3 22.0 ± 0.7
19.0 ± 2.0 8.8 ± 0.3
NO3- (mg g-1)
38.2 ± 3.7 18.5 ± 2.9
60.5 ± 4.3 22.0 ± 2.2
NH4+ (mg g-1)
42.5 ± 5.2 25.4 ± 1.5
15.4 ± 1.8 9.1 ± 1.0
PO4- (mg g-1)
30.5 ± 5.6 17.5 ± 0.5
21.1 ± 0.3 50.0 ± 10.0
β reported by species combination treatment: m = monoculture; s = species mixture, from known
planting density or estimates from n = 3 blocks.
95
data from the WorldClim database (Hijmans et al. 2005). Two sites were climatically ‘optimal’
compared to the other two ‘suboptimal’ sites, judged by ~300 mm difference in MAP. The dry
sites (MAP ~1200 mm) represent the lower end of moisture regimes for T. cacao cultivation,
while the wet sites (MAP ~1500 mm) are within climatic suitability (1250 to 2500 mm) (Wood
and Lass 2001, Snoeck et al. 2006). Within each of the two precipitation categories, one site was
characterized by sandy loam soil and the other site with loam soil, determined from soil sampling
at each site (described in ‘soil sampling and analysis’). Therefore, a farm within the cocoa-
dominated landscapes in the Western Region within a wet evergreen forest zone was classified as
‘Optimal-Loam’ (N6° 10.8 W2° 28.5). A cocoa research site in the Ashanti Region that lies
within a moist semi-deciduous forest zone was classified as ‘Optimal-Sandy’ (N6° 36.6 W0°
58.3). Further north and closer to a transition zone between moist semi-deciduous and savanna, a
farm site located in the Brong Ahafo Region was classified as ‘Suboptimal-Loam’ (N7° 07.2
W2° 21.4) and another cocoa research station at the forest-savanna transition zone in the north of
the Ashanti Region was classified as ‘Suboptimal-Sandy’ (N7° 04.9 W1° 23.7).
At all four sites, T. cacao were of similar age (~15 years old) and were the common hybrid
variety cultivated throughout Ghana (Asare et al. 2010, Zhang and Motilal 2016). At each site, I
defined areas with two management strategies where T. cacao was planted in monoculture and
where T. cacao was intercropped with a fast-growing shade tree T. ivorensis (hereafter referred
to as “monoculture” and “species mixture”, respectively). At each of the four sites, for each of
the two management strategies, three sampling blocks 10 × 10 m in size were established to
capture potential local-scale soil variation at the sites. Within each block, five T. cacao trees
were selected that were visually free of damage, healthy, and to be similar in size and stature
across the four sites (diameter at 1.3 m; DBH = 10.3 ± 1.8 cm (± S.D.), n = 120). Thus, in total
120 individual T. cacao trees were sampled for morphological and chemical root traits (described
below) in a hierarchal sampling design so that we could isolate sources of variation from
different organizational scales: i) site or farm scale, ii) management nested in each site, and iii)
sampled blocks nested within management at each site. Root sampling and installation of
ingrowth cores were completed in June 2015, while T. cacao were flowering and producing
cocoa pods and, thus, when both water and nutrient demands were presumed to be high (van
Vliet and Giller 2017).
96
5.3.2 Fine root sampling and analysis
I standardized root collection by only sampling intact ‘root branches’ (sensu McCormack et al.
(2015)) that were within 2 m from tree stems, originating from lateral surface roots (top 10 cm of
soil). Each sampled root was confirmed to originate from study trees by directly tracing the root
to the stem. Once identified, roots were cut at the point in which root diameter became 0.2 cm, or
where the root branched directly from a root that was greater than 0.2 cm. Sampled roots were
selected i) to provide sufficient material for chemical analysis, and ii) to be without, or with very
few, pioneering roots to better capture roots that were responsible for resource uptake (Iversen et
al. 2017). Samples were collected carefully to avoid loss of roots and include all attached fine
roots to distal tips. These were frozen and transported for processing at the University of Toronto
Scarborough, Canada.
Root branches were placed in reverse osmosis water to gently loosen and remove soil particles
and were subsequently separated the first three orders of roots, namely all of the ‘absorptive fine
roots’, from each root branch using a scalpel under a stereoscopic microscope. Absorptive fine
roots showed limited secondary thickening (da Silva and Kummerow 1998) and are the primary
interface of soil resource uptake, while the higher order fine roots, i.e. transport fine roots, limit
exchange of resources between root and soil and act as a conduit for water and dissolved solutes
from absorptive roots to the coarse root structure (McCormack et al. 2015, Freschet and Roumet
2017, Kong et al. 2017). Root samples were scanned using a flatbed scanner (STD4800; Regent
Instruments Inc., Canada) at 800 dpi. Images were analyzed using WinRhizo (Reg. 2016a;
Reagent Instruments Inc., Canada) to measure root length, number of tips, average diameter (D;
mm), and estimate surface area and volume. The ratio of absorptive root length to transport root
length (A:T; unitless) per branch was calculated (Kramer-Walter et al. 2016).
Root samples were then dried at 60 °C for 48 hours to determine dry weights. These weights
were used to calculate root traits based on mass, including specific root length (SRL; m g-1),
specific root area (SRA; cm2 g-1), specific root tip abundance (SRTA; tips g-1), and root tissue
density (RTD; mg cm-3)). Dried absorptive root samples were then ground into a homogeneous
powder using a ball mill (Retsch Ltd., Germany) and analyzed for root N content (Nab; mg g-1)
and C to N ratios (C:Nab; unitless) using an elemental analyzer (CN 628, LECO Instruments,
97
Canada). In sum, I measured seven morphological (SRLab, SRAab, SRTAab, Dab, RTDab) and
chemical traits (Nab, C:Nab) that have been previously shown to be related to resource uptake,
root growth, and/or longevity, as well as the architectural trait A:T and three morphological traits
of transport fine roots: SRLtr, Dtr, and RLDtr, which describe fine root construction (investment)
and function (water transport) (McCormack et al. 2015).
5.3.3 Root growth
Ingrowth cores were installed at the block scale to quantify root growth of T. cacao. Three cores
were randomly located in the same sampling blocks used in the static root trait collection
described above. An 8-cm diameter auger was used to remove soil to 10 cm depth, and all roots
were removed from the soil and root-free soil was placed back into holes within ingrowth cores
(2 mm mesh). Effort was made to emulate soil bulk density by removing soil in increments, and
then replacing the same soil to the same depths. Ingrowth cores were collected after four months,
and roots were removed for analysis. T. cacao fine roots (< 2 mm) were washed and removed
from soil, and oven dried to calculate fine root growth (i.e., fine root biomass growth rates;
GRroot). As roots from ingrowth cores were untraceable to individual study trees, GRroot values
were reported as g m-2 mo-1 and were standardized across the blocks by (dividing by) the basal
area of T. cacao trees in each sampled block.
5.3.4 Soil sampling and analysis
Soil texture analysis was carried out at the block scale using a composite of three samples and
analyzed at the Soils Institute of Ghana (Kumasi, Ghana) (n = 6 per site). Soil moisture and
nutrient analyses were carried out at the tree scale from a composite of three soil samples
collected from soil (0 to 10 cm) which roots were excised. From each of these soil samples, soil
moisture content was determined from a subsample of field moist soil that was oven dried at
105°C for 48 hours. The remainder of soil was frozen until further processing at the University
of Toronto Scarborough. Soil NO3- and NH4
+ were extracted from field moist soils in KCl
solution and filtered through Fisher P8 filter paper. Soil PO4- was extracted from air-dried and
sieved (2 mm) soils in a 1:10 soil to Bray’s 1 solution and filtered through Fisher P5 filter paper.
The concentrations of available N and P in extracted solution were then measured
colourmetrically using a flow injection analyzer (QuikChem8500; Lachat Instruments, USA).
98
5.3.5 Statistical analysis
All statistical analyses were performed in R v. 3.2.4 (R Foundation for Statistical Computing,
Austria). The study dataset contained root trait data from n = 120 individual T. cacao trees, in
addition to n = 68 observations of root GRroot (four cores were damaged and could not be
collected). I first assessed how traits coordinated with each other in bivariate and multivariate
trait space. Coordinated variation among pairs of root traits were assessed with Pearson
correlations. Coordinated trait syndromes of all traits, including a RES, were evaluated using
principal component analysis (PCA) using the ‘ade4’ R package. This unconstrained ordination
approach reflects overall variance in the data and describes the dominant trait syndromes
(gradients of variation) among individuals. Trait values were centred and scaled prior to PCA. I
ran PCA with all measured root traits to capture not only resource uptake (absorptive roots) but
also in resource transport (transport roots) and growth of fine roots to describe plant overall
resource acquisition strategies. To quantify the amount of ITV in individual traits, as well as
multivariate representations of RES trait covariation (i.e., PCA axes scores), explained by the
nested scales of sampling, I used variance decomposition with the ‘lme4’ and ‘ape’ R packages.
