Flora Diversity in Organic and Conventional Cocoa Farms

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CONTRIBUTION OF FLORA DIVERSITY TO SUSTAINABLE LIVELIHOODS OF

ORGANIC AND CONVENTIONAL COCOA FARMERS IN THE ATWIMA

MPONUA DISTRICT

JOSEPH BANDANAAH

THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN

PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF

MASTER OF PHILOSOPHY DEGREE IN CLIMATE CHANGE AND

SUSTAINABLE DEVELOPMENT

CLIMATE CHANGE AND SUSTAINABLE DEVELOPMENT

SCHOOL OF SOCIAL SCIENCES

UNIVERSITY OF GHANA, LEGON

JULY, 2015

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DECLARATION

I, Joseph Bandanaah, author of this thesis titled, “CONTRIBUTION OF FLORA DIVERSITY TO SUSTAINABLE LIVELIHOODS OF ORGANIC AND CONVENTIONAL COCOA FARMERS IN THE ATWIMA MPONUA DISTRICT”, do hereby declare that, with the exception of references to past and current literatures consulted which have been duly acknowledged, the work presented was done by me from August 2013 to July 2015 at the Climate Change and Sustainable Development, of the School of Social Sciences in the University of Ghana, Legon. This work has neither been presented in whole nor part for any other degree in this University or elsewhere.

…………………………………………

Joseph Bandanaah

Date: …………………………………..

This thesis has been presented for examination with our approval as supervisors:

……………………………… …………………………….

Prof. Isaac K. Asante Dr. (Mrs.) Irene S. Egyir (Major Supervisor) (Co-Supervisor) Date: ………………………. Date: ………………………

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DEDICATION

This thesis is dedicated to God Almighty for His grace and guidance throughout my academic pursuits.

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ACKNOWLEDGEMENT My ultimate gratitude goes to the Almighty God for His wisdom, understanding, mercies and favour to me from the beginning to the completion of this work. My sincerest appreciation goes to my supervisors, Prof. Isaac K. Asante and Dr. (Mrs.) Irene S. Egyir for their parental guidance and diverse contributions towards the success of the study. Without their advice, criticisms and directions, this work would not have been completed.

I am grateful to the Director of the Centre for African Wetlands (CAW), Prof Yaa Ntiamoa-

Baidu and the Coordinator of Climate Change and Sustainable Development Programme, Dr.

Kwadwo Owusu and most especially Open Society Foundation (OSF) for the financial support provided throughout my study period.

I also acknowledge with gratitude the role played by Dr. (Mrs.) Irene S. Egyir, Lead Researcher and Prof. Kwabena Ofosu-Budu (Co-Researcher) for securing further financial support for my field work from the Research Institute of Organic Agriculture (FBiL) led Pro Eco Africa project.

I would like to thank all the senior members and other staff of the Climate Change and

Sustainable Development Program for the constructive criticisms and knowledge imparted. My heartfelt appreciation also goes to Janvier Metoule Yirviel, a PhD candidate and Mr G.Y

Amponsah, the taxonomist for assisting me during the flora sampling. I am indebted to the

Bandanaa and Egyir families for their prayers, priceless support and contributions. Finally, I am appreciative of the contributions of many people whose names have not been mentioned here especially the cocoa farmers in Atwima Mponua district for providing me with the valuable information for the study.

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ABSTRACT The aim of the study was to determine the difference in flora diversity in two farming systems and how the diversity contributes to sustainable livelihoods of cocoa farmers in an era of changing climate in Atwima Mponua District of Ghana. Sixteen organic and sixteen conventional cocoa farms were selected in Gyereso and Pasoro in the district. In each farm, flora were sampled to include three life forms viz., trees, shrubs and herbs. Households were also interviewed with a structured questionnaire to ascertain flora contribution to livelihoods. Jaccard similarity and Shannon and Simpson diversity indices were measured to assess species similarity, abundance, evenness and dominance. The indicators used to describe sustainable livelihoods were food security, income, vulnerability and wellbeing. The key statistical tests employed in the study were the Mann-Whitney U, the Chi-square and the T-test. The results of the study showed that the level of life form abundance of organic farms is higher than that of conventional farms. Organic farms had more random and aggregated species distribution patterns than conventional. The Jaccard index of similarity obtained was 0.64 indicating a moderate similarity of flora species between the two farming systems. The Shannon index for organic farms (specie abundance and evenness) was slightly higher (0.808) than that of conventional farms (0.762); the Simpson indices (species dominance) were 0.051 and 0.084 for organic and conventional cocoa farms respectively. The results of the Mann-Whitney U test shows that, Shannon index of diversity was not statistically significant however, Simpson index of diversity was statistically significant (1%). The organic cocoa farmers studied consumed and sold more flora than their conventional counterparts; the general wellbeing (covering health cost) and resilience of the organic cocoa farmers studied was better than that of the conventional cocoa farmers. Since organic farming has more biodiversity and hence are more environmentally friendly, and households have better sustainable livelihoods outcomes, this study recommends the encouragement of farmers to practice organic farming as a climate smart option.

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TABLE OF CONTENT Content Page DECLARATION i DEDICATION ii ACKNOWLEDGEMENT iii ABSTRACT iv TABLE OF CONTENT v LIST OF TABLES viii LIST OF FIGURES ix LIST OF ACRONYMS x

CHAPTER ONE 1 INTRODUCTION 1 1.1 Background 1 1.2 Problem statement 3 1.3 Objectives of the Study 4 1.4 Justification of the Study 5 1.5 Organization of study 6

CHAPTER TWO 7 LITERATURE REVIEW 7 2.1 Introduction 7 2.2 The Concept of Organic Farming (OF), Conventional Farming (CF) and Practices 7 2.2.1 Organic farming 7 2.2.2 Conventional farming 9 2.3 Concept of Sustainable Livelihoods 9 2.4 Contribution of flora species to food security, wellbeing and income 11 2.5 Empirical Studies on Flora Diversity in Organic and Conventional farming 12 2.6 Socio-economic Characteristics of Organic versus Conventional farmers 14 2.7 The Role of Institutions in Promoting Biodiversity Conservation in Ghana 15 2.8 Evidence of Climate Change in Forest regions of Ghana 16 2.9 Organic Agriculture as Climate Smart Practice 17 2.10 Nature of Flora in Organic and Conventional cocoa farms 19

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CHAPTER THREE 21 MATERIALS AND METHODS 21 3.1 Introduction 21 3.2 Conceptual framework 21 3.3 Study area 22 3.4 Method of Data Collection 25 3.4.1 Flora survey and farm selection 25 3.4.2 Household survey 26 3.5 Methods of Data Analysis 27 3.5.1 Flora survey 27 3.5.2 Household survey 30

CHAPTER FOUR 35 RESULTS AND DISCUSSION 35 4.1 Introduction 35 4.2 Socio-Economic Characteristics of the Sampled Households 35 4.2.1 Age distribution of respondents 35 4.2.2 Gender distribution of respondents 36 4.2.3 Educational level of respondents 37 4.2.4 Place of origin of respondents 38 4.2.5 Household size distribution of cocoa farmers 39 4.2.6 Number of years of farming 39 4.2.7 Land ownership status of respondents 40 4.2.8 Land use before crop production 41 4.2.9 Perception of soil fertility status of sampled farms 42 4.3 Nature of Flora Distribution 43 4.3.1 Life form abundance 44 4.3.2 Pattern of species distribution 44 4.3.3 Species compositional similarity 45 4.4 Extent of Difference of Flora Diversity in Organic and Conventional Cocoa Farms 46 4.5 Contribution of Flora to Sustainable Livelihoods 48 4.5.1 Food security 48 4.5.2 Income from flora 56 4.5.3 Wellbeing 57 4.5.4 Vulnerability as a variable of livelihood of organic and conventional farmers 57 vi

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CHAPTER FIVE 59 SUMMARY, CONCLUSION AND POLICY RECOMMENDATION 59 5.1 Introduction 59 5.2 Summary of the Study 59 5.3 Conclusion 60 5.4 Policy Recommendation 61

REFERENCES 62

Appendix 1 Survey Questionnaire 69 Appendix 2 Flora distribution in the study area 75

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LIST OF TABLES Table Page Table 2.1 Comparison of flora diversity of organic and conventional farming on number of reviewed articles 14

Table 3.1 Socio economic variables and their measurements 30

Table 3.2 Sustainable livelihood indicators and their measurements 32

Table 4.1 Age distribution of cocoa farmers of the two farming systems 36

Table 4.2 Household size and farming experience of respondents 40

Table 4.3 Comparison of farming systems with flora diversity indices in Atwima Mponua district 48

Table 4.4 Flora availability in organic and conventional cocoa farms in the study area 50

Table 4.5 Flora consumption 53

Table 4.6 Flora sales and value (GHS) 55

Table 4.7 Range of income obtained by farming system 56

Table 4.8 Proportion of yearly health expenses financed by flora income 57

Table 4.9 Membership of a farmer based organization (FBO) as proxy to vulnerability measurement 58

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LIST OF FIGURES

Figure Page Figure 2.1 Link between Climate smart and organic agriculture 19

Figure 3.1 Conceptual framework of study 22

Figure 3.2 Location of farms in the two communities studied 24

Figure 4.1 Gender distribution of cocoa farmers 37

Figure 4.2 Educational level of respondents 38

Figure 4.3 Place of origin of respondents 38

Figure 4.4 Land ownership status of respondents 41

Figure 4.5 Land use before crop production in the study area 42

Figure 4.6 Soil fertility status of sampled farms 43

Figure 4.7 Comparison of life form abundance in organic and conventional cocoa farms in the study area 44

Figure 4.8 Species distribution pattern in organic and conventional cocoa farms in the study area 45

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LIST OF ACRONYMS

AE-LBI: Agro Eco-Louis Bolk Institute

CF: Conventional Farming

CHED: Cocoa health and Extension Division

CODAPEC: Cocoa Disease and Pest Control

DFID: Department for International Development

EWP: Edible Wild Plants

FAO: Food and Agriculture Organization

FiBL: Research Institute of Organic Agriculture

GDP: Gross Domestic Product

GHS: Ghana cedis

GMO: Genetically Modified Organisms

GPS: Global Positioning System

GSS: Ghana Statistical Service

IFOAM: International Federation of Organic Agricultural Movement

MDGs: Millennium Development Goals

MoFA: Ministry of Food and Agriculture

NGOs: Non-governmental organisations

OF: Organic Farming

PHC: Population and Housing Census

RA: Rainforest Alliance

SA: Sustainable Agriculture

SLF: Sustainable livelihoods framework

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CHAPTER ONE INTRODUCTION 1.1 Background Cocoa (Theobroma cacao) is a very important perennial crop in Ghana and serves as a major source of foreign exchange for the nation aside providing over 800,000 farmers with employment (Onumah et al., 2013). In order to meet the global demand for cocoa, the government of Ghana introduced a number of interventions including the extension information dissemination, the Cocoa Disease and Pest Control (CODAPEC) programme, the

Cocoa Hi-tech initiative programme, the payment of the remunerative producer prices and the bonus payment scheme as a means of rewarding farmers’ effort in improving yields (Teal et al., 2006). The Cocoa Hi-tech initiative programme was to boost the intensive use of fertilizers to replenish soil fertility, the application of pesticides on cocoa, and the adoption of improved planting material to improve the productivity of cocoa farms (Onumah et al., 2013).

Though the high technology initiative enhanced cocoa growth and output, it is regarded by

Ramachandran, (2007) and Scherr et al., (2008) as a form of green revolution with problems of biodiversity loss, pollution, environmental degradation and lack of sustainability. Other innovative programmes have promoted organic cocoa farming in Ghana since 2007 (Glin et al., 2015). The benefits of both organic and conventional farming have been advanced by promoters.

In January 2013, the Research Institute of Organic Agriculture (FiBL) commenced a three year project on “Productivity and Profitability of Organic and Conventional Farming Systems - A

Comparative Analysis in Sub-Saharan Africa (ProEco Organic)”. The goal of the project is to contribute to improved rural livelihoods, including food, nutrition and income security in Sub

Saharan Africa through climate-smart intensification of agricultural systems (FiBL, 2013).

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ProEco Africa is testing the difference in productivity and profitability as well as flora diversity since flora diversity is one benefit / cost specified by both proponents.

Flora diversity is a major component of agro-biodiversity and Agro-biodiversity is an important resource for agricultural productivity next to land and water in rural development (Pretty et al.,

2003). Managing plant genetic resources, crop species and variety will therefore strengthen farmers’ resilience to changing climate as well as solve the world food and livelihood security problem (Food and Agriculture Organization-FAO, 2010).

Flora diversity in organic farms has been shown to contribute to ecosystem stability. Many farmers, especially those in environments where high-yield crop and livestock varieties do not prosper, rely on a wide range of crop and livestock types. This helps them maintain their livelihood in the face of flora loss, pathogen infestation, uncertain rainfall and fluctuation in the price of cash crops, socio-political disruption and the unpredictable availability of agrochemicals. So-called minor or underutilized crops, precisely, companion crops, are frequently found next to the main staple or cash crops. They often grow side by side and their importance is often misjudged. In many cases, from a livelihood perspective, they are not minor or underutilized as they can play a disproportionately important role in food production systems at the local level (FAO, 2010).

