Farmers’ Perceptions about the Utilities of Trees Associated with...

Farmers’ Perceptions about the Utilities of Trees Associated

with Coffee Farms in Central Province, Kenya

Lindsey C. Elliott Lindsey C. Elliott Lindsey C. Elliott Lindsey C. Elliott

BSc Hons. (Biology) University of Toronto, Canada

Project submitted in partial fulfillment of the requirements for the degree of:

Sustainable Tropical Forestry (SUTROFOR) Erasmus Mundus Program

Master of Science (MSc) in Agroforestry

Bangor University

September 2009

Academic supervisor: Dr. Fergus Sinclair

Project supervisor: Dr. Fabrice Pinard

Course Director: Dr. Zewge Teklehaimanot

Student no: 500195552

School of the Environment and Natural ResourcesSchool of the Environment and Natural ResourcesSchool of the Environment and Natural ResourcesSchool of the Environment and Natural Resources

Bangor University, WalesBangor University, WalesBangor University, WalesBangor University, Wales

ii

Declaration

This work has not previously been accepted in substance for any degree and is not being

concurrently submitted in candidature for any degree.

Candidate: (Lindsey Elliott)

Date:

Statement 1: Statement 1: Statement 1: Statement 1:

This dissertation is being submitted in partial fulfillment of the requirements for the degree of

Master of Science.


Date:


This dissertation is the result of my own independent work/investigation except where otherwise stated.


Date:


I hereby give consent for my dissertation, if accepted, to be available for photocopying and for

interlibrary loan, and for the title and summary to be made available to outside organisations.


Date:

Signed: (Fergus Sinclair)

Full name of supervisor:

Date:

This work was done in association with the CAFNET project funded by the European Union (Europe Aid ENV/2006/114-382/TPS). This document has been produced with the financial assistance of the European Union and the coordination of CIRAD. The contents of this document are the sole responsibility of Lindsey Elliot and can under no circumstances be regarded as reflecting the position of the European Union.

iii

Abstract

The study was conducted from June – September, 2009 with coffee farmers across Murang’a

District and the upper part of Maragua District in Central Province, Kenya. It involved an iterative

knowledge based system (KBS) approach of alternation between different methods of knowledge

acquisition and storage of this information in a variety of forms. The primary objective of the

research was to acquire an understanding of farmers’ knowledge about tree utilities and to develop

participatory tools to encourage tree diversity and abundance on coffee farms. Additionally, the

research aimed to identify key areas where farmer knowledge could be expanded through increased

access to information and training. Two ranking and scoring approaches were also tested for future

execution.

Farmers were found to have extensive knowledge about trees which they had gained

through their own experience and from extension advice and coffee societies. Trees were found to

affect coffee productivity and profitability in a number of ways both indirectly and directly. From the

perception of farmers, the most important tree utilities were: income generation, firewood provision,

(regulating) environmental services, shade provision, medicine provision, and fodder provision.

Certain tree characteristics such as large size, slow growth, high nutrient and water requirements,

and pest abundance decreased the occurrence of many tree species on farms despite farmers’

knowledge of their potential utilities.

Knowledge about coffee shade trees, coffee quality, and regulatory tree utilities was limited

and these should be the areas of focus for future extension. It is recommended that extension may

be most effectively designed as a joint initiative by farmers, agricultural officers, and coffee

cooperative society factories. Using the second ranking/scoring approach that was tested, it is

recommended that tree ranking is continued and that the resulting information be used in

combination with data about the eco-physiological suitability of trees in the area to develop

practical decision making tools for farmers concerning the diversity of trees available for each utility.

iv

Acknowledgements

It is wonderful to get the opportunity to give much deserved thanks to many people, even if

they may not get the chance to read this. My first thanks goes to the farmers in Central Province who

volunteered their precious time to share their knowledge with us. Not only did they make this

research possible, they made it a thoroughly enjoyable experience. Thank you to Vickpreston, my

research assistant and Kikuyu connection, for his endless patience with me, his continuous hard

work and his enthusiasm. I wish him the best of luck as he starts university although I am confident

he will succeed at whatever he chooses.

I would like to thank The CAFNET Project for their financial support and for taking me on

board. I believe they are doing good work for the right reasons. Thank you to the World Agroforestry

Centre for their logistical support in Nairobi and for accepting me as a fellowship student, it was an

honour to be associated with such a prominent international organisation and I really enjoyed

meeting many wonderful people there. Thank you to Fergus Sinclair for inspiring me to attempt

social research for the first time, for connecting me to the right people, and for some wonderful

ideas and advice. I really appreciated his trust in my abilities and the freedom to do things my way.