Linear mixed models (LMM) with site, species combination, and block were the nested random
effects and the intercept was the only fixed variable. I subsequently tested for trait differences
among sites, with block as a random effect, and for within site differences between management,
with block as a random effect, using least squares means with the ‘lsmeans’ package. Direct
effects of soil environment (soil texture, moisture, and nutrient availability) on fine root traits
and traits syndromes were assessed individually using Pearson correlations. I performed another
mixed model analysis with these direct variables assigned as ‘fixed effects’ and the three nested
scales of sampling as random effects. This procedure allowed me to estimate the proportion of
ITV and coordination explained by soil variables alone (‘fixed effects r2’) and then gauge the
improvement in explanatory power resulting from random nested levels (‘fixed effects + random
effects r2’) using the ‘piecewiseSEM’ package. Prior to analyses, some root and soil variables
were log or square-root transformed to meet parametric assumptions.
99
5.4 Results
5.4.1 Intraspecific root trait (co)variation in T. cacao
T. cacao root trait coefficients of variation (CV) ranged from 13 to 72% (Table 5.2). ITV was
highest in A:T (72%) and SRTAab (46%), while Dab (14%), Nab (16%), and RTDab (18%) and
RTDtr (15%) were more constrained (Table 5.2). In bivariate relationships, acquisitive traits
(A:T, SRLab, SRAab, SRTAab, Nab, and GRroot) were, generally, positively correlated with each
other and negatively correlated with conservative traits (D, RTD, and C:Nab) (Table 5.3). These
patterns were also observed in multivariate trait space on an axis of coordinated trait variation
(PC1), which explained 42% of total trait variation and characterized a RES among individual T.
cacao trees (Fig. 5.1). On PC1, T. cacao expressing resource acquisitive strategies aligned in
opposition to T. cacao with more resource conserving strategies (Fig. 5.1). All traits were
significantly correlated with PC1 (p < 0.05) (Table A.4). The first axis prominently featured
coordinated trait variation in absorptive fine root traits (with relatively high axis loadings), while
variation in GRroot and transport fine root traits were less related to PC1 (Fig. 5.1; Table A.4). All
traits were correlated with PC2 (p < 0.01) except for A:T (p = 0.061), SRAab (p = 0.864), and
SRTAab (p = 0.107) (Table A.4). PC2 depicted an orthogonal, and somewhat weaker, resource
acquisition strategy in roots, featuring a trade off in Dab and RTDab. Furthermore, PC2 had a high
loading of GRroot variation. GRroot was positively associated with Dab and Nab, and negatively
associated with RTDab and C:Nab. These relationships were featured on PC2 and explained an
additional 15% of the total variation.
5.4.2 Abiotic effects on ITV of T. cacao
Morphological traits associated with resource acquisition (SRLab, SRAab, and SRTAab) did not
significantly correlate with any of the continuous soil variables (Table 5.3) and showed little to
no variance explained by site (Fig. 5.2; Table 5.4). However, there were significant correlations
between soil variables and fine root architecture (A:T), root diameter (Dab and Dtr), root tissue
density (RTDab and RTDtr), and chemical traits (Nab and C:Nab). A:T increased with soil NO3- (r
= 0.23; p = 0.010; n = 120) and clay content (r = 0.32; p < 0.001; n = 24), while D was generally
negatively related with soil NO3- (Dab: r = -0.36; p < 0.001; n = 120; Dtr: r = -0.47; p < 0.001; n
= 120). Both absorptive and transport fine roots were thicker with increasing sand content (Dab: r
100
Table 5.2: Summary statistics and intraspecific trait variation (coefficient of variation; CV) of
fine root traits measured from 120 individual T. cacao.
units min max mean SE CV (%)
Fine root architecture
A:T unitless 0.26 10.54 3.08 0.20 72
Absorptive fine roots
SRLab m g-1 9.10 44.10 22.47 0.66 32
SRAab m2 kg-1 13.35 44.40 24.63 0.53 24
SRTAab tips mg-1 1.53 17.20 6.43 0.27 46
Dab mm 0.28 0.52 0.36 0.00 14
RTDab g cm-3 0.28 0.72 0.48 0.01 18
Nab mg g-1 6.5 18.0 13.1 0.2 16
C:Nab unitless 21.83 65.57 33.11 0.60 20
Transport fine roots
SRLtr m g-1 0.91 4.73 2.44 0.08 35
Dtr mm 0.34 1.51 0.81 0.02 33
RTDtr g cm-3 0.25 0.68 0.52 0.01 15
Trait abbreviations: Absorptive:transport root length (A:T); Absorptive root traits: specific root - length
(SRLab), area (SRAab), and tip abundance (SRTAab), average root diameter (Dab), root tissue density
(RTDab), N content of roots (Nab), C:N in root tissue (C:Nab); Transport root traits: specific root length
(SRLtr), average diameter (Dtr), root tissue density (RTDtr)
101
Tab
le 5
.3:
Pea
rson c
orr
elat
ions
of
fine
root
trai
ts f
or
surf
ace
roots
of
T. ca
cao a
mong 1
20 i
ndiv
idual
tre
es. T
op r
ight
of
table
report
s th
e co
rrel
atio
n c
oef
fici
ents
(r)
wit
h s
ignif
ican
t val
ues
in b
old
. B
ott
om
lef
t of
table
rep
ort
s p-v
alues
.
Tra
it a
bb
rev
iati
on
s: A
bso
rpti
ve:
tran
sport
root
length
(A
:T);
Abso
rpti
ve
root
trai
ts:
spec
ific
ro
ot
- le
ng
th (
SR
Lab
), a
rea
(SR
Aab
), a
nd
tip
abun
dan
ce (
SR
TA
ab),
aver
age
root
dia
met
er (
Dab
), r
oot
tiss
ue
den
sity
(R
TD
ab),
N c
onte
nt
of
roots
(N
ab),
C:N
in
ro
ot
tiss
ue
(C:N
ab);
Tra
nsp
ort
ro
ot
trai
ts:
spec
ific
ro
ot
length
(S
RL
tr),
aver
age
dia
met
er (
Dtr),
root
tiss
ue
den
sity
(R
TD
tr);
fi
ne
roo
t g
row
th r
ate
(GR
root)
102
Figure 5.1: Principal component analysis of intraspecific root trait variation of surface roots of
T. cacao based on 120 individuals. Also shown are the 95% confidence ellipses surrounding data
from each sampled site. Trait abbreviations: Absorptive:transport root length (A:T); Absorptive
root traits: specific root - length (SRLab), area (SRAab), and tip abundance (SRTAab), average
root diameter (Dab), root tissue density (RTDab), N content of roots (Nab), C:N in root tissue
(C:Nab); Transport root traits: specific root length (SRLtr), average diameter (Dtr), root tissue
density (RTDtr); fine root growth rate (GRroot).
103
Tab
le 5
.4:
Pea
rson c
orr
elat
ions
of
fine
root
trai
ts o
f T
. ca
cao
wit
h s
oil
var
iable
s. V
alues
in b
old
indic
ate
signif
ican
t
corr
elat
ions
(p <
0.0
5).
Tra
it a
bbre
via
tions:
Abso
rpti
ve:
tran
sport
root
leng
th (
A:T
); A
bso
rpti
ve
root
trai
ts:
spec
ific
root
- le
ngth
(S
RL
ab),
are
a
(SR
Aab
), a
nd t
ip a
bundan
ce (
SR
TA
ab),
aver
age
roo
t dia
met
er (
Dab
), r
oot
tiss
ue
den
sity
(R
TD
ab),
N c
onte
nt
of
roots
(N
ab),
C:N
in r
oot
tiss
ue
(C:N
ab);
Tra
nsp
ort
root
trai
ts:
spec
ific
root
length
(S
RL
tr),
aver
age
dia
met
er (
Dtr),
root
tiss
ue
den
sity
(RT
Dtr);
fin
e ro
ot
gro
wth
rat
e (G
Rro
ot)
104
Fig
ure
5.2
: R
oot
trai
t val
ues
and P
CA
axes
sco
res
of
T. ca
cao a
cross
four
site
s (S
ubopti
mal
-San
dy (
SS
); S
ubopti
mal
-Loam
(SL
); O
pti
mal
-San
dy (
OS
); O
pti
mal
-Loam
(O
L))
and i
n m
onocu
lture
and i
n m
ixtu
re w
ith a
shad
e tr
ee T
. iv
ore
nsi
s. V
alues
pre
sent
are
leas
t sq
uar
e m
eans
± S
E w
ith s
ampli
ng b
lock
as
a ra
ndom
eff
ect.
Sam
e l
ette
rs s
ignif
y n
on
-sig
nif
ican
t dif
fere
nce
s
bet
wee
n s
ites
and a
ster
isks
show
sig
nif
ican
t dif
fere
nce
s bet
wee
n m
anag
emen
t w
ithin
sit
es
105
= 0.25; p = 0.007; n = 24; Dtr: r = 0.31; p < 0.001; n = 24), and, conversely, were thinner with
increasing clay content (Dab: r = -0.26; p = 0.004; n = 24; Dtr: r = -0.61; p < 0.001; n = 24).