Biodiversity, particularly, plant/flora diversity is not only paramount to food security, but also environmental conservation (Thrupp, 2000). Different agricultural innovations and technologies however, have led to flora diversity loss especially in conventional farming which

encourages herbicides, pesticides and other agro chemical usage (Thorbek & Bilde, 2004;

Gibson et al., 2007; Bahlai et al., 2010 and Duruigbo et al, 2013). The loss in flora diversity causes economic loss and threatens food security.

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Thrupp (2000), observed that in order to achieve sustainable ecological and socioeconomic benefits (income, wellbeing, reduce vulnerability and food security) biodiversity maintenance must be integrated with agricultural practices. Organic farming is known to provide better results in many aspects of environmental issues compared to conventional agriculture (FAO,

2008).

Organic farming could provide resilient systems for adaptation to climate change. It helps to increase resilience of farming systems especially vegetable farming through better management of soil and water, promoting biodiversity and strengthening community knowledge systems.

Agriculture is the backbone of the Ghanaian economy and it influences the quality of diets of smallholder farming households through consuming directly what households produce and the sale of agricultural goods that affect household incomes and therefore food purchases and consumption (World Bank, 2007). Farming households that consume primarily what they produce seem reasonable because flora diversity on their farms would lead to more diverse diets.

1.2 Problem statement A major driver of biodiversity loss is land use changes and the consequence of this loss disrupts the lifestyles mostly of the poor who depend upon local eco-systems for their livelihoods

(Munzara, 2007). The diversity of flora on arable and cultivated fields is further at risk due to agricultural intensification, intensive use of mineral fertilizers and herbicides (Duruigbo et al,

2013; Musters et al., 2009 and Hole et al. 2005). For instance, Bengtsson et al. (2005) in a wide meta-analysis found that organic farming often has positive effects on species richness and abundance, however, Gibson et al., (2007) argued that conventional farming can be of benefit when carefully managed.

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Mavengahama et al. (2013) and the Millennium Ecosystems Assessment (MEA) report (2006) has identified food security, income and well-being as important sustainable livelihood variables which are linked to flora diversity. The MEA report recommended speedy actions to promote sustainable use of flora as a contribution to the Millennium Development Goals

(MDGs).

It is assumed that the conventional cocoa farmers in Atwima Mponua District who were studied are vulnerable as a result of trends in biodiversity loss, seasonality and climate change shocks.

This therefore affect their natural capital (flora) and achieving the sustainable livelihood outcomes, food security, income, wellbeing and reduction in vulnerability almost impossible.

There is therefore the need to study how flora on organic cocoa farms differ from that of conventional in a changing climate since organic farming is a climate smart practice and it is assumed to contribute more to sustainable livelihoods of the studied farmers.

The specific research questions that this study seeks to address are: 1. What is the nature of flora under organic and conventional cocoa farms?

2. What are the differences between flora diversity in organic and conventional cocoa farms?

3. What is the role of flora to sustainable livelihoods of organic and conventional cocoa

farmers?

1.3 Objectives of the Study The main objective of the study is to examine the flora diversity in organic and conventional cocoa farms and its contribution to sustainable livelihood of households in the Atwima Mponua district of the Ashanti region of Ghana.

The specific objectives are to:

1. Study the nature of flora in organic and conventional cocoa farms, 2. Determine the extent to which flora diversity in organic and conventional cocoa farms differ and 3. Assess the role of flora to sustainable livelihoods of organic and conventional cocoa farmers. 4

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1.4 Justification of the Study This study is motivated by the ProEco Africa project led by FiBL. The major objective of the project is to contribute to improved rural livelihoods and food security in Sub Saharan

Africa through climate-smart intensification. It identified a study on flora diversity (species abundance, richness and distribution) in organic and conventional cocoa farms as important.

The focus of the study was on measuring abundance of species. Several studies have compared the ﬂora in different intensity of management (organic vs. conventional) in Africa and Europe.

In most cases, organic farming has been proved to enhance flora diversity (Hole et al., 2005).

However, there are knowledge gaps regarding the importance and abundance of flora species since not much empirical evidence is available to support or rebut these observations

(Mavengahama et al., 2013).

Some researchers have reported that rural people perceive the populations of flora species especially Amaranthus and other voluntary crops to be in decline. Again, people do not indiscriminately consume all of the available varieties but select, depending on certain specific

(un)desirable characteristics like leaf hairiness, bitterness and leaf size (which influences ease and speed of gathering/harvesting) (Mavengahama et al., 2013). As a result, not all that is available is consumed implying the need to closely interact with farmer households to clearly understand their perceptions and utilisation patterns at household level with respect to flora species.

Flora diversity provide ecosystem services in the form of productive soils, ecosystem stability, biological resources, food, economic benefits and income generation (FAO, 2008). This study will therefore inform farmers and stakeholders on the value and cost effective system of farming (either Organic or Conventional).

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A study on biodiversity will inform agricultural and environmental policy on the role organic farming plays in enriching and conserving biodiversity (FAO, 2008). This will support biodiversity-rich agro-ecosystems and reduce agro-biodiversity losses through weedicides and other agro chemicals use.

Food security, wellbeing and income are important sustainable development issues (Scialabba,

2007). This would also help development planners to design effective programmes, so as to improve income, food security status and reduce vulnerabilities for successful initiatives and programmes in the subject matter.

1.5 Organization of study 

The study has been organized into five chapters. The first chapter, the introduction consist of the background to the entire research, the problem statement, objectives of study, justification of the study as well as the organization of the rest of the study.

The second chapter contains the literature review which is an academic collection of findings that are related to the study.

Chapter three involves the methodology which is a systematic analysis of the methods applied to the study.

Chapter four is an embodiment of the results and discussion of findings of the research. Each finding is examined and its implication on the sustainable livelihoods of both organic and conventional cocoa farmers.

The final chapter, chapter five presents the summary, conclusions and policy recommendations of the study. The position of the researcher regarding the research questions are clearly stated here.

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CHAPTER TWO LITERATURE REVIEW 2.1 Introduction This section presents excerpts of relevant literature reviewed on the subject matter. First, is the explanation of the concepts and practices of the two farming systems (organic versus conventional); next is the explanation of the concept of sustainable livelihoods and its indicators. Then the empirical studies on flora diversity in organic and conventional farming is presented. Also a review on socio-economic characteristics of organic versus conventional farmers, the role of institutions in promoting biodiversity conservation in Ghana and evidence of climate change in forest regions of Ghana will be conducted. Finally lessons literature on organic agriculture as climate smart practice and nature of flora in organic and conventional cocoa farms will be described.

2.2 The Concept of Organic Farming (OF), Conventional Farming (CF) and Practices 2.2.1 Organic farming By default, agriculture was seen as organic farming in which simple tools and methods in crop production were used (Lockeretz, 2007; Kristiansen et al., 2006; Lotter, 2003 and Conford,

2001), however, the quest to increase crop production in order to meet the challenge of global food insecurity (green revolution era) operationalized what is termed organic farming.

Theoretically, organic farming, forbids the use of synthetic chemicals (pesticides and fertilizers) and encourages the use of compost, manure (animal droppings), bio fertilizers and biological methods of pest control.

According to International Federation of Organic Agricultural Movement - IFOAM, (2008,

2010), organic agriculture refers to a farming system that bans the use of agrochemicals such as synthetic fertilizers and pesticides and the use of Genetically Modified Organisms (GMO), as well as any synthetic compounds used as food additives (e.g. preservatives, colouring).

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IFOAM perceives organic agriculture as a production system that sustains the health of the soil, ecosystem and people by relying on ecological processes, biodiversity and the cycles adopted to local conditions, rather than the use of inputs with diverse effects (IFOAM, 2010).

This means that, the system combines traditions, innovations and science to benefit the environment and promote fair relationship and a good quality of life for all involved.

Gibson et al., (2007) and Lampkin, (2002) assert that, the concept of organic farming does not only refer to the use of living materials, but emphasizes on the concept of “wholeness”, implying the “systematic connection or co-ordination of several inputs as a whole”. Organic farming does not only differ from other farming systems (eg. conventional farming) in the range of tactics used by farmers, but also in the conceptual approaches that frame crop management strategies.

Conventional farming differs from organic farming as the latter responds to site-specific conditions by integrating cultural, biological, and mechanical practices that foster cycling of resources, promote ecological balance, and conserve biodiversity. Rather than using synthetic fertilizers, pesticides and growth regulators, organic farming systems rely on crop rotation, animal and plant manures as fertilizers, some hand weeding and biological pest control (FBiL,

2013; Letourneau et al., 2011 and Williams, 2002).

Despite the numerous definitions of organic farming, certain four key principles according to

IFOAM underline the activities and practices of this system. These principles are:

1. The principle of health: organic farming should sustain and enhance the health of soil, plant, animal and human as one and indivisible.

2. The principle of ecology: organic farming should be based on living ecological systems and cycles, work with them, emulate them and help sustain them. 8

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3. The principle of fairness: organic farming should build on relationships that ensure fairness with regard to the common environment and life opportunities.

4. The principle of care: organic farming should be managed in a precautionary and responsible manner to protect the health and wellbeing of current and future generations and the environment.

These principles are designed to ensure that all organic farming activities protect the ecosystem and ensure the sustainability of an agricultural system.

2.2.2 Conventional farming Conventional farming which is also called modern agriculture refers to a method of farming in which the use of GMOs, chemical pesticides/herbicides and chemical fertilisers are allowed.

Conventional farming systems in the Western world stems from the “green revolution” of the

1960s and heavily rely on purchased external inputs (fertilizers and pesticides). Conventional farming systems vary from farm to farm and from country to country, however, most developing countries’ conventional farming system may be classified as low input conventional

(FiBL, 2013). This is because it does not rely heavily on these external inputs: fertilizers

(chemical and organic), pesticides (herbicides, insecticides, fungicides, acaricides).

2.3 Concept of Sustainable Livelihoods The concept of sustainable livelihood has gained currency over the years as a dominant approach to implementing development interventions. The concept was first reported in the

Brundtland Commission Report of 1987 as sustainable development which was later crystallised as a concept through the works of Chambers & Conway 1992, (Solesbury, 2003).

The Brundtland Commission Report of 1987 defined livelihood as “adequate stocks and flows of food and cash to meet basic needs”. It also defined “Sustainable” as the maintenance or

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enhancement of resource productivity on a long-term basis, hence, a household may be enabled to gain sustainable livelihood in many ways: Through ownership of land, livestock or trees; through gaining of rights, hunting or gathering; through stable employment with adequate remuneration or through varied repertoires of activities. Chambers & Conway (1992) suggested that ‘livelihood comprises the capabilities, assets (stores, resources, claims and access) and activities required for a means of living. A livelihood is sustainable when it can cope with and recover from stress and shocks, maintain or enhance its capabilities and assets and provide viable opportunities for the next generation and which contributes to net benefits to other livelihoods at the local or global levels and in the long and short term.

The Department for International Development (DFID), defines sustainable livelihoods as

“coping with and recovering from stresses and shocks and maintaining or enhancing capabilities and assets both now and in the future, while not undermining the natural resource base’ (DFID, 1999).

The assumption by DFID is that all individuals in society are endowed with some level of human, financial, physical, natural and social/capital resources which describe their livelihood assets (DFID, 1999). The individual should select strategies (primary production, manufacturing or services) that are carried out to improve their level of food security, income and employment, wellbeing, reduce vulnerability and environmental degradation.

The DFID livelihood framework is therefore adopted by this study because it is holistic, people centred, builds on the strength of vulnerable people rather than focusing on their weakness, focuses on multilevel partnerships, imbibes as well as integrates all dimension of sustainable development (social, economic, institutional and ecological), (Carney et al., 1999).

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2.4 Contribution of flora species to food security, wellbeing and income Agricultural biodiversity especially the biological variety exhibited among crops provides the basic resources farmers need to adapt to variable conditions in marginal environments and the resources required to increase productivity in more favourable settings. Agriculture is fundamental to the food system as biodiversity is to food and agricultural systems by providing the variety of life (Tansey & Worsley, 2014). Food is the basic human need for survival, health and productivity. It is the foundation for human and economic development (Turyahabwe et al., 2013).

FAO (2010) as cited in Hunter & Fanzo (2013) estimates that, of a total 300,000 plant species, only 3 % have been consumed as food since the origin of agriculture. Out of the 300,000, only

150–200 species have been commercially cultivated of which only four – rice, wheat, maize and potatoes supply 50 % of the world’s energy needs, while 30 crops provide 90 % of the world’s caloric intake. Intensification of agricultural systems especially conventional farming has led to a substantial reduction in the diversity of cultivated and uncultivated flora species as pointed out by Frison et al. (2011) and Hole et al., (2005). The implications of this loss of biodiversity (as well as loss of associated ecological knowledge) for global food supply are scarcely understood, especially from the perspective of nutrition.

