Intraspecific Trait Variation in Cacao Agroecosystems: Influence of Local Conditions and Cultivars, and Role in Local

Knowledge Systems

by

Adam Kabir Dickinson

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Department of Geography

University of Toronto

© Copyright Adam Kabir Dickinson, 2017

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Intraspecific trait variation in cacao agroecosystems: influence of local conditions and cultivars, and role in local knowledge

systems

Adam Kabir Dickinson

Master of Science

Department of Geography

University of Toronto

2016

Abstract

Intraspecific functional trait variation (ITV) in agroecosystems indicates changes in crop plant

function due to genetic, environmental, and management effects. Such variation may also

underpin farmers’ knowledge and management practices. I measured ITV in six Central

American cacao cultivars (Theobroma cacao L.) by collecting leaves (n=519) from cacao trees

(n=173) in two clonal gardens and analyzing them for a suite of functional traits. Chemical traits

were related to environmental factors, morphological traits to genetic factors, and physiological

traits to a combination of the two. To assess the use of ITV in farm management, I interviewed

45 farmers using a visual elicitation device containing leaves arranged by size, colour, and

thickness. Participants linked leaf size to cacao production, leaf colour to shade and plant health,

and leaf thickness to shade. This thesis uses ITV to demonstrate how cacao cultivars respond to

environmental conditions, and how farmers interpret variation in cacao traits.

iii

Acknowledgments

I am thankful to my advisor, Dr. Marney Isaac, for her excellent mentorship and advice

throughout this thesis. From her invaluable guidance on fieldwork in Costa Rica to the very last

draft, she has made sure that I consistently improve and learn from the research process. Thanks

as well to Dr. Adam Martin and Dr. Ryan Isakson for serving on my defense committee and for

being so generous with their time and advice.

I am also thankful the many people who gave me advice, orientation, and support during my time

in Costa Rica and Nicaragua. Dr. Marie-Soleil Turmel, Dr. Eduardo Somarriba, Dr. Wilbert

Phillips-Mora, and Dr. Marie-Ange Ngo Bieng at CATIE all provided invaluable advice. In

Nicaragua, William Muñoz and Sonia Treminio made my time at FUMAT possible and were my

first guides to Waslala. This project would also not have been possible without the support of

Fundación Madre Tierra, Asociación Pro Mujer de Waslala, and Cacaonica Cooperative in

Waslala, the Costa Rican Ministry of Agriculture in Cahuita and Talamanca, and

COOPENORTE in Upala.

My deepest gratitude goes to the dozens of cacao farmers and farm workers in Costa Rica and

Nicaragua who assisted me in understanding their points of view and who were so generous in

sharing their knowledge and time with me. I am humbled to have been able to share, albeit

briefly, in their experiences.

It was my privilege to complete this work in the company of colleagues who selflessly shared

their expertise and advice. I am thankful to Kira Borden, Stephanie Gagliardi, and all of my

fellow graduate students who helped me get to this stage. I am particularly thankful to Serra

Buchanan for sharing her expertise in the lab, and to all of the volunteers who donated their time

to this research. I am also grateful for funding support from a CGS-M scholarship from the

National Science and Engineering Research Council and an Ontario Graduate Scholarship, as

well as an NSERC Discovery Grant to Dr. Isaac.

Last but not least, I am grateful without measure to all of my friends and family who have

supported me throughout this process, and who have provided suggestions and insights along the

way.

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Contents Abstract ........................................................................................................................................... ii

Acknowledgments.......................................................................................................................... iii

List of Tables ................................................................................................................................ vii

List of Figures ................................................................................................................................ ix

List of Appendices ......................................................................................................................... xi

Chapter 1: Introduction ................................................................................................................... 1

1.1 Background .......................................................................................................................... 1

1.2 Research Questions and Hypotheses .................................................................................... 3

1.3 Significance........................................................................................................................... 4

Chapter 2: Literature Review .......................................................................................................... 6

2.1 Cacao agroforestry ................................................................................................................ 6

2.2 Ecophysiology of cacao agroecosystems .............................................................................. 7

2.3 Intraspecific trait variation in cacao ...................................................................................... 9

2.3.1 Physiological traits ......................................................................................................... 9

2.3.2 Chemical traits ............................................................................................................... 9

2.3.3 Morphological traits ..................................................................................................... 10

2.4 Cacao Breeding ................................................................................................................... 10

2.5 Cacao cultivars and intraspecific trait variation .................................................................. 11

2.6 Local Knowledge and Agroecosystems .............................................................................. 12

2.7 Gaps in the Literature .......................................................................................................... 13

Chapter 3: Methods ....................................................................................................................... 15

3.1 Study Sites .......................................................................................................................... 15

3.1.1 Clonal garden sites ....................................................................................................... 15

3.1.2 Local knowledge interview sites .................................................................................. 18

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3.2 Clonal Garden Study ........................................................................................................... 22

3.2.1 Experimental Design .................................................................................................... 22

3.2.2 Functional Trait Analysis ............................................................................................. 23

3.2.3 Environmental Sampling ............................................................................................. 26

3.2.4 Statistical Analysis ....................................................................................................... 27

3.3 Local Knowledge Survey .................................................................................................... 29

3.3.1 Interview Format .......................................................................................................... 29

3.3.1.1 Participant selection .................................................................................................. 29

3.3.1.2 Participant information ............................................................................................. 30

3.3.1.3 Leaf spectra and the visual elicitation device ........................................................... 30

3.3.2 Analysis of Interview Responses ................................................................................. 33

3.3.3 Statistical analysis ........................................................................................................ 33

Chapter 4: Intraspecific trait variation in cacao clonal gardens .................................................... 35

4.1 Results ................................................................................................................................. 35

4.1.1 Variation in leaf traits .................................................................................................. 35

4.1.2 Site and cultivar effect on leaf trait variation ............................................................... 35

4.1.3 Between-site differences in individual cultivars .......................................................... 40

4.1.4 Covariance of leaf functional traits .............................................................................. 43

4.1.5 Effect of soil and light-related variables ...................................................................... 43

4.2 Discussion ........................................................................................................................... 49

4.2.1 How does site influence intraspecific functional trait variation in cacao? .................. 49

4.2.2 Do cacao genotypes have a specific functional trait profile? ...................................... 52

4.2.3. Do different cacao genotypes respond differently to environmental conditions? ...... 53

4.2.4 Implications for management and further research ..................................................... 54

Chapter 5: Local knowledge on intraspecific trait variation in cacao .......................................... 56

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5.1 Results ................................................................................................................................. 56

5.1.1 Participant and farm attributes ..................................................................................... 56

5.1.2 Leaf trait-effect relationships identified....................................................................... 58

5.1.3 Interpretation of flowering traits .................................................................................. 61

5.1.4 Interpretation of whole-plant traits .............................................................................. 67

5.1.5 Patterns in consensus on leaf trait interpretations ........................................................ 67

5.2 Discussion ........................................................................................................................... 69

5.2.1 Do farmers take functional traits into account when making management decisions? 69

5.2.2 Do farmers view leaf functional traits along a spectrum? ........................................... 71

5.2.3 How is local knowledge on functional traits distributed? ............................................ 72

Chapter 6: Conclusion................................................................................................................... 74

6.1 Conclusions ......................................................................................................................... 74

6.2 Areas of future research ...................................................................................................... 75

References ..................................................................................................................................... 77

Appendix A: Interview Questions and Resources ........................................................................ 87

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List of Tables

Table 1: Codes and descriptions of the six clones promoted by Proyecto Cacao Centroamérica

and used in the two clonal gardens in this study. Origin and average annual yield data from

Phillips-Mora et al. (2013), studies in which varieties were first described are partially listed in

the International Cocoa Germplasm Database (Turnbull & Hadley, 2015). ................................ 16

Table 2: Climate and physical data for the La Montaña and Cacaonica sites, as well as the Upala

and Talamanca interview sites. Location and elevation data are from Google Maps, temperature

and precipitation from CATIE for La Montaña and climate-data.org for Cacaonica, Upala, and

Talamanca. .................................................................................................................................... 20

Table 3: Variables measured in this study, including leaf functional traits, whole-plant traits, and

environmental data. ....................................................................................................................... 25

Table 4: Canopy openness, distance to shade tree, and soil N and C content for individual cacao

trees on cacao farms at La Montaña farm in Turrialba, Costa Rica (n=90) and Cacaonica farm in

Waslala, Nicaragua (n=83). Values presented are site-level means and standard errors, along

with the minimum and maximum values in parenthesis. ANOVA results (F and p-values) are

also given, showing differences in the four environmental parameters between the two sites. ... 28

Table 5: Intraspecific variation in T. cacao leaf traits (n=172). The maximum log-likelihood

value was calculated for each trait and is shown in bold below. Where a particular trait has a

log-normal distribution, the log-transformed values were used to calculate the mean and standard

deviation (SD). Median values, range, and coefficients of variation (CV) were also calculated for

all traits.......................................................................................................................................... 36

Table 6: Two-way ANOVA results for nine leaf traits across six T. cacao clones at two sites

(n=172). The ANOVA was applied with Site, Clone and ClonexSite as factors. A logarithmic

transformation was applied to the variables marked with an asterisk (*). .................................... 37

Table 7: Coefficients of variance (CV) of nine functional traits in T. cacao trees (n=172), as well

as a model CV, calculated based on a modeled distribution of the entire dataset, and the

corresponding 95% confidence intervals. CVs were also calculated for each site, clone, and site-

clone combination. These CVs are marked in bold and highlighted in grey when they are above

(+, dark grey) or below (-, light grey) the modeled 95% confidence intervals............................. 38

Table 8. Bivariate relationships using Pearson’s coefficient of correlation (r) for nine leaf

functional traits in T. cacao trees (n=172) (a) over the entire dataset, (b) at La Montaña and (c) at

Cacaonica. Significant bivariate relationships (p≤0.05) are indicated by grey shading. .............. 45

Table 9. Step-wise and multiple regression model analyses of the relationship between four

environmental variables (light canopy openness [CO], distance to shade tree [DST], soil C

content [SC], and soil N content [SN]) and nine leaf traits in T. cacao trees (n=172). AIC values

were calculated to identify the most parsimonious model fit, the predictor variables of which are

given in the second column. The difference (ΔAIC) between the full model (all predictor

variables) versus the best predictive model is also given. For the predictive model chosen, the

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intercept and the individual slope values for each explanatory variable are shown. The p values

associated with each model term is indicated by the following symbols: NS: not significant, *:

p<0.1, **p≤0.05, ***: p≤0.01 ....................................................................................................... 50

Table 10. Attributes of interview participants, farms, and management practices, based on

interviews with 45 participants. Values given are either mean ± standard deviation or else the

percentage of participants who responded a certain way.............................................................. 57

Table 11. Ranking of leaf, flower, and whole-plant traits in terms of their importance for cacao

performance and on-farm management decisions. The average rank is calculated by assigning a

score of 1 for the most important and 3 for the least important trait. Values are based on

interviews with n=45 participants. For both flowering traits and whole-plant traits, participants

were also asked what their preference for each trait was; their responses are given in the right-

hand column. ................................................................................................................................. 66

Table 12. Selected farm and farmer attributes for each of the study sites in which interviews were

carried out, along with consensus scores for each trait spectrum, as well as for the overall group

of leaves that participants were shown. Consensus level calculations were based on a score of 1-3

for each participant’s answer, with 3 indicating agreement with the most common interpretation

and 1 indicating agreement with the least common interpretation. .............................................. 68

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List of Figures

Figure 1: Dendrogram showing genetic relationships between the six clonal varieties of T. cacao

surveyed in this study. These relationships are based on sequencing data from 18 microsatellite

markers published in Phillips-Mora et al. (2013). Here, ‘height’ is a measure of phylogenetic

distance, indicating how closely the different clones are related. ................................................. 17

Figure 2: Study sites: Waslala, Nicaragua (where the Cacaonica clonal garden is located, as well

as the villages of Hormiga Dudú and Santa Rosa, where interviews were carried out), Turrialba,

Costa Rica (where the La Montaña clonal garden is located), and Upala and Talamanca, Costa

Rica, where interviews were conducted. ....................................................................................... 19

Figure 3: Average monthly temperature and total monthly precipitation charts for Waslala,

Nicaragua (Cacaonica), Turrialba, Costa Rica (La Montaña),), and Upala and Talamanca, Costa

Rica. Data obtained from CATIE weather station for Turrialba (1958-2012 average) and from

climate-data.org for Waslala, Talamanca, and Upala (1982-2012 average). ................................ 21

Figure 4: Diagram of how leaves are chosen. The orange circle indicates the terminal bud

(drawing A) and a recent flush (B). The third leaf back is coloured green. Adapted from Santana

& Igue (1979). ............................................................................................................................... 24

Figure 5: Leaf spectra used in the visual elicitation portion of the interview. Leaves in the top-

most row vary by colour, with the three leaves on the left showing the “greenness” spectrum,

from light (left-most leaf) to dark green (third leaf from the left). The four leaves on the left

show different common forms of discolouration. The second row from the top shows the leaf

size spectrum, and the lowest row shows the thickness spectrum. Leaves in the size and thickness

spectra are accompanied by their percentile scores based on leaf size and thickness variation in a

dataset of 519 leaves collected by the author in a separate study. ................................................ 31

Figure 6: Differences in morphological traits in six T. cacao clones between two sites: La

Montaña farm in Turrialba, Costa Rica (light grey) and Cacaonica in Waslala, Nicaragua (dark

grey). The traits measured are leaf area (chart A), leaf thickness (chart B), and specific leaf area

(SLA, chart C). The measurements shown are the means, with error bars for the standard error.

Significant between-site differences are marked by an asterisk (*). All measurements are based

on n=90 (La Montaña) and n=82 (Cacaonica). ............................................................................. 41

Figure 7: Differences in physiological traits in six T. cacao clones between two sites: La

Montaña farm in Turrialba, Costa Rica (light grey) and Cacaonica in Waslala, Nicaragua (dark

grey). The traits measured are photosynthesis at saturating irradiance (Asat, chart A), transpiration

(E, chart B), and water use efficiency (WUE, chart C). The measurements shown are the means,

with error bars for the standard error. Significant between-site differences are marked by an

asterisk (*). All measurements are based on n=90 (La Montaña) and n=82 (Cacaonica). ........... 42

Figure 8. Differences in chemical traits in six T. cacao clones between two sites: La Montaña

farm in Turrialba, Costa Rica (light grey) and Cacaonica in Waslala, Nicaragua (dark grey). The

traits measured are leaf nitrogen content (LN, chart A), leaf carbon content (LC, chart B), and the

C:N ratio (chart C). The measurements shown are the means, with error bars for the standard

x

error. Significant between-site differences are marked by an asterisk (*). All measurements are

based on n=90 (La Montaña) and n=82 (Cacaonica). ................................................................... 44

Figure 9. Significant bivariate relationships (standardized major axis model) between

morphological leaf traits (n=172): specific leaf area (SLA, log-transformed) and leaf thickness

(left-hand plot) and leaf area and leaf thickness (right-hand plot), across both sites. Red dots

indicate leaves from Cacaonica, black dots indicate leaves from La Montaña. Both correlations

are significant at the p<0.0001 level. ............................................................................................ 46

Figure 10. Significant bivariate relationships (standardized major axis model) between leaf N

content and photosynthesis at saturating irradiance (Asat, log-transformed) (left-hand plot), and

between leaf N content and water use efficiency (WUE, log-transformed) (right-hand plot). Red

dots indicate leaves from Cacaonica, black dots indicate leaves from La Montaña. Both

correlations are significant at the p<0.0001 level. ........................................................................ 47

Figure 11. Bivariate relationships (standardized major axis model) between photosynthesis at

saturating irradiance (Asat) and transpiration (E) at two sites: La Montaña farm in Turrialba,

Costa Rica, and Cacaonica in Waslala, Nicaragua. The two traits are strongly correlated

(p<0.0001) at the Cacaonica site (left-hand plot), and not significantly correlated (p>0.1) at La

Montaña (right-hand plot). ............................................................................................................ 48

Figure 12. Average interpretations of leaves of different colours by interview participants (n=45).

Participants identified leaves as coming from a tree with poor health (-1), good health (1), or

intermediate health/unsure (0). The value given for each leaf is the average interpretation along

with the standard error. ................................................................................................................. 62

Figure 13. Average interpretations of leaves along the ‘greenness’ spectrum by interview

participants (n=45). Participants identified leaves as coming from a tree under low-shade

conditions (-1), high-shade conditions (1), or intermediate shade/unsure (0). The value given for

each leaf is the average interpretation along with the standard error. .......................................... 63

Figure 14. Average interpretations of leaves along the thickness spectrum by interview

participants (n=45). Participants identified leaves as coming from a tree under low-shade

conditions (-1), high-shade conditions (1), or intermediate shade/unsure (0). The value given for

each leaf is the average interpretation along with the standard error. .......................................... 64

Figure 15. Average interpretations of leaves along the size spectrum by interview participants

(n=45). Participants identified leaves as coming from a low-producing tree (-1), high producing

tree (1), or intermediate production/unsure (0). The value given for each leaf is the average

interpretation along with the standard error. ................................................................................. 65

Figure 16. Hand-drawn visual aids for use in interviews. The left-most drawing shows variation

in canopy diameter (from top to bottom: small/medium/large canopy), the middle drawing shows

variation in the location of fruits (from top to bottom: fruits on the trunk/fruits on the

branches/fruits all over the tree), and the right-most drawing shows variation in plant height

(from top to bottom: tall/medium/short plant). ............................................................................. 91

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List of Appendices

Appendix A: Interview Questions and Resources ........................................................................ 87

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Chapter 1: Introduction

1.1 Background

Agricultural ecosystems, or agroecosystems, are crucial for both food security and responding to

climate change. Agricultural land occupies a unique position in climate change processes:

because of land clearance and deforestation, they represent about 25% of global emissions

(IPCC, 2014). At the same time, these systems have also been identified as important

components of climate change mitigation and adaptation strategies (Verchot et al., 2007).

Management practices such as conservation tillage and crop rotation can increase soil carbon,

while agroforestry systems, which integrate trees, store carbon in woody biomass (Lal, 2004).

Finally, agroecosystems are also vulnerable to global change processes, as changes in

temperature, rainfall, and increasing unpredictability of weather patterns (Morton, 2007), which

underscores the importance of understanding the consequences of global change on these

systems.

Crop plant response to environmental change and management practices is an important priority

for research. Currently, research in this area tends to use crop physiological models to determine

plant responses to differing conditions (e.g. Alvim & Kozlowski, 1977; Malézieux et al., 2009).

Other researchers examine the landscape-level effects of different agroecological approaches

(Scherr & McNeely, 2008; Perfecto et al., 2009). More recently, a functional trait approach has

been discussed as it applies to agroecosystems (Garnier & Navas, 2011; Martin & Isaac, 2015;

Wood et al., 2015; Prieto et al., 2017). Functional traits, which are physiological, morphological,

and chemical characteristics of organisms, have been used in the ecological literature to provide

an understanding of abiotic and species-level interactions (Westoby et al., 2002; McGill et al.,

2006). This approach allows researchers examine the dynamic processes that underpin ecosystem

processes, such as succession, restoration, and adaptation to change (Tilman et al., 1997; Dıaz &

Cabido, 2001).

One major focus of functional trait-based approaches is investigating community assemblages

along biophysical gradients (Ordoñez et al., 2009; Jager et al., 2015). This allows an

understanding of plant community change under different climate, soil, or management

characteristics. A further benefit of this approach is that plant community changes can be tracked

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at the local or regional level (Vitousek et al. 1995; Gagliardi et al., 2015), or the continental or

global scale (ter Steege et al., 2006; Ordoñez et al., 2009).

Variation in functional traits has been described using the concept of economic spectra (Reich et

al., 2003). This concept refers to plant response to resources insofar as when plant-available

resources are more abundant or environmental pressures weaken, organisms’ responses are

evident in certain functional traits. The most well-studied example of this is the leaf economics

spectrum (Wright et al., 2004), which shows the way in which plants in resource-rich

environments tend to have more ‘resource-acquiring’ leaves, which are short-lived, and whose

high metabolism emphasizes short-term productivity. At the trait level, this is reflected in high

photosynthetic rates and leaf nitrogen, markers of high metabolism, and high specific leaf area

and short lifespan, markers of low energy investments in each leaf. On the other hand, plants in

resource-poor environments tend to have more ‘resource-conserving’ leaves, which are long-

lived and whose metabolism is slower (Liu et al., 2010; Poorter et al., 2012). Thus, the variation

in leaf traits is seen as occurring along a spectrum, with various leaf traits changing in concert

depending on resource availability and environmental constraints.

As mentioned, the bulk of functional trait research tends to focus on variation between species,

also termed interspecific variation. However, significant variation has also been observed at the

species level, especially along environmental gradients – this is known as intraspecific trait

variation, and is useful in determining the response of individual species to environmental

conditions (Bolnick et al., 2011; Violle et al., 2012). Recent studies in agroecosystems, including

coffee (Gagliardi, et al., 2015; Martin et al., 2017) and pasture grasses (Prieto et al., 2017) have

shown that the leaf economic spectrum can be observed within a species, and that the

intraspecific trait variation concept is useful to show the interplay of environmental and

management practices on agricultural crops.

The concept of trait spectra, and specifically leaf spectra, in local knowledge systems is one that

has not been widely researched. However, research into participatory plant breeding shows that

leaf traits, including morphology and phenology, are important to farmers when choosing new

varieties (Gibson et al., 2008; Mwanga et al., 2011). Coffee farmers in Mexico and Central

America make deliberate decisions on which shade trees to plant, maintain, and remove based on

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a series of preferred traits, including leaf texture, size, foliage density, and growth rates, as well

as information received from outside sources (Cerdán et al., 2012; Valencia et al., 2015).

1.2 Research Questions and Hypotheses

This project is comprised of two studies carried out in parallel with the goals of understanding

(1) the effect of genotype and environmental conditions on intraspecific trait variation in disease-

resistant cultivars of Theobroma cacao (cacao), and (2) to what extent cacao farmers take this

intraspecific trait variation into account when making management prescriptions.

In order to understand trends in intraspecific trait variation in cacao, I analysed two populations

of clonal cacao with the objective of determining the patterns of intraspecific trait variation

between different cultivars, between the two sites, and the variation that resulted from the

interplay between the two factors (site and cultivar). Specifically, my research question was:

what is the extent of intraspecific leaf trait variation in six Central American cacao cultivars,

and how is it related to environmental factors (site), genetic factors (cultivar), and to the

interplay of these factors?