RTDtr was positively related to sand (r = 0.31; p < 0.001; n = 24) and negatively related to clay
content (r = -0.37; p < 0.001; n = 24) and soil NO3- (r = -0.25; p = 0.007; n = 120). Conversely,
RTDab was negatively related to sand content (r = -0.41; p < 0.001; n = 24) and positively related
to clay content (r = 0.42; p < 0.001; n = 24) and soil moisture (r = 0.23; p = 0.011; n = 120) and
soil NO3- (r = 0.28; p = 0.002; n = 120). C:Nab was negatively related with sand content (r = -
0.26; p = 0.004; n = 24) and conversely Nab was positively related with sand content (r = 0.30; p
< 0.001; n = 24) and negatively related to soil moisture (r = -0.28; p = 0.002; n = 120) and soil
NH4+ (r = -0.31; p < 0.001; n = 120). Dtr was positively correlated with soil PO4
- (r = 0.22; p =
0.017; n = 120) while GRroot decreased with increasing PO4- availability (r = -0.33; p < 0.001; n
= 120) (Table 5.4).
Although I found no significant relationships with continuous soil variables and PC1, PC1 axis
scores were significantly different among the sites (p = 0.03) in which T. cacao roots at the OL
site had the lowest PC1 scores (more conservative) and was significantly lower compared to SL
(p = 0.02) (Fig. 5.2). PC2 was negatively related to sand content (r2 = 0.23; p = 0.002) and
positively related to clay content (r2 = 0.23; p < 0.001), and NO3- availability (r2 = 0.25; p <
0.001) (Table 5.3). Of the traits that were more prominently aligned with PC2, RTDab had similar
patterned variation with soil variables with increasing PC2 scores, whereas Dab and Nab showed
an opposite trend: these traits were higher in sandier conditions with fewer soil resources (Table
5.3). There were few consistent patterns in mean trait values or mean coordinate trait values
among the sites, with the exception of chemical traits that were significantly lower in Nab and
conversely higher in C:Nab at the OL site (Fig. 5.2). Variance partitioning revealed that total ITV
of SRLab, Dab, and A:T, were moderately explained by both fixed and random variables (22 to
33%) (Table 5.4). However, these variables and nested factors explained a high proportion of
ITV in the chemical variation in absorptive fine roots (Nab, C:Nab), morphological variation in
transport fine roots (SRLtr, Dtr), GRroot, and the PCA axes scores (50 to 80%) (Table 5.5).
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Table 5.5: Sources of intraspecific trait variation (ITV) in fine roots of T. cacao. Variance
decomposition was based on a nested analysis of variance, which for each trait was based on 120
individual trees sampled in shallow soil (0 to 10 cm). Largest source of variation is in bold. Also
presented are the total explained variance associated by continuous soil variables (NO3-, NH4
+,
PO4-, soil moisture, sand content, and clay content) (“Fixed effects r2”) and the explained
variance associated with both the fixed effects and random effects.
Variance decomposition Mixed model
Root trait Site
Species
combination Block Within/Error
‘fixed
effects r2’
‘fixed +
random
effects r2’
Absorptive fine roots
SRLab 0.00 0.06 0.06 0.89 0.04 0.22
SRAab 0.00 0.12 0.03 0.85 0.07 0.41
SRTAab 0.00 0.11 0.05 0.84 0.12 0.40
Dab 0.06 0.03 0.10 0.80 0.14 0.22
RTDab 0.27 0.09 0.00 0.64 0.15 0.38
Nab 0.30 0.09 0.03 0.58 0.11 0.68
C:Nab 0.25 0.11 0.00 0.65 0.05 0.54
Transport fine roots
SRLtr 0.30 0.00 0.14 0.57 0.24 0.80
Dtr 0.69 0.00 0.02 0.29 0.29 0.75
RTDtr 0.17 0.01 0.02 0.80 0.13 0.25
Fine root system
A:T 0.29 0.00 0.10 0.61 0.20 0.33
GRroot* 0.37 0.30 NA 0.34 0.16 0.76
Coordinated root variation
PC1 0.00 0.13 0.04 0.83 0.14 0.50
PC2 0.54 0.03 0.17 0.26 0.25 0.75
Trait abbreviations: Absorptive:transport root length (A:T); Absorptive root traits: specific root - length
(SRLab), area (SRAab), and tip abundance (SRTAab), average root diameter (Dab), root tissue density
(RTDab), N content of roots (Nab), C:N in root tissue (C:Nab); Transport root traits: specific root length
(SRLtr), average diameter (Dtr), root tissue density (RTDtr).
*measured at the block scale using ingrowth cores, not by individual T. cacao
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5.4.3 Shade tree effects on ITV of T. cacao
Species combination was relatively important on controlling coordinated root trait variation of
individual T. cacao described by PC1 (i.e., the RES) and the traits which were strongly
associated with that axis. Fine roots of T. cacao in mixture with T. ivorensis were generally more
acquisitive. At three of the four sites, acquisitive traits (SRLab, SRTAab, SRAab, and Nab) were
generally higher in mixture than in monoculture, and conservative traits (Dab, RTDab, C:Nab)
were generally lower in mixture than in monoculture (Fig. 5.2). An opposite trend was observed
at the SL site where absorptive fine roots traits of T. cacao in mixture were significantly more
conservative compared to in monoculture as described by PC1 axis scores (p < 0.01). Species
combination nested in site explained little of the total variation in PC2 scores, but significant
differences of PC2 between monoculture and mixture were detected at the two suboptimal sites
(Table 5.4; Fig. 5.2). Differences among sites and continuous soil variations were relatively
important in controlling coordinated trait variation described by PC2. Inclusion of the nested
factors with soil variables explained approximately 50% of the total variation in PC1. PC2 was
poorly explained by species combination (management), while site and soil variables were
strongly related, with 54% of total variation in PC2 attributed to site and 25% explained by the
fixed factors (Table 5.5).
5.5 Discussion
5.5.1 How do root resource acquisition strategies of T. cacao vary across
climatic conditions?
In support of my first hypothesis, T. cacao coordinated and aligned onto a RES describing trade-
offs in the construction and function of fine roots. Notably, there was a trade-off in the first three
orders of fine roots (i.e., absorptive fine roots), with traits that are commonly associated with
higher rates of resource acquisition but lower root lifespan (i.e., ‘less expensive’ roots) in
opposition to traits that are commonly associated with lower rates of resource acquisition but
longer root lifespan (i.e., ‘expensive’ roots) (Roumet et al. 2016, Freschet and Roumet 2017).
Abiotic control on the first axis (RES) was limited, but overall T. cacao fine roots were more
conservative in the higher resource site (i.e., optimal climate with loam soils), which was in
support of my second hypothesis. Generally, however, traits associated with a trade-off described
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by PC2, including GRroot, tissue density, elemental composition of root tissue, and root diameter,
were better explained by soil conditions. For example, Nab was significantly lower at the OL site
and also correlated negatively with soil moisture, which also corroborates negative trends
observed between precipitation and Nab across species and communities (Freschet et al. 2017)
and suggests that root respiration and metabolic processes per unit of biomass is lower as water
becomes less limiting. Results also show lower RTDab in drier soil and that sandy soils may
accentuate this effect. Lower RTDab may reduce metabolic costs of the root under increased
resource limitation (Postma and Lynch 2011), while conversely higher RTDab may improve root
strength penetration (Freschet and Roumet 2017), which is likely required in fine-texture soils
versus coarse-textured soils.
It has been proposed that across taxa and at the whole-plant scale, conservative traits should be
better to withstand drought stress, with an improved ability to maintain hydraulic functions under
low water potentials (Lopez-Iglesias et al. 2014, Reich 2014). When water limitation is a
concern, retention of invested biomass (e.g., expressed through lower SRL) may permit
continued uptake of soil resources with lower root turnover. However, maintaining resource
uptake in soil with lowered investment to roots (e.g., higher SRL) may also be useful given less
fixed C, as stomatal closure is important in regulating water loss for T. cacao in dry
environments (de Almeida et al. 2016). Preference for one strategy over another may largely
depend on the temporal patterns in precipitation and resource availability, with acquisitive
strategies optimal for short periods of drying (Fort et al. 2015). The sites from this present study
represent the lower end of annual rainfall requirements for T. cacao and there was indication that
absorptive roots of T. cacao were more acquisitive with drier soils, and thus presumably more
responsive to influxes of resource availability. Further research should focus on variation in trait
and crop performance over time through variable precipitation.
Including measurements of both absorptive and transport fine roots proved interesting. First,
variation in transport fine roots were generally better explained by site than were absorptive fine
roots. While for the most part the directional patterns in transport fine roots matched that of
absorptive fine roots, results show the potential for contrasting directional signals between
functionally distinct fine root organs. Of note, RTD of transport fine roots expressed an opposing
response to RTDab, with denser transport fine roots in sandier soils. Transport fine roots are
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presumably longer lived than absorptive roots, provide structural framework for absorptive fine
roots, and transporting conduits to move resources from absorptive roots to the plant
(McCormack et al. 2015). To this end, more ‘conservative’ transport fine roots that can better
withstand drought stress in soils with low water availability are likely thinner in diameter. This
was observed at the suboptimal site with higher clay content. Inclusion of transport fine roots
may be useful in elucidating plant resource acquisition strategies as they capture important trade-
offs in root growth and construction for transporting resources from absorptive roots, particularly
in studies testing root response to moisture or precipitation gradients.