For instance a large proportion of the Ghanaian population do not consume adequate amounts of flora species to meet their daily requirement of nutrition (Ghana Statistical Service-GSS,

2013; Hunter & Fanzo, 2013). Even what is consumed has a large proportion of these nutrients destroyed or lost during preparation and cooking. There is reduced effectiveness in ensuring food security all year round due to the fact that very few flora species (plantain, cassava, and yam) are cultivated, with the majority being collected from the wild or fields and plantations

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as stated earlier. In some of the ecosystems they are regarded as weeds and are often weeded out and are not available during the minor season (Rubaihayo, 2002 and Rubaihayo, 1995).

Diversity of plant divisions, species and gene pools can increase the productivity of farming systems in a range of growing conditions, and more diverse farming systems are also generally more resilient in the face of perturbations, thus enhancing food security (Frison et al., 2011).

2.5 Empirical Studies on Flora Diversity in Organic and Conventional farming A meta- analysis by Bengtsson et al. (2005) on the effects of organic farming on flora diversity was positive on species richness and abundance: 53 out of the 63 studies (84%) showed higher species richness in organic agriculture systems. Similarly, Hole et al., (2005) analyzed the impacts of organic farming on biodiversity and suggested that though there is a positive effect of organic farming on biodiversity, conventional farming could reach such value if it’s properly managed. This finding was supported Gibson et al., 2007. Hole et al., (2005) however indicated the need for a long term, system-level studies of the biodiversity response to organic farming.

Roschewitz et al., (2005) have compared the effects of landscape complexity on arable weed species diversity in organic and conventional farming. They indicated that, local management

(farming system) and complexity of the surrounding landscape has an effect on alpha, beta and gamma diversities of weeds in 24 winter wheat fields in Germany. The authors indicated that, species diversity under organic farming systems was clearly higher in simple landscapes, but conventional vegetation reached similar diversity levels when the surrounding landscape was richer because of the presence of a refugium. A refugium is an area in which the weed populations could survive through a period of unfavorable conditions.

Hyvönen et al., (2003) also conducted a study on weed species diversity and community composition in organic and conventional cropping of spring cereals in Finland. They concluded 12

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that organic cropping tends to promote weed species diversity at an early phase of cropping history, in particular for species susceptible to herbicides.

Weibull et al., (2003) studied species richness of plants, butterflies, carabids, rove beetles and the diversity of spiders in cereal fields, leys (grass and clover crop) and semi-natural pastures at 16 farms in Central East Sweden. The authors concluded that, there is no difference when comparing the biodiversity and abundance of plants, butterflies, rove beetles and spiders in organic and conventional farms, however, carabids richness was higher in conventional farms.

Weibull et al., (2003), claimed that species richness was higher on farms with a heterogeneous landscape, while farming practice was of relatively less importance in relation to landscape features for species richness.

A meta-data analysis by Rahmann, (2011) to ascertain whether organic farming has an advantage for biodiversity (fauna and flora) in comparison to conventional farming systems found that 327 out of 396 (83%) relevant scientific papers indicated a higher degree of biodiversity in organic farming when compared to conventional farming. The author concluded that, organic farming produces more biodiversity.

Several other articles have made comparison of organic and conventional farming systems in terms of flora diversity and the positions are diverse (Table 2.1). For instance about 12 articles on flora in perennial crop land indicates that, there is more flora diversity in organic farms compared to only 2 articles argued that there is more flora diversity in conventional farms.

However, one article showed no clarity in flora diversity in the two systems (Rahmann, 2011).

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Table 2.1 Comparison of flora diversity of organic and conventional farming on number of reviewed articles

Number of published articles Type of land use More diversity in More diversity in Unclear or organic farms conventional farms indifferent Flora on arable land 62 0 3 Flora on grass land 19 0 5 Flora on perennial crop land 12 2 1 Total 93 2 9 Source: Rahmann, (2011)

2.6 Socio-economic Characteristics of Organic versus Conventional farmers A relationship between socio-economic status, such as age, gender, education, farming experience and innovativeness was generalized from many adoption studies in the adoption model (Rogers 1995).

Duram (1999) and Tovey (1997) found that, a high proportion of organic farmers are people with urban backgrounds, high levels of general academic education, younger farmers and less farming experience. A study by Comer et al., (1999) on socio-economic characteristics of sustainable and conventional farmers showed a positive relationship between farmers’ education and Sustainable Agriculture System (SAS).

There is some indication that gender is a factor in the decision to convert to organic farming, although the role of women in organic agriculture in general and in the decision making in particular has not been studied in detail. On several of the 100 organic farms whose motives to go organic were studied in a qualitative social study in Switzerland, the initial ‘organic’ ideas came from the woman (Fischer, 1982). Women’s influence is also likely to be important where reasons of family health are cited, as traditionally it is the women’s role to look after nutrition and health of the family.

A survey of organic and conventional horticultural farmers in the United Kingdom by Burton et al.’s (1997) concluded gender to be important with women farmers dominating.

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2.7 The Role of Institutions in Promoting Biodiversity Conservation in Ghana According to Hodgson, (2006), “Institutions are systems of established and embedded social rules that structure social interactions”. This definition however integrates both formal and informal systems and encompasses organisations, social and cultural norms as well as conventions which promote the conservation of biodiversity.

Historically, the dominant approach to conservation strategies by institutions was the establishment of protected areas from which humans and other species were excluded (Adams,

2004). This model has been referred to as the 'fortress conservation'. The “Fortress conservation involved the creation of protected areas, the exclusion of people as residents, the prevention of consumptive use and minimisation of other forms of human impact (Brockington & Schmidt-

Soltau, 2004). This narrative was very influential in sub-Saharan Africa as it led to the creation of national parks. The 'fortress conservation' was challenged not to exclude local people, either physically from protected areas or politically from the conservation policy process, but to include their participation. This new way of conservation was referred to as “community conservation” (Adams & Hulme, 2001). This initiative included community-based natural resource management, state/community co-management and integrated conservation and development programmes (Barrow & Murphree, 2001).

For instance according to Boaten, (1998) as cited in Hens, (2006), trees which are regarded as housing spirits in the Ashanti region of South – Western Ghana are not to be felled without performing rituals. This custom had a protective effect on trees as odum (Chlorophora excelsa),

African mahogany (Khaya ivorensis) and tall palm trees as betene (Elaeis guineensis) and osese

(Funtumia sp.). Also in the Northern savannah zone of Ghana, shea butter (Butyrospermum parkii) and the Dawadawa (Parkia clappertoniana) trees are subject to the same traditional protection system.

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Attuquayefio & Fobil (2005) observed that there have been collaborative research projects between the Wildlife Division (Forestry Commission) and counterparts departments in the various universities, research institutes and some environmental NGOs, notably the Ghana

Wildlife Society (GWS), Friends of the Earth (FOE), Green Earth Organisation and

Conservational International. This is an effort to implement biodiversity conservation programmes through sound environmental practices and sustainable resource utilization.

2.8 Evidence of Climate Change in Forest regions of Ghana Climate change refers to a change in the state of the climate that can be identified (eg. Using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer, (Inter governmental Panel on Climate

Change- IPCC, 2007).

It is however attributable to both natural and anthropogenic causes increasing the concentration of greenhouse gases in the atmosphere. Gases responsible for the greenhouse effect are termed greenhouse gases and they allow the passage of the sun’s radiation (visible light) but absorb reradiated heat (infrared) energy emitted from the earth and these gases occur naturally.

The evidence of climate change in Ghana is manifested through: rising temperatures; declining rainfall totals and increased variability; rising sea levels and high incidence of weather extremes and disasters. There has been a 1°C increase in the average annual temperature in the last 30 years (Minia et al., 2004). In the Forest zones, reductions in rainfall are reported (Owusu

& Waylen 2009) to be about 20% which is far larger than the 10% reduction in the Transition and Savannah to the north. The major challenge in the forest however, is that the reduction permeates the entire rainfall regime.

Forests in Ghana are characterised by their show of a strong floristic and structural gradient correlated with climate (Swaine & Hall 1986), however, Ghana’s vulnerability to climate

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change especially in the Forest zone is evident in the various impacts of droughts and floods.

This is largely due to the over dependence on climate-sensitive sectors for instance agriculture and natural resources.

Agricultural production is predominantly rain-fed and any changes in rainfall pattern would have serious impact on productivity. These changes in the climatic conditions is deepening rural vulnerability to poverty and enhancing the process of land degradation and desertification making agriculture a more risky venture.

The major sources of wealth and livelihoods for majority of rural Ghanaians and the state is natural resources. Sustainable management of these resources is crucial for generating food, income, tourism, foreign exchange and biodiversity needed for ecosystem services. Climate change is said to impact on natural resources negatively both directly and indirectly. Directly, the increasing frequency of droughts reduces biodiversity; while low levels of rainfall, high temperatures and winds exacerbate bush fires. Indirectly, climate change impacts on the ability of economic sectors to provide adequate income and services to the population which is translated into unsustainable harvesting of natural resources through intensification of agriculture which have negative consequences for biodiversity, a major source of tourism and ecosystem services (FAO, 2010).

2.9 Organic Agriculture as Climate Smart Practice Concerns over climate change and increasing pressure on land have resulted in increased promotion of sustainable production methods that increase yields, while protecting the environment as well as increasing the resilience of crops to climatic change (Branca et al.,

2011). Such agro-ecological practices form part of organic agriculture principles, but in practice, low-input production with none, or very little sustainable soil and water management practices are frequently certified as organic in many developing countries.

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Climate smart agriculture has its mandate to sustainably increase food production, build resilience to climate change (adaptation), reduces/removes greenhouse gases (mitigation) and enhance the achievement of national food security and development goals (FAO, 2010).

There is a link between Climate smart agriculture and organic farming. While the two aim to build resilience and adaptation, reduce greenhouse gas emissions and enhance food security for a national and developmental goal in the face of climate change, organic farming is usually perceived in most literature as a climate smart activity (FAO, 2013).

As discourse on climate change and agriculture has advanced, climate-smart agriculture has emerged as a framework to capture the concept that agricultural systems can be developed and implemented to simultaneously improve food security and rural livelihoods, facilitate climate change adaptation and provide mitigation benefits (Scherr et al.,2012).

The World Bank asserts that climate smart agriculture seeks to increase sustainable productivity, strengthen farmers’ resilience, reduce agriculture’s greenhouse gas emissions and increase carbon sequestration. It strengthens food security and delivers environmental benefits.

Organic farming is a low cost tool for addressing mitigation, adaptation, resilience, food security and sustainable rural development (FAO, 2008).

Climate smart agriculture promotes agricultural best practices, particularly integrated crop management, conservation agriculture, intercropping, improved seeds and fertilizer management practices, as well as supporting increased investment in agricultural research.

Organic agriculture as a climate smart activity has outputs, outcomes and impacts to society and the environment (figure 2.1).

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Organic Farming  Payment for Ecosystem Services  Crop insurance

CSA Activity

 Soil organic carbon levels increased  Above and below ground biomass increases

 Soil depth increased Reduction in resource use

Outputs (labour, energy, water etc)  Soil water retention increased

 GHG emissions reduced  Climate related yield variability is reduced  Carbon sequestration increased  Agric. productivity and income

 Increased drought resilience increased Outcomes  Better water & nutrient availability on soil  Natural, Physical & financial assets stocks increased

Contribute to climate Adaptation to climate Improve food security change mitigation change Impacts

Figure 2.1 Link between Climate smart and organic agriculture Source: Adopted from FAO Climate Smart Agriculture Source book, (2013) The outputs, outcomes and impacts are defined as; Outputs: goods or services delivered by organic agriculture

Outcomes: short term changes in the population or system targeted by the intervention that result from delivering or using outputs.

Impacts: longer term changes that result from outputs and outcomes, either within or outside the population or system the intervention targets (e.g. the intervention may produce or influence wider societal changes).

2.10 Nature of Flora in Organic and Conventional cocoa farms The nature of flora are in several different ways based on its classification, and the further away from cocoa farms (organic and conventional) we get, the more the name indicates a flora’s relationship to other flora, and tells about its place in the plant world rather than in the cocoa farm.

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Usually, only the order, family, genus and species are of concern to the farmer, although the subspecies, variety or cultivar to identify a particular plant are sometimes included.

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CHAPTER THREE MATERIALS AND METHODS 3.1 Introduction This chapter presents the materials and methods employed to accomplish the objectives of the study. It includes the conceptual framework and the methods of data collection and analysis.

The scope and limitation of the study is also included in this chapter.

3.2 Conceptual framework Conventional farming dominates cocoa production in Ghana (Appiah et al., 2000). The trade- offs between conventional farming practices for greater livelihood security and concerns about environmental sustainability have been recognised. There is also the general view that conventional farming maximises production or income in the short term but guarding against vulnerability to external shocks and building resilience of farms in the longer term is not ascertained (Edwin & Masters, 2005).