I hypothesize the following relationships between these factors and intraspecific trait variation in

cacao:

1. Physiological traits will be significantly different between cultivars as opposed to

between sites because of the selection of cacao cultivars for physiological performance.

2. Morphological traits will significantly differ at the site level over the genetic level

because of the strong environmental drivers of these traits.

3. Chemical traits will differ significantly at the site level because of the correlation of leaf

chemistry with soil nutrients and climatic conditions.

My second study, on local knowledge, sought to examine whether intraspecific trait variation in

cacao, especially as observed through the ecological concept of leaf trait spectra, is meaningful

to farmers in terms of tracking changes in their cacao crops and making management decisions.

My central research question for this study was: how do cacao farmers take intraspecific trait

variation into account when making management decisions?

In this study, I hypothesize the following:

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1. Cacao farmers do take intraspecific trait variation into account when making

management decisions.

2. Leaf trait spectra are recognized by cacao farmers, who observe changes in management-

relevant effects in relation to changes along a leaf trait spectrum or spectra.

3. Certain farm or farmer attributes can be used to describe the distribution of local

knowledge on intraspecific trait variation.

1.3 Significance

Despite the potential of functional trait-based research for understanding cacao cultivation in the

context of management practices and environmental change, very few studies have applied this

approach in its entirety. Though several studies look at certain groups of traits (Daymond et al.,

2011; Acheampong et al., 2015; Ávila-Lovera et al., 2015), the co-variation in these traits and

their variation from one site to another is less well established. Particularly, studies that examine

any set of traits in-depth tend to be located in a single site, making an assessment of genetic and

environmental underpinnings of functional trait relationships difficult. This study, by examining

contrasting cultivars and locations, will be helpful in this regard.

In Central America, this study has special relevance because of the history of cacao cultivation in

the area. Though cacao has been farmed in the region for thousands of years, it is currently not a

major economic crop in any of the Central American countries for a number of reasons. One of

these reasons is the outbreak of diseases, especially frosty pod rot, which is difficult and costly to

combat, and which is currently the main limiting factor on Central American cacao (Wilbert

Phillips-Mora et al., 2009). The six cacao clones that are being investigated here have been

chosen for their disease resistance and high production, but little is yet known about their

performance across the region (Phillips-Mora et al., 2013). As such, this study provides a useful

first step in documenting how well-suited these novel varieties are for use in the region.

The local knowledge study will be useful for future work on functional trait ecology in

agroecosystems. This is a relatively new area of research that promises to provide a framework

for charting crop responses to environmental and management conditions. While recent studies

have shown the utility of such an approach (Martin & Isaac, 2015; Wood et al., 2015; Prieto et

al., 2017), the extent to which this knowledge is already reflected in farmer decision-making is

less well-understood. Ensuring that farmers’ existing conceptions of functional trait variation in

5

their crops is well-understood can form the basis of a dialogue between researchers and farmers

about how to adapt to changing conditions.

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Chapter 2: Literature Review

2.1 Cacao agroforestry

As the main ingredient in chocolate, cacao (Theobroma cacao L.) is both an economically

important crop and a global passion. It is one of the world’s most important tree crops, with over

10 million hectares dedicated to its production, and 4.5 million tons produced annually (FAO,

2017), and is farmed by over 6 million people worldwide (Baligar et al., 2008). Cacao is an

understorey tree native to the western Amazon, and is currently cultivated in the tropical regions

of South and Central America, Africa, and Asia (Young, 2007). It grows in wet tropical

environments with annual rainfall of between 1,150-2,500 mm yr-1 and a mild dry season (Wood

& Lass, 1985). Cacao grows in warmer regions with mean monthly minimum temperatures of

18-21°C and mean monthly maximum temperatures of 30-32°C (Wood & Lass, 1985). Cacao is

particularly sensitive to low temperatures, with inhibited growth observed under prolonged

exposure to temperatures below 15°C, although short-term exposure may be harmless (Alvim &

Koslowski, 1977; Leibel, 2008).

Cacao is often grown in agroforestry systems, with a variety of trees used for shade on cacao

farms. Cacao grows well under moderate shade, though short-term gains in yield can be realized

if shade is removed entirely (Almeida & Valle, 2007). Several studies have pointed out the

economic and ecological downsides of monoculture cacao production (Ramírez et al., 2001;

Isaac et al., 2007; Clough et al., 2009; Isaac et al., 2014), which include reduced longevity of

cacao trees, lower nutrient inputs from litterfall, and increased dependence on fertilizer. In South

and Central America, the predominant form of cacao cultivation is a polyculture with shade trees

(Almeida & Valle, 2007; Somarriba, Cerda, et al., 2013).

Cacao agroforests are important both because of their long-term benefits for the crop as well as

for a host of ecological and economic benefits. One such benefit is enhanced nutrient cycling,

with nutrient inputs into the ecosystem through litterfall and nitrogen fixation, which has been

shown to contribute to increased nutrient uptake by cacao (Beer et al., 1998; Isaac et al., 2007;

Dawoe et al., 2009; Munroe & Isaac, 2014). Shade trees also play an important role in carbon

sequestration, holding 65% of the biomass in mature cacao agroforests (Somarriba et al., 2013a).

Many shade trees also provide fruit, medicinal products, timber, and other products that

contribute to the economic value of cacao agroforests (Almeida & Valle, 2007). Indeed, one

7

long-term study concludes that these economic benefits equal or surpass the added production of

cacao grown in a monoculture (Ramírez et al., 2001).

2.2 Ecophysiology of cacao agroecosystems

Growing cacao in agroforestry systems involves careful management of shade, nutrient levels,

and water stress. As mentioned previously, cacao is an understorey tree generally best grown

under moderate shade. Typically, this involves growing young plants under heavy shade, usually

comprised of fast-growing plants such as bananas (Musa spp), while other shade trees grow up

alongside the cacao (Beer et al., 1998). As cacao matures, shade is generally reduced by

eliminating or pruning the shade trees (Wood & Lass, 1985). This is important because

particularly high levels of shade can result in lower photosynthetic rates and increased

susceptibility to disease (Almeida & Valle, 2007). Cacao is also sensitive to high light levels:

photosynthetically active radiation (PAR) levels of 1050 µmol photons m-2 s-1 – still lower than

full sun, under which PAR levels can be around 1800 µmol photons m-2 s-1 – can be detrimental

to growth rates (Baligar et al 2008).

Water stress due to a high vapour pressure deficit (VPD) leads to stomatal closure, with

decreases in stomatal conductance beginning at VPD values of 0.5-1.0 kPa (Baligar et al., 2008).

The effects on photosynthetic rates are not noticeable until VPD levels reach around 2.0 kPa

(Balasimha et al., 1991). Baligar et al. (2008) have observed that transpiration rates tend to

increase steadily with increasing VPD up to around 2.5 kPa, and the rate of increase in cacao is

higher than comparable rainforest trees, indicating an inability to reduce transpiration in

situations of water stress. However, at high levels of VPD, stomatal closure leads to a decrease in

transpiration (Carr & Lockwood, 2011).

During the dry and rainy seasons, the effects of water stress and shade work in tandem. During

the rainy season, when VPD and irradiance is low, shaded cacao has lower rates of

photosynthesis, transpiration, and stomatal conductance when compared with non-shaded cacao

(Carr & Lockwood, 2011). Conversely, during the dry season, high VPD levels (averaging

around 2.7 kPa), particularly in non-shaded areas, leads to comparatively lower rates of

photosynthesis, transpiration, and stomatal conductance among non-shaded cacao (Acheampong

et al., 2015).

8

As a result, cacao yield is highly sensitive to prolonged periods of drought, especially in the

absence of shade trees. Indeed, a production model formulated by Zuidema et al. (2005)

identifies rainfall levels in the two driest months and irradiance levels as the two most important

determinants of cacao yield, collectively explaining about 70% of the variation in yield levels.

One important measure of cacao’s sensitivity to dry conditions is water use efficiency (WUE),

which is a ratio of the photosynthetic rate (A) to water losses due to stomatal opening. It may be

expressed as either instantaneous WUE (ratio of A to transpiration [E]) or intrinsic WUE (ratio of

A to stomatal conductance [gs]) (Seibt et al., 2008). Both measures of WUE exhibit seasonal

variation, with most cacao cultivars having higher WUE in the dry season due to stomatal

closure (Tezara et al., 2009). One study that examined both intrinsic and instantaneous WUE

values for contracting cacao cultivars showed differences in both measures among cultivars

(Daymond et al., 2011).

Intrinsic WUE is often related to the relative concentrations of Carbon-12 and Carbon-13

isotopes in plant tissue relative to the environment (δ13C) (IAEA, 2012). Daymond et al. (2011)

observed genetic differences in both instantaneous and intrinsic WUE between cultivars under

constant environmental conditions, though no differences were observed in δ13C. Conversely,

field studies have shown significant differences in δ13C between cultivars, and have suggested its

use in determining drought resistance among cacao cultivars (Tezara et al., 2009; Ávila-Lovera

et al., 2016).

In a fruit crop such as cacao, the main physiological determinants of yield are the size of the

canopy, the photosynthetic rate, and the partitioning of biomass to the harvested pods (Alvim &

Kozlowski, 1977). As mentioned above, the photosynthetic rate is affected by seasonal

differences and factors such as shade levels, though genetic differences have been observed

between cultivars growing in identical conditions (Daymond et al., 2011). The partitioning of

biomass between vegetative growth and fruiting is seasonal, with leaf flushes coming after the

advent of the rainy season and followed by flowering (Alvim & Kozlowski, 1977). However, the

ratio of vegetative growth to total yield differs between cultivars by as much as a factor of ten

(Daymond et al., 2002). Canopy size and architecture, in addition to being affected by light

levels and planting density, also differs between genotypes (Daymond, 2002).

9

2.3 Intraspecific trait variation in cacao

Intraspecific trait variation has been the focus of numerous recent studies that seek to understand

the extent and causes of functional trait variation within an individual species (Violle et al.,

2012). Species-level studies have uncovered certain patterns in intraspecific trait variation,

notably in the extent of variation chemical, morphological, and physiological traits. Chemical

traits tend to be more variable than morphological traits, and are affected more strongly by

environmental conditions, especially soil nutrients (Siefert et al., 2015), though these findings

were not replicated in a coffee study, where chemical traits were found to be less variable than

morphological traits (Martin et al., 2017). That study also found that physiological traits varied

more strongly than either chemical or morphological traits, and were most related to the site

where the coffee was growing rather than management practices such as shade or fertilizer levels

(Martin et al., 2017). Such a systematic investigation of intraspecific trait variation has not been

carried out in cacao; however, numerous studies have been carried out on variation in individual

functional traits, the importance of these traits for plant performance, and how these traits vary

between cultivars and under different environmental conditions. The following sections will

explore trends of functional trait variation in the cacao literature.

2.3.1 Physiological traits

Leaf physiology in cacao tends to vary with light and moisture levels. As an understorey tree,

cacao generally grows under relatively low light levels; usually between 163-304 μmol photons

m-2 s-1, except where it is grown in full-sun conditions (Daymond et al., 2011). Photosynthetic

rates tend to increase in the outer canopy, which has the highest exposure to sunlight, reaching a

maximum at light levels of around 400 μmol photons m-2 s-1 (Baligar et al., 2008). Both

photosynthetic rates and stomatal conductance are highly seasonal, with lower values in the dry

season due to stomatal closure at higher vapour pressure deficits (Ávila-Lovera et al., 2015). In

the rainy season, stomatal conductance and photosynthesis values tend to be both higher and

more variable (Acheampong et al., 2015).

2.3.2 Chemical traits

Leaf nutrient content is related to soil and climate factors. A number of studies on fertilizer use

indicate that increasing soil nitrogen (N) through fertilizer application tends to be reflected in

higher leaf N (Burridge et al., 1964; Ahenkorah, 1975; Acheampong et al., 2012). Leaf nitrogen

10

content correlates with photosynthetic rates, largely because of the high N content of the foliar

structures associated with photosynthesis (Ávila-Lovera et al., 2015). Cacao partitions N to these

compounds, including chlorophyll and thykaloids, in a seasonal manner, with more

photosynthetic material being formed under low irradiance levels (Evans, 1989), resulting in

higher leaf N concentrations in the rainy season (Leibel, 2008). The other major N sinks in cacao

are pods and beans, whose N concentrations are similar to those found in leaves (Lockard &

Burridge, 1965).

2.3.3 Morphological traits

Morphological traits are highly sensitive to light levels, with high degrees of variability observed

within a single plant’s canopy (Miyaji et al., 1997). Both leaf thickness and specific leaf area

tend to increase in unshaded cacao trees, or even in leaves that are closer to the outside of the

canopy, and hence receive more direct sunlight (Baligar et al., 2008). Under lower levels of

irradiance, however, this variability is lessened, with larger differences in leaf thickness and SLA

between plants than within a single plant’s canopy (Leibel, 2008).

2.4 Cacao Breeding

Traditionally, cacao has been grouped into three distinct varieties: Criollo, Forastero, and

Trinitario. Recent work disputes this grouping, and indicates that “Forastero” cacao may be

further subdivided into as many as eight other groups, while Trinitario cacao is likely a hybrid

created in early domestication efforts (Motamayor et al., 2008). In Central America, the

Trinitario and Criollo varieties are most common, with most landraces showing some

combination of Trinitario and Criollo genetics (Trognitz et al., 2013). When grown from seed,

cacao exhibits a high degree of genetic diversity, partially due to low rates of self-compatibility

and high heterozygosity (Trognitz et al., 2011).

Grafting is a common form of reproduction in cacao, though its use by smallholder farmers in

Central America is still relatively scarce (Trognitz et al., 2011). Traditionally, grafting has been a

way to reproduce the best individuals on a farm, since one result of cacao’s high levels of genetic

diversity is that certain beneficial characteristics (e.g. high yield or disease resistance) may not

be passed down from one generation to another (Wood & Lass, 1985).

Recently, the role of clonal cacao cultivars in Central America has been highlighted due to

outbreaks of frosty pod (Moniliophthora roreri) and black pod (Phytophthora palmivora), which

11

have severely diminished yield levels in the region (Phillips-Mora et al., 2006). Solutions such as

fungicides or manual control are costly or labour intensive. This has led to the use and

widespread promotion of disease-resistant clones by regional agricultural institutions, such as

CATIE in Costa Rica, INTA in Nicaragua, and FHIA in Honduras (Phillips-Mora et al., 2013;

Somarriba et al., 2013b). As a result, there is significant interest in identifying and researching

new clonal varieties of cacao in the area.

2.5 Cacao cultivars and intraspecific trait variation

Given the importance of working with high-performing and adaptable cultivars, a number of

studies have explored the contrasts in plant function between common clonal varieties, showing

significant variability in the functional trait profile of different cultivars. These include canopy

architecture (Daymond, 2002), photosynthetic rates and their relationship to stomatal

conductance and leaf nitrogen (Daymond et al., 2011), and biomass partitioning to yield

(Daymond et al., 2002).

A growing body of literature has also tracked the response of different cultivars to rainy- and

dry-season conditions, and to shade and fertilizer treatments. While these studies provide insight

into the responses of cacao cultivars to environmental pressures, they are almost always limited

to a single site. With that said, one benefit of basing these studies out of a single site is that it is

easier to isolate individual environmental differences, such as changes in soil nutrients or

seasonal changes in temperature and rainfall. Acheampong et al. (2015) showed that there is

genetic variability in stomatal conductance both seasonally and under different nutrient and

shade levels, while photosynthetic rates varied based on nutrient and shade levels but not by

cultivar. Young cacao plants belonging to different cultivars were shown to have different

responses to shade levels, as indicated by changes in leaf morphology and growth rates

(Acheampong et al., 2012). Further studies show differences in physiological cacao traits,

including water use efficiency and photosynthetic rates, among Venezuelan cultivars under

conditions of water stress (Araque et al., 2012; Ávila-Lovera et al., 2015; Tezaraet al., 2015; De

Almeida et al., 2016).

A notable exception to the studies that focus on a single site is a number of studies carried out at

the University of Reading, UK, which has greenhouse facilities that simulate temperature and

climate conditions at a number of tropical field sites (e.g. Daymond & Hadley, 2004, 2008).

12

These studies have documented the interplay of climate and genetic factors in cacao growth. For

example, the response of cacao bean size and lipid content to temperature varies significantly

between genotypes (Daymond & Hadley, 2008), as does the response of leaf chlorophyll content

to temperature stress (Daymond & Hadley, 2004). In addition, a study by Tezara et al. (2009),

showed that improved criollo varieties had higher water use efficiency in periods of drought in

comparison with older varieties at three different sites in Venezuela.

2.6 Local Knowledge and Agroecosystems

The existing literature on cacao, as well as a larger body of research on agricultural systems

shows that intraspecific trait variation is important in terms of understanding the response of crop

plants to different environmental conditions and management practices (e.g. Gagliardi et al.,

2015; Prieto et al., 2017; Martin et al., 2017). Notably, however, it is unclear the extent to which

farmer perceptions of crop performance or plant well-being take this variation into account. By

extension, it is also unclear to what extent farmers use functional traits in their crop plants to

inform their management decisions. This is especially relevant given the fact that changes in

certain functional traits that are linked to environmental or management conditions can be

perceived visually or tactically without the aid of specialized equipment – e.g. leaf thickness or

discolouration stemming from nutrient, light, or water stresses (Chepote et al., 2005).

Farmers’ knowledge on these changes in plant traits may be a useful indicator of how they

engage with their crops – that is, the extent to which they manage for shade levels, nutrient

levels, or overall plant well-being. This may, in turn, help inform the scientific exploration of

intraspecific trait variation in agroecosystems: agricultural research spanning decades has shown

the benefits of examining agronomic and ecological phenomena through the lens of farmers’

experience, and of the utility of engagement between scientific research and local practice (e.g.

Richards, 1985). Furthermore, farmers’ beliefs regarding the relationship between crop plant

traits and management decisions has important implications for the implementation and viability

of agroforestry systems, as indicators of how they manage shade and nutrient levels in these

systems.

Several recent studies have engaged with issues of local knowledge and agroecosystems,

particularly regarding the choices cacao and coffee farmers make with respect to shade tree

selection. Farmers make conscious decisions about the shade trees they plant, maintain, and

13

eliminate, with preferences regarding leaf phenology, canopy density, and crown shape, as well

as economic utility (Soto-Pinto et al., 2006; Isaac & Dawoe, 2009; Cerdán et al., 2012). Valencia

et al. (2015) indicate that coffee farmers in Mexico have a preference for fast-growing timber

trees for economic reasons, as well as for leguminous Inga spp. trees because of advice they have

received from agricultural extension workers. A pair of studies from northern Nicaragua

conclude that farmers show a near-universal understanding of the importance of protecting cacao

from direct sunlight using shade trees, and prefer shade trees that have wide canopies at a

medium height, so that they can be managed (Matey et al., 2013; Silva et al., 2013).

Furthermore, there is some evidence that farmers take leaf traits into account when making

decisions about using new cultivars. Participatory plant breeding programs have shown that

farmers take a wide range of plant traits into account when making decisions on adopting a new

cultivar. For example, in addition to yield-related characteristics, Andean farmers choose new

maize and quinoa varieties based on leaf colour and phenology (Danial et al., 2007). Two studies

on sweet potato cultivars show a similar preference for a leaf colour and ample foliage (Gibson

et al., 2008; Mwanga et al., 2011).

2.7 Gaps in the Literature

A significant body of cacao research exists on cacao response to environmental pressures as well

as shade and nutrient interactions in the context of on-farm and controlled trials. While this work

often involves measurements of one or several functional traits, the conclusions are made not in

the context of functional trait variation but rather as beneficial or negative plant responses. Two

key limitations to this research are (i) a tendency to work with single traits or groups of traits in

isolation, rather than examining a wide suite to determine their co-variation, and (ii) the lack of

multi-site projects, even for research that deals with differences in climate or soil conditions.

These limitations make it difficult to accurately portray the consequences of changing climatic or

soil conditions on cacao performance, or the suitability of individual cultivars for different

climatological conditions.

While recent work shows farmer preferences for shade tree traits and cacao agronomic traits, no

work to date has documented farmer perception and preference for cacao functional traits.

Participatory breeding programs suggest that farmers have a nuanced view of the plants with

which they interact, including an understanding and preference for certain traits that can be

14

contextualized as “functional”. These preferences have an impact on the selection of shade trees

and new cultivars, and agroforestry management. What is less well understood is the way in

which this body of local knowledge interprets intraspecific trait variation within a crop species,

and how farmers relate this variation to changes in plant-level phenomena such as production

levels, health, or nutrient status, or management issues such as shade levels. In this respect,

farmer engagement with intraspecific trait variation could be seen as an indicator of the extent of

management of shade, nutrients, and plant well-being, among others. Given the novel but

growing interest in intraspecific trait variation in agroecosystems (Martin & Isaac, 2015; Wood

et al., 2015), it is important to catalogue this knowledge in order to facilitate dialogue between

researchers and farmers.

15

Chapter 3: Methods

3.1 Study Sites

3.1.1 Clonal garden sites

A cacao breeding project, Proyecto Cacao Centroamérica (PCC), began in the 1990s in response

to the devastating outbreak of frosty pod rot or moniliasis (Moniliophthora roreri) and black pod

disease (Phytophthora palmivora) in the region, with yield reductions ranging from 30-100% on

small-holder farms (Phillips-Mora et al., 2009). The purpose of PCC was to identify and breed

high-yielding varieties of cacao that were resistant to frosty pod rot and black pod disease. Six

varieties that showed an optimal combination of yield and disease resistance were chosen for

further study and massive propagation (Table 1).

Five of the varieties chosen under the PCC were developed from breeding programs of Trinitario

cacao in Costa Rica – three were developed under the CATIE breeding program in the 1990s,

namely CATIE-R1, CATIE-R4, and CATIE-R6. These are crosses of existing disease-resistant

varieties contained in the CATIE international cacao genebank (Phillips-Mora et al., 2009). Two

of the others were developed through previous CATIE breeding programs: CC-137 in the 1970s

under CATIE’s Cacao Centre (Centro de Cacao) (Engels, 1981, cited in Engels, 1983), and

PMCT-58 in the 1980s under the Tropical Crop Improvement Program (Programa para el

Mejoramiento de Cultivos Tropicales) (Morera et al. 1991, cited in Turnbull & Hadley, 2015).