Thanks to Fabrice Pinard for giving me confidence in what I was doing in Murang’a and for his

calming support. Thank you so very much to Genevieve Lamond and Tim Pagella for their feedback

and friendship. Thank you to everyone at the Mugama Union for their help and for welcoming me in

Murang’a; especially to Mr. Wanjohi for his dedication and hard work (and the occasional Tusker).

I would also like to thank my family for their endless support and understanding. It has been

very difficult to be so far from them this past two years and I hope they will one day forgive me for

being half the world away. Thank you so very much to Elena, Emily and Tom for housing me

periodically during the research, and to my other amazing friends Benson, Charles, Florence, Jane,

Kurt, Martha, Paige, Phil, Ruth, Sanjeeb, and all the guys at Murang’a Mukawa for their support and

for listened to me go on and on. I am so fortunate to have such wonderful people around me and I

sincerely look forward to returning the countless favours! Thanks to the cafés of East Africa for

acting as my office and for providing me with internet and caffeine as I wrote this thesis. And last

but most of all a big thank you to Chris for always being there no matter what.

Research can be a selfish endeavor and I sincerely hope that everyone involved saw this

research as a participatory process and feels that they got out of it what they had hoped. I certainly

did.

v

List of Abbreviations

AKT5 Agroecological Knowledge Toolkit

C Carbon

CAFNET Coffee Agroforestry Network

CBD Coffee berry disease

CBK Coffee Board of Kenya

CDM Clean Development Mechanism

CRF Coffee Research Foundation

GD Group discussion

ICO International Coffee Organization

ICRAF World Agroforestry Centre

KPCU Kenya Planters Cooperative Union

KB Knowledge base

KBS Knowledge based systems approach

KSH Kenya shillings

LK Local knowledge

m a.s.l. Meters above sea level

MPT Multipurpose trees

Mugama Union Mugama Farmer’s Cooperative Union

PES Payment for environmental services

PNVT Potential natural vegetation type

spp. Species

vi

Table of Contents

DeclarationDeclarationDeclarationDeclaration iiiiiiii

AbstractAbstractAbstractAbstract iiiiiiiiiiii

AcknowledgementsAcknowledgementsAcknowledgementsAcknowledgements iviviviv

List of AbbreviaList of AbbreviaList of AbbreviaList of Abbreviationstionstionstions vvvv

List of FiguresList of FiguresList of FiguresList of Figures ixixixix

List of TablesList of TablesList of TablesList of Tables xxxx

List of BoxesList of BoxesList of BoxesList of Boxes xxxx

1.1.1.1. Background: Literature ReviewBackground: Literature ReviewBackground: Literature ReviewBackground: Literature Review 1111

1.11.11.11.1 Coffee Agroforestry SystemsCoffee Agroforestry SystemsCoffee Agroforestry SystemsCoffee Agroforestry Systems 1111

1.1.1 World Coffee Trends and Outlooks 2

1.1.2 Coffee in Kenya 3

1.21.21.21.2 Ecosystem ServicesEcosystem ServicesEcosystem ServicesEcosystem Services 5555

1.2.1 Ecosystem Services and Agroforestry 6

1.2.2 Biodiversity in Working Landscapes 8

1.2.3 Other Ecosystem Services 9

1.1.1.1.3333 Local KnowledgeLocal KnowledgeLocal KnowledgeLocal Knowledge 10101010

1.3.1 Local Knowledge in Combination with Scientific Knowledge 10

2.2.2.2. Research ObjectivesResearch ObjectivesResearch ObjectivesResearch Objectives 12121212

2.12.12.12.1 Organisational SettingOrganisational SettingOrganisational SettingOrganisational Setting 12121212

2.22.22.22.2 RationaleRationaleRationaleRationale 12121212

2.32.32.32.3 ObjectivesObjectivesObjectivesObjectives 13131313

2.42.42.42.4 Research TopicsResearch TopicsResearch TopicsResearch Topics 13131313

3.3.3.3. MethodsMethodsMethodsMethods 14141414

3.13.13.13.1 Study AreaStudy AreaStudy AreaStudy Area 14141414

3.23.23.23.2 Local Local Local Local Knowledge AcquisitionKnowledge AcquisitionKnowledge AcquisitionKnowledge Acquisition 16161616

3.2.1 Scoping Stage 16

3.2.2 Definition Stage 17

3.2.3 Compilation Stage 18

3.2.3.1 Informal Semi-Structured Interviews 19

3.2.3.2 Group Discussions 20

vii

3.2.3.3 Telephone Interviews 20

3.2.3.4 Farm Sketches 20

3.2.3.5 Tree Spreadsheet 21

3.2.3.6 Tree Utility Ranking 21

3.2.3.7 Ranking/Scoring – Method 1 22

3.2.3.8 Ranking/Scoring – Method 2 23

3.2.4 Feedback Sessions 25

3.33.33.33.3 Representation of Local KnowledgeRepresentation of Local KnowledgeRepresentation of Local KnowledgeRepresentation of Local Knowledge 27272727