Additional dimensions of trait covariation may be important to plant performance. GRroot, a
dynamic trait describing overall investment to fine roots (Weemstra et al. 2017), was poorly
related to the absorptive organ-scale trade offs described by the RES. Instead, faster root growth
occurred for T. cacao with thicker but less dense and N-rich absorptive root tissue. Fine root
growth is important for T cacao in water-limited environments (da Silva and Kummerow 1998)
and has been shown to be a strategy for drought tolerant T. cacao seedlings (dos Santos et al.
2016). Yet, the response can likely be modified by soil texture that can alter hydraulic (and
nutrient) properties of the soil environment. Among the two climatically optimal sites, faster root
growth (150% higher growth rate) was observed in sandier soils compared to loam soils.
Weemstra et al. (2017) also reported higher root growth in sandy versus clayey soils within
species (Fagus sylvatica and Picea abies), suggesting a tree response to lower resource
availability in sandy soils. However, in this present study, the opposite pattern was also observed
at the climatically suboptimal sites, with 53% reduced growth rate at the sandier site compared to
the loam site. One possible explanation is that optimal conditions for T. cacao growth given
certain soil physical properties will be a function of precipitation, known as the ‘inverse texture
effect’, in which water limitation is accentuated in fine-textured soils (Fernandez-Illescas et al.
2001). Although this effect is common to more arid conditions, this assertion was reflected by
the trait values and coordinated root strategies at the suboptimal-sandy site which were more
similar to the two optimal sites than to the suboptimal-loam site. Another possible explanation
could be that the sites with the lowest amounts of available P also showed the highest GRroot,
which may indicate an overall plant allocation response to P limitation. Further study is required
to identify the casual mechanisms.
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5.5.2 How do shade trees modify resource acquisition strategies of T.
cacao?
I found mixed evidence for my third hypothesis. Interspecific interactions with T. ivorensis were
important for some root traits and less so for others. Morphological traits describing surface area
in relation to unit of biomass of absorptive roots (SRL, SRA, and SRTA) were more affected by
presence of the shade tree T. ivorensis than to the overall variation across sites or continuous soil
variables. This confirms previous work on tropical tree-crop C. arabica roots in which variation
of acquisitive morphological traits such as SRA and SRTA (reported instead as specific root tip
density) was better explained by interspecific interactions with the shade tree Erythrina
poeppigiana than by sites that spanned variable climatic conditions and from which C. arabica
were sampled (Isaac et al. 2017). These traits were among those strongly featured in the
dominate coordinated root trait strategy (RES, or PC1), and, relatedly, shade management, or
species combination may be an important driver in determining a tree crop’s position on the
RES.
Under more optimal precipitation regimes, soil physical properties and species selection for
cocoa agroecosystems are less consequential. For example, in Bolivia under optimal climatic
conditions, more complete (i.e., complementary) moisture acquisition with depth was observed
in cocoa agroforestry compared to cocoa monoculture as well as improved soil moisture in
shallow soils under agroforestry with diverse species of shade trees (Niether et al. 2017).
However, careful shade tree management that accounts for soil physical properties may be more
critical in suboptimal climates. Moser et al. (2010) and Schwendenmann et al. (2010) found T.
cacao could maintain sap flux during artificially-induced drought conditions when next to G.
sepium in sandy soils: T. cacao acquired water from shallow soils, while G. sepium acquired
water from deeper in the soil profile. However, in another study in Ghana, mortality of mature T.
cacao during a drought was shown to be higher in agroforestry with either Albizia ferruginea and
Antiaris toxicaria than in monoculture (Abdulai et al. 2018) (but see Norgrove (2018) and
Wanger et al. (2018)). In that study, soil texture was not controlled across the different species
combinations (sandy loam and loams) (Abdulai et al. 2018, Wanger et al. 2018). Management
strategies that are finely tuned to environment are being increasingly recognized. Cocoa farmers
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in Ghana already draw on complex knowledge of shade tree species for their management needs
and objectives contingent on climatic conditions (Graefe et al. 2017), but improved
understanding of the interactions of soil hydraulic properties in conjunction with the effects of
functionally distinct shade trees in regulating microclimate and soil resources could further
support shade tree management towards maintaining T. cacao function in more diverse
agroecosystems.
5.6 Conclusions
This study shows that the influence of a shade tree on T. cacao resource acquisition strategies
can vary depending on environment. Thus, selection of species combination for cocoa
agroecosystems should be environment specific, inclusive of both climate and soil physical
properties. This study demonstrates ITV and a RES in T. cacao. However, as previous studies
have shown for roots across species, multidimensional trait coordination is also important
(Weemstra et al. 2016). The functionality of trait research for informing agroecological practices
is being increasingly recognized (Garnier and Navas 2012, Martin and Isaac 2015, Wood et al.
2015, Barot et al. 2017, Damour et al. 2018). The findings from this present study mark
significance towards including ITV of cultivated species in larger assessments of ecosystem
processes and modelling. For example, root traits describing the chemical and tissue composition
of absorptive roots, such as Nab, C:Nab, RTDab, predictably varied with continuous soil variables
and were significantly related to the growth rate of fine roots. The predictive capacity of ITV of
root traits could inform management decisions (Garnier and Navas 2012) as well as species
combinations in agroforestry.
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Chapter 6 Discussion
6.1 Contributions and future research directions
My PhD research highlights the importance of context-specific understandings of plant trait
variation in evaluating agroecosystem function. Phenotypic plasticity is expected to occur among
plant individuals of the same species cultivated across diverse and heterogeneous agricultural
landscapes, such as in low-input, multispecies tropical agroecosystems. The observed variation
in root traits within one of the most socially and economically influential tree crops: Theobroma
cacao, was large, at a scale relevant to broader root trait datasets. In Figure 6.1, I present data
collected in my PhD research in comparison to data from the largest global root trait database:
Fine Root Ecology Database (FRED 2.0; Iversen et al. 2018). Trait values measured within and
across individual T. cacao (of the same hybrid and of the same age) distribute over a relatively
large portion of trait values representing upwards of 1392 observations of live roots from woody
plants and trees (Fig. 6.1). Additionally, bivariate relationships between some of the more
commonly measured fine root (< 2mm) traits and absorptive fine root (orders 1-3) traits show
that species management can influence trait values within T. cacao but generally have similar
trait trade-offs that are observed in larger-scale datasets (Fig. 6.1.C & D).
Overall, my PhD research shows that species composition and edaphic and climatic conditions
can result in a phenotypic response that systematically affects root traits associated with plant
and ecosystem function (e.g. C storage, nutrient cycling). In the case of T. cacao, root trait
variation could have regional-scale implications due to the artificially high occurrence of T.
cacao throughout Ghana. Potential applications from my PhD research include supporting plant
and farm diagnostics as well as informing management for enhancing the provision of critical
ecosystem services, which I discuss in section 6.2. However, first, in this section, I summarize
the scientific and applied contributions from each study and highlight important research
questions that emerged.
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Figure 6.1: Data on T. cacao roots reported in my PhD thesis, according to shade tree
management (i.e., T. cacao in different species compositions: T. cacao in monoculture (C-C), T.
cacao in mixture with E. angolense (C-E), and T. cacao in mixture with T. ivorensis (C-T)), in
comparison to all available ‘woody’ root trait data in the FRED 2.0 database: A: root to shoot
(RS) ratio using data of T. cacao n = 15 (Chp. 2) and FRED data n = 87; B: average fine root
diameter (D) within individual root systems of T. cacao n = 360 (Chp. 3) and FRED data n =
3891; C: relationships between fine root diameter (D) and specific root length (SRL) of T. cacao
n = 54 (Chp. 4) and FRED n = 1392; D: absorptive fine root RTDab and SRLab of T. cacao n =
120 (Chp. 5) and FRED n = 565.
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6.1.1 Chapter 2: Root biomass variation in Theobroma cacao and implications for carbon stocks in agroforestry systems
This study reports some of the first data on directly measured coarse root biomass in a mature
cocoa agroecosystem. I found that the amount of biomass allocated belowground versus
aboveground can vary substantially within this perennial tree crop. This is intriguing as
measurements were carried out on same-age and same-genotype trees at the same site, with the
only distinct influencing factor being species combination. Experiments on T. cacao seedlings
have shown differential RS allocation patterns depending on above and belowground regulation
of resources (Isaac et al. 2007b) as well as water limitation (dos Santos et al. 2016); however,
allocation patterns in relation to environment in mature and productive T. cacao had yet to be
tested. The data indicate that T. cacao grown in mixture with shade trees, particularly with a
pioneer tree species, may allocate relatively greater amounts of biomass belowground, or,
conversely T. cacao in monoculture show reduced allocation to coarse root biomass. Previous
research shows that species composition can affect soil resources and the nutritional status of T.
cacao (Isaac et al. 2007a, Dawoe et al. 2014, Blaser et al. 2017). Therefore, variation in
allocation patterns at the plant scale may relate to differential allocation for soil-based resources
versus light requirements. While these conditions were not evaluated directly in this study,
neighbouring tree species used in this study are associated with distinct canopy and rooting
strategies that could lead to these differences in resource availability and use.
Differential allocation at the plant scale can impart variation on delivery of ecosystem services,
such as C cycles. Inclusion of shade trees and increases in soil C on cocoa farms are the primary
pathways to increasing and maintaining total C stocks closer to forest levels (Dawoe et al. 2014).