This study assumes that conventional cocoa farmers are vulnerable as a result of biodiversity loss (trend) due to agricultural intensification which is based on use of inorganic inputs.

Seasonality and climate change shocks further affects the natural capital (flora) therefore achieving food security, income, wellbeing and reduction in vulnerability seems a mirage.

Innovative processes and structures are needed to transform the system. In the communities studied, public sector institutions such as Cocoa health and Extension Division (CHED), non- governmental organisations (NGOs) such as Agro Eco-Louis Bolk Institute (AE-LBI) and

Rainforest Alliance (RA) and private cocoa beans buyers such as Armajaro Limited, are supporting the incorporation of organic cocoa farming. These organisations view organic farming practices as climate smart since it not only reduces biodiversity loss but also supports carbon sequestration.

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The expectation is that households that have adopted organic farming will have more diverse and abundant species. It is then assumed that the consumption and sale of such species will lead to increased food security, income and well-being as well as reduced vulnerability for practicing households. The sustainable livelihoods framework (SLF) developed by the

Department for International Development (DFID) (2000) captures the sense of the argument

(Figure 3.1). It is noted that the vulnerability context influences the level of livelihoods assets which include natural (N), human (H), financial (F), physical (P) and social (S) capital. The influence of transforming structures and processes lead to adoption of strategies that result in improved outcomes.

LIVELIHOOD ASSETS TRANSFORMING LIVELIHOOD

VULNERABILITY STRUCTURES & ORDERIN TO ACHIEVE OUTCOMES H PROCESSES* CONTEXT • More income STRUCTURES • Increased LIVELIHOOD wellbeing • SHOCKS S NN • Levels of Influence STRATEGIES government • TRENDS & access • Reduced • Laws vulnerability • Private • SEASONALITY sector • Policies P F • Improved food h • Culture security Conventional • Institutions • More sustainable use

farming PROCESSES of NR base

Figure 3.1 Conceptual framework of study

Source: Author’s adaptation of DFID’s SLF (DFID, 2000)

3.3 Study area This study was carried out in the Atwima Mponua District of the Ashanti Region of Ghana.

The district is located in the south-western part of the Ashanti Region and covers an area of approximately 894.15 square kilometres (Figure 3.2).

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The district shares boundaries with four (4) other districts, the Amansie West District to the south, Ahafo Ano South District to the north, Atwima Nwabiagya District to the East and

Bibiani–Anwhiaso–Bekwai District of the Western Region to the west

(http://www.ghanadistricts.gov.gh).

The district is marked by double maxima rainfall seasons with the major rainfall period begining from March to July peaking in May. The average annual rainfall for the major season is about 170 cm– 185 cm per year. The minor rainfall period begins in August tapering off in

November with an average minor annual rainfall of 100 cm - 125 cm per year. Mean annual temperatures of 27oC are recorded in August and in March. The climate in the district is ideal for the cultivation of cash and food crops such as cocoa, cola, oil palm, maize, cocoyam, plantain, cassava, rice and all kinds of vegetables. Even though the rainfall is adequate for agriculture, its erratic and unpredictable nature and concentration have adverse implications for rain fed agriculture. The soil type in the district is the forest ochrosol. The vegetation is basically of the semi deciduous type.

The flora and fauna are diverse and composed of different species of both economic and ornamental tree species with varying heights and game and wildlife (http://www.mofa.gov.gh).

The district has organic and conventional cocoa farmers located in several communities within the Tano Dumase Area Council, however, this study was conducted in two communities namely Gyereso and Pasoro. The two communities have similar land forms and so biases in comparison has been reduced.

In order to locate the communities, waypoints of each study site were recorded using a Global

Positioning System (GPS) unit (GeoXT™ Pocket PC handheld GPS unit with FastMAP CE™ software). The waypoint data are inserted in figure 3.2 showing the location of communities studied.

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Figure 3.2 Location of farms in the two (2) communities studied

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3.4 Method of Data Collection 3.4.1 Flora survey and farm selection In August 2014, the ProEco Africa project selected 150 households for farming systems’ study in Gyereso and Pasoro. The ProEco Organic Africa database was examined for relevance in

January 2015. A total of thirty two (32) cocoa farmers managing organic and conventional farms in the two communities were purposively selected from the data base. Only farmers who managed cocoa farms that were aged between 2 and 3 years inclusive, were selected. The canopies of these farms were not closed and therefore above ground flora growth was in progress. After one month, those who were unwilling to participate in the experiment were replaced with willing farmers.

For simplicity, there was an even distribution of the thirty-two farmers among the farming systems and among the communities: eight each of organic and conventional cocoa farms were selected from Gyereso and Pasoro. Organic farms were selected and paired with a conventional farm of similar size within 5 km where there was no adjoining ﬁelds between them. The pairing was done to minimize differences in landscape and elevation as well as soil types and characteristics (Anglaaere et al., 2011).

Procedure for sampling the flora

In each cocoa farm, a 25 m x 25 m main plot was marked out randomly. This plot was subdivided into five sub-plots of 5 m x 5 m out of which one was randomly selected to determine the quantitative abundance of herbaceous plants. The number of individual plant species in each plot was recorded. For each main plot, a species composition list was prepared to include trees, shrubs and herbs of the two farming systems. It was noted that the medicinal flora as well as crop flora like cocoyam and plantain were largely voluntary plants. Species were identified as far as possible on site with the help of a Taxonomist (Mr. G.Y Amponsah, Botany

Department, University of Ghana, Legon). Samples which could not be identified were

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collected and pressed for later identification at the herbarium in the Department of Botany,

University of Ghana, Legon.

3.4.2 Household survey Household heads of cocoa farms that were sampled were interviewed to assess the contribution of flora diversity on their cocoa farms to their livelihood. Secondary data on the following variables were obtained from the ProEco Africa data base:

1. Respondent’s bio data,

2. Household composition,

3. Farming activities,

4. Agro-chemical application information,

5. Membership of a farmer-based organization,

6. Health status of farmers’ family and

7. Cost of production of farm produce.

A face to face discussion was held with the farmers to validate the secondary data and collect new information through questionnaire administration (see appendix 1). The new data collected was on:

1. Frequency of identified flora species consumption (as a measure of desirability or

popularity),

2. Trends in sales,

3. Availability of flora species all year round (2014)

4. Income from sale of flora and proportion used to cover cost of health.

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3.5 Methods of Data Analysis 3.5.1 Flora survey i. Measuring species compositional similarity: Jaccard index The Jaccard index of similarity was used to calculate the similarity between the two farming systems using the following formula:

풂 퐉 = (ퟑ. ퟏ) (풂 + 풃 + 풄)

Where, J = Jaccard index of similarity

a= the number of species found in both organic and conventional farms

b = the number of species in only organic cocoa farms c = the number of species in only conventional cocoa farms and 0≤ J ≥1

Where, 0= complete dissimilarity

1= complete similarity

A study by Gnanakumar et al., (2012) in comparing the diversity of egg parasitoids in organic and conventional paddy ecosystems used the Jaccard similarity index as a measure of how different (or similar) ranges of habitats were. Varón et al., (2007) used the Jaccard’s index of similarity to compare A. cephalotes harvest profiles from the farm management types, based on the %age of each of the plant species observed being harvested and the % availability based on the leaf area index (LAI) measurements.

ii. Measuring the diversity among species: Shannon diversity index (H’) and Simpson’s index (D)

The Shannon diversity index (H’) and Simpson’s index (D) were used to determine the species diversity of the Cocoa farms (Asase et al., 2014; McCune & Grace, 2002 and Magurran,

1988).The Biodiversity Professional Software (free version) was applied in calculating the indices. 27

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Shannon diversity index (H’)

It was calculated using the following formula:

푠

퐇′ = − ∑(푝푖In 푝푖) (3.2) 푖=1

And p = n/N (3.3)

Where,

H’ = Shannon diversity index

n = the number of individuals of one particular species found

N = total number of individuals found

ln is the natural log

∑ is the sum of the calculations, and

s is the number of species in a community

H’ accounts for both abundance and evenness of the species present in a community.

Simpson’s index (D) is a measure of diversity that takes into account both species richness and evenness. Simpson’s index (D) measures the probability that two organisms sampled from a community will belong to the same species.

Species richness is a relative term that refers to the number of species in a community and

Species evenness defines the number of individuals from each species in a given area or community. The formula for the Simpsons index is as follows:

푠 [푛푖 (푛푖 − 1)] 퐃 = ∑ (3. 4) [푁 (푁 − 1) 푖=1

Where n and N are as defined in equation 3.3.

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iii. Hypothesis testing of indices

Ho 1: H’ (OF) - H’ (CF) =0 (There is no difference in Shannon index of diversity of flora in organic and conventional cocoa farms)

Ha 1: H’ (OF) - H’ (CF) > 0 (The Shannon index of diversity of flora in organic cocoa farms is greater than that of conventional)

Ho 2: D (OF) - D (CF) =0 (There is no difference in Simpson diversity index of flora in organic and conventional cocoa farms)

Ha 2: D (OF) - D (CF) > 0 (The Simpson diversity index of flora in organic cocoa farms is greater than that of conventional) iv. Test statistic for hypothesis A non-parametric test, the Mann-Whitney U test was used to test the significant differences in flora diversity among organic and conventional farms based on the indices and measures obtained, viz, Shannon and Simpson’s indices and the total species and total individuals calculated. v. Decision criterion

Reject the null hypothesis (Ho) in favour of the alternative hypothesis (Ha).

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3.5.2 Household survey The socio economic variables, their measurements and a prior expectations are shown in table 3.1. Table 3.1 Socio economic variables and their measurements

A prior expectation (s) Variable (s) Description Measurement Organic Conventional farmers farmers Age Age of farmer years +/- +/- Gender Sex of farmer 1= male 0= female +/- +/-

Education Level of education none, basic and + - of farmer secondary Origin Place of origin of 1=native +/- +/- farmer 0=settler/migrant Household size Household size number + - Years of Number of years years in farming + - farming of farming Land Land ownership 1=Own land allocated by +/- +/- ownership status village head 2=Own land inherited from family 3=Own land purchased 4=Rented on longer term basis Land use Land use before 1=arable land +/- +/- crop production 2=fallow lands 3=virgin forest lands Soil fertility Perception of soil 1=high, 2=moderate + - status fertility status 3=low Source: Author, (2015)

i. Description of variables selected and statement of hypothesis Age of the farmer (Age): Age was measured as a continuous variable in years. An aged farmer is believed to be wise in resource use and it is expected to have a positive effect on the choice of farming system. It is hypothesized that age has a positive impact on the type of farming system one engages in. Mazvimavi & Twomlow (2009) argue that, an older farmer may also be tradition-bound and not willing to adopt new technology (organic farming- climate smart). In this study, age is expected to have a positive or negative impact on farming system.

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Gender: This is defined as the sex of the respondent and is measured as a dummy variable where a male is assigned one (1) and female is assigned zero (0). This study expects gender to either have a positive or negative impact on farming systems.

Education: Education was measured as the highest level of formal education the farmer had.

None means the farmer has no formal education. Basic means the farmer has had either primary, JHS or middle school education. Secondary means the farmer has had a senior high school education. Education leads to knowledge acquisition, skills, problem-solving ability, discipline, motivation and self-confidence. An educated farmer will be able to understand the opportunities organic and conventional farming presents. Duram (1999) and Tovey (1997) suggested that organic farmers are people with high levels of general academic education.

This study expects organic farmers to be more educated (positive).

Place of origin: This was measured as native (1) and settler/migrant (0). Place of origin is expected to either be positive or negative for organic or conventional farmers.

Household size: This is the total number of dependents in the house. It was measured as a continuous variable. In this study, it is expected that organic farming households will have a greater number of household members because of the labour requirement of the practice (Lohr

& Park, 2009), therefore household size is expected to be negative for conventional farmers.

Number of years of farming: The number of years of farming was measured as a continuous variable in years. Duram (1999) and Tovey (1997) suggested that organic farmers are people with less farming experience. Therefore conventional farmers are expected to be more experienced.

Land ownership status: This was measured as own land allocated by village head (1), own land inherited from family (2), own land purchased (3) and land rented on longer term basis (4). It was expected to either be positive or negative for organic or conventional farmers.

Land use before crop production: This was measured as arable land under cultivation (1), 31

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fallow lands (2) and virgin forest lands (3). It was expected that, the different land uses will either be positive or negative for organic or conventional farmers.

Perception of soil fertility status: This was measured by how farmers perceived soil fertility status of their farms to be better than that of their neighbours’. It was expected that, organic farmers will perceive their soils to be more fertile than their neighbours.

ii. Test statistic for hypothesis

A chi square contingency test was used to test for association between the farming systems

(organic and conventional) and the socio economic variables (see table 3.1).