The final clone, ICS-95 Type 1, is a Trinitario-Criollo hybrid developed in Trinidad & Tobago

that has been in use for several decades longer than the other varieties, and which was included

for its broad geographic applicability and its proven disease resistance (Phillips-Mora et al.,

2005).

All of the varieties selected by the PCC are high yielding (789-2363 kg ha-1 yr-1) and show low

to medium incidences of frosty pod disease (5-32% loss per year) and low incidences of black

pod (0-7% loss per year) (Phillips-Mora et al., 2013, see also Table 1). An analysis of

microsatellite markers (Figure 1, with data from Phillips-Mora et al., 2013) shows the close

relationship between the newer CATIE varieties. Unsurprisingly, the two most closely

genetically related clones are CATIE-R4 and CATIE-R6, which come from the same two parent

clones. These two clones form a clade with CATIE-R1 and one of the older Costa Rican clones,

16

Table 1: Codes and descriptions of the six clones promoted by Proyecto Cacao Centroamérica

and used in the two clonal gardens in this study. Origin and average annual yield data from

Phillips-Mora et al. (2013), studies in which varieties were first described are partially listed in

the International Cocoa Germplasm Database (Turnbull & Hadley, 2015).

Clone Origin Average

annual yield

First

described

Developed

CATIE-R1

Costa Rica Trinitario,

cross between UF-273

T1 X CATIE-1000.

1674 kg ha-1

yr-1

Arciniegas

Leal, 2005 1990s

CATIE-R4

Costa Rica Trinitario,

cross between UF-273

T1 X PA-169.

2070 kg ha-1

yr-1

Arciniegas

Leal, 2005 1990s

CATIE-R6

Costa Rica Trinitario,

cross between UF-273

T1 X PA-169.

2363 kg ha-1

yr-1

Arciniegas

Leal, 2005 1990s

CC-137

Costa Rica Trinitario,

open-pollenated cross

with UF-12.

1321 kg ha-1

yr-1

Engels, 1981 1970s

ICS-95

Trinidad & Tobago

Trinitario-Criollo cross,

unknown parents.

926 kg ha-1 yr-

1

Holliday,

1950, possibly

earlier

Already

widespread by

1940s

PMCT-58 Costa Rica Trinitario,

unknown parents.

789 kg ha-1 yr-

1

Morera et al.,

1991 1980s

17

Figure 1: Dendrogram showing genetic relationships between the six clonal varieties of T. cacao

surveyed in this study. These relationships are based on sequencing data from 18 microsatellite

markers published in Phillips-Mora et al. (2013). Here, ‘height’ is a measure of phylogenetic

distance, indicating how closely the different clones are related.

18

PMCT-58. The other older Costa Rican clone, CC-137, forms a more distantly related clade with

the Trinidadian clone, ICS-95.

In order to promote these new varieties, 50 demonstration sites were set up under PCC in

collaboration with local NGOs and cooperatives throughout Central America (Somarriba et al.,

2013b). The sites were termed ‘clonal gardens’ (jardines clonales) because of the use of clonal

reproduction in planting the new varieties. This study was conducted on two of these clonal

gardens: La Montaña site in Turrialba, Costa Rica, managed by the CATIE agricultural

university, and the Cacaonica site in Waslala, Nicaragua, managed by the cacao cooperative of

the same name (Figure 2). These two clonal gardens, established in 2007, use the same six

genetic varieties as well similar management practices. Cacao trees were planted at both sites in

2007 in a 3-metre grid under a canopy of already-mature shade trees. Cacao trees are fertilized

and pruned twice yearly at both sites and harvested roughly once per month during the non-peak

periods, and once per week during the production peaks in April and December.

These two clonal gardens vary in their physical, climate and edaphic characteristics. La Montaña

farm is located in the Central Valley of Costa Rica at an elevation of 590 metres above sea level

(masl). The climate is on the cool side for a cacao-growing region, with average temperatures of

20-22°C. There is a dry season from January to April, though there is regular rainfall even during

the dry season, with an average of 142 mm of rainfall per month. Cacaonica farm is located in

the hilly Waslala municipality at an elevation of 500 masl (Table 2). The climate is hotter and

drier than the La Montaña site: average temperatures range from 22-27°C, with a dry season

from January to April during which average monthly rainfall is around 60 mm and often much

lower (Figure 3).

3.1.2 Local knowledge interview sites

Interviews with cacao farmers were carried out in three cacao-growing regions: Waslala,

Nicaragua, Upala, Costa Rica, and Talamanca, Costa Rica. These sites were chosen for their

geographic diversity and their varying history with respect to cacao farming. All three have also

been well-studied by local cacao research programs, including those of CATIE in Costa Rica and

CIAT in Nicaragua. The sites vary in terms of their geography, encompassing inland hills

(Waslala), inland plains (Upala), and coastal hills (Talamanca) and climate; Waslala and Upala

19

Figure 2: Study sites: Waslala, Nicaragua (where the Cacaonica clonal garden is located, as well

as the villages of Hormiga Dudú and Santa Rosa, where interviews were carried out), Turrialba,

Costa Rica (where the La Montaña clonal garden is located), and Upala and Talamanca, Costa

Rica, where interviews were conducted.

20

Table 2: Climate and physical data for the La Montaña and Cacaonica sites, as well as the Upala

and Talamanca interview sites. Location and elevation data are from Google Maps, temperature

and precipitation from CATIE for La Montaña and climate-data.org for Cacaonica, Upala, and

Talamanca.

La Montaña Cacaonica

Location 9.8757°N, 83.6549°W 13.3337°N, 85.3565°W

Elevation 590 masl 500 masl

Precipitation Temperature Precipitation Temperature

Dry season 142 mm 21.3°C 64 mm 23.3°C

Rainy season 269 mm 22.2°C 268 mm 24.0°C

Upala Talamanca

Location 10.91-10.97°N, 85.00-85.08°W 9.51-9.60°N, 82.60-83.00°W

Elevation 120-140 masl 20-200 masl

Precipitation Temperature Precipitation Temperature

Dry season 60 mm 26.9°C 164 mm 26.3°C

Rainy season 224 mm 26.2°C 210 mm 25.8°C

21

Figure 3: Average monthly temperature and total monthly precipitation charts for Waslala,

Nicaragua (Cacaonica), Turrialba, Costa Rica (La Montaña),), and Upala and Talamanca, Costa

Rica. Data obtained from CATIE weather station for Turrialba (1958-2012 average) and from

climate-data.org for Waslala, Talamanca, and Upala (1982-2012 average).

22

both have pronounced dry seasons, in which average monthly rainfall drops below 50 mm for

three months, while Talamanca is wet year-round.

Cacao cultivation has been practiced in the region for millennia, but the recent history of cacao

farming in each of the study areas has been quite different. Waslala experienced a migration

wave in the second half of the 20th century of Mestizo farmers from the Western half of the

country, who began cacao farming in the 1960s and 1970s (Lok & Sandino, 1999). As a result,

most participants at the Nicaraguan sites of Hormiga Dudú and Santa Rosa were first- or second-

generation cacao farmers. Both Upala and Talamanca have longer histories with cacao farming,

particularly Talamanca, in which most of the population is indigenous or Afro-Caribbean in

origin, with strong cultural ties to cacao cultivation. However, both areas were heavily affected

by the outbreaks of frosted pod disease, with many farmers abandoning the crop. Thanks to

efforts to combat the disease, both through more intensive management and the introduction of

disease-resistant varieties, cacao has recently begun to be considered as a viable economic

activity once again. Upala and Talamanca have been involved with efforts to promote disease-

resistant varieties, specifically through the six CATIE clones. As such, cacao farmers grow these

clones alongside traditional seed-grown Criollo and Trinitario cacao, while farmers in Waslala

grow seed-grown cacao almost exclusively (Trognitz et al., 2011).

3.2 Clonal Garden Study

3.2.1 Experimental Design

Both the La Montaña and Cacaonica sites are 1-2 hectares in size, with the six clones planted in

alternating, randomly ordered rows. Each site was divided into three blocks that contained

roughly equal numbers of individuals, and in which all six clones were present. At the Montaña

site, the site is already divided into six blocks containing all six clones, which are pruned at

different intervals. The three blocks that had been left undisturbed (i.e. neither pruned nor

fertilized) for the longest time were chosen for this study. In each block, five individuals of each

clone were selected, for a total of 15 trees per clone per site, or 90 trees per site in total. All

individuals were within five metres of a shade tree in order to ensure consistent shade tree effects

(using a threshold identified by Isaac et al. (2007)). In Waslala, the entire site was first surveyed

for trees that fit the selection criteria. Contiguous blocks were then selected to include five

23

individuals of each clone, which resulted in an irregular block size due to the more irregular

distribution of the clones in the site.

At the La Montaña site, shade tree composition consisted of non-leguminous mahogany

(Swietenia macrophylla) and laurel (Cordia alliodora) while at the Cacaonica site, several

leguminous trees of the genus Inga were present. Given spatial constraints, I ensured that half of

the trees chosen at the Cacaonica site were adjacent to leguminous trees and half were adjacent

to non-leguminous trees, mostly consisting of C. alliodora, with two avocado (Persea

americana) trees were also present.

3.2.2 Functional Trait Analysis

Cacao leaf traits vary within a tree due to factors such as the leaf’s age and its exposure to

sunlight (Miyaji et al., 1997). In order to reduce this within-tree variation in leaf traits, a

protocol developed by Santana & Igue (1979) was employed, in which the third leaf back on one

side of the twig from a terminal bud or recent flush was selected (Figure 4). The leaves selected

were 60% of the way up the canopy (2.5-3.0m above the ground) and partially exposed to direct

sunlight in order to minimize the trait variation due to sun exposure. A series of physiological,

morphological, and chemical traits were measured for three leaves per tree, as well as whole-

plant traits and environmental characteristics, as follows (Table 3).

Photosynthesis at saturating irradiance (Asat, µmol CO2 m-2 s-1) and transpiration (E, mmol H2O

m-2 s-1) was measured using a LICOR LI-6400 gas exchange system (Li-Cor Biosciences,

Lincoln, Nebraska, USA) equipped with a red/blue light source (6000-2B Red-Blue SI-0951)

with leaf chamber conditions set to 400 µmol mol-1 CO2, leaf temperature of 25°C, and light

levels of 1000 µmol m-2 s-1 photosynthetically active radiation, well over the levels at which

maximum photosynthesis occurs (400-800 µmol m-2 s-1) according to studies by Acheampong et

al. (2015), Ávila-Lovera et al. (2015), and Baligar et al. (2008). Relative humidity was

maintained at 30-70% and VPD at under 3 kPa. Measurements were taken between 7-11 AM to

avoid midday stomatal closure. For each leaf, the chamber was closed 2/3 of the way from the

base of the leaf to its tip, taking care to avoid the midrib so as to exclude non-photosynthesizing

parts of the leaf. Three measurements were taken per leaf and an average was subsequently

calculated. Physiological traits were measured in this way for three leaves from each individual

(n=173

24

Figure 4: Diagram of how leaves are chosen. The orange circle indicates the terminal bud

(drawing A) and a recent flush (B). The third leaf back is coloured green. Adapted from Santana

& Igue (1979).

25

Table 3: Variables measured in this study, including leaf functional traits, whole-plant traits, and

environmental data.

Measurement Abbreviation Unit Method

1. Leaf traits

Photosynthetic rate

under saturating

irradiation

Asat µmolCO2 m-2 s-1 LICOR LI-6400, preset to CO2 =

400 ppm, PAR 1000 µmol m-2 s-1,

Tleaf 25°C Transpiration E molH2O m-2 s-1

Leaf area Area cm2 Photographic analysis using ImageJ

Leaf dry weight Dry weight g Scale

Leaf thickness Thickness mm Calipers

Leaf nitrogen content LN mg g-1 LECO C/N analyzer

Leaf carbon content LC mg g-1 LECO C/N analyzer

Specific leaf area SLA mm2 mg-1 Ratio using area and dry weight

Leaf carbon-to-

nitrogen ratio C:N ratio Unitless Ratio using LECO data

Water use efficiency WUE μmolCO2mmolH2O

Ratio using LICOR data (Asat/E)

2. Whole-plant traits

Basal diameter D30 cm DBH tape

Plant height H m Altimeter

Canopy width CW m Measuring tape

Branching ratio BR Branches/m Measuring tape; it is a ratio of the

number of sub-branches to the total

length of the longest branch

3. Environmental data

Total soil nitrogen SN mg g-1 LECO C/N analyzer

Total soil carbon SC mg g-1 LECO C/N analyzer

Canopy openness CO Percentage Image analysis using Gap Light

Analyzer

26

individuals; n=519 leaves). Instantaneous water use efficiency (WUE, mmol CO2 mol-1 H2O)

was calculated as WUE=Asat/E.

After photosynthesis measurements were taken, the leaf was collected and placed in water such

that the petioles were completely submerged in order to maintain turgidity (per Pérez-

Harguindeguy et al., 2013). Immediately upon returning from the field, leaves were removed

from water and leaf lamina thickness (in mm) was measured as the average of three

measurements taken using a low-force micrometer (No. 227-101, Mitutoyo Co., Mississauga,

Ontario, Canada), with care taken to avoid major leaf veins. Leaf area (in cm2) was calculated

based on digital photographs analyzed using ImageJ software (Abramoff et al., 2004). Leaves

were then dried in an oven at 60°C for 72 hours to obtain a constant mass. The relationship

between leaf area and leaf dry weight was used to calculate specific leaf area (SLA, mm2 mg-1).

Dried leaves were subsequently transported to the University of Toronto Scarborough, Canada

for laboratory analysis. The leaves were ground into a fine powder using a ball mill (Retsch Ltd.,

Haan, Germany) and then analyzed for leaf carbon (LC, mg C g-1 dry weight) and nitrogen (LN,

mg N g-1 dry weight) concentrations using a LECO 628 Series C/N Analyzer (LECO Corp.,

Saint Joseph, Michigan, USA).

Certain whole-plant traits were also measured. Trunk diameter was measured at a height of 30

cm from the ground (D30, cm) using a diameter tape, which is common practice for cloned cacao

trees because they usually branch well before 1.3 m, the height at which trunk diameter at breast

height (DBH) is commonly measured (Somarriba et al., 2013a). Plant height was measured using

a Suunto PM-5/360 clinometer (Suunto, Vantaa, Finland), and canopy diameter using a

measuring tape. Branching complexity was measured using a measuring tape and the branching

ratio described in Pérez-Harguindeguy et al. (2013).

3.2.3 Environmental Sampling

Composite soil samples (4 per sample) were collected using a 0.5 L soil augur at a depth of 20

cm and at a distance of 50 cm from the base of each individual (n=173). A subsample was used

to determine soil N (SN, mg N g-1 dry weight) and soil C (SC, mg C g-1 dry weight) content. This

subsample was oven-dried at 105°C for 24 hours and ground into a fine powder using a ball mill

(Retsch Ltd., Haan, Germany). A 0.1g sample of the powdered soil was used to determine SC

and SN using a LECO 628 Series C/N Analyzer (LECO Corp., Saint Joseph, Michigan, USA).

27

Canopy density was calculated based on hemispherical photos taken with a Nikon CoolPix 950

digital camera fitted with a Nikon Fisheye Converter FC-E8 0.21x lens (Nikon, Tokyo, Japan).

Four photos were taken in each cardinal direction at 1m from the base of the cacao tree and 1m

above ground level. The colour photographs were then converted to binary images and analyzed

for total light transmission, expressed as a percentage of open sky using Gap Light Analyzer

(Frazer et al., 1999).

The two sites showed significant differences in their shade and soil characteristics (Table 4).

There were significant differences in SN (F=46.63, p≤0.0001) and SC (F=124.1, p≤0.0001)

between the two sites. La Montaña had lower SN (2.49±0.03 mg g-1) and higher SC (31.19±0.39

mg g-1) than Cacaonica (SN: 2.89±0.05 mg g-1; SC: 24.10±0.51 mg g-1). There was a significant

difference in canopy openness, with higher levels at La Montaña (12.78±0.38%) than at

Cacaonica (10.50±0.32%). There was no difference in the distance to shade tree, though this is to

be expected given that the selection criteria for cacao individuals included that they be within 5m

of a shade tree.

3.2.4 Statistical Analysis

All statistical analyses were performed using R version 3.3.1 (R Core Team, 2016). Nine

functional traits were chosen for the overall analysis, encompassing chemical (LC, LN, C:N

ratio), physiological (Asat, E, WUE), and morphological traits (SLA, area, thickness) (Table 3).

Outliers were determined using a Lund’s Test for each trait (Lund, 1975). Once extreme values

were removed, the maximum likelihood of each trait to fit normal and log-normal distributions

was determined using the ‘fitdistrplus’ R package (Delignette-Muller & Dutang, 2015). The raw

data range, mean, median, and standard deviation were reported along with the parameters for

each best-fit distribution model. Where traits were log-normally distributed, the log-transformed

data were used in all subsequent analyses.

A two-way analysis of variance (ANOVA) was performed to test for differences in leaf traits

between varieties and sites and their interaction. A Tukey post-hoc test was then used to

determine whether significant differences existed between mean trait values between sites,

varieties, or site/variety combinations. The coefficient of variation (CV) was calculated for each

trait along the entire dataset. CVs were then calculated for each trait by site and by clone-site

combination. A bootstrapping with replacement procedure was then used to generate 999

28

Table 4: Canopy openness, distance to shade tree, and soil N and C content for individual cacao

trees on cacao farms at La Montaña farm in Turrialba, Costa Rica (n=90) and Cacaonica farm in

Waslala, Nicaragua (n=83). Values presented are site-level means and standard errors, along

with the minimum and maximum values in parenthesis. ANOVA results (F and p-values) are

also given, showing differences in the four environmental parameters between the two sites.

Site ANOVA

La Montaña Cacaonica F p

Canopy openness (%) 12.73±0.38

(5.49-20.91)

10.50±0.32

(5.04-16.96) 19.61 <0.0001

Distance to shade tree (m) 2.9±0.1

(0.6-5.0)

3.4±0.2

(0.1-6.0) 3.566 0.0611

Total Soil N (mg g-1) 2.49±0.03

(1.59-3.51)

2.89±0.05

(1.99-4.06) 46.63 <0.0001

Total Soil C (mg g-1) 31.19±0.39

(20.59-41.77)

24.10±0.51

(14.92-36.87) 124.1 <0.0001

29

randomized trait datasets, using the entire leaf dataset (n=519), which was used to generate a

modeled mean CV for the trait, along with 95% confidence limits of the randomized distribution.

The subset CV values were then interpreted as either being within, below, or above the 95%

confidence limits of the randomized distribution, which corresponded to a subset of the data

having equivalent, lower, or greater variation than the dataset as a whole.

A standardized major axis (SMA) linear regression was performed to test for bivariate

relationships between the traits included in the analysis and calculate the correlation coefficient

(r). Once this was done for the full dataset, SMA linear regressions were also performed for the

nine traits at the site and clone level to examine patterns of covariation at these levels.

In order to determine the effect of environmental variables – specifically, light levels and soil

nutrients – on the selected leaf traits, a backwards stepwise linear model was used. SN, SC,

canopy openness, and distance from shade tree were used as predictor variables in the full model.

Models were compared using Akaike’s Information Criteria (AIC), whereby the lowest AIC

indicated the most parsimonious model fit. A least-squares regression using the explanatory

variables identified in the parsimonious model fit was then applied to calculate the significance

and direction of the relationships between environmental variables and leaf traits.

3.3 Local Knowledge Survey

3.3.1 Interview Format

3.3.1.1 Participant selection

Forty-five semi-structured interviews were conducted in Santa Rosa and Hormiga Dudú,

Nicaragua, and Upala and Talamanca, Costa Rica. In Nicaragua, local partners included two

NGOs (Fundación Madre Tierra and the Asociación Pro Mujer de Waslala) and the cacao

cooperative Cacaonica, all of whom were participating in a regional cacao project coordinated by

Bioversity International and the International Centre for Tropical Agriculture (CIAT). In Upala,

introductions were made by CATIE researchers who had completed a study in the region in the

previous year, and who were in the process of providing participants with the results of the

previous study. For the work in Talamanca, contact with participants was made with the help of

local promotores, extension staff of the Costa Rican Ministry of Agriculture, on visits to cacao

farms. This research received ethics approval from the Social Sciences, Humanities, and

30

Education Research Ethics Board, University of Toronto, for research involving human

participants. Informed consent was secured verbally in advance of every interview.

3.3.1.2 Participant information

Information was collected on the participants’ demographics, farming history, farm

characteristics, and farm management (the complete list of interview questions is provided in

Appendix A). Participants’ demographic information consisted of their age, the number of years

they had farmed cacao, what they had done prior to farming cacao, and their membership in

cooperatives or community organizations. The questions on farm characteristics included the size

of farm in hectares, the number of people working on the farm, and the farm’s prior land-use. In

terms of management practices, participants were asked about the shade trees they used, whether

they pruned their shade trees, whether they pruned their cacao trees, how often pruning was

carried out, whether they used different types of fertilizer, and which varieties of cacao they

planted.

Farmers were also asked about the visual cues they used in making management decisions.

These questions focused on the way the cacao trees looked when pruning or fertilizer application

was necessary. Finally, participants were asked to identify the sources of information they

consulted with regards to establishing and maintaining their cacao farms, as well as people or

organizations that had provided information on different cacao varieties and on leaf traits.

3.3.1.3 Leaf spectra and the visual elicitation device

A visual elicitation tool based on empirically framed researcher-created visual data (Prosser &

Loxley, 2008; Bagnoli, 2009) was used to examine participants’ knowledge of different

functional traits in cacao. This included three sets of leaves, which showed differences in colour,

size, and thickness, respectively, as well as a set of drawings that demonstrated differences in

canopy size, height, and the distribution of pods on the tree (Figure 5; see Appendix A for scans

of the drawings used for the interviews).

The concept of trait-effect relationships was used to analyze participant responses. A trait-effect

relationship was defined as the relationship between variation in a trait spectrum (i.e. size,

colour, or thickness) and an effect on the crop – either a difference in production, health, or

shade level. A total of twelve trait-effect combinations were examined, given four trait spectra

(size, thickness, colour, and ‘greenness’ as a subset of leaf colour) and three effect groupings

31

Figure 5: Leaf spectra used in the visual elicitation portion of the interview. Leaves in the top-

most row vary by colour, with the three leaves on the left showing the “greenness” spectrum,

from light (left-most leaf) to dark green (third leaf from the left). The four leaves on the left

show different common forms of discolouration. The second row from the top shows the leaf

size spectrum, and the lowest row shows the thickness spectrum. Leaves in the size and thickness

spectra are accompanied by their percentile scores based on leaf size and thickness variation in a

dataset of 519 leaves collected by the author in a separate study.