However, in most cocoa agroecosystems, T. cacao trees represent an important portion of
vegetative C. I show that T. cacao root biomass needs to be included in C stock studies as coarse
roots contribute 6 to 18% of total biomass C to these systems. Conventionally, researchers rely
on allometric equations often developed from forest data to estimate belowground biomass. This
approach has appeal for coarse-scale inventories; however, these equations may lead to
inaccuracies of estimates in cultivated environments (Kuyah et al. 2012, Borden et al. 2014).
Additionally, lower accuracy may occur if there is some environmental condition that is related
to systematic variation in allocation patterns. To illustrate, with lower biomass allocation to T.
cacao roots in monoculture, using a generic allometric equation can lead to overestimates of C
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stocks in these more intensified production systems. As C markets evolve, more accurate and
precise accounting is required. For example, inclusion of species-specific allometric estimates at
the country level for root biomass is part of Tier 2 level C accounting practices (Zomer et al.
2016).
When the objective is to detect systematic variation, more accurate estimation is required. In this
study, the use of GPR combined with physical sampling was more accurate at estimating coarse
root biomass of T. cacao than applying a generic allometric equation. To this end, it is worth
noting methodological advancements from previous GPR-root research (Borden et al. 2014,
2017b); for example, in estimating biomass that is not detected by GPR, such as the taproot
(Samuelson et al. 2010, Butnor et al. 2015). Additionally, this was a first-time application of
combining GPR estimates of large coarse roots with physical sampling of smaller coarse roots
that would otherwise go undetected by GPR. This is important for trees such as T. cacao that
have long fibrous root systems with many small to medium sized coarse roots that can constitute
a large proportion of lateral coarse root biomass. Relatedly, it was important to select roots of
variable sizes during the calibration phase. This user-guided approach was also suggested by
Butnor et al. (2015) and was important in my study to ensure that the larger coarse roots, which
were detectable by GPR, were represented within the calibration curve.
6.1.2 Chapter 3: Fine root distribution and morphology of Theobroma cacao reveals nutrient-specific acquisition strategies in a multispecies agroecosystem
This study shows that T. cacao are adapted to forage for nutrients in heterogeneous soil
environments through fine-scale modifications in the modular development of the root system.
Primarily, this can occur through root growth and lateral root initiation where there is higher
nutrient availability. In this study, the distribution of fine root length and biomass related to
gradients of localized availability of soil nutrients. Furthermore, these spatial changes in root
densities were coupled with variation in root morphology: T. cacao invested more to individual
roots with increasing nutrient availability. These findings are in line with numerous other studies
examining root variation over larger scales or across species that show more permanent, longer-
lived roots in resource-rich environments (e.g., Ostonen et al. (2007b), Holdaway et al. (2011),
and Weemstra et al. (2017)), but are the first, to my knowledge, to demonstrate this pattern
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within the scale of individual tree root systems and under field conditions. However, for
gradients of more mobile nutrients, responses showed a tendency towards more acquisitive root
organs, which is in support of theories on plant economics as well as empirical evidence from
field and laboratory experiments (e.g., Fort et al. (2016)). In particular, soil NO3- was related to
increases in acquisitive root morphology, which could signify a plastic response for foraging of a
transient nutrient.
Species combination can have multiple direct and indirect effects on the root systems
development of T. cacao. First, root-root interactions can affect the fine-scale modifications
described above. Although the signal was weak, this study provides some evidence that
heterospecific neighbour tree roots are related to plastic responses in root morphology. While
root distributions may be altered by neighbouring shade tree, fine-scale variation in root
morphology may also be an important mechanism for T. cacao to forage for nutrients when in
proximity to neighbouring tree roots. Thus, root system models that predict patterns of nutrient
acquisition and utilization may need to be parameterized for neighbour interactions. Second,
species combination can have differential effects on total nutrient stocks and cycling rates. I
found general support in soil nutrient improvements with shade trees, with some nutrient-specific
exceptions. Further work is needed to confirm the nutrient status of T. cacao within these species
compositions. Notably species mixture may also be characterized by faster nutrient cycling,
which was indicated by systematic changes in root morphology of T. cacao in mixture compared
to monoculture. Previous studies have shown variation in nutrient cycling under different species
combinations in cocoa agroecosystems (Hartemink 2005, Dawoe et al. 2010, Fontes et al. 2014).
Shade tree leaves can be an important source of nutrients in cocoa agroecosystems, with faster
decomposition rates reported for shade tree opposed to T. cacao leaves (Fontes et al. 2014).
More direct evidence linking T. cacao root traits to nutrient cycling comes from observations in
Indonesia, where Rajab et al. (2018) found differences in T. cacao fine root turnover rates among
different species combinations, and these differences seemed to be reflected in root trait variation
(generally higher SRL and SRA, and lower D). These plant-soil feedbacks are an exciting area of
research that can heavily draw on trait-based approaches to develop and test hypotheses (e.g.,
Semchenko et al. (2017)).
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The integrated response occurring within individual roots systems is impressive given that
certain trait variation can optimize acquisition for one resource but not another within the same
soil environment. The study of complex processes of root-soil relationships within the scale of
individual root systems are generally limited to controlled laboratory settings, with plants grown
in containers that feature artificially generated gradients and patches of soil resources (e.g., Drew
(1975), Hodge (2006), and Cahill et al. (2010)). Rarely do we observe these soil-root processes
in situ (Poorter and Ryser 2015) and especially within more extensive and complex mature tree
root systems, at least not without active manipulation of nutrient availability (e.g., Eissenstat et
al. (2015) and Liu et al. (2015)). Other research has shown the spatial patterning of root systems
and soil conditions (e.g., Cardinael et al. (2015)), yet this is the first, to my knowledge, to capture
the interface of an individual with a neighbour and also characterize soil nutrient availability.
Previous research has also shown high heterogeneity in soil conditions within the scale of an
individual root system (e.g., Jackson and Caldwell (1993) and Stoyan et al. (2000)) as well as the
importance of this heterogeneity on plant growth (e.g., Hutchings and John (2004) and Hu et al.
(2014)). Therefore, this study represents a novel multi-nutrient analysis that captured soil-root
interactions given heterogeneous nutrient availability under field conditions.
Future research can focus on sampling within the scale of individual root systems and measuring
multiple aspects of root modular development. A trait-based approach can aid in this process if
specific traits measured are associated with the function of interest. For example, in P-limited
tropical soils, studies should include the functional trait root hair length, which was not captured
in this study, but is related to P uptake (Fort et al. 2015). More generally trait variation that
captures root growth, lateral root initiation, and investment to absorptive organs (such as FRLD
and SRTA) can be useful to describe nutrient uptake but also ecosystem function, such as
nutrient cycling.
6.1.3 Chapter 4: Shade trees regulate the fine root trait response of Theobroma cacao to fertilization
Neighbouring species controlled coordinated root trait syndromes in T. cacao, suggesting that the
abiotic and biotic conditions invoked by different species of trees can lead to variation in the
resource acquisition strategies of T. cacao. There was some evidence that fertilization shifted T.
cacao towards conservative resource acquisitions strategies in surface roots. Directional trends in
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root morphological variation in this experiment are similar to those reported in long-term field
fertilization and controlled laboratory experiments in which there was a decrease in acquisitive
root trait values (Ostonen et al. 2007b, Kramer-Walter and Laughlin 2017). Among species
combinations and with depth, T. cacao fine root responses were directionally inconsistent and
seemingly non-linear, demonstrating the complexity in belowground interactions in multispecies
agroecosystems. However, the existence of a root economics spectrum (RES) among individuals
of the same species within the same site is interesting as it could indicate that patterns predicted
across larger scales and across species are generalizable to variation within a farm (Messier et al.
2017). Furthermore, patterns in root trait ITV can describe important trade-offs in the growth and
function within a species and, in turn, how they relate to environment (Isaac et al. 2017). This
study detected a RES but also demonstrates the importance of multidimensional trait spectra in
roots.
Results show a limit to morphological variation for the fine roots collected from surface soils,
with no difference in response between two levels of fertilizer inputs used in this study.
However, additional increases in fertilizer dosages resulted in different rooting patterns with
depth. These effects could be the result from increased competition with neighbouring trees and
increased nutrient availability with depth resulting from higher nutrient leaching. Future studies
should pair root trait variation with nutrient leaching measurements to determine how root trait
response relates to nutrient losses. This study is representative of field conditions and active
management strategies employed by farmers in Ghana and, thus, can provide relevant insight
into the applied ecology of roots in these agroecosystems. In T. cacao agroecosystems, fertilizers
are typically broadcast applied as solid prills, which will likely result in non-uniform application
across the soil at intermittent times during the year. Therefore, it is useful to understand how T.
cacao roots may respond to these forms of fertilizer applications. In this regard, the results from
this study indicate that shifts in traits and trait syndromes can be used as indicators of plant
response to fertilizer application. These root trait response to fertilization could be useful in
diagnosing nutrient amendments. Snoeck et al. (2006) mapped fertilizer amendments that were
specific to soil conditions across the cocoa growing region. Similarly, nutrient amendments
could also be specified to species combination. These more refined nutrient diagnostics and
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prescriptions are essential to maintain high nutrient use efficiency in the agroecosystem and
minimize nutrient losses.