The contribution of flora to sustainable livelihoods was measured using the DFID sustainable livelihood outcomes. The livelihood outcomes measured were improvement in level of food security, wellbeing, income from flora and reduced vulnerability. The livelihood outcomes were measured as shown in table 3.2.

Table 3.2 Sustainable livelihood indicators and their measurements

A prior expectation (s) Indicator Measurement Organic Conventional farmers farmers Food security Percentage of flora availability all + - season (2014), flora sale and flora + - consumption + - Wellbeing Health cost funded from flora income; + - cost contributed was measured using percentages Income Earnings made from the sale of flora + - in GHS Vulnerability Improved social inclusion; + - membership of farmer based organization Source: Author, (2015)

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i. Description of sustainable livelihood indicators and statement of hypothesis

Food security: A “situation when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life”. This study assumed that, edible flora species identified on the farms were nutritious and once available, will be consumed or sold. It was therefore measured as the percentage of flora available all season, flora consumption, and percentage of flora sale.

The percentage of flora availability all season (2014), consumption habits and incomes through the sale of the edible flora were compared between organic and conventional cocoa farmers. It was hypothesised that, since there is high flora diversity in the organic cocoa farms studied, organic cocoa farmers will consume and sell more flora than their conventional counterparts, however the limitation of this measurement is that, there should have been a measure for farmers who didn’t have the species available on their farms to purchase.

Wellbeing: It refers to dynamic process as that give people a sense of how their lives are evolving. Well-being range from the need for an adequate income to meet basic needs to people's social involvement and interaction with others (Nimpagaritse & Culver, 2010).

Farmers who spent more than 40% of flora income to finance the cost of their health bills were considered to have better wellbeing. The cost contributed was measured using percentages. It is expected that, flora income will contribute more to the wellbeing of organic farmers compared to that of conventional.

Income: This refers to the earnings made from the sale of flora. It was measured using the following income ranges in GHS: 10-50, 51-100, 101-150, 201-250, 251-300 and 301-400. The percentage of response for each income range was presented. It is expected that, organic farmers will earn more income from flora sale compared to their conventional counterparts.

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Vulnerability reduction: This was measured as improved social inclusion; membership of farmer based associations. It was analysed using percentages to compare organic farmers and conventional farmers. It is hypothesised that the organic cocoa farmers were more resilient than that of the conventional cocoa farmers.

ii. Test statistic for hypothesis

A t-test statistic was used to test for mean differences in flora availability, consumption and sale (food security) of the two systems.

iii. Decision criterion

Reject the null hypothesis (Ho) in favour of the alternative hypothesis (Ha).

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CHAPTER FOUR RESULTS AND DISCUSSION

4.1 Introduction This chapter presents and discusses the results of the analysis of data gathered during the study.

The first section covers the socio-economic characteristics of the farming households selected.

The second section presents a description of the nature of flora in organic and conventional cocoa farms; the extent to which flora diversity in organic and conventional cocoa farms differ is presented in section three. Finally, the contribution of flora to livelihoods of organic and conventional cocoa farming households is discussed.

4.2 Socio-Economic Characteristics of the Sampled Households 4.2.1 Age distribution of respondents Table 4.1 shows the results of age distribution of cocoa farmers of the two farming systems in the district. The mean ages for organic and conventional cocoa farmers were 47 years and 55 years respectively. This implies that farmers in the different farming systems are relatively old but can still be considered as an economically active group in Ghana. From the results, 37.5% of the organic farmers interviewed were in the age range of 46 years to 55 years compared to

25% of conventional farmers. These findings are in line with Theocharopoulos et al., (2012) who had 41.4% of organic farmers in the age range of 46 years to 55 years compared to 21% of their conventional counterparts. About 25% of the youth (35-45 years) were into both organic and conventional farming because they are usually engaged in off farm agricultural activities like tree nursery, trading and craft. About thirteen % of organic cocoa farmers in the sample were less than or equal to 35 years compared to 6 % of conventional farmers; about 6

% of organic cocoa farmers were above 65 years compared to 19 % of conventional cocoa farmers. Results on farmers’ age less than 35 years showed that 12.5% of organic farmers were in this age range compared to 6% of their conventional counterparts.

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These finding are consistent with Duram (1999) and Tovey (1997) who indicated that high proportion of organic farmers are younger farmers, nevertheless, all the above-mentioned differences in ages were not found to be statistically significant given the chi square (χ2) value

=1.876, d.f = 4 and p=0.759.

Table 4.1 Age distribution of cocoa farmers of the two farming systems

Age distribution Organic farmers (%) Conventional farmers (%) ≤35 12.5 6 36-45 25 25 46-55 37.5 25 56-65 19 25 Above 65 6 19 Mean 47 55 Minimum 31 35 Maximum 66 79 Source: Compiled from data base of ProEco Africa, 2015

4.2.2 Gender distribution of respondents The results of gender distribution of respondents are presented in figure 4.1. Men dominated both farming systems with 81% of males engaged in organic cocoa farming and 62.5 % conventional cocoa farming. This reflects the fact that men have easier access to land for tree crop farming. Another reason for male dominance is that cocoa farming activities (especially harvesting and pruning) involve a lot of vigorous work. Interestingly, conventional farming had more (38%) female farmers compared to their organic counterparts (19%). This observation is consistent with findings of Hall & Mogyorody (2007) where female involvement in farming declines when they shift from conventional to organic farming. The chi square test however provided values that suggested that gender does not influence choice of farming system: χ2 value =1.39, d.f = 1 and p=0.238.

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Conventional farmers

Female Male Organic farmers

0 20 40 60 80 100 Percentage

Figure 4.1 Gender distribution of cocoa farmers

4.2.3 Educational level of respondents The results on educational level of the respondents is presented in figure 4.2. The result shows that majority (68%) of organic farmers have had basic education (primary/junior high school/middle school) compared to 38% of their conventional counterparts. About 56 % of conventional farmers and 19% of organic farmers had no formal education, hence they cannot read and write the English language. The illiteracy rate of 38% is above both the Ashanti

Regional average of 35 % and the national average of 22.1 % (GSS, 2013). About 19 % of organic farmers and 6% of conventional farmers had attained either technical or secondary education. The result supports the national trend of increasing number of literates engaged in cocoa farming (GSS, 2013). The high level of education among organic farmers is consistent with findings of Duram (1999) and Tovey (1997), that a high proportion of organic farmers are people with high levels of general academic education. The results of the chi square test is as follows: χ2 value =5.0, d.f = 2 and p=0.082, showing that there is significant association between farming systems and level of formal education of respondents (albeit, weak).

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Secondary

Basic Conventional farmers Organic farmers None

0 20 40 60 80 Percentage

Figure 4.2 Educational level of respondents

4.2.4 Place of origin of respondents The place of origin of the respondents is presented in figure 4.3. The results shows that majority of the respondents in conventional farmers are natives of the communities (Gyereso and

Pasoro) they reside. There are more settlers/migrants who are organic farmers than conventional farmers: (50% compared to 38%). The chi square (χ2) value =0.508, d.f = 1 and p=0.476 shows that place of origin does not influence adoption of farming systems.

Conventional farmers

Settler/migrant Native Organic farmers

0 10 20 30 40 50 60 70 Percentage

Figure 4.3 Place of origin of respondents 38

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4.2.5 Household size distribution of cocoa farmers Distribution of household size is presented in Table 4.2. The mean household size for both organic and conventional cocoa farmers was eight. The mean household size was above the recorded 4.6 for the district in the 2010 Population and Housing Census (PHC) report (GSS,

2013). None of the households studied had a household size between 1 and 3. About 56.3% of organic farming household sizes ranged between 7 and 9 compared to 37.5% of their conventional counterparts. This means that organic farming households have high household sizes because of the labour requirements of the practice which is also consistent with findings of Lohr & Park, (2009). The chi square (χ2) value =1.053, d.f = 2 and p-value=0.591 nonetheless showed no significant association between the farming system and household size distribution.

4.2.6 Number of years of farming The results on number of years of farming is shown in Table 4.2. The mean years of experience in farming for conventional farmers was 28 years compared to 23 years of organic farmers.

There was an indication that, 37.5% of organic farmers had between 21 to 30 years of farming experience while that of conventional farmers was 25%. About 18.8% of organic farmers had farming experience between 31 and 40 years compared to 12.5% of their conventional counterparts. Similarly, 31.3% of organic farmers had farming experience ranging between 10 and 20 years. This follows findings of Duram (1999) and Tovey (1997) that organic farmers have less farming experience. The mean years of experience follows the pattern of

Santos & Escalante (2010) who had mean years for conventional and organic farmers to be 20 and 14 years respectively. The chi square (χ2) value =0.588, d.f = 2 and p-value=0.745 shows no significant association between farming system and number of years of farming experience.

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Table 4.2 Household size and farming experience of respondents

Variables Organic farmers (%) Conventional farmers (%)

Household size 1-3 0 0 4-6 37.5 37.5 7-9 56.3 37.5 10-12 6.3 18.8 Above 12 0 6.3 Mean 8 8 Minimum 6 5 Maximum 12 20

Experience in farming <10 6.3 18.8 10-20 31.3 25 21-30 37.5 25 31-40 18.8 12.5 Above 40 6.3 18.8 Mean 23 28 Minimum 9 6 Maximum 46 65 Source: Compiled from data base of ProEco Africa, 2015

Summary: Apart from education, a person’s socio-economic characteristics is not likely to influence choice of farming system.

4.2.7 Land ownership status of respondents The distribution on land ownership status of respondents is shown in figure 4.4. The results show that, for the two farming systems, 50% of the respondents own ed land by inheritance.

Respondents who owned land through lease were 25 % and 18.8 % for organic and conventional farmers, respectively. The results also showed that, respondents who owned land through village head allocation were 18.7 % and 6.2 % for organic and conventional farmers respectively. Land ownership through long term lease for organic and conventional farmers however was 6.3 % and 25 % respectively. There was no significant association between farming system and land ownership status of respondents as given by the chi square (χ2) value

=5.94, d.f = 5 and p=0.312.

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Own land, allocated by village head

Own land, Inherited from family

Conventional farmers Organic farmers Own land, purchased

Rented on longer term basis

0 10 20 30 40 50 60 Percentage of ownership

Figure 4.4 Land ownership status of respondents

4.2.8 Land use before crop production The type of land use before crop production is shown in figure 4.5. The results indicated that, farmers selected arable land under cultivation, fallow lands and virgin forest lands to establish cocoa farming. About 44 % and 31 % of organic and conventional farmers respectively selected arable lands. The proportion that selected fallow lands under the two systems was 31.3 % and

37.5 % for organic and conventional farms respectively. Interestingly, fewer (25%) organic farmers used areas known to be surrounded by forest compared 31% of conventional farmers.

During further interaction with farmers to validate results, it was explained that most organic farms were under conventional operations; farmers were sensitised and trained to leave lands fallow or stop applying conventional inputs for three years for farms to be considered organic.

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Land use before crop production was not significant with the type of farming system as shown in the results; chi square (χ2) value =0.535 d.f =2 and p=0.765.

Arable land

Fallow land Conventional farmers

Organic farmers

Forest

0 10 20 30 40 50

Percentage of land use before crop production

Figure 4.5 Land use before crop production in the study area

4.2.9 Perception of soil fertility status of sampled farms The perception of soil fertility status of the cocoa farms sampled is shown in figure 4.6. The results show that, majority (75%) of the conventional farm respondents perceived soil fertility status of their farms to be better than that of their neighbours. Fewer organic farmers (43.8%) perceived soil fertility status of their farms to be better than that of their neighbours. A chi square (χ2) value =5.316, d.f = 2 and p=0.07 shows that a significant number of organic farmers perceived the soil fertility on their farms as better than that of their neighbours, though the strength of perception by the Phi and Cramer's V tests showed a moderate strength.

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High

Average Conventional farms Organic farms

Low Percieved soil fertility status fertilitysoil Percieved

0 20 40 60 80 Percentage

Figure 4.6 Soil fertility status of sampled farms

4.3 Nature of Flora Distribution 4.3.1 Life form abundance The life form abundance of the flora species in the two farming systems indicates that, for all the three life forms (herbs, shrubs and trees) the organic cocoa farms had more life form abundance than conventional cocoa farms (see figure 4.7). Out of the 93 flora species sampled in organic cocoa farms, 20 (22%), 32 (34%) and 41 (44%) were herbs, shrubs and trees respectively. Out of the 82 flora species sampled in Conventional cocoa farms, 16 (19.5%), 31

(37.8%) and 35 (42.7%) were herbs, shrubs and trees respectively. Overall, the life form abundance under organic was higher than under conventional. Nonetheless the difference in life forms were not significant as shown in the results chi square (χ2) value =2.0, d.f =2 and p=0.157 among the farming systems.