32

(production, health, and shade level). The underlying assumption was that participants would

link variation in a given trait spectrum to an effect on the cacao tree.

Leaf size, as measured by area, was included because of its tendency to vary with shade levels

(Galyuon et al., 1996). The leaf area spectrum included five leaves, whose size was based the

leaf areas observed in the dataset of 519 leaves generated in the clonal garden section of this

study, in which leaf area varied between 114.8-779.4 cm2, with a mean of 297.2 cm2. The sizes

represented were the mean area, the 5th, 25th, 75th, and 95th percentiles of leaf area observed in

that dataset.

Leaf thickness tends to vary with shade levels, as well as between cultivars (Baligar et al., 2008;

Leibel, 2008). The leaf thickness spectrum was also based on the clonal garden dataset, in which

leaf thickness ranged from 0.21-0.38 mm, with a mean of 0.28 mm. The spectrum that was

shown to informants was comprised of the mean thickness, as well as the 5th, 25th, 75th, and 95th

percentiles of that dataset.

The leaf colour spectrum consisted of three leaves that were judged to represent light, medium,

and dark green colour, an approach used to assess leaf chlorophyll content (Carter & Knapp,

2001; Netto et al., 2005). These three leaves were included in the overall colour spectrum as well

as in a separate ‘greenness’ spectrum (see Figure 5). Four leaves with common discolouration

were also chosen: one with some yellow discolouration, one with extensive yellow

discolouration, one with brown patches, and one with white spots. These discoloured leaves were

chosen based on published charts of the effects of nutrient deficiency and sun/shade damage

(Wood & Lass, 1985; Chepote et al., 2005), as well as common leaf colouration patterns in the

area.

Participants were asked what they thought may cause a leaf to look or feel a certain way, and

which leaves they believed exhibited signs of environmental stress such as too much sun, shade,

or plant diseases, as well as which leaves they believed to come from healthy or productive trees.

Leaves were collected every 1-2 days while interviews were being carried out from locally

available cacao trees.

The whole-plant traits included in this study depend either on management decisions (plant

height and canopy width) or on plant age (trunk diameter). Participants were asked to rank the

33

three traits in order of their importance for cacao production, and to indicate their preference of

each. To ensure a common understanding of these subjective criteria, a series of drawings were

used to illustrate a narrow, medium, and wide canopy, and short, medium, and tall trees

(Appendix A).

Three flowering traits were included in this study: number and distribution of flowers, and

flower size. Little work has been done on cacao flower traits, though research suggests that

pollination rates are more important in determining yield than intraspecific trait variation in

cacao flowers (Young, 2007; Groeneveld et al., 2010). Pictures were used to communicate the

distribution of flowers and fruit– whether it was preferable for flowers and fruit to be located on

the trunk, on the branches, or be distributed evenly. For flower size and number, a verbal

assessment was used: small/medium/large for size, and few/many for number.

3.3.2 Analysis of Interview Responses

The interpretation of each leaf in the spectrum was assessed to be either positive, unsure/neutral,

or detrimental, with a value of 1, 0, or -1, respectively, assigned to the responses. In the case of

production, a positive assessment was one that indicated that the leaf came from a high-yielding

tree, and a negative assessment was one that indicated that the leaf came from a low-yielding

tree. For health, a positive response indicated that the leaf was healthy, while a negative response

indicated that the leaf has a nutrient deficiency or is infected with a pest or disease. With regards

to shade, a negative score indicated a lack of shade, while a positive score indicated heavy shade.

While both overshading and undershading can be detrimental for cacao, this assessment allowed

for the responses to be arranged along a continuum.

For each leaf in the spectrum, the number of responses that each of the three interpretations

(positive, unsure/neutral, negative) received was recorded. An average of the numerical values

was also calculated for each individual in order to describe the consensus view.

3.3.3 Statistical analysis

Two statistical methods were used to measure the variation in participants’ interpretations of

leaves. Firstly, the standard deviation in the numerically coded responses was measured for each

participant and each trait-effect combination – for example, a participant who stated that three of

the five leaves in the size spectrum came from trees under intermediate shade, one from a tree

under high shade, and one from a tree under low shade, would have a size-shade score of SD

34

(1,0,0,0,-1)=0.707. Secondly, the average of the interpretation values for each leaf over all

interviews was calculated. For each trait-effect combination, the range was taken as the

difference between the highest average interpretation value and the lowest. The result was a

single standard deviation and range for each trait-effect combination. A larger standard deviation

and range were taken to mean that participants believed that variation in a given trait spectrum

was indicative of a large change in the management-relevant effect.

A subsequent analysis was carried out to measure the degree to which individual farmers agreed

or disagreed with a “consensus view”. The concept of the consensus view, for the purpose of this

analysis, was defined as the most common interpretation for each leaf in the interview. For each

leaf, the most common response (positive, unsure/neutral, or negative) was given a value of 3,

the second-most common response a value of 2, and the least common response a value of 1.

Using this system, an average could be calculated for each group of farmer responses to indicate

the extent to which they agreed with the overall consensus. An average closer to 3 indicated

“consensus” participants, while an average closer to 1 indicated more “contrarian” participants.

The variation in responses was measured by locality by calculating the average consensus score

for each set of traits – size, colour, and thickness – for each study area. This was compared with

the personal and farm attributes in order to form a qualitative assessment of the variation in

consensus with regard to the location and demographics of participants.

Finally, participant rankings of certain leaf, flower, and whole-plant traits were assessed: these

placed certain functional traits in order of their importance for cacao production and overall

function. For each of these rankings, an average rank for each trait was calculated between 1

(most important) and 3 (least important). Participants’ preferences for each of the traits were also

recorded – for example, what size of canopy or what number of flowers was viewed as ideal.

35

Chapter 4: Intraspecific trait variation in cacao clonal gardens

4.1 Results

4.1.1 Variation in leaf traits

Nine leaf traits were examined, consisting of three physiological traits (photosynthesis at

saturating irradiance [Asat], transpiration [E], water use efficiency [WUE]), three morphological

traits (leaf thickness, leaf area, specific leaf area [SLA]), and three chemical traits (leaf carbon

[LC], leaf nitrogen [LN], and C:N ratio). Six of the nine functional traits surveyed were log-

normally distributed, while LN, leaf area, and leaf thickness were normally distributed.

Coefficients of variation (CVs) of the nine measured traits ranged from 2.54 to 51.63 (Table 5).

The largest variation was detected in physiological traits, all of which were distributed more

widely. Asat ranged from 0.71-10.56 µmolCO2 m-2 s-1 (CV=39.41), while E ranged from 0.46-

3.28 mmolH2O m-2 s-1 (CV=33.08), and WUE ranged from 0.88-7.50 mmolCO2 mol-1H2O

(CV=51.63). Chemical traits showed the lowest ranges of variation, with LC ranging from 419.8-

486.5 mg g-1 (CV=2.54, the lowest of any trait), LN ranging from 16.61 mg g-1 (CV=10.84), and

the C:N ratio ranging from 16.45-25.93 (CV=10.49). Morphological traits showed intermediate

levels of variation: leaf area ranged from 114.9-474.6 cm2 (CV=21.81), leaf thickness from 0.22-

0.34 mm (CV=6.86), and SLA from 8.79-17.81 mm2 mg-1 (CV=14.67).

4.1.2 Site and cultivar effect on leaf trait variation

Analysis of variance showed a significant effect (p≤0.05) of treatment (site and cultivar) on

nearly all of the nine traits surveyed (Table 6). The exceptions were leaf thickness and WUE, for

which there was no significant effect at the site level (thickness: F=0.782, p=0.378; WUE:

F=0.805, p=0.371) and LC, for which there was no significant effect at the cultivar level

(F=1.298, p=0.267). The interaction of treatment (site x cultivar) had a highly significant effect

on all traits (p≤0.0001 for all traits, except for SLA, for which p=0.0002).

The within-site variation in leaf traits was lower than, or within, the modeled 95% confidence

intervals (CI) for every trait (Table 7). Specifically, the within-site CVs for chemical traits were

almost all lower than the modeled 95% CI, with LN having a CV of 6.64 at La Montaña and 8.81

at Cacaonica, and C:N ratio having a CV of 7.60 at La Montaña and 8.49 at Cacaonica. LC was

the only exception, with a lower CV at La Montaña (CV=2.10), but a CV within the 95% CI at

Cacaonica (CV=2.45). The within-site CVs for morphological traits were all within the 95% CIs

36

Table 5: Intraspecific variation in T. cacao leaf traits (n=172). The maximum log-likelihood value was calculated for each trait and is

shown in bold below. Where a particular trait has a log-normal distribution, the log-transformed values were used to calculate the

mean and standard deviation (SD). Median values, range, and coefficients of variation (CV) were also calculated for all traits.

Trait Log-likelihood values

Range Mean Median SD CV Normal Log-normal

Morphological traits

Area (cm2) -381 -383 114.9-474.6 297.5 293.4 87.5 21.81

Thickness (mm) 425 425 0.22-0.34 0.28 0.28 0.02 6.86

SLA (mm2 mg-1) -346 -344 8.79-17.81 13.11 12.90 1.92 14.67

Physiological traits

Asat (µmolCO2 m-2 s-1) -334 -322 0.71-10.56 4.59 4.36 1.81 39.41

E (mmolH2O m-2 s-1) -136 -125 0.46-3.28 1.68 1.59 0.55 33.08

WUE (mmolCO2 mol-1H2O) -308 -272 0.88-7.50 2.97 2.50 1.54 51.63

Chemical traits

LN (mg g-1) -381 -383 16.61-26.60 21.91 21.84 2.38 10.84

LC (mg g-1) -643 -642 419.8-486.5 447.6 447.7 11.4 2.54

C:N ratio -366 -364 16.45-25.93 20.65 20.49 2.16 10.49

37

Table 6: Two-way ANOVA results for nine leaf traits across six T. cacao clones at two sites

(n=172). The ANOVA was applied with Site, Clone and ClonexSite as factors. A logarithmic

transformation was applied to the variables marked with an asterisk (*).

Factor Type Variable DF Sum of

Squares

Mean

squares F-value P-value

Site

Chem.

Leaf N 1 515.2 515.2 200 <0.0001

Leaf C* 1 0.0222 0.0222 43.32 <0.0001

C:N ratio* 1 0.8083 0.8083 132.4 <0.0001

Morph.

Thickness 1 0.0003 0.0003 0.782 0.378

Area 1 18183 18183 4.408 0.0373

SLA* 1 0.127 0.127 6.08 0.0147

Phys.

Asat* 1 0.970 0.970 5.859 0.0166

E* 1 3.006 3.006 31.98 <0.0001

WUE* 1 0.0038 0.0038 0.805 0.371

Clone

Chem.

Leaf N 5 69.1 13.83 2.565 0.0291

Leaf C* 5 0.0041 0.0008 1.298 0.267

C:N ratio* 5 0.144 0.029 2.786 0.0192

Morph.

Thickness 5 0.012 0.0025 8.261 <0.0001

Area 5 178397 35679 11.02 <0.0001

SLA* 5 0.5179 0.10358 5.436 0.0001

Phys.

Asat* 5 5.506 1.101 7.771 <0.0001

E* 5 3.745 0.749 8.162 <0.0001

WUE* 5 0.163 0.033 8.388 <0.0001

ClonexSite

Chem.

Leaf N 11 621.2 56.5 27.4 <0.0001

Leaf C* 11 0.078 0.0026 5.22 <0.0001

C:N ratio* 11 1.057 0.096 19.61 <0.0001

Morph.

Thickness 11 0.018 0.0016 6.03 <0.0001

Area 11 239145 21740 7.313 <0.0001

SLA* 11 0.720 0.065 3.556 0.0002

Phys.

Asat* 11 8.665 0.788 6.207 <0.0001

E* 11 8.529 0.775 11.99 <0.0001

WUE* 11 0.239 0.021 6.133 <0.0001

38

Table 7: Coefficients of variance (CV) of nine functional traits in T. cacao trees (n=172), as well as a model CV, calculated based on

a modeled distribution of the entire dataset, and the corresponding 95% confidence intervals. CVs were also calculated for each site,

clone, and site-clone combination. These CVs are marked in bold and highlighted in grey when they are above (+, dark grey) or below

(-, light grey) the modeled 95% confidence intervals.

Leaf N Leaf C C:N ratio Thickness Area SLA Asat E WUE

Overall 10.84 2.54 10.49 6.86 21.81 14.67 39.41 33.08 51.63

Model CV – mean

and 95% CI

10.82

(9.94,1.7

1)

2.55

(2.25,2.85

)

10.48

(9.53,11.42

)

7.16

(6.18,8.14)

21.82

(19.52,24.11

)

14.62

(13.25,15.9

8)

39.31

(35.50,43.13)

34.23

(30.40,38.08)

51.88

(47.42,56.33)

Site

La Montaña 6.64- 2.10- 7.60- 6.73 22.10 15.76 40.91 34.79 47.92

Cacaonica 8.81- 2.45 8.49- 7.73 21.29 13.15- 33.68- 29.74- 28.17-

Clone

CATIE-R1 7.26- 3.92+ 8.03- 7.28 23.20 15.44 39.17 21.06- 53.91

CATIE-R4 12.28+ 2.10- 11.85+ 7.12 14.74- 14.64 36.50 35.25 41.05-

CATIE-R6 9.98 1.87- 9.98 6.26 16.40- 12.90- 28.85- 22.87- 32.15-

CC-137 9.97 2.74 10.19 4.84- 15.86- 15.21 36.74 30.00- 53.66

ICS-95 11.27 2.37 10.12 6.19 24.58+ 10.53- 37.39 29.69- 27.46-

PMCT-58 11.86+ 2.28 10.66 8.19+ 20.12 12.82- 36.67 47.03+ 51.37

Site*Clone

La

Mo

nta

ña

CATIE-R1 7.11- 3.58+ 8.57- 5.31- 24.65+ 17.40+ 42.26 34.32 53.93

CATIE-R4 4.66- 1.47- 5.52- 7.08 14.97- 12.91- 44.77+ 37.11 41.30-

CATIE-R6 8.29- 1.36- 9.22- 5.96- 17.54- 15.54 19.54- 19.24- 29.80-

CC-137 6.95- 2.14- 8.99- 5.58- 17.02- 19.39+ 29.28- 18.15- 35.56-

ICS-95 5.92- 1.69- 5.76- 4.79- 19.25- 8.11- 35.10- 28.02- 29.56-

PMCT-58 4.62- 1.29- 4.63- 3.78- 19.31- 12.32- 29.01- 18.25- 19.66-

Cac

aonic

a

CATIE-R1 7.49- 3.88+ 7.36- 6.86 17.29- 9.37- 17.19- 7.94- 23.70-

CATIE-R4 8.24- 2.41 7.12- 7.36 14.91- 14.88 28.51- 26.32- 34.43-

CATIE-R6 6.52- 2.30 6.88- 5.68- 15.09- 9.88- 35.66 26.44- 19.34-

CC-137 6.14- 3.00+ 6.75- 3.95- 15.25- 9.83- 34.41- 26.79- 28.18-

ICS-95 6.47- 1.85- 7.19- 7.51 22.07 12.03- 38.08 26.09- 24.18-

PMCT-58 11.39 1.57- 9.45- 11.36+ 20.04 13.56 35.19- 37.28 30.72-

39

for the entire dataset, with the exception of SLA at Cacaonica (CV=13.15). Meanwhile, the CVs

of physiological traits all fell within the 95% CI values at the La Montaña site, while they were

below the 95% CI values at Cacaonica for Asat (CV=33.68), E (CV=29.74), and WUE

(CV=28.17).

The ‘newer’ Costa Rican clones (CATIE-R1, CATIE-R4, and CATIE-R6) showed higher

variation in chemical leaf traits in certain cases: CATIE-R4 had higher variation in LN

(CV=12.28) and C:N ratio (CV=11.85), CATIE-R1 showed variation above the 95% CI for LC

(CV=3.92), though it also showed lower variation in its C:N ratio (CV=8.03). CATIE-R6 also

showed lower variation in LC (CV=1.87). Both of the ‘older’ Costa Rican clones (CC-137 and

PMCT-58) had variation that generally fell within the 95% CIs, except for PMCT-58, which had

higher variation in LN (CV=11.86). The oldest cultivar, and the only non-Costa Rican clone

(ICS-95), had CVs for chemical traits that fell within the 95% CIs.

The variation in physiological traits was also lower for these newer clones, all of which had

variation in the three physiological traits surveyed that was within or below the modeled 95%

CV values. The Trinidadian clone, ICS-95, also had variation below the 95% CI in E

(CV=29.69) and WUE (CV=27.46), though the variation in its Asat (CV=36.74) fell within the

95% CI values. The older clones generally fell within the modeled 95% CIs, although PMCT-58

had higher variation in E (CV=47.03) and CC-137 had lower variation in E (CV=30.00).

Variation in morphological traits (leaf area, thickness, and SLA) at the clone level was generally

within or lower than the modeled 95% CIs for the entire dataset. The only clone that showed

higher variation in morphological traits was PMCT-58, whose leaf thickness varied more than

the overall dataset (CV=8.19), though the variation in its SLA was lower than the overall 95% CI

(CV=12.82).

The majority of clone-site combinations showed levels of variation in all traits that was below

the modeled 95% CI values for that trait. The newer Costa Rican clones, however, showed levels

of variation in morphological and physiological traits that were at or above the levels of variation

present in the entire dataset. At La Montaña, CATIE-R1 showed higher variation in leaf area

(CV=24.65) and SLA (CV=17.40), and levels of Asat (CV=42.26), E (CV=34.32), and WUE

(CV=53.93) that were within the 95% CIs, while CATIE-R4 had higher variation in Asat

(CV=44.77), while its variation in E (37.11) and leaf thickness (7.08) fell within the modeled

40

95% CIs. CATIE-R6, on the other hand, showed lower levels of variation in all leaf traits except

for SLA, which was within the 95% CIs (CV=15.54). At Cacaonica, the three newer clones

showed levels of variation in LC that was within or greater than the modeled 95% CIs (CATIE

R1: 3.88; CATIE-R4: 2.41; CATIE-R6: 2.30), and CATIE-R1 and CATIE-R4 had levels of

variation in leaf thickness that were within the modeled 95% CIs (CV=6.86 and CV=7.36,

respectively). Only CATIE-R6 showed levels of variation in a physiological trait at Cacaonica

that were within the 95% CIs, specifically Asat (CV=35.66).

At La Montaña, the older Costa Rican clones had levels of trait variation that were uniformly

lower than the modeled CIs, with the exception of CC-137, which had higher variation in SLA

(CV=19.39). At Cacaonica, however, PMCT-58 showed levels of variation that were within or

higher than the modeled 95% CIs in LN (CV=11.39), leaf thickness (CV=11.36), leaf area

(CV=20.04), SLA (CV=13.56), and E (CV=37.28).

Similarly, ICS-95 showed levels of trait variation at La Montaña that were consistently lower

than the modeled 95% CIs. At Cacaonica, though, its levels of variation in leaf

thickness(CV=7.51), leaf area(CV=22.07), and Asat (CV=30.08) were all within the 95% CIs.

4.1.3 Between-site differences in individual cultivars

The analysis of variance showed that all of the traits showed significant (p≤0.0002) interactions

at the site*cultivar level. It should be noted, however, that this only means that some

site*cultivar combinations are different from others – significant differences for an individual

cultivar between one site and another were less common. Very few clones had significant

between-site differences in morphological leaf traits. The only clones that did were ICS-95,

which had significantly larger leaves at Cacaonica (La Montaña: 263.56±13.85 cm2; Cacaonica:

338.65±19.30cm2; p=0.0155), and CATIE-R1, which had significantly higher leaf thickness at

Cacaonica (La Montaña: 0.260±0.003 mm; Cacaonica: 0.287±0.007 mm; p=0.0499) (Figure 6).

The transpiration rate, E, was higher in Cacaonica for the two older Costa Rican clones, CC-137

(La Montaña: 1.32±0.06 mmolH2Om-2s-1; Cacaonica: 1.92±0.12 mmolH2Om-2s-1; p=0.0050) and

PMCT-58 (La Montaña: 1.06±0.05 mmolH2Om-2s-1; Cacaonica: 1.84±0.14 mmolH2Om-2s-1;

p≤0.0001), as well as for one of the newer clones, CATIE-R4 (La Montaña: 1.44±0.14

mmolH2Om-2s-1; Cacaonica: 2.07±0.12 mmolH2Om-2s-1; p=0.0018; Figure 7). This resulted in

41

Figure 6: Differences in morphological traits in six T. cacao clones between two sites: La Montaña farm in Turrialba, Costa Rica

(light grey) and Cacaonica in Waslala, Nicaragua (dark grey). The traits measured are leaf area (chart A), leaf thickness (chart B), and

specific leaf area (SLA, chart C). The measurements shown are the means, with error bars for the standard error. Significant between-

site differences are marked by an asterisk (*). All measurements are based on n=90 (La Montaña) and n=82 (Cacaonica).

42

Figure 7: Differences in physiological traits in six T. cacao clones between two sites: La Montaña farm in Turrialba, Costa Rica (light

grey) and Cacaonica in Waslala, Nicaragua (dark grey). The traits measured are photosynthesis at saturating irradiance (Asat, chart A),

transpiration (E, chart B), and water use efficiency (WUE, chart C). The measurements shown are the means, with error bars for the

standard error. Significant between-site differences are marked by an asterisk (*). All measurements are based on n=90 (La Montaña)

and n=82 (Cacaonica).

43

significantly lower WUE for both CC-137 (La Montaña: 4.79±0.46 molH2O mmol-1CO2;

Cacaonica: 2.11±0.15 molH2O mmol-1CO2; p≤0.0001) and PMCT-58 (La Montaña: 5.94±0.30

molH2O mmol-1CO2; Cacaonica: 2.29±0.17 molH2O mmol-1CO2; p≤0.0001), though this

difference was not significant for CATIE-R4. Neither of the other two newer Costa Rican clones

nor ICS-95 had significant between-site differences for any of the physiological traits measured.