6.1.4 Chapter 5: Effects of interspecific interactions on Theobroma cacao root strategies across optimal and suboptimal climates
Variation in root traits may be useful in prescribing management strategies for suboptimal
conditions. In varietal development, dos Santos et al. (2016) used a multivariate trait-based
approach to characterize which varieties of T. cacao seedlings were most tolerant to drought and
waterlogging. Root traits and coordinated plant trait strategies of young and mature T. cacao
trees may also have potential in characterising germplasm for new varietal development to face
current and future production limitations. These efforts would be more appropriate where genetic
diversity is expected to be high in Central and South America (Motamayor et al. 2008). A trait-
based approach may also inform shade tree management strategies. Importantly, this study shows
that T. ivorensis influenced the resource acquisition strategies of T. cacao in both optimal and
suboptimal climates, but that these effects were seemingly regulated by edaphic conditions,
specifically soil texture. This means that selection of species combination may need to consider
certain environmental factors, including soil physical attributes.
Specific species interactions under different environments are often studied in controlled
experiments (e.g., Callaway et al. (1991), Isaac et al. (2007b), and Cahill et al. (2010)) and rarely
in field conditions and particularly not in trees. Unique to this study was the controlled
examination of root phenotypic response of T. cacao to the same neighbour species across
multiple environments. Thus, this study also highlights the usefulness in using agroecosystems
for testing hypotheses in ecology more broadly. This study aligned T. cacao trees along the
hypothesized RES, which was also observed in Chapter 4 for individual plants within a site.
Interestingly, the relative position of the trees on this RES was largely unrelated to measured
environmental conditions at local to regional scales, despite the large amount of variation in soil
environment that can occur within and across sites. Instead, there was indication that the
presence of a T. ivorensis regulated this position. A secondary axis was better explained by site
(climate and soil textural class) and soil resource availability.
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Notably, findings from this study showed that the growth of fine roots was uncoordinated with
the RES. This suggests alternative trade-offs to the fast-slow plant economics spectrum (Reich
2014). Furthermore, measurements on fine roots by their dominant functional role (i.e.,
absorption vs. transport) supported the assertion of multiple resource acquisition strategies.
Critically this could indicate why predictable root trait variation could be less detectable if not
divided into functional classes. While many articulate the need to differentiate roots according to
their primary functional role in trait-based studies (e.g., McCormack et al. (2015), Freschet and
Roumet (2017), and Kong et al. (2017)), to my knowledge none have integrated absorptive and
transport fine roots into one analytical framework. In this case, it was important given the study’s
objective of elucidating water acquisition strategies across variable moisture regimes and the
differential role absorptive and transport roots. I also found absorptive fine roots to be more
acquisitive in monoculture as opposed to in species mixture at the suboptimal loam site. This
trend was the opposite to what I found in Chapter 3 and may indicate the importance of
environmental factors in regulating how trait expressions are influenced by neighbouring species,
but also suggests measuring fine roots by functional classification may elucidate different
patterns than when, for example, more permanent transport fine roots are also included in the
analysis.
6.2 Recommendations for management
Refocusing agricultural production away from intensification and towards agroecological
practices is needed in order to offset the detrimental effects of agriculture that threaten critical
earth system processes. Specifically, management strategies must reduce nutrient losses, increase
C sequestration and storage, slow and reverse biodiversity loss, and mitigate the effects of
environmental perturbations, pests, and pathogens. Environment-specific management that can
utilize information from trait-based understandings of agroecosystems offers a promising
pathway towards achieving these objectives. To this end, I hope insights from my PhD research
can contribute towards more specific and precise usability of agroecological principles. In the
following subsections, I make recommendations on how this can be achieved by drawing on
findings from my PhD research.
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6.2.1 How can root traits be used as indicators of plant and agroecosystem function and ultimately inform management?
Plant roots are integral in driving and regulating critical ecosystem processes (Bardgett et al.
2014, Laliberté 2017). When their functional role can be interpreted from their form, root traits
can serve as indicators for ecosystem function (Mommer and Weemstra 2012, Freschet and
Roumet 2017). Some root traits are widely used in estimating ecosystem processes. For example,
root biomass and root growth are commonly used in estimating C dynamics (Jackson et al. 1997)
and root distribution is commonly used to estimate nutrient and water uptake patterns (Schenk
and Jackson 2002). There is now growing interest in the use of other traits for estimating other
ecosystem processes, such as SRL and Nroot which positively correlate with root decomposition
rates (Prieto et al. 2016). In managed environments, traits could also be used to monitor and
inform management strategies designed to shift agroecosystems towards functional trait targets,
for example, to be functionally more similar to forest (e.g., higher decomposition rates) (Wood et
al. 2015).
The usability of a trait-based approach hinges on trait-environment (response) and trait-function
(effect) relationships being sufficiently generalizable across different environments (Damour et
al. 2018). For example, Nab, and RTDab, predictably varied with continuous soil variables and
were significantly related to the growth rate of fine roots. However, for farmers, usability of trait-
based approaches will also hinge on usability in the field. Therefore, while root traits describing
the chemical and tissue composition of roots may be good candidates as ‘indicator traits’, they
are not easily accessible for farmers to measure in the field, especially for fine absorptive roots.
On the other hand, root diameter could be more easily measured. Diameter of thicker transport
roots, Dtr, showed patterned variation to edaphic and moisture conditions, and, thus, might be
used in suboptimal environments to gauge the moisture status of the farm and effects from
different shade trees.
Another application likely of interest to farmers would be in monitoring plant and ecosystem
response to fertilization. If traits can be used to estimate the point at which crops reach luxury
consumption of resources, or the maximum extent to which roots can respond to nutrient influx,
farmers can more accurately apply fertilizer dosages that increase the overall nutrient use
efficiency of the system and limit nutrient losses to the wider environment. Future research is
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needed to chart these trends and to develop extension efforts that can translate trait- based
understandings of plant and ecosystem function with traits on plants that are readily (or could be)
interpreted by farmers (Isaac et al. 2018).
These data can also contribute to growing global plant trait databases, such as FRED, which can
be useful in predicting response-effects on ecosystem and comparability among species, studies,
and systems. As explained by Garnier and Navas (2012), Martin and Isaac (2015), and Damour
et al. (2018), ancillary data on the conditions in which root traits are collected should be reported
with the trait values. For example, soil physical properties, i.e., soil texture, was important in
driving root ITV across the cocoa-growing region in Ghana (and similar patterns have been
reported across species and communities (Freschet et al. 2017)). In my PhD research,
neighbouring shade tree species also influenced root traits at various units of analysis: whole
plant to absorptive fine root organs. Thus, consolidated databases that include management
parameters, such as shade management, are critically needed.
Root system responses to environment occur over time and at different scales; for example, total
plant biomass allocation patterns (e.g., RS ratio) are developed over a different time scale than
absorptive root trait modifications. Similarly, more distal fine roots are more ephemeral
compared to more permanent higher-order fine roots of trees. In this regard, assessing a suite of
traits can help distill some of this complexity into coordinated plant root strategies. This could be
particularly useful in interpreting how trait trade-offs are expected to optimize growth and
survival under given conditions (Callaway et al. 2003). Findings from my PhD research re-iterate
this complexity and highlight the need to reconcile multidimensional variation in root traits. The
challenge in assigning definitive relationships for root trait response is largely attributed to the
multiple roles that roots play in acquiring resources from a complex soil matrix, providing
stability for the plant, and interacting with soil biota. Indeed, it has been recently suggested that
mycorrhizal associations are an integral component to a root economics spectrum, with species
that have thick roots of low SRL being associated with mycorrhizae in contrast to species with
non-mycorrhizal, thin, and rapidly growing roots (Ma et al. 2018). Far less is known on these
relationships within species. However, from a management standpoint we could use these
understandings, such as known positive relationships between first order root thickness and
mycorrhizal associations (Guo et al. 2008), to monitor multi-tropic interactions following
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management intervention; considering, for example, that mycorrhizal associations with crop
plants can be suppressed with fertilization (Liu et al. 2015, Wang et al. 2017).
6.2.2 What management strategies will maintain or improve plant and ecosystem function in multispecies agroecosystems?
Strategically incorporating more planned species diversity may improve multiple ecosystem
services by mixing species with complementary and facilitative interactions to optimize resource
acquisition and use (Schroth 1999, Isaac et al. 2007a, Bergeron et al. 2011, Tully et al. 2012,
Zhang et al. 2014, Brooker et al. 2015). In optimal growing environments, farmers can
incorporate a greater diversity of trees to achieve higher overall productivity. These systems
could be particularly effective in C sequestration and biodiversity protection. Conversely, in
suboptimal environments it may be critical to focus on mixing shade trees with root systems that
are more spatially, temporally, and functionally complementary. For example, trees that root
deeply and facilitate hydraulic redistribution can have facilitative effects on shallow lateral root
activity, which may improve resiliency through climatic variability.
Farmers select species for diverse reasons that are often context dependent (Graefe et al. 2017).