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Trees

Shrubs Organic Cocoa farms Life Life forms Conventional Cocoa farms

Herbs

0 10 20 30 40 50 Percentage

Figure 4.7 Comparison of life form abundance in organic and conventional cocoa farms in the study area

4.3.2 Pattern of species distribution The species distribution pattern of organic and conventional farming systems are shown in figure 4.8.The results show that, organic cocoa farms had 53 species distributed randomly and

40 distributed aggregately compared to 47 and 35 species respectively of conventional farms.

Findings from the current work agree with that of Kershaw (1973) who postulated that, a farm has more random and aggregate distribution than the other, when more individual species variance mean ratio is 1 and greater than 1 for distribution patterns of species respectively.

There was therefore more random and aggregate species distribution under organic than under conventional farming. However the chi square (χ 2) value =2.0, d.f =1 and p=0.157 shows that there was significant association between the farming system and species distribution patterns.

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Random

Organic Cocoa farms Conventional Cocoa farms Aggregated Patterns of species distribution

0 10 20 30 40 50 60 Percentage

Figure 4.8 Species distribution pattern in organic and conventional cocoa farms in the study area

4.3.3 Species compositional similarity Over all, a total of 111 species were sampled in the 32 organic and conventional cocoa farms selected and they belonged to 47 families from 24 orders (See Appendix 2). Thirty-one of the species were found in only organic cocoa farms whilst 18 species were found in only conventional cocoa farms and 62 species were found in both.

The calculated Jaccard index of similarity in flora species in organic and conventional cocoa farms was 0.64 (moderate similarity). The figure indicates that only 64% similarity exist between flora distributions of the two farming systems. The similarity between the two systems is due to the fact that they both lie in the forest zone, but are not completely similar due to the difference in characteristics and farming practices (chemical usage) that affect the flora distribution.

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4.4 Extent of Difference of Flora Diversity in Organic and Conventional Cocoa Farms Flora diversity indices by study area The values of flora diversity indices obtained for each farming system in the two communities of study (Gyereso and Pasoro) and the total for Atwima Mponua District are shown in Table

4.3. The result shows there was a difference in flora diversity under the two farming systems in the two communities. The difference in the total number of species in organic and conventional farms in Gyereso was statistically significant at 5% with a Mann-Whitney U value of 10.5. The Shannon index of diversity for organic farms sampled in Gyereso was higher

(0.914) than conventional farms sampled (0.818) in the same community.

In Pasoro, the difference in the total number of species in organic and conventional farms was statistically significant at 1% with a Mann-Whitney U value of 0.

The Shannon index value for organic farms was higher (0.805) than that of conventional farms

(0.795). This implies that, the abundance and evenness of the species present in the sampled organic farms was higher than that of the sampled conventional farms in Gyereso and Pasoro, respectively, however, this difference was not statistically significant for both Gyereso and

Pasoro.

From Table 4.3, the Simpson index of organic farms shows a higher (0.048) diversity as compared to conventional cocoa farms (0.101) at Gyereso. The results further showed that, the

Simpson index of sampled organic farms showed higher (0.062) diversity as compared to sampled conventional farms at Pasoro. This is based on the principle that, the higher the

Simpson index, the less the diversity and vice versa. The difference in Simpson index between organic and conventional farming systems was statistically significant at 10% and 5% for

Gyereso and Pasoro respectively with a Mann-Whitney U value of 14 and 11.

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Flora diversity indices of Atwima Mponua District is shown in Table 4.3. The results show that, sampled organic cocoa farms had higher flora diversity than sampled conventional cocoa farms in the district. The difference in the total number of species in organic and conventional farms was statistically significant at 1% with a Mann-Whitney U value of 35.5. The difference in total individuals in organic and conventional farms was statistically significant at 10% with a Mann-Whitney U value of 80.The Shannon index of diversity for organic farms sampled was higher (0.808) than that of conventional farms sampled (0.762) in Atwima Mponua District.

This implies that, the abundance and evenness of species present in the sampled organic farms was higher than that of the sampled conventional farms in the district. The hypothesis proposed that flora diversity on organic farms is higher than conventional using Shannon (H’) diversity index. This corroborates findings of Jastrzebska et al., (2013) and Edesi et al., (2012) who had

Shannon diversity index values for organic farms higher than that of conventional farms. This difference however was not statistically significant.

Simpson index of diversity showed that there was high flora dominance in organic cocoa farms than in conventional cocoa farms. The value for Simpson indices were 0.051 and 0.084 for organic and conventional farms respectively. The difference between these values was statistically significant at 1% with a Mann-Whitney U value of 56.5. The value of Simpson index of diversity suggest that organic farming had more flora dominance, as compared with conventional farming. The hypothesis that flora diversity and dominance on organic farms is higher than conventional ones using Simpson index of diversity is in line with findings of

Solomou et al., (2013).

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Table 4.3 Comparison of farming systems with flora diversity indices in Atwima Mponua district

Community Measure Organic Conventional U index index Gyereso Total species 117 79 10.5** Total individuals 483 349 17 Shannon index 0.914 0.818 21 Simpson index 0.048 0.101 14*

Pasoro Total species 197 112 0*** Total individuals 695 538 19 Shannon index 0.805 0.795 19 Simpson index 0.062 0.078 11**

Atwima Mponua district Total species 314 191 35.5*** Total individuals 1178 887 80* Shannon index 0.808 0.762 124.5 Simpson index 0.051 0.084 56.5*** Source: Author, (2014) *, ** and *** denote 10%, 5% and 1% significance, respectively

4.5 Contribution of Flora to Sustainable Livelihoods The DFID sustainable livelihood framework was used as a guide to determine the contribution of flora to livelihoods of cocoa farmers. The livelihood variables were food security, income, wellbeing and vulnerability.

4.5.1 Food security A total of 26 flora species were used for food and medicine by the cocoa farmers as shown in Table 4.4. About 50 % of the flora species were shrubs whiles 23 and 27 % were herbs and trees respectively. The contribution of flora species to food security will be enhanced if there is high flora diversity (access to flora). This will lead to an increased consumption of flora species. The commercialization (using chemicals, synthetic fertilizers and pesticides) of agriculture has displaced many flora species (especially volunteer flora) that used to ensure a balanced rural diet (Rubaihayo, 2002).

During focus group discussions it was learnt that about 90 % of the 26 species identified were commonly known and utilized by majority of the local people, while the remaining 10% were 48

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only known and consumed by a few households in the study area. All the 26 flora species were available all season. The survey results showed that there were slight differences with respect to availability of specific flora to organic and conventional farmers. Other species such as Musa paradisiaca, Capsicum frutescens and Carica papaya. are equally available to all the farmer categories. The mode of propagation of these plants are usually volunteer, cultivated or both.

A paired t-test was run on a sample of 26 edible flora species to determine whether there was a statistically significant mean difference between their perceived availability all season in organic and conventional cocoa farms. The results showed that, organic cocoa farms have

(63.96 ± 24.44) as opposed to conventional cocoa farms (60.11 ± 26.69). This means flora is more available all season in organic farms by 3.84 compared to conventional farms with (95% confidence interval of 1.64 – 9.33), t (25) = 1.44 and Pr (T>t) = 0.0807.

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Table 4.4 Flora availability in organic and conventional cocoa farms in the study area

Species Life Mode of % respondents on form propagation availability Organic Conventional Ageratum conyzoides (Abrakyie S volunteer 62.5 50 abodwese) Alchornea cordifolia (Ogyama) S volunteer 62.5 68.8 Ananas comosus (AborƆbɛ) S Both 68.8 81.3 Antiaris toxicaria (kyenkyen) T volunteer 43.8 50 Baphia nitida (Odwene) S volunteer 43.8 25 Blighia unijugata (Akye) T volunteer 12.5 6.3 Boerhavia diffusa (NkokƆdwe) H volunteer 31.3 43.8 Capsicum frutescens (Moko) S Both 93.8 93.8 Cardiospermum halicacabum (FƆtƆ) S volunteer 100 62.5 Carica papaya (Boferi) T cultivated 87.5 87.5 Ceiba pentandra (Onyaa/Onyina) T volunteer 87.5 81.3 Colocasia esculenta (Kooko) S volunteer 50 43.8 Cyathula prostrata (Apupuaa) H volunteer 37.5 18.8 Dioscorea bulbifera (Cocoa asi bayere) S volunteer 75 93.8 Elaeis guineensis (Abɛ) T Both 81.3 75 Emelia sonchifolia (Guakuro) H volunteer 56.3 37.5 Lycopersicon esculentum (Nsusuwa) S volunteer 56.3 75 Mangifera indica (Mango) T cultivated 75 75 Manihot esculentum (Bankye) S Both 87.5 93.8 Momordica charantia (Nyanya/Nyinya) S volunteer 93.8 75 Musa paradisiaca (bode/mpusae) H Both 100 100 Persea americana (pea/paya) T cultivated 56.3 62.5 Phyllanthus amarus (abomaguwakyi) H volunteer 68.8 62.5 Synedrella nodiflora (Kwadupo) H volunteer 56.3 31.3 Talinum triangulare (ɛfan/bokoboko) S volunteer 12.5 12.5 Vernonia amygdalina (awonwene) S volunteer 62.5 56.3 Mean 63.96 60.11

T test P<0.0807 t(25)=1.443 Source: Author computation, (2014)

The results in Table 4.5 show the percent response of flora consumption. Majority of the parts of species consumed were the leafy or vegetative parts. Thirty one percent of the species were consumed as medicine whiles 50 % were used as food.

Fifteen percent of the species were consumed for both food and medicinal purposes.

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Ananas comosus (AborƆbɛ) was consumed by 93.8 % of respondents from both organic and conventional farmers. Talinum triangulare (ɛfan/bokoboko) was relished by 12.5% of organic farmers compared to 18.8% of conventional farmers.

Momordica charantia (nyanya/nyinya) and Vernonia amygdalina (awonwene) were relished by 81.3% and 93.8% respectively of organic cocoa farmers as compared to the 68.8 % and 75% consumption by conventional cocoa farmers. Ageratum conyzoides (Abrakyie abodwese) was consumed by 6.3% of conventional cocoa farmers despite its availability in high amounts

(62.5%) in organic cocoa farms as compared to conventional cocoa farms (50%). The differences in consumption of the flora species is due to the time of need and purpose for consumption. This confirms Dokosi’s (1987) claim that flora species are usually consumed as beverages for medicinal purposes (used in treating fever), as food or as a relish to accompany diets.

The results also show that, about 43.8 % of organic cocoa farmers consume Boerhavia diffusa

(NkokƆdwe) compared to the 31.3 % of conventional cocoa farmers. It is used as a substitute for okra when okra is not in season, however, Musa paradisiaca (bode/mpusae) is an important ingredient in fufu, a traditional meal cherished by respondents of the study area. One hundred percent of both farmer groups consume the species.

Emelia sonchifolia (Guakuro) is both used as medicine in treating fever, and also used to garnish palm nut soap for consumption. About 81.3 % of organic cocoa farmers consume this species as compared to the 50 % of conventional cocoa farmers.

Blighia unijugata (Akye) is used in soups to induce breast milk production for newly delivered mothers, however 25 % of organic cocoa farmers consumed it compared to 12.5% for conventional cocoa farmers. Antiaris toxicaria (kyenkyen) was consumed by 18 % of both organic and conventional farmers. As claimed by Dokosi (1987), it is usually consumed as a beverage for medicinal purposes. Elaeis guineensis (Abɛ) was consumed by 100 % of organic 51

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cocoa farmers as compared to 93.8 % of conventional cocoa farmers. Fifty six percent of conventional cocoa farmers consumed Persea americana (pea/paya) compared to 37.5% of organic farmers. This is because Persea americana (pea/paya) is usually cultivated and is not largely available in most farms.

Eighty seven and a half percent of organic farmers consumed Phyllanthus amarus

(abomaguwakyi) compared to 69% of conventional farmers. The difference in consumption is consistent with the postulation by Dokosi (1987) that the species is used in managing blood pressure.

A paired t-test was run on a sample of 26 edible flora species to determine the whether there is a statistically significant mean difference between their consumption by organic and conventional cocoa farmers. The mean and standard deviation of organic cocoa farmers was

(63.0 ± 30.87) as opposed to conventional cocoa farms (54.83 ± 33.82). This means flora is consumed more by organic farmers compared to conventional farmers by 8.16 (95% confidence interval of 1.83 – 14.50), t (25) = 2.65 and Pr (T>t) = 0.007.