The analysis of variance showed highly significant (p≤0.0001) between-site differences for all

chemical traits across all clone. This was reflected in the between-site differences for individual

clones, which were significant for LN and C:N ratio for all clones except CATIE-R1 (Figure 8).

Additionally, significant between-site differences in LC was observed in ICS-95 (La Montaña:

452.9±2.1 mg g-1; Cacaonica: 438.9±2.1 mg g-1 p=0.0135) and PMCT-58 (La Montaña:

454.2±1.5 mg g-1; Cacaonica: 439.6±2.0 mg g-1 p=0.0078).

4.1.4 Covariance of leaf functional traits

Several significant bivariate relationships were observed in the overall dataset, as well as at the

site level (Table 8). There was significant covariance between morphological traits, with a

positive correlation between thickness and SLA (r=-0.32, p<0.0001) and a positive correlation

between leaf thickness and leaf area (r=0.15, p<0.0001; Figure 9). Leaf chemical traits,

specifically, LN and LC, also showed significant covariance, with a positive correlation between

the two (r=0.32, p<0.0001). LN was also significantly correlated with two physiological traits:

LN had a positive correlation with Asat (r=0.26, p<0.0001) and a positive correlation with WUE

(r=0.40, p<0.0001; Figure 10). Two of the physiological traits, Asat and E, covaried over the

entire dataset, with a positive correlation (r=0.19, p=0.0128), but the correlation was quite strong

and positive at Cacaonica (r=0.65, p<0.0001), and not significant at La Montaña (r=0.09,

p=0.4163; Figure 11).

4.1.5 Effect of soil and light-related variables

The stepwise regression yielded parsimonious model fits for each trait except for leaf area, for

which no combination of explanatory variables was found to explain its variance. The

parsimonious fit for the remaining traits was generally some combination of SN and SC, except

for leaf thickness, whose parsimonious model included CO and SC (though the relationship with

CO was not significant), and LC, whose parsimonious model included DST, along with SC and

SN.

44

Figure 8. Differences in chemical traits in six T. cacao clones between two sites: La Montaña farm in Turrialba, Costa Rica (light

grey) and Cacaonica in Waslala, Nicaragua (dark grey). The traits measured are leaf nitrogen content (LN, chart A), leaf carbon

content (LC, chart B), and the C:N ratio (chart C). The measurements shown are the means, with error bars for the standard error.

Significant between-site differences are marked by an asterisk (*). All measurements are based on n=90 (La Montaña) and n=82

(Cacaonica).

45

Table 8. Bivariate relationships using Pearson’s coefficient of correlation (r) for nine leaf

functional traits in T. cacao trees (n=172) (a) over the entire dataset, (b) at La Montaña and (c) at

Cacaonica. Significant bivariate relationships (p≤0.05) are indicated by grey shading.

Leaf N log-Leaf

C

log-C:N

ratio Thickness Area log-SLA log-Asat log-E log-WUE

a) Overall

Leaf N ---

log-Leaf C r = 0.32

p < 0.0001 ---

log-C:N ratio r = -0.98

p < 0.0001 r = -0.11

p = 0.1518 ---

Thickness r = 0.09

p = 0.2646

r = -0.01

p = 0.8572

r = -0.09

p = 0.2409 ---

Area r = -0.08

p = 0.3365

r = -0.15

p = 0.0533

r = 0.05

p = 0.5181

r = 0.16

p =0.0450 ---

log-SLA r = -0.04

p = 0.7890

r = 0.09

p = 0.2287

r = 0.04

p = 0.5902

r = -0.31

p < 0.0001

r = 0.41

p<0.0001 ---

log-Asat r = 0.26

p =0.0027

r = 0.11

p = 0.1638

r = -0.26

p =0.0009

r = -0.11

p = 0.1562

r = -0.14

p =0.0837

r = -0.15

p = 0.0499 ---

log-E r = -0.23

p = 0.0027

r = -0.03

p = 0.7058

r = 0.23

p = 0.0030

r = -0.12

p = 0.1392

r = -0.20

p = 0.0106

r = 0.09

p=0.2535

r = 0.19

p = 0.0128 ---

log-WUE r=0.40

p<0.0001 r=0.12

p=0.1363 r=-0.40

p<0.0001 r=-0.02

p=0.7784 r=0.02

p=0.7947 r=-0.19

p=0.0164 r=0.73

p<0.0001 r=-0.52

p=<0.0001

b) La Montaña

Leaf N ---

Leaf C r = -0.25

p = 0.0184 ---

C:N ratio r = -0.97

p < 0.0001 r = 0.49

p < 0.0001 ---

Thickness r = 0.13

p = 0.2397

r = -0.05

p = 0.6762

r = -0.12

p = 0.2518 ---

Area r = 0.21

p = 0.0481

r = -0.04

p = 0.7156

r = -0.20

p = 0.0668

r = 0.42

p < 0.0001 ---

SLA r = 0.10

p = 0.3506

r = 0.07

p = 0.5095

r = -0.07

p = 0.5470

r = -0.18

p 0.1010

r = 0.52

p<0.0001 ---

Asat r = 0.07

p = 0.5077 r = -0.08

p = 0.4771 r = -0.08

p = 0.4396 r = -0.21

p = 0.0506 r = 0.01

p = 0.9536 r = -0.13

p = 0.2220 ---

E r = -0.04

p = 0.7278

r = 0.22

p = 0.0436

r = 0.09

p = 0.3834

r = -0.20

p = 0.0681

r = 0.12

p = 0.2683

r = 0.19

p=0.0857

r = 0.09

p = 0.4163 ---

WUE r=0.09

p=0.4196 r = -0.21

p = 0.0534 r = -0.13

p = 0.2129 r = -0.05

p = 0.6404 r = 0.08

p = 0.4413 r = -0.22

p = 0.0426 r = 0.77

p < 0.0001 r = -0.56

p < 0.0001

c) Cacaonica

Leaf N ---

Leaf C r = 0.17

p = 0.1393 ---

C:N ratio r = -0.96

p < 0.0001 r = 0.12

p = 0.3143 ---

Thickness r = -0.04

p = 0.7348

r = -0.07

p = 0.5564

r = 0.02

p = 0.8985 ---

Area r = -0.11

p = 0.3338

r = -0.14

p = 0.2172

r = 0.07

p = 0.5661

r = -0.09

p = 0.4410 ---

SLA r = 0.24

p =0.0314

r = 0.36

p = 0.0014

r = -0.15

p = 0.2010

r = -0.47

p < 0.0001

r = 0.23

p=0.0428 ---

Asat r = 0.23

p = 0.0465

r = 0.08

p = 0.4629

r = -0.20

p = 0.0728

r = -0.04

p = 0.7144

r = -0.24

p = 0.0349

r = -0.09

p = 0.4162 ---

E r = 0.24

p = 0.0329

r = 0.16

p = 0.1720

r = -0.20

p = 0.0763

r = 0.03

p = 0.7838

r = -0.49

p <0.0001

r = -0.22

p=0.0480

r = 0.65

p < 0.0001 ---

WUE r = 0.08

p =0.4653

r = -0.05

p = 0.6755

r = -0.10

p = 0.3950

r = -0.11

p = 0.3361

r = 0.17

p = 0.1445

r = 0.11

p = 0.3271

r = 0.66

p < 0.0001

r = -0.12

p = 0.2947

46

Figure 9. Significant bivariate relationships (standardized major axis model) between

morphological leaf traits (n=172): specific leaf area (SLA, log-transformed) and leaf thickness

(left-hand plot) and leaf area and leaf thickness (right-hand plot), across both sites. Red dots

indicate leaves from Cacaonica, black dots indicate leaves from La Montaña. Both correlations

are significant at the p<0.0001 level.

47

Figure 10. Significant bivariate relationships (standardized major axis model) between leaf N

content and photosynthesis at saturating irradiance (Asat, log-transformed) (left-hand plot), and

between leaf N content and water use efficiency (WUE, log-transformed) (right-hand plot). Red

dots indicate leaves from Cacaonica, black dots indicate leaves from La Montaña. Both

correlations are significant at the p<0.0001 level.

48

Figure 11. Bivariate relationships (standardized major axis model) between photosynthesis at

saturating irradiance (Asat) and transpiration (E) at two sites: La Montaña farm in Turrialba,

Costa Rica, and Cacaonica in Waslala, Nicaragua. The two traits are strongly correlated

(p<0.0001) at the Cacaonica site (left-hand plot), and not significantly correlated (p>0.1) at La

Montaña (right-hand plot).

49

The variation in chemical traits was most extensively explained by environmental variables (LN:

r2=0.473; LC: r2=0.229, C:N ratio: r2=0.379). Both LN and LC were negatively correlated with

soil N (p≤0.01 in both cases) and positively correlated with soil C (p≤0.01 in both cases). LC

was also positively correlated with DST, though only at p<0.1. The C:N ratio was negatively

correlated with SC (p≤0.01) and positively correlated with SN (p≤0.01) (Table 9).

The extent to which these variables explained trait variation was lowest in morphological traits

(Area: N/A; Thickness: r2=0.018; SLA: r2=0.035). Neither leaf area nor thickness were

significantly correlated with any of the four environmental variables. SLA was correlated

negatively with soil carbon (p≤0.05) (Table 9).

Physiological traits showed intermediate r2 values (Asat: r2=0.053; E: r2=0.185; WUE: r2=0.278),

indicating some degree of influence by site-specific characteristics. SC had a significant effect on

all physiological traits: it was correlated positively with Asat (p≤0.01) and WUE (p≤0.01), but

negatively with E (p≤0.01). SN was significantly and positively correlated with E (p≤0.01) and

negatively correlated with WUE (p≤0.01). None of the physiological traits were correlated with

canopy openness or distance to shade tree (Table 9).

4.2 Discussion

4.2.1 How does site influence intraspecific functional trait variation in cacao?

A number of cacao growth studies track cacao leaf response to nutrient stimuli (Ahenkorah et al.,

1987; Isaac et al., 2010) or to climatic conditions (Schwendenmann et al., 2010; Araque et al.,

2012; Acheampong et al., 2015; Ávila-Lovera et al., 2015). The general trend for cacao under

conditions of water stress is for the stomata to close, thereby decreasing Asat and E and increasing

WUE during the dry season (Farquhar & Richards, 1984; Rada et al., 2005; Acheampong et al.,

2015). In this study, there was no significant decrease in Asat or E in the drier Cacaonica site, and

WUE decreased rather than increase as expected due to the drier conditions. This puzzling result

likely points to an interplay of site and cultivar (see section 4.2.3).

In contrast, chemical traits, namely LN, LC, and the C:N ratio, strongly differed between sites.

The variation in chemical leaf traits across climate and soil conditions remains poorly studied.

Single-site studies have shown that LN varies between the rainy and dry season, with higher N

content in the rainy season than the dry season (Santana & Igue, 1979), and this variation is

50

Table 9. Step-wise and multiple regression model analyses of the relationship between four environmental variables (light canopy

openness [CO], distance to shade tree [DST], soil C content [SC], and soil N content [SN]) and nine leaf traits in T. cacao trees

(n=172). AIC values were calculated to identify the most parsimonious model fit, the predictor variables of which are given in the

second column. The difference (ΔAIC) between the full model (all predictor variables) versus the best predictive model is also given.

For the predictive model chosen, the intercept and the individual slope values for each explanatory variable are shown. The p values

associated with each model term is indicated by the following symbols: NS: not significant, *: p<0.1, **p≤0.05, ***: p≤0.01

Trait Predictor variables Step-wise regression Multiple regression model terms

Model AIC ΔAIC Intercept CO DST SC SN Model r2

Area N/A 1078.6 -1.9 - - - - - -

Thickness CO+SC -1029.9 -3.7 0.278*** 0.001* - -0.0004NS - 0.018

SLA SC -495.3 -5.8 2.723*** - - -0.0055** - 0.035

Asat SC -247.8 -4.1 0.977*** - - 0.018*** - 0.053

E log(SN) + SC -331.5 -3.9 0.700*** - - -5.835*** 3.252*** 0.185

WUE log(SN) + SC -240.7 -2.6 0.391NS - - 0.048*** -0.739*** 0.278

LN log(SN) + SC 148.4 -2.1 20.47*** - - 0.293*** -6.813*** 0.473

LC DST + log(SN) + SC 567.5 -1.9 439.19*** - 1.360* 0.867*** -22.287*** 0.229

C:N log(SN) + SC -633.7 -2.8 3.100*** - - -0.011*** 0.253*** 0.379

51

linked to higher photosynthetic rates (Leibel, 2008; Ávila-Lovera et al., 2015). Thus, it is not

surprising that LN was higher at the site with higher dry-season rainfall (La Montaña) and lower

in Cacaonica, where rainfall was more limited over the study period. It is also noteworthy that

Asat tends to follow this same seasonal trend, and was significantly higher at La Montaña as well.

Several studies have shown that increasing fertilizer levels increases LN (Burridge et al., 1964;

Santana & Igue, 1979; Acheampong, et al., 2015), though these studies did not measure the

levels of SN to show the exact relationship between soil nutrient levels and leaf nutrient levels.

However, at a global level and across multiple species, LN is known to increase with increasing

SN (Ordoñez et al., 2009).

The present study found a relationship between SN and LN, but this relationship is negative

rather than positive – that is, LN actually decreases with increasing SN. This unexpected

relationship likely has to do with the fact that only total soil N was measured, rather than

mineralized, or plant-available N. The fact that LC was also negatively correlated with soil N

compounds this finding and demonstrates the need for a study the relationships between these

traits and plant-available N. Indeed, a similar study on total soil N and LN levels showed that

total SN was not a good predictor of LN (Wessel, 1971). A further possibility is that low SN

values were a result of the soil N supply having been taken up by cacao and stored in plant

tissue. Indeed, care was taken to sample during a period when the cacao plantation was not being

fertilized in order to eliminate variations due to recent fertilization. As a result, the soil may have

been depleted of nutrients at the time it was collected. LN was positively and significantly

correlated with SC content, showing that the posited relationship between leaf nutrients and soil

nutrients is likely present.

This finding supports the hypothesis that chemical traits are more closely linked to

environmental (site) factors. However, it is also possible that varieties were selected for high LN

content, as this is positively correlated with high Asat values – as such, the variation in LN may

be artificially lower among clones with lower Asat values.

Shade levels – as measured by canopy openness at 1m and distance to shade tree – had almost no

significant effect on any of the functional traits measured, despite changes in shade being an

important factor in cacao leaf nutrient levels (Burridge et al., 1964; Isaac et al., 2007), leaf

morphological traits, and leaf physiology (Miyaji et al., 1997). One potential reason for the lack

52

of a relationship between shade levels and morphological leaf traits is that the sampling design of

this study was specifically designed to minimize shading differences: almost all of the cacao

trees sampled were within 5m (or 6m at an absolute maximum) of a shade tree and the overall

shade tree density, though not precisely measured, was similar at both sites. Isaac et al. (2007)

observe that measurable effects of shade trees on biomass and leaf nutrient concentrations occur

within a threshold of 5m from the shade tree. It is also possible that different measurements of

incident light, such as light transmittance above the cacao canopy (per Isaac et al., 2007;

Schwendenmann et al., 2010), might have shown a greater effect of shade levels on these

morphological and physiological traits; in this paper, light levels were made at a height of 1m,

which includes the cacao canopy, and so cannot be directly compared to these findings.

The strength of certain bivariate relationships also changed from one site to another. The

relationships between LN/Asat has been well-documented in cacao (Daymond et al., 2011), as

well as between species (Wright et al., 2004). However, while this relationship was significant

over the entire dataset (r=0.26, p=0.0027) and at Cacaonica (r=0.23, p=0.0465), it was not

significant at La Montaña. This may indicate a closer coupling of these traits at the drier

Cacaonica site, or a higher variation in cultivar trait variation at La Montaña – indeed, higher

variation in all physiological traits was observed at the La Montaña site. This higher degree of

variability in physiological traits may also explain the weaker relationship between Asat and E at

La Montaña.

4.2.2 Do cacao genotypes have a specific functional trait profile?

In addition to arising from environmental conditions, as mentioned above, intraspecific trait

variation can also be caused by genetic factors. Genetic variation in physiological traits,

including Asat and WUE, has been well studied in the context of finding productive or drought-

resistant varieties (Medrano et al., 2015). In cacao, a number of studies have compared

physiological traits between clonal cultivars (Tezara et al., 2009; Daymond et al., 2011; Araque

et al., 2012; Ávila-Lovera et al., 2015; Acheampong et al., 2015) or between varieties such as

Criollo and Forastero (Tezara et al., 2016).

This study showed little evidence for cultivar-level effects on leaf chemical traits, though

differences were seen in physiological and morphological traits. However, physiological

53

differences were largely seen in changes within individual cultivars from one site to the other

(see Section 4.2.3).

Morphological traits varied significantly between cultivars. Additionally, within-site differences

in soil or light levels did little to affect morphological characteristics, despite light levels being

identified as a determinant of SLA and leaf thickness (Miyaji et al., 1997; Baligar et al., 2008).

Once again, this may be due to the generally low levels of variation in light levels in this study.

Indeed, the fact that cacao trees were chosen within 5m of a shade tree may help to explain the

variation between cultivars: Leibel (2008) suggests that under shaded conditions, genetic

differences in cacao leaf morphology may find greater expression than under unshaded

conditions, in which leaf thickness and SLA tend to be more homogenous between varieties.

This indicates that the variation in morphological traits observed in this study is likely due to

genetic differences between the cultivars rather than site characteristics.

4.2.3. Do different cacao genotypes respond differently to environmental conditions?

While several of the studies mentioned previously do compare intraspecific trait variation in

different cacao cultivars, the majority of these studies take place at a single site. This has the

potential to miss out on the variation in cultivar responses to environmental conditions,

especially given cacao’s sensitivity to changes in climatic conditions such as prolonged drought

or extreme temperatures (Zuidema et al., 2005; Leibel, 2008). Recent work in coffee has shown

the utility of investigating functional trait variation along climate and edaphic gradients in order

to explain large-scale patterns in variation (Martin et al., 2017) or to examine responses to

agroforestry management (Gagliardi et al., 2015). However, these studies employed plants of the

same variety and therefore did not examine the role of genetic expression.

These climate-dependent changes in physiological traits may underpin the higher photosynthetic

rates in the site with a less severe dry season, La Montaña. However, the finding that WUE

tended to be lower in the drier of the two sites, Cacaonica, seems to contradict the prevailing

literature on water use efficiency in cacao, which notes that cacao plants tend to undergo

stomatal closure in response to drought conditions, thereby decreasing E and increasing WUE

(Farquhar & Richards, 1984; Rada et al., 2005). However, studies that examine several cacao

cultivars have shown that this trend is not uniform, with some cacao cultivars having similar or

54

even lower WUE in the dry season (Tezara et al., 2009; Araque et al., 2012; Ávila-Lovera et al.,

2015).

Of the six clones, two showed significant differences in physiological traits between the two

sites, namely the older Costa Rican clones, CC-137 and PMCT-58. These had significantly

higher transpiration rates and significantly lower WUE at the more drought-prone site, which

may indicate a lack of suitability to drier climates. At the very least, it may indicate that their

high performance in La Montaña (Phillips-Mora et al., 2013) may not be replicated in the drier

Cacaonica site, since the rates of WUE for both clones, while significantly lower in Cacaonica

than at La Montaña, are not lower than any of the other clones Cacaonica. Meanwhile, the lack

of a significant difference in the newer Costa Rican clones (CATIE-R1, CATIE-R4, and CATIE-

R6) and the older, widely-used Trinidadian clone, ICS-95, indicates that these are less affected

by the change in climate and soil conditions.

This study has shown that the newer varieties, CATIE-R1, CATIE-R4, and CATIE-R6, as well

as the oldest regional favourite, ICS-95, showed no change in physiological or morphological

traits between the two sites. However, the older Costa Rican varieties, CC-137 and PMCT-95,

did show a decrease in Asat and WUE at Cacaonica, where rainfall is more limited. This finding is

preliminary and does not include rainy season measurements, but may indicate a change in the

fitness of these two clones in the drier site. It may also be instructive to examine intrinsic WUE,

calculated by the ratio of Asat to stomatal conductance, as differences in both intrinsic and

instantaneous WUE have previously been identified between cacao genotypes (Daymond et al.,

2011).

The three physiological traits measured showed the highest levels of variation of any traits: Asat

(CV=39.41), E (CV=33.08), and WUE (CV=51.63). These have been shown to fluctuate based

on microsite conditions such as canopy position and light levels (Miyaji et al., 1997), which may

explain their high degree of variation. The notably lower levels of variation in these traits at

Cacaonica (Asat: CV=33.68; E: CV=29.74; WUE: CV=28.17) may reflect a restriction in the

range of these traits, with fewer extreme values possible within the drier conditions at Cacaonica.

4.2.4 Implications for management and further research

The six clones included in this study were chosen for massive propagation in Central America

because of their high yield potential and resistance to diseases, especially frosty pod disease.

55

This disease is caused by the pathogen Moniliophthora roreri and is currently widespread

throughout northern South America and Central America (Phillips-Mora et al., 2007). Despite its

limited range, the pathogen has become the main constraint on cacao production in Central

America ever since it began appearing in the region in the 1970s (Phillips-Mora et al., 2006).

Currently, farmers depend on labour-intensive means to reduce losses to the disease such as

manually removing infected cacao pods (Phillips-Mora et al., 2006). As such, researchers believe

that disease-resistant varieties such as the six cultivars included in this study may be the best way

forward (Ploetz, 2016).

Given the economic importance of the six clones examined in this study, it is important to

understand how each clone performs under different environmental constraints. Currently, multi-

year data on yield and disease susceptibility is only available for these clones in Costa Rica

(Phillips-Mora et al., 2013), and the present study is the first to examine functional traits among

these cultivars in multiple sites. The differences in physiological and chemical traits between the

Costa Rican and Nicaraguan sites demonstrate that these clones may not perform equally in

different parts of the region; especially in regions with challenging climatic or edaphic

conditions.

56

Chapter 5: Local knowledge on intraspecific trait variation in cacao

5.1 Results

5.1.1 Participant and farm attributes

Participants ranged in age from 18-70 years (mean=44.4±14.8), and had been farming cacao for

between 2-60 years (mean=20.2±16.2). A majority (60%, n=27) had been cacao farmers all their

lives, while most of the remaining participants had begun by farming different crops (33%,

n=15), and a smaller number (7%, n=3) had non-farming careers before farming cacao. Most

participants (82%, n=37) belonged to some form of cooperative or local organization, though a

significant minority (18%, n=8) did not (Table 10).