For one, farmers are primarily interested in crop yield. In C. arabica, coffee cherries were
significantly related to leaf traits (Gagliardi et al. 2015) and, across species, seed size was found
to be significantly related to root traits (Bergmann et al. 2017), but more is required to
understand which root traits or coordinated root trait strategies may be related to yield and
specifically for T. cacao. Potential relationships may be context specific, in suboptimal growing
conditions, where the structure and function of roots are integral in overall plant performance.
Furthermore, farmer decision-making will have to adapt to present and future environmental
threats to crop production (e.g., climate change, diseases). In this regard, management targets,
based on functional traits, can inform management strategies that are specific to environmental
conditions and interspecific interactions (Wood et al. 2015). For example, this could include
accounting for plant trait interactions with pests and pathogens that are known to supress yields
(e.g., black pod disease in T. cacao), whether that be through incorporating plants with traits that
feedback into environments which are less favourable for pests or pathogens, or the plant traits
themselves offer some resistance to the pests and pathogens.
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When designing and managing multispecies agroecosystems, intraspecific variation may be
consequential to ecosystem function. Increasing planned species diversity can manipulate abiotic
and biotic conditions and, thus, acts as a filter on trait variation in relation to environmental
variables across agroecosystems (Damour et al. 2018). Intercropping with shade trees has been
shown to have a major impact on crop leaf ITV (Martin et al. 2016) and species interactions are
related to phenotypic plasticity in plant roots, as shown in this thesis but also shown in other
studies on roots (e.g., Callaway et al. (2003), Isaac et al. (2014), Cardinael et al. (2015), Duan et
al. (2017), and Rajab et al. (2018)). Generally, however, much remains to be revealed on crop
root ITV in relation to environmental gradients when grown in mixture with another species, and
ultimately the resulting effects on ecosystem processes. To better assess and predict ITV in
multispecies agroecosystems, conventional factorial experiments, which are common to
agronomy, cannot feasibly account for the various permutations of species combinations given
diverse environmental conditions. Developing methods for high-throughput phenotyping can aid
in identifying species combinations suitable for key environmental variables.
Research presented here focuses on species diversity of one or two tree species in regular and
relatively controlled spacing, yet more work is needed in more complex polycultures featuring a
diversity of plant species. How the density and spatial configuration of planned diversity interact
with the expression of functional traits, and the resulting effects on ecosystem services are
largely unknown (Damour et al. 2018). Other than the potential to increase the effects of plant-
plant interactions on resource acquisition (competition) (Kumar and Jose 2018), we could also
expect that density would affect environmental factors, such as microclimate, nutrient cycling,
and related interactions with soil biota (de Kroon et al. 2012, Semchenko et al. 2017).
Ultimately, strategic design in agroecosystems requires mechanistic understandings that matches
that of intensive monoculture farming systems and supports farmers of more complex
agroecosystems, ideally, towards outperforming their intensified counterparts in the long term.
6.2.3 How can ecological intensification be achieved and maintained in the cocoa producing region in Ghana?
Reconciling production and conservation in the Guinea forest zone is critical in Ghana and
fundamentally affects the biodiversity and human communities in this region (Oke and Odebiyi
2007, Norris et al. 2010, Gockowski and Sonwa 2011, Phalan et al. 2011, Schroth and Ruf 2014,
125
Ruf et al. 2015, Asare and Ræbild 2016). Reversing forest degradation and deforestation is
essential, but pathways to achieving these objectives, and whether through agricultural or
ecological intensification in cocoa producing lands, are hotly debated. Ecological intensification,
through increased planned biodiversity on farms, offers important benefits. Namely, the structure
and function in these agroecosystems should be more similar to that of Guinean forest than
intensified monocultures. To this end, understanding how ecosystem function can be enhanced
through management should be a priority.
The ecological impact of cocoa agroecosystems in Ghana is immense given that the current land
under cocoa cultivation in the country covers approximately 1.6 million ha (FAO 2013). While
proponents of agricultural intensification argue more can be produced on less land, this approach
is tenuous given declines in soil quality (Dawoe et al. 2014), increased pathogen incidence
(Ploetz 2016), and limits to pollination (Toledo-Hernández et al. 2017). Therefore, it is also
imperative to further investigate the functional status of cocoa agroecosystems over time. For T.
cacao in West Africa, this should also include variation with seasons due to strong climatic
variation between the distinct rainy and dry seasons and the seasons’ role in plant function and
nutrient cycling in cocoa agroecosystems (e.g., litterfall production) (Dawoe et al. 2010). For
instance, the morphological and physiological traits that are associated with survivability during
increasingly hot dry seasons could be examined, and the resulting interactive effects of these
traits from shade trees. These efforts can lead to better prescriptions of species combinations in
climatically vulnerable locations.
Cocoa is a commodity crop that is inextricably linked to the country’s economy and the
livelihoods of over 700,000 farmers and their families. Recent developments and growth in
alternative markets in Ghana offer a premium price on cocoa beans that are from farms certified
as environmentally and socially responsible, essentially by including a price premium for
maintaining management practices that improve ecosystem function above that of more
intensified forms of management. The implementation and outcome of these payment schemes is
controversial, but recent evidence from Ghana suggests there are measurable enhancements to
both farmer livelihoods and the farming ecosystems they manage (Fenger et al. 2017). However,
the country-wide impact from specialized markets in Ghana could be limited given the
126
importance of the centralized marketing board and the on-the-ground complexities of cocoa bean
purchasing (Ryan 2011).
More widespread change towards ecological intensification will likely require specific
management strategies that also demonstrate benefits to cocoa production itself. The current
major concerns about production pertain to climate change, pest and pathogen regulation, and
yield stability and quality (Ruf et al. 2015, Schmitz and Shapiro 2015, Ploetz 2016, Toledo-
Hernández et al. 2017). To this end, using trait-based approaches to link T. cacao function and
performance in multispecies agroecosystems across different environmental conditions could be
used towards extension efforts and training that strive to mitigate these concerns. Most
smallholder cocoa farmers employ to some degree of species diversification, often with shade
trees. Context-specific understandings of multispecies agroecosystems could be used to
encourage increasingly more diverse systems that also lead to more sustained cocoa production
for farmers in the long term.
6.3 Final remarks
Species composition and interactions, nutrient management, and environmental conditions can
vary at multiple scales, making it challenging to diagnose and specify appropriate management
strategies across diverse environments. My PhD research links specific management strategies to
the root system of an economically and socially critical tree crop Theobroma cacao within
complex agroecosystems. Plasticity in root system development for individual trees under
cultivation is impressive and this thesis highlights the importance of neighbour tree species in
influencing the expression of root system form and function. Findings here can contribute
towards the design of more ecologically sustainable and resilient agricultural landscapes that
feature a higher species diversity.
127
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Appendix
Table A.1: Ground penetrating radar settings used during this study.
Nominal frequency 1000 MHz
Antenna separation 7.62 cm
Pulser voltage 100 V
Step size 1 cm
Sampling interval 0.1 ns
Trace stacking 8
158
Table A.2: Details on the conditions of the site, plant, and radar signal response during GPR
survey.
Condition Details
Site
Volumetric soil moisture 21% and 32% From two environmental sensor readings
taken every 30 minutes during the study
Plant
Volumetric coarse root
moisture content
68.8 ± 16.8%
(mean ± SD)
Coarse root samples dried at 70 °C until
constant mass (n = 23) to determine
moisture content; volume assumed
cylindrical based on diameter and length
Root biomass-diameter
relational equation y = 0.042x1.84
Dry weight biomass of 10 cm long root
segments (y) with root diameter (x) of cocoa
roots r² = 0.98; n = 33; data not shown
Radar signal response
Radar signal velocity 0.07 m ns-1
Calculated from time required for radar
signals to reflect at known-depth reflectors
(metal rods) at the site
GPR-biomass calibration
relationship y = 4.9 + 0.02x
Where y is root biomass and x is the number
of pixels above the image intensity threshold
of 202 (p < 0.001; F1,28 = 18.5; r = 0.63;
Figure A.1)a
a Determined through an automated sequence of linear regressions for calibration geo-images
and their matched root biomass by i) tallying the number of pixels above image intensities (8-bit
greyscale; 0 to 255) and ii) sequentially using each as a predictor variable for root biomass to
discern best correlations.
159
Table A.3: Results tables of linear mixed models with soil nutrients, root density of
heterospecific neighbour, and sampling depth as fixed variables. Soil interface (i.e., soil profile)
was a random factor. Significant coefficients are in bold (p < 0.05). Results are synthesized in
Table 3.2.