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Table 4.5 Flora consumption

Species % respondents on Uses Part used consumption Organic Conventional Food Medicine Ageratum conyzoides (Abrakyie 0 6.3 Leaves, abodwese) Vegetable Alchornea cordifolia (Ogyama) 43.8 37.5 Fruit, Leaves Ananas comosus (AborƆbɛ) 93.8 93.8 Fruit Antiaris toxicaria (kyenkyen) 18.8 18.8 Fruit Baphia nitida (Odwene) 68.8 31.3 Seeds Blighia unijugata (Akye) 25 12.5 Fruit, Flowers Boerhavia diffusa (NkokƆdwe) 43.8 31.3 Leaves Capsicum frutescens (Moko) 93.8 100 Fruit Cardiospermum halicacabum (FƆtƆ) 75 37.5 Leaves Carica papaya (Boferi) 93.8 100 Fruit Ceiba pentandra (Onyaa/Onyina) 56.3 31.3 Leaves Colocasia esculenta (Kooko) 75 81.3 Underground stem Cyathula prostrata (Apupuaa) 25 6.3 Leaves Dioscorea bulbifera (Cocoa asi bayere) 25 6.3 Underground stem Elaeis guineensis (Abɛ) 100 93.8 Fruit Emelia sonchifolia (Guakuro) 81.3 50 Leaves Lycopersicon esculentum (Nsusuwa) 75 87.5 Fruit Mangifera indica (Mango) 93.8 87.5 Fruit Manihot esculentum (Bankye) 81.3 93.8 Underground stem Momordica charantia (Nyanya/Nyinya) 81.3 68.8 Young shoots Musa paradisiaca (bode/mpusae) 100 100 Bunches Persea americana (pea/paya) 37.5 56.3 Fruit Phyllanthus amarus (abomaguwakyi) 87.5 68.8 Fruit/vegetabl e Synedrella nodiflora (Kwadupo) 56.3 31.3 Stock Talinum triangulare (ɛfan/bokoboko) 12.5 18.8 Leaves Vernonia amygdalina (awonwene) 93.8 75 Leaves, stem Mean 63.0 54.83

T test P<0.007 t(25)=2.65 Source: Author computation, (2014) *The shaded portion shows the type of use

Table 4.6 show flora sales and value in Ghana Cedis. Seventy three percent of the 26 edible flora species were sold. Majority (75%) of conventional cocoa farmers have sold Manihot esculentum (Bankye), likewise Lycopersicon esculentum (Nsusuwa) (50%) and Ananas comosus (AborƆbɛ) (25%).

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On Dioscorea bulbifera (Cocoa asi bayere), organic cocoa farmers dominate (56.3%) in its sale, likewise Colocasia esculenta (Kooko) (25%), Cardiospermum halicacabum (FƆtƆ)

(12.5%), Capsicum frutescens (Moko) (37.5%) and Baphia nitida (Odwene) (12.5%).

Ninety four percent of the respondents that have sold Musa paradisiaca (bode/mpusae) were conventional cocoa farmers whiles 87.5% were organic farmers. Thirteen percent of respondents of both systems were reported to have sold Phyllanthus amarus (abomaguwakyi).

However, Cyathula prostrata (Apupuaa), Emelia sonchifolia (Guakuro) and Synedrella nodiflora (Kwadupo) percent sales was reported to be 12.5%, 25% and 12.5% respectively for organic farmers. Percent sales for conventional farmers was 0%, 12.5% and 6.3% respectively.

Twenty five percent of organic cocoa farmers had sold Emelia sonchifolia (Guakuro) as compared to 12.5 % of conventional cocoa farmers.

Despite the availability and consumption of Elaeis guineensis (Abɛ) by organic farming households, 37.5 % sold this species compared to 43.8 % of conventional farming households.

Also, conventional farming household dominated (25%, 25%, 12.5% and 6.3 %,) in the sales of Carica papaya (Boferi), Mangifera indica (Mango), Persea americana (pea/paya) and

Antiaris toxicaria (kyenkyen) respectively. Twenty five percent of organic cocoa farmers however had Ceiba pentandra (Onyaa/Onyina) as compared to (6.3%) of conventional cocoa farmers.

A paired t-test was run on a sample of 19 edible flora species sold to determine whether there is a statistically significant mean difference between sales by organic and conventional cocoa farmers. The results show that, organic cocoa farmers have (25.0 ± 22.63) as opposed to conventional cocoa farmers (26.3 ± 25.8). This means that there is no statistically significant difference in flora sales among both organic and conventional farmers with 1.32 (95% confidence interval of -7.47 – 4.82), t (18) = 0.45 and Pr (T>t) = 0.67.

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Table 4.6 Flora sales and value (GHS)

% respondents on sale Price (GHS) Species Organic Conventional

Ananas comosus (AborƆbɛ) 6.3 25 1.00-5.00/piece Antiaris toxicaria (kyenkyen) 0 6.3 0.5/hand full Baphia nitida (Odwene) 12.5 0 1.00/five pieces Capsicum frutescens (Moko) 37.5 31.3 2.00/mug

Cardiospermum halicacabum (FƆtƆ) 12.5 6.3 0.5/hand full Carica papaya (Boferi) 0 25 0.5-1.00/piece Ceiba pentandra (Onyaa/Onyina) 25 6.3 0.5/hand full Colocasia esculenta (Kooko) 25 18.8 1.00-5.00/mug Cyathula prostrata (Apupuaa) 12.5 0 0.5-1.00/hand full Dioscorea bulbifera (Cocoa asi 56.3 50 1-2.00/tuber bayere) Elaeis guineensis (Abɛ) 37.5 43.8 2-5.00/mug Emelia sonchifolia (Guakuro) 25 12.5 0.5-1.00/hand full Lycopersicon esculentum (Nsusuwa) 43.8 50 1.00-2.00/mug Mangifera indica (Mango) 18.8 25 0.2-0.5/fruit Manihot esculentum (Bankye) 50 75 1.00-1.50/tuber Musa paradisiaca (bode/mpusae) 87.5 93.8 0.5-1.00/finger Persea americana (pea/paya) 0 12.5 0.5-1.00/fruit Phyllanthus amarus (abomaguwakyi) 12.5 12.5 0.5-1.00/hand full Synedrella nodiflora (Kwadupo) 12.5 6.3 0.5-1.00/hand full Mean 25.0 26.3 T test P<0.67 t (18)=0.45

Source: Author computation, (2014)

The sale of the different species by both organic and conventional households suggest that there is a market for flora. Farmers were able to indicate the average unit prices and the measures used. There is an indication that, the various species contribute more to food security of organic

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farmers than conventional ones. Indeed, households that do not harvest organic species can obtain them for use in the market.

4.5.2 Income from flora The income information was obtained as qualitative data. Farmers were asked to indicate the range of income they obtained during the 2014 cropping season. The results of the study showed that cocoa farmers in the Atwima Mponua District were able to make some amount of money monthly from sale of flora. About 37.5%, 19%, 18.8% and 18.8% of organic farmers made between GHS151-200, GHS10-50, GHS201-250, and GHS301-400 respectively for year

2014 compared to their conventional counterparts (31.3%, 16%, 12.5% and 12.5%) (See Table

4.7). The results show that, flora contribute to income of especially organic cocoa farmers more than conventional cocoa farmers. The difference in income categories of organic farmers according to Neudeck et al., (2012) will diversify their diets and enhance food security at the household level.

Table 4.7 Range of income obtained by farming system

% respondents Income range (GHS) Organic Conventional 10-50 18.8 6.3

51-100 18.8 31.3

101-150 37.5 31.3

151-200 18.8 12.5

201-250 6.3 6.3

251-300 18.8 12.5

301-400 18.8 6.3

Source: Author computation, (2015) 56

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4.5.3 Wellbeing The proportion of health expenditure funded from flora income for organic and conventional cocoa farmers is shown in Table 4.8. The wellbeing of the farmer was measured by using the cost of health care that was funded with income from flora. About 31% of organic cocoa farmers did not finance the cost of their health care with income from flora. The remaining

68.8 % did. Fifty six percent of conventional cocoa farmers financed the cost of their health care with income from flora whiles 44% did not.

Table 4.8 Proportion of yearly health expenses financed by flora income

Proportion by flora income (%) % of farmers Organic Conventional 0 31.3 43.8 1-20 18.8 12.5 21-40 18.8 18.8 41-60 25.0 18.8 61-80 0 0 81-100 6.3 6.3 Source: ProEco Africa database and Author, (2015)

It is therefore concluded that income from the sale of flora was critical in improving the wellbeing of both organic and conventional farmers. The hypothesis that organic farmers has a better wellbeing is consistent with findings of Maroyi, (2009) in a sustainable agroforestry system rural livelihoods in Zimbabwe.

4.5.4 Vulnerability as a variable of livelihood of organic and conventional farmers Table 4.10 shows membership of a farmer based organization (FBO) as a proxy to vulnerability measurement. All the organic cocoa farmers in the survey belonged to a farmer based organization (FBO) known as Tano Biakoye Group, however, 75% of conventional cocoa farmers belonged to an FBO (Cocoa Abrabopa, a subsidiary of Wienco Ghana Ltd.).

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In terms of vulnerability, organic farming especially is seen as a tool that could make cocoa farmers less vulnerable to the impacts of climate change and variability and other land use changes.

The activities farmers engaged in as group members included farmer field day/schools, participation in fora, workshops and seminars as well as weekly meetings. During the focus group discussion, the farmers from both groups admitted that meetings and training of organic farmers was more regular than that of the conventional farmers group. This suggests that organic cocoa farmers are more socially included and less vulnerable compared to conventional cocoa farmers.

Table 4.9 Membership of a farmer based organization (FBO) as proxy to vulnerability measurement

Membership of an FBO % respondents Organic Conventional

Yes 100 75 No 0 25

Source: ProEco Africa Database (2015)

This means that higher flora diversity will increase purchasing power and food production capacity which has a direct impact on household nutrition, health and food security.

For instance edible flora play an important role in household livelihoods, especially during periods of both natural and man-made stresses. They have significant nutritional, economic, ecological and socio-cultural values (Neudeck et al., 2012; Dovie et al., 2007 and Scoones et al. 1992) and also increase the nutritional quality of rural diets (Neudeck et al., 2012).

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CHAPTER FIVE SUMMARY, CONCLUSION AND POLICY RECOMMENDATION

5.1 Introduction Chapter five presents the summary, conclusions and policy recommendations of the study. The chapter is therefore subdivided into three main sections.

5.2 Summary of the Study The study was conducted to assess flora diversity in two different cocoa farming systems,

(organic and conventional cocoa farms) and its contribution to sustainable livelihoods in

Atwima Mponua District of the Ashanti Region of Ghana. A total of thirty two cocoa farms

(16 organic and 16 conventional) were randomly selected for the study. For each cocoa farm, a sample plot of 25 m x 25 m was marked out from which 5 m x 5 m sub plots were demarcated to determine quantitative abundance of flora species. Flora identification was done and species composition list prepared for each farm. The life form abundance and species distribution was shown and the Jaccard index of similarity was calculated to ascertain the similarity of species in the two farming systems. The extent of differences in flora diversity for the two farming systems was calculated using the Shannon and Simpson diversity indices and the significance tested using the Mann-Whitney U test.

Data on the contribution to sustainable livelihood were collected based on a structured questionnaire administration to assess the following livelihood variables; food security, income, vulnerability reduction and well-being. Food security was measured by flora availability, consumption and sale and tested using a paired t test, Income was measured by the amount of money (GHS) made from sale of flora, Wellbeing was measured by cost of health care that was funded from flora income and Vulnerability was measured by being a member of a farmer based organization.

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5.3 Conclusion Based on the findings of the study, the following conclusions were drawn:

1. On the nature of flora;

- Life form abundance under organic farms was higher than conventional ones.

- Organic farms had more random and aggregate distribution of species than under

conventional farms.

- The Jaccard index indicate moderates similarity: 64% similarity exist between flora

species distributions of the two farming systems.

2. On the extent of difference in flora diversity;

- Flora diversity was higher in organic cocoa farms than in conventional cocoa farms

using the Shannon and Simpson diversity indices.

- There was no difference in species evenness (Shannon index insignificant) but in

species dominance (Simpson index significant) in organic farms to the conventional

ones.

3. On the contribution of flora diversity to sustainable livelihoods;

- There was an indication that, the various species contribute more to food security

of the studied organic farmers in terms of flora availability and consumption

compared to their conventional counterparts, however, there was no statistical

significance in the mean response of sale of flora by farmers of both systems.

- Flora contribute to income of organic cocoa farmers’ than conventional cocoa

farmers.

- Income from the sale was critical in improving the wellbeing of organic farmers

since it was used in funding health cost.

- Organic cocoa farmers were more socially included and less vulnerable compared

to conventional cocoa farmers.

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5.4 Policy Recommendation 1. Organic farms accommodates more species in a changing climate and therefore for

biodiversity conservation, organic farming should be encouraged.

2. Though there was no clear difference in species diversity with respect to evenness and

since the species dominance in organic farms was higher, it should be the preferred

farming system. It aids the mitigation of climate change shocks, therefore in order to

increase adoption of organic farming in the face of climate change, more awareness

should be created by public sector institutions such as Cocoa health and Extension

Division (CHED) of the Ghana Cocoa Board, non-governmental organisations (NGOs)

such as Agro Eco-Louis Bolk Institute (AE-LBI) and Rainforest Alliance (RA) and

private cocoa beans buyers such as Armajaro Limited to encourage farmers to practice

organic farming and adhere to standards set.

3. There is the need for policies and legislation that involve all stakeholders in wild

resource conservation and management to consider value of especially voluntary flora

species and their sustainable use since edible flora does not only provide food for the

family but also serves as an alternative source of income.