Farms ranged in size from very small (0.5 ha) to quite large (17.5 ha), with an average size of 4.2

± 3.6 ha. A majority of farms used only seed-grown cacao (64%, n=29), while several (29%,

n=13) used a mix of seed-grown and grafted varieties, and a smaller number (7%, n=3) used only

grafted cacao. An average of 3.9 varieties of cacao were identified on farms, with the number of

varieties ranging between 1 and 9 (Table 10).

Most farms (62%, n=28) used only on-farm resources (banana stalks, cacao husks, and fallen

leaves) as fertilizer, while a smaller number (27%, n=12) used organic fertilizer made from

household or animal waste. Very few (11%, n=5) farms used inorganic fertilizer – almost all of

these were farms that had previously been large-scale plantain or banana farms where the use of

such fertilizer was common. All participants pruned their cacao trees, but several (33%, n=15)

did not prune or thin their shade trees (Table 10). The use of shade trees was ubiquitous among

participants, this being the norm in all of the study areas. Participants who did not prune their

shade trees generally indicated that this was because the trees were either too small or too large

to require pruning.

Both farmer and farm attributes varied across study sites. The average experience levels of

participants were over 10 years at all of the sites, with farmers at Hormiga Dudú having the least

experience (12.8±3.1 years) while farmers in Upala had the most experience (30.4±5.8 years).

Farms in Hormiga Dudú had the highest number of workers, though at all sites the workers

tended to be family members rather than paid employees. All sites had high (75-85%) rates of

membership in cooperatives or farmer organizations. Management techniques did differ more

57

Table 10. Attributes of interview participants, farms, and management practices, based on

interviews with 45 participants. Values given are either mean ± standard deviation or else the

percentage of participants who responded a certain way.

Attribute Value

Participant attributes

Age 44.4 ± 14.8 years (Range: 18.0-70.0)

Sex 62% male (n=28), 38% female (n=17)

Years spent farming cacao 20.2 ± 16.2 (Range: 2.0-60.0)

Membership in cooperative/local organization 82% yes (n=37), 18% no (n=8)

Activities before farming cacao 60% lifelong cacao farmers (n=27), 33%

other farming experience (n=15), 7% non-

farming background (n=3)

Number of workers 3.4 ± 1.0 (Range: 1.0-11.0)

Cacao farming aspects for which advice is

sought

3.0 ± 1.0 (Range: 0.0-5.0)

Farm attributes

Farm size 4.2 ± 3.6 ha (Range: 0.5-17.5)

Number of cacao varieties planted 3.9 ± 2.3 (Range: 1.0-9.0)

Management practices

Shade tree pruning 67% yes (n=30), 33% no (n=15)

Cacao tree pruning 100% yes (n=45)

Planting techniques 64% seed-planted only (n=29), 29% mix

of grafting and seed-planting (n=13), 7%

grafted only (n=3)

Fertilizer use 62% on-farm inputs only (n=28), 27% on-

farm and household organic waste (n=12),

11% inorganic fertilizer (n=5)

58

starkly: farmers in Talamanca used 7.3±0.4 varieties, more than twice the number used in any of

the other sites (Table 11) – this was due to the fact that several participants had received the six

clonal varieties from the CATIE breeding programs.

Fertilizer use was far higher at the Hormiga Dudú site (58% of participants) than at any of the

other sites (22-31%), though the types of fertilizers used differed from house to house – from

using stove ash to an organized system of household compost or animal manure. Interestingly,

Talamanca had the highest rates of inorganic fertilizer use, with 23% (n=3) of participants using

inorganic fertilizer (Table 11), because of the presence of banana farmers who transferred their

use of agricultural inputs from banana operations, where use of such fertilizer is common.

5.1.2 Leaf trait-effect relationships identified

Farmer reported variance, measured by the total dispersion of interpretations across the dataset,

and range, measured by the average interpretations for each leaf across the dataset and

represented as a single value calculated by the difference of the highest and lowest average

interpretation for a given trait-effect combination, is given in Table 12. Variance and range were

calculated for each of the twelve trait-effect interpretations. For each leaf spectrum shown to

participants, the variation in participants’ responses (i.e. they see differences in the leaves) varied

for each effect group (a leaf’s relationship to production, health and shade) (Table 12). Leaf

colour was identified to vary most strongly with plant health, with the colour-health relationship

showing the highest global variance (SD=0.82) and response range (1.16) compared to other

effect groups (productivity and shade level). Both leaf greenness and thickness were identified to

vary most strongly in relation to shade levels (greenness: SD=0.54, range=0.57; thickness:

SD=0.65, range=0.59). For leaf size, the greatest variance (SD=0.69) and range (0.86) was

observed for participant interpretation of plant production potential.

Each of the trait-effect combinations with high levels of variation and range showed a degree of

directionality from one end of the spectrum to the other. The colour-health relationship, for

example, showed that the ‘greenness’ spectrum leaves were seen to be healthy overall (average

interpretations of 0.39-0.61). This is contrary to expectations, since yellowy-green leaves can be

an indicator of N deficiency (Wood & Lass, 1985). Yellow or brown discolouration, which is

generally a sign of nutrient deficiency or sun damage (Wood & Lass, 1985; Chepote et al.,

59

Table 11. Farmer and farm attributes at the different study sites, based on interviews with 45 participants. Values shown are averages

and standard errors, with the exception of membership in cooperatives/farmer groups, for which the percentage of participants

answering ‘yes’ is given, fertilizer use, which reflects the percentage of participants who use any form of organic or non-organic

fertilizer from outside the farm, and use of grafting is the percentage of participants who use any amount of grafted cacao on their

farms.

Farmer and farm attributes

Site Years farming

cacao No. workers Membership No. varieties Fertilizer use

Use of

grafting

Sources of

advice

Santa Rosa 20.8±4.9 2.8±0.5 75% 2.0±0.2 25% 0% 2.5±0.5

Hormiga Dudú 12.8±3.1 4.2±0.8 83% 2.8±0.2 58% 0% 3.1±0.2

Upala 30.4±5.8 2.9±0.4 78% 2.8±0.1 22% 44% 2.9±0.4

Talamanca 19.9±5.5 3.2±0.5 85% 7.3±0.4 31% 85% 3.1±0.2

Overall 20.2±2.4 3.4±0.3 82% 3.9±0.4 38% 36% 3.0±0.1

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Table 12. Standard deviation, as a measure of dispersion across the whole dataset, and range (in

parentheses) of participant (n=45) trait-effect interpretations for three leaf spectra (size, colour,

and thickness) and one subset of the colour spectrum for leaves without discolouration but whose

colour ranged from light to dark green. The number in bold is the trait-effect combination for

which the highest variance in interpretations was observed.

Leaf trait spectrum

Effect Size Greenness Discolouration Thickness

Production 0.69

(0.86)

0.41

(0.27)

0.59

(0.66)

0.46

(0.45)

Health 0.47

(0.32)

0.42

(0.23)

0.82

(1.16)

0.47

(0.50)

Shade 0.60

(0.50)

0.54

(0.57) 0.68

(0.57)

0.65

(0.59)

61

2005), was identified as an indicator of an unhealthy tree (AI -0.55 to -0.36). Opinions were

divided on the seventh leaf, which had white spots (AI 0.02) (Figure 12).

Both the greenness (Figure 13) and thickness (Figure 14) spectra were linked to high variation in

shade levels. A clear direction was present in both cases, with thinner or lighter green leaves

being interpreted as coming from low shade conditions (light green AI: -0.27, thinnest AI: -0.23).

Thicker or dark green leaves were interpreted as coming from high shade conditions (dark

green=0.30, thickest=0.36). Intermediate thickness and greenness values fell between the two

extremes in terms of how much shade participants believed the tree to be under.

Leaf size and plant productivity were seen to be positively related (Figure 15), with smaller

leaves indicative of a tree that produces less cacao. This relationship stayed constant until the 4th-

largest leaf, representing the 75th percentile of leaf size, which was seen as being most likely to

come from a high-producing tree (AI: 0.61). The largest leaf showed a decrease from this

maximum value (AI: 0.34), with some participants indicating that such a leaf might come from a

tree that has very dense foliage and has no energy to dedicate to production.

5.1.3 Interpretation of flowering traits

Location of flowers was the highest-ranked trait in terms of impact on plant performance, with

an average rank of 1.49 ± 0.72. Participants were divided on the optimal location of flowers, with

32% (n=14) responding that it was better for flowers to be located on the trunk. Twenty-five

percent (n=11) indicated that it was preferable for flowers to be well-distributed throughout the

tree (Table 13).

Number of flowers was the second-ranked of the three flowering traits that respondents were

asked about, with an average rank of 1.84 ± 0.72. Participants were split on what the optimal

number of flowers was: 44% (n=19) said that more flowers were better, while 25% (n=11) said

that a smaller number was better and the remaining participants (32%, n=14) said that the

number of flowers was unimportant (Table 13). Many of those who favoured a smaller number

indicated that trees with a large number of flowers often had comparatively low rates of

pollination.

Flower size was the lowest-ranked flowering trait, with an average rank of 2.58 ± 0.64. The

majority of respondents (75%, n=33) indicated that size made no difference to cacao production

62

Figure 12. Average interpretations of leaves of different colours by interview participants

(n=45). Participants identified leaves as coming from a tree with poor health (-1), good health

(1), or intermediate health/unsure (0). The value given for each leaf is the average interpretation

along with the standard error.

63

Figure 13. Average interpretations of leaves along the ‘greenness’ spectrum by interview

participants (n=45). Participants identified leaves as coming from a tree under low-shade

conditions (-1), high-shade conditions (1), or intermediate shade/unsure (0). The value given for

each leaf is the average interpretation along with the standard error.

64

Figure 14. Average interpretations of leaves along the thickness spectrum by interview

participants (n=45). Participants identified leaves as coming from a tree under low-shade

conditions (-1), high-shade conditions (1), or intermediate shade/unsure (0). The value given for

each leaf is the average interpretation along with the standard error.

65

Figure 15. Average interpretations of leaves along the size spectrum by interview participants

(n=45). Participants identified leaves as coming from a low-producing tree (-1), high producing

tree (1), or intermediate production/unsure (0). The value given for each leaf is the average

interpretation along with the standard error.

66

Table 11. Ranking of leaf, flower, and whole-plant traits in terms of their importance for cacao

performance and on-farm management decisions. The average rank is calculated by assigning a

score of 1 for the most important and 3 for the least important trait. Values are based on

interviews with n=45 participants. For both flowering traits and whole-plant traits, participants

were also asked what their preference for each trait was; their responses are given in the right-

hand column.

Trait Average rank

(± SD)

Preferences

a) Leaf traits

Leaf colour 1.14 ± 0.35

Leaf size 2.00 ± 0.54

Leaf thickness 2.86 ± 0.35

b) Flower traits

Flower location 1.49 ± 0.72

Flowers on trunk: 32% (n=14)

Flowers all over: 43% (n=19)

Location unimportant: 25% (n=11)

Flower number 1.84 ± 0.72

More better: 43% (n=19)

Intermediate number better: 25% (n=11)

Number unimportant: 32% (n=14)

Flower size 2.58 ± 0.64

Larger better: 18% (n=8)

Smaller better: 7% (n=3)

Size unimportant: 75% (n=33)

c) Whole-plant traits

Height 1.45 ± 0.71

Taller better: 5% (n=2)

Medium better: 48% (n=21)

Shorter better: 30% (n=13)

No difference: 18% (n=8)

Canopy size 2.13 ± 0.66

Larger better: 20% (n=9)

Medium better: 39% (n=17)

Smaller better: 16% (n=7)

No difference: 25% (n=11)

Trunk diameter 2.35 ± 0.82

Larger better: 41% (n=18)

Medium better: 25% (n=11)

Smaller better: 5% (n=2)

No difference: 30% (n=13)

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(Table 13). Several respondents indicated that they had not noticed whether flower size varied

from one tree to another, and those who had noticed this said that this was generally due to the

cacao variety rather than any difference between trees in the same variety.

5.1.4 Interpretation of whole-plant traits

Plant height was the highest-ranked whole-plant trait (average rank=1.45 ± 0.71). Participants

generally preferred smaller or medium-sized plant, indicating a preference for plants that were

easier to work (Table 13). Taller plants were sometimes referred to as being stronger or more

vigorous, but they were difficult to harvest.

Canopy size was the second-ranked whole-plant trait (average rank=2.13 ± 0.66), with an overall

preference for medium-sized plants (Table 13). Participants had varying explanations for their

preference: those who favoured smaller canopies referred to ease of harvesting and avoiding

broken branches, while those who favoured more open, extensive canopies talked about the

penetration of light and air into the lower layers of the canopy and the increased number and size

of branches on which cacao pods could grow. Trunk diameter was the lowest-ranked whole plant

trait, though its average rank was quite close to that of the second-ranked canopy size (average

rank=2.35 ± 0.82). Participants preferred medium-sized or thicker trunks (Table 13). The general

consensus was that a strong trunk indicated a vigorous plant and would positively impact

production by providing an area for pods to grow. On the other hand, several participants related

thick trunks to older, unproductive cacao trees.

5.1.5 Patterns in consensus on leaf trait interpretations

The average level of consensus across all leaves and sites was 2.34±0.03 on a 3-point scale, with

3 being the closest to the consensus view and 1 representing a contrarian view. The two Costa

Rican sites, Upala and Talamanca, had consensus levels that were higher than the average

(2.46±0.06 and 2.50±0.04, respectively), while the two Nicaraguan sites, Santa Rosa and

Hormiga Dudú, had lower-than-average consensus levels (2.30±0.03 and 2.12±0.03,

respectively) (Table 13). This pattern was repeated for each of the individual trait spectra as well,

with higher levels of consensus at the Costa Rican sites than at the Nicaraguan sites. Consensus

levels also differed by the trait being discussed. Leaf thickness had the highest consensus

(2.41±0.05), followed by leaf size (2.34±0.04) and leaf colour (2.29±0.04) (Table 13).

68

Table 12. Selected farm and farmer attributes for each of the study sites in which interviews

were carried out, along with consensus scores for each trait spectrum, as well as for the overall

group of leaves that participants were shown. Consensus level calculations were based on a score

of 1-3 for each participant’s answer, with 3 indicating agreement with the most common

interpretation and 1 indicating agreement with the least common interpretation.

Consensus level

Site Leaf

colour

Leaf

size

Leaf

thickness All leaves

Santa Rosa 2.17±0.08 2.43±0.05 2.24±0.05 2.30±0.04

Hormiga

Dudú 2.12±0.07 1.97±0.07 2.35±0.07 2.12±0.03

Upala 2.29±0.09 2.50±0.04 2.56±0.10 2.46±0.06

Talamanca 2.48±0.05 2.47±0.04 2.56±0.08 2.50±0.04

All sites 2.29±0.04 2.34±0.04 2.41±0.05 2.34±0.03

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5.2 Discussion

5.2.1 Do farmers take functional traits into account when making management decisions?

The clearest evidence for farmer preferences in terms of leaf traits comes from the literature on

participatory plant breeding, where farmers often indicate a preference for leaves of a certain

colour, shape, or phenology (Gibson et al., 2008; Mwanga et al., 2011). This shows that farmers

do notice functional traits that are not directly connected to yield, though most studies do not

indicate why these preferences exist, their basis in overall plant function, or how they connect to

management decisions.

Conversely, studies on shade tree selection show that farmers have an overall preference in terms

of growth rate, canopy density, rooting characteristics, and leaf texture and size (Cerdán et al.,

2012; Valencia et al., 2015). Cacao farmers in West Africa are aware of the links between shade

tree species and ecosystem processes such as litterfall and organic matter accumulation (Isaac et

al., 2009).

In my study, I show that participants had a variety of interpretations for functional trait variation,

as evidenced by the high range and total standard deviation in their responses. When participants

were shown the leaves, many made comments regarding the variety that the leaf came from, the

quality of the trees that the leaves came from, or similarities to leaves on their own farm. The

differing amounts of variation in responses for each trait-effect combination also indicates that

participants noticed links between variation in certain functional traits and effects on the crop.

The interviews also provided some evidence that participants notice certain leaf traits when

making management decisions, especially regarding shade and nutrient management. When

participants were asked about their pruning techniques, many indicated that their decision to

prune their trees was based on an observation of the plant’s canopy. This included observations

that over-shaded cacao had a very dense canopy (copa muy frondosa) and darker, larger leaves.

Other cues included a drop in production and an increase in diseases affecting cacao pods, such

as frosty pod disease. Shade management is widely reported as important for mature cacao

farms, as over-shading can limit production and lead to higher levels of losses to disease (Wood

& Lass, 1985). Although only 38% of farmers (n=17) used fertilizer, several participants who did

use fertilizer noted that cacao leaves tended to be yellowy and the canopy became sparse due to

dieback if not enough fertilizer was used.

70

A large majority of participants (68-82%) indicated some preference for whole-plant and

flowering traits. The only exception was flower size, for which 75% of participants indicated that

they have no preference for a specific flower size. The justifications for preferences were also

quite similar – participants who talked about flower number often referred to the fact that a large

number of flowers was necessary for high production, but that trees with excessive numbers of

flowers often ended up forming very few fruits. Flower location was usually explained in terms

of either maximizing yield by having flowers well-distributed on the branches and trunk, or in

terms of having flowers grow on the trunk and major branches so that fruit would grow stronger

(tener más fuerza).

Preferences for whole-plant traits were generally expressed in terms of ease of access, with a

general preference for low or medium trees with a medium-sized canopy to facilitate pruning and

harvesting. Here, too, participants phrased their preferences in terms of a certain narrative: for

example, a smaller, more compact canopy was seen as desirable because it allowed for ease of

access, yet a wider canopy was seen as more ‘open’, allowing light and air to penetrate beneath

the canopy. This was seen as good for overall plant health, and especially for the prevention of

disease. Participants’ preferences for one canopy size or another was generally framed as a

compromise between these two priorities.

These findings support the idea that farmers’ interpretations of functional traits can be seen as an

‘indicator’ of management practices. Farmers tended to frame their interpretation of leaf trait

spectra in terms of management effects. This was evident in farmers’ interpretation of variation

in certain functional traits and its relationship to effects at the plant level – for example, relating

leaf size to production or leaf colour to health. Especially in the case of shade levels,

participants’ interpretation of leaf and canopy traits showed their conception of a link between

plant traits – leaf colour, thickness, and canopy density – and the need to either prune the cacao

trees or to reduce the amount of shade by pruning the shade trees. With regards to nutrient levels,

it was primarily farmers who used some form of fertilizer who associated leaf discolouration

with nutrient deficiencies. Participants also formed hypotheses about the links between plant

traits and phenomena such as yield and disease – for example, positing a link between leaf size

and cacao yield, or linking leaf discolouration or an overly dense canopy to higher incidence of

71

disease. These findings indicate that farmers’ management practices inform, and are informed

by, their understanding of intraspecific trait variation in their crop plants.

5.2.2 Do farmers view leaf functional traits along a spectrum?

Studies on intraspecific trait variation have catalogued the functional trait response to differing

environmental characteristics or, in the case of agricultural studies, to management practices

(Gagliardi et al., 2015; Niinemets, 2015; Martin et al., 2017). Other studies have linked variation

in certain traits to plant-level phenomena such as yield (Gagliardi et al., 2015), plant health, or

nutrient levels (Chepote et al., 2005). In these studies, the variation of a trait along an effect

gradient is measured in the amount and direction of change in that trait. In this study,

participants’ perception of variation is used analogously to determine whether participants

believe there to be a link between leaf traits and plant-level effects such as health, production, or

shade level. Thus, in this study, the trait-effect combinations with the highest total variance and

range were chosen for further analysis. Specifically, for each of the four trait spectra, the effect

that was linked to the widest variation in participant interpretations was investigated further.

Certain trait-effect combinations show high variation and a clear direction in participant

responses. Both the greenness-shade and the thickness-shade combinations show that

participants have a clear idea of certain leaf traits increasing in magnitude under shady

conditions. Here, participant interpretations tended to view darker-green, thicker leaves as

coming from trees under heavier shade, and lighter-green, thinner leaves as coming from trees

under lower shade.

The relationship between leaf greenness and shade is supported by the literature: leaf greenness

is often used as a correlate of chlorophyll concentration in leaves (Netto et al., 2005), meaning

that leaves that are deeper green in colour generally have higher chlorophyll concentrations than

lighter-green leaves of the same species. In cacao, leaf chlorophyll concentration is negatively

correlated with light levels, meaning that higher light levels are associated with lower

chlorophyll and less green leaves (Daymond & Hadley, 2004). Indeed, pale green leaves are a

common symptom of sun damage in cacao, along with die-back in the upper canopy (Wood &

Lass, 1985).

In this study, participants’ views on leaf thickness, however, were counter to expectations. Leaf

thickness in cacao is closely correlated with specific leaf area and is positively correlated with

72

irradiance (Miyaji et al., 1997; Baligar et al., 2008), meaning that leaves with higher shade would

tend to be thinner. A possible confounding factor is the fact that leaf thickness also tends to

increase with age (Wessel, 1971; Santana & Igue, 1979), and leaves that are older tend to be

located further down a branch and subject to more self-shading. Thus, participants may have

seen the thicker leaves as being similar to older, more self-shaded leaves, and interpreted them as

being related to high shade levels.

Participants indicated a positive relationship between leaf size and overall cacao production, with

a drop-off at the largest leaf, which was seen as reflecting a tree that was overgrown with foliage

and would not produce a great deal. Some participants also indicated that this overgrowth with

foliage may be related to high levels of shade, indicating the need to prune the cacao tree to

‘open up’ the canopy or to prune shade trees to allow more light and air to enter. Interestingly, a

similar belief has been observed in coffee farmers, with a preference for larger leaves to a

threshold and then preference declining for very large leaves (Isaac et al., 2017 [in review]).

These farmers indicated that coffee plants with such very large leaves would require excessive

fertilizer to maintain production (Isaac et al., 2017 [in review]) – indicating that farmers may be

identifying trade-offs between foliage and fruit production.