T. cacao in monoculture
Model
variables Coeff. SE d.f. t-value p-value
Model
variables Coeff. SE d.f. t-value p-value
logFRLD logSRL
(Intercept) -0.431 0.449 88 -0.961 0.339 (Intercept) 1.815 0.508 82 3.571 0.001
depth -0.025 0.006 88 -3.968 0.000 depth 0.001 0.008 82 0.139 0.890
logNO3- -0.163 0.134 88 -1.217 0.227 logNO3
- 0.318 0.143 82 2.224 0.029
logNH4+ 0.159 0.137 88 1.153 0.252 logNH4
+ -0.385 0.172 82 -2.237 0.028
logPO4- -0.255 0.332 88 -0.770 0.444 logPO4
- -0.323 0.383 82 -0.844 0.401
logK+ -0.207 0.082 88 -2.530 0.013 logK+ 0.103 0.107 82 0.958 0.341
logCa2+ 0.746 0.236 88 3.155 0.002 logCa2+ -0.168 0.204 82 -0.820 0.415
logMg2+ 0.236 0.233 88 1.013 0.314 logMg2+ -0.286 0.291 82 -0.982 0.329
logFRBD logSRTA
(Intercept) -1.412 0.718 82 -1.965 0.053 (Intercept) 1.131 0.630 82 1.795 0.076
depth -0.026 0.011 82 -2.389 0.019 depth 0.002 0.010 82 0.177 0.860
logNO3- -0.465 0.220 82 -2.111 0.038 logNO3
- 0.571 0.177 82 3.222 0.002
logNH4+ 0.480 0.232 82 2.070 0.042 logNH4
+ -0.437 0.214 82 -2.047 0.044
logPO4- 0.440 0.549 82 0.801 0.425 logPO4
- 0.032 0.474 82 0.067 0.947
logK+ -0.222 0.142 82 -1.556 0.123 logK+ 0.118 0.133 82 0.888 0.377
logCa2+ 0.684 0.374 82 1.830 0.071 logCa2+ -0.881 0.253 82 -3.476 0.001
logMg2+ 0.544 0.388 82 1.402 0.165 logMg2+ -0.179 0.361 82 -0.497 0.621
logD
(Intercept) -0.452 0.206 88 -2.200 0.030
depth 0.001 0.003 88 0.436 0.664
logNO3- -0.038 0.064 88 -0.587 0.559
logNH4+ 0.144 0.067 88 2.148 0.034
logPO4- -0.087 0.160 88 -0.543 0.588
logK+ -0.001 0.040 88 -0.024 0.981
logCa2+ 0.107 0.111 88 0.964 0.338
logMg2+ -0.023 0.114 88 -0.198 0.843
160
T. cacao in mixture with E. angolense
Model
variables Coeff. SE d.f. t-value p-value
Model
variables Coeff. SE d.f. t-value p-value
logFRLD logSRL
(Intercept) -2.116 1.532 67 -1.381 0.172 (Intercept) 1.028 1.349 60 0.762 0.449
depth -0.035 0.008 67 -4.594 <0.001 depth 0.018 0.007 60 2.394 0.020
logNO3- -0.059 0.085 67 -0.691 0.492 logNO3
- -0.066 0.073 60 -0.907 0.368
logNH4+ 0.061 0.14 67 0.437 0.663 logNH4
+ -0.161 0.124 60 -1.299 0.199
logPO4- 1.636 1.126 67 1.452 0.151 logPO4
- 0.260 0.986 60 0.263 0.793
logK+ 0.030 0.347 67 0.086 0.932 logK+ -0.022 0.311 60 -0.07 0.945
logCa2+ 0.380 0.215 67 1.768 0.082 logCa2+ -0.085 0.183 60 -0.467 0.643
logMg2+ 0.065 0.412 67 0.158 0.875 logMg2+ 0.444 0.373 60 1.189 0.239
FRBDhetero -0.089 0.212 67 -0.419 0.677 FRBDhetero -0.152 0.181 60 -0.839 0.405
logFRBD logSRTA
(Intercept) -1.616 2.163 62 -0.747 0.458 (Intercept) 1.746 1.778 62 0.982 0.330
depth -0.052 0.011 62 -4.564 <0.001 depth 0.025 0.009 62 2.638 0.011
logNO3- 0.067 0.118 62 0.563 0.575 logNO3
- 0.011 0.097 62 0.115 0.909
logNH4+ 0.132 0.194 62 0.681 0.499 logNH4
+ -0.121 0.162 62 -0.744 0.460
logPO4- 0.725 1.641 62 0.442 0.660 logPO4
- -0.398 1.31 62 -0.304 0.762
logK+ -0.391 0.475 62 -0.825 0.413 logK+ 0.296 0.397 62 0.744 0.460
logCa2+ 0.423 0.322 62 1.315 0.193 logCa2+ -0.219 0.244 62 -0.896 0.374
logMg2+ 0.014 0.581 62 0.024 0.981 logMg2+ 0.465 0.491 62 0.946 0.348
FRBDhetero 0.049 0.287 62 0.169 0.866 FRBDhetero -0.064 0.241 62 -0.265 0.792
logD
(Intercept) -0.137 0.573 67 -0.239 0.812
depth -0.011 0.003 67 -3.916 <0.001
logNO3- 0.004 0.032 67 0.132 0.895
logNH4+ 0.010 0.052 67 0.200 0.842
logPO4- -0.071 0.425 67 -0.166 0.869
logK+ 0.064 0.129 67 0.498 0.620
logCa2+ -0.027 0.082 67 -0.325 0.746
logMg2+ -0.209 0.153 67 -1.361 0.178
FRBDhetero 0.017 0.079 67 0.213 0.832
161
T. cacao in mixture with T. ivorensis
Model
variables Coeff. SE d.f. t-value p-value
Model
variables Coeff. SE d.f. t-value p-value
logFRLD logSRL
(Intercept) -0.969 0.777 56 -1.247 0.218 (Intercept) 2.557 0.891 51 2.871 0.006
depth -0.025 0.01 56 -2.402 0.020 depth -0.005 0.011 51 -0.487 0.629
logNO3- 0.037 0.103 56 0.356 0.724 logNO3
- 0.069 0.108 51 0.637 0.527
logNH4+ 0.612 0.31 56 1.975 0.053 logNH4
+ -0.676 0.312 51 -2.167 0.035
logPO4- -0.244 0.313 56 -0.781 0.438 logPO4
- -0.228 0.35 51 -0.652 0.517
logK+ -0.096 0.225 56 -0.427 0.671 logK+ 0.048 0.235 51 0.205 0.839
logCa2+ 0.712 0.481 56 1.481 0.144 logCa2+ 0.296 0.544 51 0.545 0.588
logMg2+ -0.322 0.356 56 -0.907 0.369 logMg2+ 0.300 0.383 51 0.783 0.437
FRBDhetero -0.027 0.106 56 -0.251 0.803 FRBDhetero 0.189 0.101 51 1.861 0.068
logFRBD logSRTA
(Intercept) -2.496 1.238 52 -2.016 0.049 (Intercept) 2.708 1.032 51 2.625 0.011
depth -0.009 0.016 52 -0.592 0.556 depth -0.007 0.013 51 -0.548 0.586
logNO3- -0.017 0.153 52 -0.111 0.912 logNO3
- 0.018 0.126 51 0.146 0.885
logNH4+ 1.002 0.444 52 2.258 0.028 logNH4
+ -0.801 0.359 51 -2.235 0.030
logPO4- 0.127 0.495 52 0.256 0.799 logPO4
- -0.168 0.413 51 -0.405 0.687
logK+ -0.098 0.335 52 -0.293 0.771 logK+ 0.059 0.273 51 0.215 0.831
logCa2+ 0.777 0.745 52 1.044 0.301 logCa2+ -0.306 0.612 51 -0.499 0.620
logMg2+ -0.624 0.52 52 -1.200 0.236 logMg2+ 0.843 0.424 51 1.986 0.052
FRBDhetero -0.202 0.145 52 -1.400 0.168 FRBDhetero 0.171 0.117 51 1.460 0.151
logD
(Intercept) -0.837 0.28 56 -2.992 0.004
depth -0.001 0.003 56 -0.373 0.711
logNO3- -0.036 0.037 56 -0.984 0.329
logNH4+ 0.222 0.100 56 2.212 0.031
logPO4- 0.088 0.119 56 0.742 0.461
logK+ 0.006 0.077 56 0.081 0.936
logCa2+ -0.066 0.166 56 -0.395 0.694
logMg2+ -0.251 0.118 56 -2.126 0.038
FRBDhetero -0.047 0.034 56 -1.362 0.179
162
Table A.4: Trait loadings on the first two axes from principal component analyses of T. cacao
root traits. Significant correlations between traits and component coordinates are indicated in
bold with their significance level.
trait PC1 PC2
A:T 0.22*** 0.13
SRLab 0.41*** 0.14*
SRAab 0.40*** -0.01
SRTAab 0.38*** 0.11
Dab -0.32*** -0.39***
RTDab -0.27*** 0.39***
Nab 0.34*** -0.28***
CNab -0.33*** 0.35***
SRLtr 0.16*** 0.36***
Dtr -0.17*** -0.21**
RTDtr -0.08* -0.18*
BGR 0.08* -0.49*** *** p < 0.001 ; ** p <0.01; * p < 0.05
163
Figure A.1: Calibration model used to estimate root biomass from GPR response (following
geo-image processing) (n = 30); y = 4.9 + 0.02x where x is the number of pixels above the image
intensity threshold of 202.
164
Copyright Acknowledgements
Borden KA, Anglaaere LCN, Adu-Bredu S, Isaac ME. 2017. Root biomass variation of cocoa
and implications for carbon stocks in agroforestry systems. Agroforestry Systems. (online first).
DOI: 10.1007/s10457-017-0122-5. Permission to reprint this article within my thesis (presented
as Chapter 2) was secured through Springer Nature publishing: license number 4276571233845.