4. For future and further studies, it is recommended that a study should test diversity of

flora by comparing organic, conventional and those farmers who practice both. The

outcome of flora diversity in organic, conventional and mixed systems will contribute

immensely to literature.

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Appendix 1 Survey Questionnaire

UNIVERSITY OF GHANA CLIMATE CHANGE AND SUSTAINABLE DEVELOPMENT PROGRAMME

Flora Diversity in Organic and Conventional Cocoa farms and its Contribution to Sustainable Livelihood in the Atwima Mponua District

This study is being carried out by Joseph Bandanaah, da gra uate student of the University of Ghana, Legon. The objective of the survey is estimate the contribution of flora to sustainable livelihood in Organic and Conventional Cocoa farming households. All information gathered will be treated with much confidentiality and would solely be for academic purposes. Your support and contribution would be very much appreciated. For further enquiries, please contact him on [email protected] 0244546590

Questionnaire ID: ………...…….. Date of interview: ...... Name of respondent: ...... Community: …………………….... Telephone number: ......

SECTION A: BIODATA

1. Are you the head of your household 1=Yes 0= No

2. How old are you? Years 3. What is your gender? 1=Male 0= Female 4. Highest level of formal education? 0= None 1=Basic (Primary/JHS/Middle) 2=Secondary (SHS) 3=Vocational 4=Tertiary (training college/polytechnic/university) 5=Islamic education 6=other, specify 5. Are you able to read and write 1=Read 2=Write English 3= Read and Write 4= None 6. Marital status of respondent? 1=Married 2=Single 3= Divorced /Separated 4=Widowed 7. Place of origin? 1=Native 2=Settler/migrant 8. What is your religious affiliation? 1=Christian 2=Muslim 3= Traditional 4=other 9. What is your household size? (specify)………………………….

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SECTION B: HOUSEHOLD INCOME IN 2014

10. Please state the income you made from the sale of crops in 2014 Crop groups Income made from sale (GhS)

Cultivated crops - - - - - Non cultivated or voluntary crops - - - - -

11. Please state the income you made from the sale of livestock in 2014 Livestock groups Income made from sale (GhS)

Own rearing - - - - - Hunted in wild - - - - -

12. Please state the income you made from other activities other than the sale of your crops or livestock Other income activities Income made (GhS)

- - - - -

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SECTION C: FARMING ACTIVITIES

13. Do you consider farming to be your major occupation 1=Yes 0=No 14. How long have you been farming [……..] years 15. Wha t is the main purpose of you r farming (choose one )? 1= household consumption 2= sale 3=both consumption and sale 4= other (specify)………… 16. Wha t is your total farm size (Cocoa farm in hectares)? (…………………………..) 17. What is your source of labour for your farm? 1= Hired labour 2= Family labour 3= Both 18. What is your land ownership status? 1=Own land allocated by village head, 2=Own land

inherited from family, 3=Own land purchased, 4=Rented on longer term basis

19. What is your soil fertility status? 1=Low 2=Average 3=High 20. How many times do you apply chemical fertilizer or spray your Cocoa farm in a year? (...... ) 21. What time do you apply the chemical fertilizer or spray? 1=Morning 2=Afternoon 3=Evenings 22. What was your land used for before crop production? 1=Forest 2=Fallow land 3=Grazing land 4=Other specify

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SECTION D: FOOD SECURITY AND DIETARY DIVERSITY

23. Which of the following crop species found on your farm contribute to your food security? Crop species Common name Part eaten Available Consumed Sold Ever sold Total on farm? value 1=Yes 1=Yes 1=Yes 1=Yes (GhS) 0=No 0=No 0=No 0=No Ageratum conyzoides (Goat weed) Abrakyie abodwese Leaves, Vegetable Alchornea cordifolia (Christmas bush) gyama Fruit, Leaves - drink, Leaves Ananas comosus (Pineapple) Abor)b3 Fruit Antiaris toxicaria (False iroko, Ako,) kyenkyen Fruit Baphia nitida (Camwood) Ɔdwen(e) Seeds Blighia sapida (Akee apple) Ankye Fruit, Aril, Flowers - flavour Blighia unijugata (Blighia, Triangle tops) Akye Seeds, Fruit, Leaves, Boerhavia diffusa (Pig weed) NkokƆdwe Leaves Bombax buonopozense (Cotton tree, Red flower silk cotton Leaves, Flowers, Seeds, tree) Okum Calyces, Fruits, Canna indica Ahabia Underground stem Capsicum frutescens Pepper Spice Cardiospermum (Balloon vine) FƆtƆ Leaves halicacabum Carica papaya Pawpaw/Papaya Fruit Ceiba pentandra (Kapok, White silk cotton tree) Seeds, Leaves, vegetable Onyaa/Onyina Colocasia esculenta (Cocoyam) Kooko Underground stem Commelina diffusa Onyamebewunamawu Leaves Croton lobatus AkƆnansa Leaves Cyathula prostrata Apupuaa Leaves Dioscorea bulbifera (Potato yam) Cocoa asi bayere Underground stem 72

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Elaeis guineensis (Oil palm) Abɛ Fruit/oil Emelia sonchifolia Guakuro Leaves Euphorbia heterophylla Stock Euphorbia hirta (Australian asthma herb) Animakoa Leaves Lycopersicon esculentum (Tomato) Nsusuwa Fruit Mangifera indica Mango Fruit Manihot esculentum (Cassava) Bankyɛ Underground stem Momordica charantia (African cucumber) Young shoots/spice Nyanya/Nyinya Musa paradisiaca (Plantain) bode/mpusae Bunches Persea americana (Avocado pea) pεa/paya Fruit/vegetable Phyllanthus amarus (Fly roost) abomaguwakyi Fruit/vegetable Synedrella nodiflora Kwadupo/MampƆnfoapƆw Stock Talinum triangulare ɛfan/bokoboko Leaves Vernonia amygdalina (bitter leave) awonwẽnẽ Leaves, Vegetable, Stems,

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24. What types of foods did you or anyone else in your household eat during the past 7 days?

Food groups Examples Yes=1 Number of days for which food group is No=0 consumed A. CEREALS bread, rice water ,rice balls, biscuits, banku or any other foods made from millet, sorghum, maize, rice, wheat etc B. ROOT AND TUBER potatoes, plantain, cocoyam, yams, carrots, cassava, fufu, gari, tapioca etc C. VEGETABLES AND Leaves, pumpkin, kenaf, orange, banana, melon, FRUITS mango, pawpaw D. MEAT beef, pork, lamb, goat, rabbit wild game, chicken, duck, grasscutter or other birds, liver, kidney, heart, or other organ meats E. EGGS F. FISH fresh or dried fish or shellfish G. LEGUME, NUTS AND beans, peas, lentils, or nuts SEEDS H. MILK AND MILK cheese, yogurt, milk, wagashi or other milk products PRODUCTS I. OILS AND FATS oil, fat, or butter used during cooking J. SWEETS sugar or honey , chocolate etc K. SPICES, CONDIMENTS Salt; sauces; coffee, tea, or other alcoholic beverages AND BEVERAGES

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Appendix 2 Flora distribution in the study area

Family Order Genus , Specie (s) Life form Distribution Org. Con.

Acanthaceae Lamiales Justicia flava H Random + -

Amaranthaceae Caryophyllales Cyathula prostrata H Aggregated + +

Anacardiaceae Sapindales Mangifera indica T Random + -

Annonaceae Magnoliales Monodora tenuifolia T Aggregated + -

Alstonia boonei T Aggregated + + Funtumia africana T Aggregated + + Holarrhena floribunda S Random + + Parquetina nigrescens S Random + + Apocynaceae Gentianales Rauvolfia vomitoria S Random + +

Anchomanes dalzielii S Random + - Araceae Alismatales Colocasia esculenta S Random + +

Arecaceae Arecales Elaeis guineensis T Random + +

Asclepiadaceae Gentianales Secamone afzelii H Aggregated + +

Asparagaceae Asparagales Dracaena manii T Random - +

Asteraceae Asterales Ageratum conyzoides S Aggregated + + Aspilia africana H Aggregated + - Chromolaena odorata H Aggregated + + Emelia sonchifolia H Random + - Synedrella nodiflora H Random + + Vernonia amygdalina S Random + + Vernonia colorata S Aggregated + + Newbouldia laevis T Random + + Bignoniaceae Lamiales Spathodea campanulata T Random + + Bromeliaceae Poales Ananas comosus S Aggregated + +

Celtis mildbraedii T Aggregated + + Cannabaceae Rosales Trema guineensis T Random - +

Cannaceae Zingiberales Canna indica S Aggregated - +

Caricaceae Brassicales Carica papaya T Aggregated + +

Cecropiaceae Rosales Myrianthus arboreus T Aggregated + -

Combretum racemosum H Aggregated + + Terminalia ivorensis T Aggregated + - Combretaceae Myrtales Terminalia superba T Aggregated + +

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Commelinaceae Commelinales Commelina diffusa H Random + -

Connaraceae Rosales Byrsocarpus coccineus S Random + +

Connaraceae Oxalidales Cnestis ferruginea S Aggregated + +

Convolvulaceae Solanales Ipomoea mauritiana S Random - +

Crassulaceae Saxifragales Bryophyllum pinnatum H Aggregated + -

Cucurbitaceae Cucurbitales Momordica charantia S Aggregated + +

Dichapetalaceae Malpighiales Dichapetalum madagascariense H Random + -

Dioscoreaceae Dioscoreales Dioscorea bulbifera S Random + -

Alchornea cordifolia S Random + + Croton lobatus H Random + - Euphorbia heterophylla S Random + + Euphorbia hirta H Random - + Mallotus oppositifolius S Random + + Manihot esculentum S Random + + Mareya micrantha S Random + + Macaranga barteri S Random - + Euphorbiaceae Malpighiales Ricinodendron heudelotii T Random + +

Baphia nitida S Random + + Baphia pubescens S Aggregated + + Cassia sieberiana T Aggregated + - Griffonia simplicifolia S Random + + Leucaena leucocephala T Random - + Millettia chrysophylla T Random + - Millettia zechiana T Random + + Mucuna pruriens H Random + - Senna alata T Random - + Fabaceae Fabales Lithocarpus sericus T Random - +

Gentianaceae Gentianales Anthocleista vogelii T Random + -

Lamiaceae Lamiales Gmelina arborea T Aggregated + +

Lauraceae Laurales Persea americana T Aggregated + +

Leguminosae Fabales Albizia adianthifolia T Aggregated + +

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Albizia ferruginea T Aggregated + + Albizia glaberrima T Aggregated + + Acacia kamerunensis S Aggregated + + Amphimas pterocarpoides T Random + - Albizia zygia T Aggregated + +

Loganiaceae Gentianales Spigelia anthelmia H Aggregated + +

Bombax buonopozense T Random + - Ceiba pentandra T Random - + Cola millenii T Random + + Glyphaea brevis S Random + - Nesogordonia papaverifera T Random + - Sida acuta S Aggregated + + Sterculia oblonga T Random + + Sterculia tragacantha T Random + + Malvaceae Malvales Theobroma cacao T Aggregated + +

Meliaceae Sapindales Trichilia monadelpha T Aggregated + +

T Random + + Antiaris toxicaria T Aggregated + + Ficus exasperata T Random + - Ficus sur T Random + - Ficus zygia T Random + + Milicia excelsa T Random + + Morus mesozygia S Random + - Moraceae Rosales Myrianthus libericus Musaceae Zingiberales Musa paradisiaca H Random + +

Myristicaceae Magnoliales Pycnanthus angolensis T Random + +

Nyctaginaceae Caryophyllales Boerhavia diffusa H Random - +

Pandaceae Malpighiales Microdesmis puberula H Random - +

Margaritaria discoidea T Random - + Phyllanthus amarus H Random + - Phyllanthus mauritiana T Aggregated + + Phyllanthaceae Malpighiales Phyllanthus niruri S Random - +

Digitaria insularis H Random - + Poaceae Poales Panicum maximum H Aggregated + +

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Setaria barbata H Random + + Sporobolus pyramidalis H Random - +

Morinda lucida H Random + + Rubiaceae Gentianales Rothmannia longiflora S Aggregated + +

Salicaceae Malpighiales Homalium hypolasium S Random + -

Blighia sapida T Aggregated + - Blighia unijugata T Aggregated + + Cardiospermum halicacabum S Random + + Lecaniodiscus cupanioides S Aggregated + - Sapindaceae Sapindales Paullinia pinnata S Random + +

Smilacaceae Liliales Smilax rotundifolia H Random + +

Capsicum frutescens S Aggregated - + Lycopersicon esculentum S Random + - Solanaceae Solanales Solanum torvum S Aggregated + +

Talinaceae Caryophyllales Talinum triangulare S Aggregated - +

Source: Field data (2015) H-Herb, S-Shrub, T-Tree (+) =species available (-) =species not

available