Leaf colour, on the other hand, followed a more dichotomous interpretation: the leaf-colour

combination did not show a progression from a positive to a negative effect. Instead, participants

categorized leaves that were light to dark green, but without discolouration, as being reflective of

a healthy tree, while discoloured leaves were generally seen as coming from an unhealthy tree. It

is interesting that the ‘greenness’ levels were not associated with plant health, since pale green

leaves are known to be a symptom of nitrogen deficiency in cacao (Wood & Lass, 1985). This

may be due to N deficiency being less of a problem on the farms surveyed or a lack of awareness

of the symptoms of nutrient deficiency. This latter interpretation is supported by the fact that

only slightly more than one in three of the farmers surveyed use any kind of off-farm fertilizers

on their cacao crops.

5.2.3 How is local knowledge on functional traits distributed?

It is possible that the higher levels of consensus in the Costa Rican sites are due to the influence

of agricultural extension programs in the area, such as the PCC and CATIE projects in both

Upala and Talamanca, as well as active co-operatives in both areas. This could explain the

73

disparity with the Santa Rosa site, which had the lowest rate of membership in cooperatives or

farmer organizations (though still high, at 75%), and the lowest number of sources of advice,

averaging 2.5±0.5. However, this was not the case at Hormiga Dudú, which had the second-

highest rate of membership in cooperatives (83%), including the active involvement of the

Cacaonica cooperative, and a similar number of average sources of advice (3.1±0.2), yet which

had the lowest level of consensus (2.12±0.03 for all leaves).

The higher levels of consensus around leaf thickness and the lower levels of consensus around

leaf size and leaf colour are interesting given the relatively lower importance given to leaf

thickness by participants. This may be due to the fact that there was little variation in participant

responses regarding the relationship between plant health or production and leaf thickness. The

low preference score further shows that leaf thickness is considered relatively unimportant for

overall plant performance. Leaf colour, on the other hand, was related to both shade levels (in

terms of ‘greenness’ levels) and health (in terms of discolouration), and participants identified it

as the most important leaf trait. Leaf size was related to productivity and identified as the

second-most important. This shows that there is lower consensus regarding leaf traits that are

seen as more important for plant performance.

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Chapter 6: Conclusion

6.1 Conclusions

My thesis consists of two studies, whose goals are (i) to measure intraspecific trait variation in

cacao in six Central American cacao cultivars at two sites, and the extent to which this variation

is due to environmental (site) factors, genetic (cultivar) factors, or the interplay of the two, and

(ii) to understand how cacao farmers take intraspecific trait variation into account when making

management decisions.

As I had hypothesized, chemical traits (leaf C, leaf N, and C:N ratio) varied significantly at the

site level and did not vary between cultivars. An analysis of the within-site underpinnings

showed that soil C and N content explained much of the variation in LN, LC, and C:N ratio. In

addition, leaf N was lower at the drier site (Cacaonica) than at the wetter site (La Montaña),

which may reflect increased N partitioning to leaves at La Montaña due to lower irradiance and

vapour pressure deficits (Evans, 1989; Almeida & Valle, 2007). The fact that little variation was

measured across cultivars could be due to a selection pressure on the high-yielding clonal

varieties, where selecting for yield implies a generally high level of leaf N, which is linked to

photosynthetic rates.

Morphological traits, contrary to expectations, did not vary significantly between sites despite

environmental factors, especially light levels, being the main driver of variance in these traits. It

is possible that this is due to the relatively homogenous management practices at the two sites,

which both use similar shade levels. When irradiance is kept beneath a certain threshold, its

effect on leaf morphology is lessened and differences due to cacao genetics may become more

apparent (Baligar et al., 2008; Leibel, 2008), as seems to be the case in this study.

Physiological traits were the most responsive to the interplay between environment and genetic

factors. As hypothesized, there was significant variation between cultivars in Asat, E, and WUE.

The variation in these traits between sites was also significant. Most interesting, however, was

the significant increase in E and drop in WUE in the two older Costa Rican clones, CC-137 and

PMCT-95, between La Montaña (wetter) and Cacaonica (drier). Only these clones showed a

significant change in E and WUE between sites, and in both cases, it went against expectations

for a more water-limited site, where the expectation would be for stomatal closure to result in

75

higher WUE and lower E. This seems to point to either a convergence in physiological traits at

the Cacaonica site, or else a reduction in fitness in the two older Costa Rican clones.

Given this wide range and variation in cacao leaf responses to cultivar selection, site conditions

and the interaction of these two principal factors, it is not surprising that cacao farmers do take

intraspecific trait variation into account when making management decisions, as hypothesized.

Farmers’ interpretation of functional trait variation can be seen as an ‘indicator’ of management

practices or awareness of the causes of certain phenomena: in certain cases, farmers identify

links between a functional trait spectrum (in this study, size, colour, ‘greenness’ as a subset of

colour, and thickness) and a management-relevant effect (namely plant health, production, and

shade level). Some of the relationships between leaf response and effects such as shade that were

identified by participants are consistent with ecological research, specifically between leaf colour

and plant health (Wood & Lass, 1985), and between leaf greenness and shade levels (Daymond

& Hadley, 2004). Participants also identified a link between leaf size and cacao production,

which has not been confirmed by research but seems to be similar to views held by coffee

farmers (Isaac et al., 2017 [in review]). A link between leaf thickness and shade levels runs

counter to the literature (Miyaji et al., 1997), but this may be due to participants forming an

association between thicker leaves and older, self-shaded leaves.

6.2 Areas of future research

While there is widespread consensus on the need to research the effect of environmental

conditions and new genetic varieties on cacao production, current research in this area tends to

be narrowly focused on a small number of traits and be limited to a single site. This project,

which is an effort to examine the contrasting variance in a suite of traits among several cacao

cultivars, has been made possible because of the network of clonal gardens established through

Proyecto Cacao Centroamérica. This network now consists of dozens of farms of the same age,

similar management techniques, and identical clones across six countries of Central America

(Somarriba et al., 2013b). It would be interesting to apply these methods on a wider geographic

scale and over a longer time period – at least encompassing a set of rainy- and dry-season

measurements to show seasonal variation.

This study provides one of the first explorations of the link between farmers’ knowledge

regarding plant functional traits and management-related decisions on the farm. Certain linkages

76

were found between trait variation and its effect on plant health, yield, and shade level, among

other factors. This demonstrates the need for further work in understanding the ways in which

farmers’ interaction with their crop plants informs their decisions regarding management

practices. Using farmer interpretations of trait variation as an indicator of management practices

will allow a more thorough understanding of farmers’ priorities and beliefs regarding their crops.

Furthermore, it remains important to understand the factors that underpin the opinions that

farmers have regarding their crops. In particular, social network analysis could be a promising

avenue for future research because of its ability to track the social relationships and networks

that underpin how people learn about different management techniques. While this study did

identify contributing factors, a more in-depth analysis may identify the dynamics by which

knowledge is created and shared around cacao management.

77

References

Abramoff, M. D., Magalhães, P. J., & Ram, S. J. (2004). Image processing with ImageJ [Article].

Retrieved April 28, 2017, from http://dspace.library.uu.nl/handle/1874/204900

Acheampong, K., Hadley, P., & Daymond, A. J. (2012). Photosynthetic activity and early growth

of four cacao genotypes as influenced by different shade regimes under West African dry

and wet season conditions. Experimental Agriculture, 49(1), 31–42.

https://doi.org/10.1017/S0014479712001007

Acheampong, K., Hadley, P., Daymond, A. J., & Adu-Yeboah, P. (2015). The influence of shade

and organic fertilizer treatments on the physiology and establishment of Theobroma

cacao clones. American Journal of Experimental Agriculture, 6(6), 347–360.

Ahenkorah, Y. (1975). Use of radio-active phosphorus in determining the efficiency of fertilizer

utilization by cacao plantation. Plant and Soil, 42(2), 429–439.

https://doi.org/10.1007/BF00010018

Ahenkorah, Y., Halm, B. J., Appiah, M. R., Akrofi, G. S., & Yirenkyi, J. E. K. (1987). Twenty

years’ results from a shade and fertilizer trial on Amazon cocoa (Theobroma cacao) in

Ghana. Experimental Agriculture, 23(1), 31–39.

https://doi.org/10.1017/S0014479700001101

Almeida, A.-A. F. de, & Valle, R. R. (2007). Ecophysiology of the cacao tree. Brazilian Journal

of Plant Physiology, 19(4), 425–448. https://doi.org/10.1590/S1677-04202007000400011

Alvim, P. de T., & Kozlowski, T. T. (1977). Ecophysiology of Tropical Crops. Academic Press.

Araque, O., Jaimez, R. E., Tezara, W., Coronel, I., Urich, R., & Espinoza, W. (2012).

Comparative photosynthesis, water relations, growth and survival rates in juvenile Criollo

cacao cultivars (Theobroma cacao) during dry and wet seasons. Experimental

Agriculture, 48(4), 513–522. https://doi.org/10.1017/S0014479712000427

Arciniegas Leal, A. M. (2005). Caracterización de árboles superiores de cacao (Theobroma

cacao L.) seleccionados por el programa de mejoramiento genético del CATIE (M. Sc.).

CATIE, Turrialba, Costa Rica.

Ávila-Lovera, E., Coronel, I., Jaimez, R., Urich, R., Pereyra, G., Araque, O., … Tezara, W.

(2015). Ecophysiological traits of adult trees of Criollo cocoa cultivars (Theobroma cacao

L.) from a germplasm bank in Venezuela. Experimental Agriculture, 52(1), 137–153.

https://doi.org/10.1017/S0014479714000593

Bagnoli, A. (2009). Beyond the standard interview: the use of graphic elicitation and arts-based

methods. Qualitative Research, 9(5), 547–570.

https://doi.org/10.1177/1468794109343625

Balasimha, D., Daniel, E. V., & Bhat, P. G. (1991). Influence of environmental factors on

photosynthesis of cocoa trees. Agricultural and Forest Meteorology, 55, 15-21.

78

Baligar, V. C., Bunce, J. A., Machado, R. C. R., & Elson, M. K. (2008). Photosynthetic photon

flux density, carbon dioxide concentration, and vapor pressure deficit effects on

photosynthesis in cacao seedlings. Photosynthetica, 46(2), 216–221.

https://doi.org/10.1007/s11099-008-0035-7

Beer, J., Muschler, R., Kass, D., & Somarriba, E. (1998). Shade management in coffee and cacao

plantations. In P. K. R. Nair & C. R. Latt (Eds.), Directions in Tropical Agroforestry

Research (pp. 139–164). Springer Netherlands. Retrieved from

http://link.springer.com/chapter/10.1007/978-94-015-9008-2_6

Bolnick, D. I., Amarasekare, P., Araújo, M. S., Bürger, R., Levine, J. M., Novak, M., …

Vasseur, D. A. (2011). Why intraspecific trait variation matters in community ecology.

Trends in Ecology & Evolution, 26(4), 183–192.

https://doi.org/10.1016/j.tree.2011.01.009

Burridge, J. C., Lockard, R. G., & Acquaye, D. K. (1964). The levels of nitrogen, phosphorus,

potassium, calcium and magnesium in the leaves of cacao (Theobroma cacao L.) as

affected by shade, fertilizer, irrigation, and season. Annals of Botany, 28(3), 401–418.

Carr, M. K. V., & Lockwood, G. (2011). The water relations and irrigation requirements of

cocoa (Theobroma cacao L.): a review. Experimental Agriculture, 47(4), 653–676.

https://doi.org/10.1017/S0014479711000421

Carter, G. A., & Knapp, A. K. (2001). Leaf optical properties in higher plants: linking spectral

characteristics to stress and chlorophyll concentration. American Journal of Botany,

88(4), 677–684.

Cerdán, C. R., Rebolledo, M. C., Soto, G., Rapidel, B., & Sinclair, F. L. (2012). Local

knowledge of impacts of tree cover on ecosystem services in smallholder coffee

production systems. Agricultural Systems, 110, 119–130.

https://doi.org/10.1016/j.agsy.2012.03.014

Chepote, R. E., Sodré, G. A., Reis, E. L., Pacheco, R. G., Marrocos, P. C. L., & Valle, R. R.

(2005). Recomendações de corretivos e fertilizantes na cultura do cacaueiro no sul da

Bahia.

Clough, Y., Faust, H., & Tscharntke, T. (2009). Cacao boom and bust: sustainability of

agroforests and opportunities for biodiversity conservation. Conservation Letters, 2(5),

197–205. https://doi.org/10.1111/j.1755-263X.2009.00072.x

Danial, D., Parlevliet, J., Almekinders, C., & Thiele, G. (2007). Farmers’ participation and

breeding for durable disease resistance in the Andean region. Euphytica, 153(3), 385–

396. https://doi.org/10.1007/s10681-006-9165-9

Dawoe, E. K., Isaac, M. E., & Quashie-Sam, J. (2009). Litterfall and litter nutrient dynamics

under cocoa ecosystems in lowland humid Ghana. Plant and Soil, 330(1–2), 55–64.

https://doi.org/10.1007/s11104-009-0173-0

79

Daymond, A. J. (2002). Canopy characteristics of contrasting clones of cacao (Theobroma

cacao). Experimental Agriculture, 38(3), 359–367.

Daymond, A. J., & Hadley, P. (2004). The effects of temperature and light integral on early

vegetative growth and chlorophyll fluorescence of four contrasting genotypes of cacao

(Theobroma cacao). Annals of Applied Biology, 145(3), 257–262.

https://doi.org/10.1111/j.1744-7348.2004.tb00381.x

Daymond, A. J., & Hadley, P. (2008). Differential effects of temperature on fruit development

and bean quality of contrasting genotypes of cacao (Theobroma cacao). Annals of

Applied Biology, 153(2), 175–185. https://doi.org/10.1111/j.1744-7348.2008.00246.x

Daymond, A. J., Hadley, P., Machado, R. C. R., & Ng, E. (2002). Genetic variability in

partitioning to the yield component of cacao (Theobroma cacao L.). HortScience, 37(5),

799–801.

Daymond, A. J., Tricker, P. J., & Hadley, P. (2011). Genotypic variation in photosynthesis in

cacao is correlated with stomatal conductance and leaf nitrogen. Biologia Plantarum,

55(1), 99–104. https://doi.org/10.1007/s10535-011-0013-y

De Almeida, J., Tezara, W., & Herrera, A. (2016). Physiological responses to drought and

experimental water deficit and waterlogging of four clones of cacao (Theobroma cacao

L.) selected for cultivation in Venezuela. Agricultural Water Management, 171, 80–88.

https://doi.org/10.1016/j.agwat.2016.03.012

Delignette-Muller, M. L., & Dutang, C. (2015). fitdistrplus: An R Package for Fitting

Distributions. Journal of Statistical Software, 64, 1–34.

Dıaz, S., & Cabido, M. (2001). Vive la différence: plant functional diversity matters to

ecosystem processes. Trends in Ecology & Evolution, 16(11), 646–655.

https://doi.org/10.1016/S0169-5347(01)02283-2

Engels, J. M. M. (1981). Genetic Resources of Cacao: a catalogue of the CATIE collection.

Technical bulletin/CATIE, 7.

Engels, J. M. M. (1983). A systematic description of cacao clones. III. Relationships between

clones, between characteristics and some consequences for the cacao breeding.

Euphytica, 32(3), 719–733. https://doi.org/10.1007/BF00042152

Evans, J. R. (1989). Photosynthesis and nitrogen relationships in leaves of C3 plants. Oecologia,

78(1), 9–19. https://doi.org/10.1007/BF00377192

Farquhar, G., & Richards, R. (1984). Isotopic composition of plant carbon correlates with water-

use efficiency of wheat genotypes. Functional Plant Biology, 11(6), 539–552.

Food and Agriculture Organization of the United Nations (FAO). (2017). FAOSTAT Database.

Retrieved May 3, 2017, from http://www.fao.org/faostat/

80

Frazer, G. W., Canham, C. D., & Lertzmen, K. P. (1999). Gap Light Analyzer (GLA), Version

2.0: Imaging software to extract canopy structure and gap light transmission indices

from true-colour fisheye photographs, users manual and program documentation. Simon

Fraser University, Burnaby, BC and the Institute of Ecosystem Studies, Millbrook, NY.

Gagliardi, S., Martin, A. R., Filho, E. de M. V., Rapidel, B., & Isaac, M. E. (2015). Intraspecific

leaf economic trait variation partially explains coffee performance across agroforestry

management regimes. Agriculture, Ecosystems & Environment, 200, 151–160.

https://doi.org/10.1016/j.agee.2014.11.014

Galyuon, I. K. A., Mcdavid, C. R., Lopez, F. B., & Spence, J. A. (1996). The effect of irradiance

level on cocoa (Theobroma cacao L.) : I. Growth and leaf adaptations. Tropical

Agriculture, 73(1), 23–28.

Garnier, E., & Navas, M.-L. (2011). A trait-based approach to comparative functional plant

ecology: concepts, methods and applications for agroecology. A review. Agronomy for

Sustainable Development, 32(2), 365–399. https://doi.org/10.1007/s13593-011-0036-y

Gibson, R. W., Byamukama, E., Mpembe, I., Kayongo, J., & Mwanga, R. O. M. (2008).

Working with farmer groups in Uganda to develop new sweet potato cultivars:

decentralisation and building on traditional approaches. Euphytica, 159(1–2), 217–228.

https://doi.org/10.1007/s10681-007-9477-4

Groeneveld, J. H., Tscharntke, T., Moser, G., & Clough, Y. (2010). Experimental evidence for

stronger cacao yield limitation by pollination than by plant resources. Perspectives in

Plant Ecology, Evolution and Systematics, 12(3), 183–191.

https://doi.org/10.1016/j.ppees.2010.02.005

Holliday, P. (1950). The control of witches’ broom disease of cacao. Proceedings of the

Agricultural Society of Trinidad and Tobago, 50, 393–399.

IPCC. (2014). Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global

and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report

of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J.

Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada,

R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and

L.L. White (eds.)]. Cambridge, United Kingdom and New York, NY, USA: Cambridge

University Press.

Isaac, M. E., Adjei, E. O., Issaka, R. N., & Timmer, V. R. (2010). A strategy for tree-perennial

crop productivity: nursery phase nutrient additions in cocoa-shade agroforestry systems.

Agroforestry Systems, 81(2), 147–155. https://doi.org/10.1007/s10457-010-9365-0

Isaac, M. E., Anglaaere, L. C. N., Borden, K., & Adu-Bredu, S. (2014). Intraspecific root

plasticity in agroforestry systems across edaphic conditions. Agriculture, Ecosystems &

Environment, 185, 16–23. https://doi.org/10.1016/j.agee.2013.12.004

81

Isaac, M. E., Cerda, R., Rapidel, B., Martin, A. R., Dickinson, A. K., & Sibelet, N. (2017). “Yo

toco hojas”: farmer perception and valuation of leaf functional traits in agroecosystems.

Journal of Applied Ecology.

Isaac, M. E., & Dawoe, E. (2009). Integrative Management Of Cocoa Agroforestry Systems:

Promoting Long-Term On-Farm Diversity. Journal of Science and Technology (Ghana),

29(2). https://doi.org/10.4314/just.v29i2.46220

Isaac, M. E., Dawoe, E., & Sieciechowicz, K. (2008). Assessing Local Knowledge Use in

Agroforestry Management with Cognitive Maps. Environmental Management, 43(6),

1321–1329. https://doi.org/10.1007/s00267-008-9201-8

Isaac, M. E., Timmer, V. R., & Quashie-Sam, S. J. (2007). Shade tree effects in an 8-year-old

cocoa agroforestry system: biomass and nutrient diagnosis of Theobroma cacao by vector

analysis. Nutrient Cycling in Agroecosystems, 78(2), 155–165.

https://doi.org/10.1007/s10705-006-9081-3

Jager, M. M., Richardson, S. J., Bellingham, P. J., Clearwater, M. J., & Laughlin, D. C. (2015).

Soil fertility induces coordinated responses of multiple independent functional traits.

Journal of Ecology, 103(2), 374–385. https://doi.org/10.1111/1365-2745.12366

Lal, R. (2004). Soil carbon sequestration to mitigate climate change. Geoderma, 123(1–2), 1–22.

https://doi.org/10.1016/j.geoderma.2004.01.032

Leibel, N. T. M. (2008). The environmental constraints on cocoa (Theobroma cacao) production

in north Australia (Thesis). Retrieved from

https://opus.lib.uts.edu.au/handle/10453/29533

Liu, G., Freschet, G. T., Pan, X., Cornelissen, J. H. C., Li, Y., & Dong, M. (2010). Coordinated

variation in leaf and root traits across multiple spatial scales in Chinese semi-arid and arid

ecosystems. New Phytologist, 188(2), 543–553. https://doi.org/10.1111/j.1469-

8137.2010.03388.x

Lockard, R. G., & Burridge, J. C. (1965). The levels of macro- and micronutrients in the beans of

cacao (Theobroma cacao L.) in relation to shade, fertilizer, irrigation, and season. Annals

of Botany, 29(3), 377–382.

Lok, R., & Sandino, D. (1999). Traditional cocoa agroforestry systems in Waslala, Nicaragua:

adoption of technology and adaptation to local environment and priorities. Reuniones

Técnicas (CATIE), 5, 211–215.

Lund, R. E. (1975). Tables for An Approximate Test for Outliers in Linear Models.

Technometrics, 17(4), 473–476. https://doi.org/10.1080/00401706.1975.10489374

Malézieux, E., Crozat, Y., Dupraz, C., Laurans, M., Makowski, D., Ozier-Lafontaine, H., …

Valantin-Morison, M. (2009). Mixing Plant Species in Cropping Systems: Concepts,

Tools and Models: A Review. In E. Lichtfouse, M. Navarrete, P. Debaeke, S. Véronique,

82

& C. Alberola (Eds.), Sustainable Agriculture (pp. 329–353). Springer Netherlands.

Retrieved from http://link.springer.com/chapter/10.1007/978-90-481-2666-8_22

Martin, A. R., & Isaac, M. E. (2015). Plant functional traits in agroecosystems: a blueprint for

research. Journal of Applied Ecology. https://doi.org/10.1111/1365-2664.12526

Martin, A. R., Rapidel, B., Roupsard, O., Van den Meersche, K., de Melo Virginio Filho, E.,

Barrios, M., & Isaac, M. E. (2017). Intraspecific trait variation across multiple scales: the

leaf economics spectrum in coffee. Functional Ecology, n/a-n/a.

https://doi.org/10.1111/1365-2435.12790

Matey, A., Zeledón, L., Orozco, L., Chavarría, F., & López, A. (2013). Composición florística y

estructura de cacaotales y parches de bosque en Waslala, Nicaragua. Agroforestería En

Las Américas, 49, 61–67.

McGill, B. J., Enquist, B. J., Weiher, E., & Westoby, M. (2006). Rebuilding community ecology

from functional traits. Trends in Ecology & Evolution, 21(4), 178–185.

https://doi.org/10.1016/j.tree.2006.02.002

Medrano, H., Tomás, M., Martorell, S., Flexas, J., Hernández, E., Rosselló, J., … Bota, J. (2015).

From leaf to whole-plant water use efficiency (WUE) in complex canopies: Limitations

of leaf WUE as a selection target. The Crop Journal, 3(3), 220–228.

https://doi.org/10.1016/j.cj.2015.04.002

Miyaji, K.-I., Da Silva, W. S., & Alvim, P. de T. (1997). Productivity of leaves of a tropical tree,

Theobroma cacao, grown under shading, in relation to leaf age and light conditions

within the canopy. New Phytologist, 137(3), 463–472. https://doi.org/10.1046/j.1469-

8137.1997.00841.x

Morera, J., Paredes, A., & Mora, A. (1991). Germoplasma de cacao en el CATIE entre 1947 y

1991, Programa II: Generacion y transferencia de tecnologia. IICA, San José, Costa Rica.

Morton, J. F. (2007). The impact of climate change on smallholder and subsistence agriculture.

Proceedings of the National Academy of Sciences, 104(50), 19680–19685.

https://doi.org/10.1073/pnas.0701855104

Motamayor, J. C., Lachenaud, P., da Silva e Mota, J. W., Loor, R., Kuhn, D. N., Brown, J. S., &

Schnell, R. J. (2008). Geographic and Genetic Population Differentiation of the

Amazonian Chocolate Tree (Theobroma cacao L). PLoS ONE, 3(10), e3311.

https://doi.org/10.1371/journal.pone.0003311

Munroe, J. W., & Isaac, M. E. (2014). N2-fixing trees and the transfer of fixed-N for sustainable

agroforestry: a review. Agronomy for Sustainable Development, 34(2), 417–427.

https://doi.org/10.1007/s13593-013-0190-5

Mwanga, R. O. M., Niringiye, C., Alajo, A., Kigozi, B., Namukula, J., Mpembe, I., … Yencho,

G. C. (2011). “NASPOT 11”, a Sweetpotato Cultivar Bred by a Participatory Plant-

breeding Approach in Uganda. HortScience, 46(2), 317–321.

83

Netto, A. T., Campostrini, E., Oliveira, J. G. de, & Bressan-Smith, R. E. (2005). Photosynthetic

pigments, nitrogen, chlorophyll a fluorescence and SPAD-502 readings in coffee leaves.

Scientia Horticulturae, 104(2), 199–209. https://doi.org/10.1016/j.scienta.2004.08.013

Niinemets, Ü. (2015). Is there a species spectrum within the world-wide leaf economics

spectrum? Major variations in leaf functional traits in the Mediterranean sclerophyll

Quercus ilex. New Phytologist, 205(1), 79–96. https://doi.org/10.1111/nph.13001

Ordoñez, J. C., Van Bodegom, P. M., Witte, J.-P. M., Wright, I. J., Reich, P. B., & Aerts, R.

(2009). A global study of relationships between leaf traits, climate and soil measures of

nutrient fertility. Global Ecology and Biogeography, 18(2), 137–149.

https://doi.org/10.1111/j.1466-8238.2008.00441.x

Pérez-Harguindeguy, N., Díaz, S., Garnier, E., Lavorel, S., Poorter, H., Jaureguiberry, P., …

Cornelissen, J. H. C. (2013). New handbook for standardised measurement of plant

functional traits worldwide. Australian Journal of Botany, 61(3), 167–234.

Perfecto, I., Vandermeer, J., & Wright, A. (2009). Nature’s Matrix: Linking Agriculture,

Conservation and Food Sovereignty. Earthscan.

Phillips-Mora, W., Aime, M. C., & Wilkinson, M. J. (2007). Biodiversity and biogeography of

the cacao (Theobroma cacao) pathogen Moniliophthora roreri in tropical America. Plant

Pathology, 56(6), 911–922. https://doi.org/10.1111/j.1365-3059.2007.01646.x

Phillips-Mora, W., Arciniegas-Leal, A. M., Mata-Quirós, A., & Motamayor-Arias, J. C. (2013).

Catalogue of cacao clones selected by CATIE for commercial plantings. Turrialba, Costa

Rica: CATIE, Cacao Genetic Improvement Program.

Phillips-Mora, W., Castillo, J., Arciniegas, A., Astorga, C., Motamayor, J. C., Guyton, B., …

Schnell, R. (2009). Overcoming the main limiting factors of cacao production in Central

America through the use of improved clones developed at CATIE. In Proceedings (pp.

93–99). Bali, Indonesia.

Phillips-Mora, W., Castillo, J., Krauss, U., Rodríguez, E., & Wilkinson, M. J. (2005). Evaluation

of cacao (Theobroma cacao) clones against seven Colombian isolates of Moniliophthora

roreri from four pathogen genetic groups. Plant Pathology, 54(4), 483–490.

https://doi.org/10.1111/j.1365-3059.2005.01210.x

Phillips-Mora, W., Ortiz, C. F., & Aime, M. C. (2006). Fifty years of frosty pod rot in Central

America: Chronology of its spread and impact from Panama to Mexico. In Proceedings

of the 15th International Cocoa Research Conference. San José, Costa Rica: Cocoa

Producers’ Alliance (COPAL)/CATIE.

Ploetz, R. (2016). The Impact of Diseases on Cacao Production: A Global Overview. In B. A.

Bailey & L. W. Meinhardt (Eds.), Cacao Diseases (pp. 33–59). Springer International

Publishing. Retrieved from http://link.springer.com/chapter/10.1007/978-3-319-24789-

2_2

84

Poorter, H., Niklas, K. J., Reich, P. B., Oleksyn, J., Poot, P., & Mommer, L. (2012). Biomass

allocation to leaves, stems and roots: meta-analyses of interspecific variation and

environmental control. New Phytologist, 193(1), 30–50. https://doi.org/10.1111/j.1469-

8137.2011.03952.x

Prieto, I., Litrico, I., Violle, C., & Barre, P. (2017). Five species, many genotypes, broad

phenotypic diversity: When agronomy meets functional ecology. American Journal of

Botany. https://doi.org/10.3732/ajb.1600354

Prosser, J., & Loxley, A. (2008). Introducing Visual Methods (Working Paper). NCRM.

Retrieved from http://eprints.ncrm.ac.uk/420/

Rada, F., Jaimez, R., Núñez, C. G., Azócar, A., & Ramírez, M. (2005). Water relations and gas

exchange in Theobroma cacao var. Guasare under periods of water deficit. Revista de La

Facultad de Agronomía, Zulia, 22(2).

Ramírez, O. A., Somarriba, E., Ludewigs, T., & Ferreira, P. (2001). Financial returns, stability

and risk of cacao-plantain-timber agroforestry systems in Central America. Agroforestry

Systems, 51(2), 141–154. https://doi.org/10.1023/A:1010655304724

Reich, P. B., Wright, I. J., Cavender‐Bares, J., Craine, J. M., Oleksyn, J., Westoby, M., &

Walters, M. B. (2003). The Evolution of Plant Functional Variation: Traits, Spectra, and

Strategies. International Journal of Plant Sciences, 164(S3), S143–S164.

https://doi.org/10.1086/374368

Richards, P. (1985). Indigenous agricultural revolution: ecology and food production in West

Africa. London: Hutchinson.

Santana, M. B. M., & Igue, K. (1979). The mineral composition of cacao leaves as a variation of

season and leaf age [Theobroma cacao; Brazil]. Revista Theobroma (Brazil). Retrieved

from http://agris.fao.org/agris-search/search.do?recordID=BR8000390

Scherr, S. J., & McNeely, J. A. (2008). Biodiversity conservation and agricultural sustainability:

towards a new paradigm of “ecoagriculture” landscapes. Philosophical Transactions of

the Royal Society B: Biological Sciences, 363(1491), 477–494.

https://doi.org/10.1098/rstb.2007.2165

Seibt, U., Rajabi, A., Griffiths, H. & Berry, J. A. (2008). Carbon isotopes and water use

efficiency: sense and sensitivity. Oecologia, 155(3), 441-454.

https://doi.org/10.1007/s00442-007-0932-7

Siefert, A., Violle, C., Chalmandrier, L., Albert, C. H., Taudiere, A., Fajardo, A., … Wardle, D.

A. (2015). A global meta-analysis of the relative extent of intraspecific trait variation in

plant communities. Ecology Letters, 18(12), 1406-1419.

https://doi.org/10.1111/ele.12508

Silva, C., Orozco, L., Rayment, M., & Somarriba, E. (2013). Conocimiento local sobre los

atributos deseables de los árboles y el manejo del dosel de sombra en los cacaotales de

Waslala, Nicaragua. Agroforestería En Las Américas, 49, 51–60.

85

Somarriba, E., Cerda, R., Orozco, L., Cifuentes, M., Dávila, H., Espin, T., … Deheuvels, O.

(2013a). Carbon stocks and cocoa yields in agroforestry systems of Central America.

Agriculture, Ecosystems & Environment, 173, 46–57.

https://doi.org/10.1016/j.agee.2013.04.013

Somarriba, E., Villalobos, M., Cerda, R., Astorga, C., Orozco, S., Escobedo, A., … Salazar, J.

(2013b). ¿Cómo diseñamos y ejecutamos el Proyecto Cacao Centroamérica para

estimular al sector cacaotero de Centroamérica? Agroforestería en las Américas, 1(49),

127–134.

Soto-Pinto, L., Villalvazo-López, V., Jiménez-Ferrer, G., Ramírez-Marcial, N., Montoya, G., &

Sinclair, F. L. (2006). The role of local knowledge in determining shade composition of

multistrata coffee systems in Chiapas, Mexico. Biodiversity and Conservation, 16(2),

419–436. https://doi.org/10.1007/s10531-005-5436-3

ter Steege, H., Pitman, N. C. A., Phillips, O. L., Chave, J., Sabatier, D., Duque, A., … Vásquez,

R. (2006). Continental-scale patterns of canopy tree composition and function across

Amazonia. Nature, 443(7110), 444–447. https://doi.org/10.1038/nature05134

Tezara, W., Coronel, I., Urich, R., Marín, O., Jaimez, R., & Chacón, I. (2009). Plasticidad

ecofisiológica de árboles de cacao (Theobroma cacao L.) en diferentes ambientes de

Venezuela (pp. 1–5). Presented at the III Congreso Latinoamericano de Ecología y IX

Congreso de Ecología de Brazil. Sâo Lorenço pp.

Tezara, W. T., Almeida, J. D. A. D., Valencia, E. V., Cortes, J. L. C., & Bolaños, M. J. B.

(2015). Actividad fotoquímica de clones élites de cacao (Theobroma cacao L.)

eduatoriano en el norte de la provincia Esmeraldas. Investigación y Saberes, 4(3), 37–51.

Tezara, W., Urich, R., Jaimez, R., Coronel, I., Araque, O., Azocar, C., & Chacón, I. (2016). Does

Criollo cocoa have the same ecophysiological characteristics than Forastero? Botanical

Sciences, 94(3), 563–574.

Tilman, D., Knops, J., Wedin, D., Reich, P., Ritchie, M., & Siemann, E. (1997). The Influence of

Functional Diversity and Composition on Ecosystem Processes. Science, 277(5330),

1300–1302. https://doi.org/10.1126/science.277.5330.1300

Trognitz, B., Cros, E., Assemat, S., Davrieux, F., Forestier-Chiron, N., Ayestas, E., … Hermann,

M. (2013). Diversity of Cacao Trees in Waslala, Nicaragua: Associations between

Genotype Spectra, Product Quality and Yield Potential. PLoS ONE, 8(1).

https://doi.org/10.1371/journal.pone.0054079

Trognitz, B., Scheldeman, X., Hansel-Hohl, K., Kuant, A., Grebe, H., & Hermann, M. (2011).

Genetic Population Structure of Cacao Plantings within a Young Production Area in

Nicaragua. PLoS ONE, 6(1), e16056. https://doi.org/10.1371/journal.pone.0016056

Turnbull, C. J., & Hadley, P. (2015). International Cocoa Germplasm Database (ICGD). CRA

Ltd./ICE Futures Europe/University of Reading, UK. Retrieved from

http://www.icgd.reading.ac.uk

86

Valencia, V., West, P., Sterling, E. J., García-Barrios, L., & Naeem, S. (2015). The use of

farmers’ knowledge in coffee agroforestry management: implications for the

conservation of tree biodiversity. Ecosphere, 6(7), art122. https://doi.org/10.1890/ES14-

00428.1

Verchot, L. V., Noordwijk, M. V., Kandji, S., Tomich, T., Ong, C., Albrecht, A., … Palm, C.

(2007). Climate change: linking adaptation and mitigation through agroforestry.

Mitigation and Adaptation Strategies for Global Change, 12(5), 901–918.

https://doi.org/10.1007/s11027-007-9105-6

Violle, C., Enquist, B. J., McGill, B. J., Jiang, L., Albert, C. H., Hulshof, C., … Messier, J.

(2012). The return of the variance: intraspecific variability in community ecology. Trends

in Ecology & Evolution, 27(4), 244–252. https://doi.org/10.1016/j.tree.2011.11.014

Vitousek, P. M., Turner, D. R., & Kitayama, K. (1995). Foliar Nutrients During Long-Term Soil

Development in Hawaiian Montane Rain Forest. Ecology, 76(3), 712–720.

https://doi.org/10.2307/1939338

Wessel, M. (1971). Fertilizer requirements of cacao (Theobroma cacao L.) in south-western

Nigeria (Vol. 61). Koninklijk Institut voor de Tropen.

Westoby, M., Falster, D. S., Moles, A. T., Vesk, P. A., & Wright, I. J. (2002). Plant Ecological

Strategies: Some Leading Dimensions of Variation between Species. Annual Review of

Ecology and Systematics, 33, 125–159.

Wood, G. A. R., & Lass, R. A. (1985). Cocoa (4th ed.). Great Britain: John Wiley & Sons.

Wood, S. A., Karp, D. S., DeClerck, F., Kremen, C., Naeem, S., & Palm, C. A. (2015).

Functional traits in agriculture: agrobiodiversity and ecosystem services. Trends in

Ecology & Evolution, 30(9), 531–539. https://doi.org/10.1016/j.tree.2015.06.013

Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch, Z., Bongers, F., … Villar, R.

(2004). The worldwide leaf economics spectrum. Nature, 428(6985), 821–827.

https://doi.org/10.1038/nature02403

Young, A. M. (2007). The Chocolate Tree: A Natural History of Cacao. University Press of

Florida.

Zuidema, P. A., Leffelaar, P. A., Gerritsma, W., Mommer, L., & Anten, N. P. R. (2005). A

physiological production model for cocoa (Theobroma cacao): model presentation,

validation and application. Agricultural Systems, 84(2), 195–225.

https://doi.org/10.1016/j.agsy.2004.06.015

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Appendix A: Interview Questions and Resources

Spanish

Nombre: Edad:

Preguntas iniciales

1. ¿Cuánto tiempo tiene de sembrar cacao?

Parcela propia Otras parcelas Otros cultivos/trabajo

2. ¿Ha vivido toda su vida en esta zona?

3. ¿Alguien más trabaja con usted en la finca?

Familiares Empleados

4. ¿Cuáles parcelas trabaja? ¿De qué tamaño son?

5. ¿Cómo era esta finca antes de tener cacao?

6. ¿Es (era) miembro de alguna organización de finqueros o

cooperativa? ¿Cuál(es)?

Preguntas sobre el manejo del cacaotal

1. ¿Tiene árboles de sombra en su cacaotal? ¿Cuántos y de

cuáles especies?

Especie: _________ Nat. Sem. Especie: _________ Nat. Sem.

2. ¿En algún momento poda o hace un raleo de sus árboles de

sombra?

a. ¿Hace este raleo o poda en una época específica del año?

b. ¿Hay algunas características de los árboles de cacao que

le demuestran que necesita podar o hacer un raleo?

English

Name: Age:

Introductory questions

1. How long have you been planting cacao?

Own farm: Other farms: Other crops/work:

2. Have you lived in this area for your entire life?

3. Does anyone else work with you on the farm?

Family members: Employees:

4. How many pieces of land do you work? How large are they?

5. What was this farm like before cacao was planted here?

6. Are (or have you ever been) a member of a farmers’

organization or cooperative? Which one(s)?

Questions about cacao farm management

1. Do you have shade trees on your cacao farm? How many,

and of which species?

Species: ____ Natural/Planted Species: ______ Natural/Planted

2. Do you prune or thin your shade trees at any time?

a. Do you prune/thin the shade trees at a particular time of

year?

b. Are there any characteristics of the cacao trees that tell

you that it is time to prune or thin your shade trees?

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3. ¿Cuáles son los abonos que usa en su finca? ¿Son

orgánicos o inorgánicos o ambos?

Abonos naturales hojarasca - tallo de banano - estiércol

animal - ceniza - mazorca de cacao Otros:

Abonos químicos cal (fumigado o en polvo) – fertilizante

químico Otros:

4. ¿Cómo son los árboles de cacao cuando necesitan el

abono?

Preguntas sobre variedades de cacao

1. ¿Cuáles variedades de cacao siembra?

Variedad: ________Sem. Inj. Variedad: __________

Sem. Inj.

a. Ponga las variedades en orden desde la más

productiva a la menos productiva:

b. Ponga las variedades en orden desde la más resistente

a plagas y enfermedades a la menos resistente.

c. Ponga las variedades en orden desde la que más

aguanta la sombra a la que menos aguanta la sombra.

d. ¿Poda o abona a sus árboles de manera diferente según

la variedad?

3. What fertilizers do you use on your farm? Are they

organic, inorganic, or both?

Natural fertilizers leaf litter – banana stalks –

manure – ash – cacao husks Others:

Chemical fertilizers lime (as a spray or powder) –

chemical fertilizer Others:

4. What do cacao trees look like when they need fertilizer?

Questions about cacao varieties

1. What varieties of cacao do you plant?

Variety: ______ Seed/Graft Variety: ______

Seed/Graft

a. Please place the varieties in order from most to least

productive.

b. Please place the varieties in order from most to least

resistant to disease

c. Please place the varieties in order from most shade-

tolerant to least shade-tolerant

d. Do you prune or apply fertilizer to your trees

differently from one variety to the next?

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Entrevista visual

1. ¿Cuál de estas hojas cree que proviene de un árbol de

alta producción? ¿De un árbol de baja producción?

1 2 3 4 5 6 7

Color

Tamaño

Espesor

2. ¿Cuál de estas hojas cree que proviene de un árbol

sano? ¿De un árbol en mal estado?

1 2 3 4 5 6 7

Color

Tamaño

Espesor

3. ¿Cuál de estas hojas proviene de un árbol con alta o

baja sombra?

1 2 3 4 5 6 7

Color

Tamaño

Espesor

Visual Interview

1. Which of these leaves do you think came from a high-

producing tree? From a low-producing tree?

1 2 3 4 5 6 7

Colour

Size

Thickness

2. Which of these leaves do you think came from a

healthy tree? From a sick one?

1 2 3 4 5 6 7

Colour

Size

Thickness

3. Which of these leaves came from a tree under high or

low shade?

1 2 3 4 5 6 7

Colour

Size

Thickness

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Características de la planta entera

1. Por favor, ponga las siguientes características en orden

desde la más importante a la menos importante con

relación a la producción de cacao: altura de la planta,

tamaño de la copa y diámetro del tronco principal.

2. Por favor, ponga las siguientes características en orden

desde la más importante a la menos importante con

relación a la producción de cacao: color de las hojas,

tamaño de las hojas y espesor/textura de las hojas.

3. Por favor, ponga las siguientes características en orden

desde la más importante a la menos importante con

relación a la producción de cacao: número de flores,

tamaño de flores, y la ubicación de las flores y las frutas

en el árbol (ya sea en las ramas o en el tronco o en

ambos).

Whole-plant traits (see Figure 16)

1. Please place the following traits in order from the most

to least important for cacao production: plant height,

canopy size, and diameter of the main trunk.

2. Please place the following traits in order from the most

to least important for cacao production: leaf colour, leaf

size, and leaf thickness/texture.

3. Please place the following traits in order from the most

to least important for cacao production: number of

flowers, size of flowers, and location of fruits/flowers

on the plant (namely, on the branches, the trunk, or

spread out throughout the tree)

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Figure 16. Hand-drawn visual aids for use in interviews. The left-most drawing shows variation in canopy diameter (from top to

bottom: small/medium/large canopy), the middle drawing shows variation in the location of fruits (from top to bottom: fruits on

the trunk/fruits on the branches/fruits all over the tree), and the right-most drawing shows variation in plant height (from top to

bottom: tall/medium/short plant).

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Preguntas sobre redes sociales

1. ¿Con quién discutió sobre la conversión de esta parcela

a la producción de cacao? O ¿Con quién discutió el

manejo de esta parcela cuando Ud. empezó a

manejarla?

2. ¿Buscó información de otros agricultores cuando

estableció su plantación de cacao? Si es así, ¿de cuáles?

a. ¿Buscó información de alguna organización,

cooperativa o técnico cuando estableció su

plantación de cacao? Si es así, ¿de cuáles?

3. ¿Hay alguien en esta lista que sabe mucho sobre una

variedad en específico? ¿Hay alguien que sabe mucho

sobre todas las variedades?

4. ¿De quién recibió las semillas, las plántulas y/o los

injertos para su plantación de cacao?

5. Con base en nuestra discusión sobre las características

de las hojas de cacao, por favor indique cuáles personas

le han enseñado sobre las hojas de cacao.

Questions on social networks

1. With whom did you discuss the conversion of this farm

to cacao production? OR With whom did you discuss

the management of this farm when you took it over?

2. Have you sought advice from other farmers since taking

over this cacao plantation?

a. Did you seek information from any

organization, cooperative, or technician after

taking over this cacao plantation, if so, from

whom?

3. Is there someone in this community who knows a lot

about a particular cacao variety? About cacao varieties

in general?

4. From whom did you receive seeds, seedlings, or

grafting material for your cacao plantation?

5. With regards to our discussion on cacao leaf traits,

please indicate which people have taught you about

cacao leaves