3.3.1 Agroecological Knowledge Toolkit (AKT5) 27

3.3.2 Other Methods 28

3.43.43.43.4 Limitations of the Limitations of the Limitations of the Limitations of the MethodologyMethodologyMethodologyMethodology 29292929

4.4.4.4. ResultsResultsResultsResults 30303030

4.14.14.14.1 Respondent StratificationRespondent StratificationRespondent StratificationRespondent Stratification 30303030

4.24.24.24.2 Knowledge DerivationKnowledge DerivationKnowledge DerivationKnowledge Derivation 33333333

4.34.34.34.3 FactFactFactFactors Affecting Coffee Productivityors Affecting Coffee Productivityors Affecting Coffee Productivityors Affecting Coffee Productivity 36363636

4.3.1 Shade 36

4.3.2 Intercropping 40

4.3.3 Input Application 41

4.44.44.44.4 Factors Affecting Farm ProfitabilityFactors Affecting Farm ProfitabilityFactors Affecting Farm ProfitabilityFactors Affecting Farm Profitability 44444444

4.4.1 Coffee Price Instability 46

4.4.1.1 Factors Affecting the Price of Coffee 46

4.4.1.2 The Impact of Changing Prices on Farm Activities 48

4.4.2 Trees as a Source of Income 49

4.54.54.54.5 Tree Utilities on Coffee FarmsTree Utilities on Coffee FarmsTree Utilities on Coffee FarmsTree Utilities on Coffee Farms 52525252

4.5.1 Occurrence of trees on Farms 52

4.5.2 Factors Limiting Tree Presence 55

4.5.3 Tree Location 57

4.5.4 Priority of Tree Utilities 58

4.5.5 Selection of Most Important Trees 59

4.5.5.1 Ranking/Scoring Approach 1 Results 60

4.5.5.2 Ranking/Scoring Approach 2 Results 63

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5.5.5.5. DiscussionDiscussionDiscussionDiscussion 65656565

5.15.15.15.1 Problems Facing Coffee FarmersProblems Facing Coffee FarmersProblems Facing Coffee FarmersProblems Facing Coffee Farmers 65656565

5.1.1 Unstable Coffee Price 66

5.1.2 Decreased Coffee Yield and Profitability 66

5.1.3 Climate Change 67

5.25.25.25.2 Farmers’Farmers’Farmers’Farmers’ Knowledge LimitationsKnowledge LimitationsKnowledge LimitationsKnowledge Limitations 68686868

5.2.1 Shade 68

5.2.2 Coffee Quality 68

5.2.3 Regulating tree utilities 68

5.2.1 Extension Approaches 69

5.35.35.35.3 Important Tree UtilitiesImportant Tree UtilitiesImportant Tree UtilitiesImportant Tree Utilities 70707070

5.3.1 Ranking and Scoring – the Way Forward 71

5.3.2 Tree Diversification 72

6.6.6.6. ConclusionsConclusionsConclusionsConclusions 74747474

6.16.16.16.1 RecommendationsRecommendationsRecommendationsRecommendations 74747474

ReferencesReferencesReferencesReferences 76767676

Appendix A Appendix A Appendix A Appendix A –––– Research PamphletResearch PamphletResearch PamphletResearch Pamphlet 81818181

Appendix B Appendix B Appendix B Appendix B –––– Source InformationSource InformationSource InformationSource Information 83838383

Appendix C Appendix C Appendix C Appendix C –––– Tree Spreadsheet Tree Spreadsheet Tree Spreadsheet Tree Spreadsheet (legend on pg. 97)(legend on pg. 97)(legend on pg. 97)(legend on pg. 97) 87878787

Appendix D Appendix D Appendix D Appendix D –––– Pairwise Ranking of Tree UtilitiesPairwise Ranking of Tree UtilitiesPairwise Ranking of Tree UtilitiesPairwise Ranking of Tree Utilities 99999999

Appendix E Appendix E Appendix E Appendix E –––– Ranking/Scoring Sheets (sample)Ranking/Scoring Sheets (sample)Ranking/Scoring Sheets (sample)Ranking/Scoring Sheets (sample) 100100100100

Appendix F Appendix F Appendix F Appendix F –––– Feedback Session OutlineFeedback Session OutlineFeedback Session OutlineFeedback Session Outline 101101101101

Appendix G Appendix G Appendix G Appendix G –––– Farm SketchesFarm SketchesFarm SketchesFarm Sketches 111103030303

AppeAppeAppeAppendix H ndix H ndix H ndix H –––– Average Tree Utility ScoresAverage Tree Utility ScoresAverage Tree Utility ScoresAverage Tree Utility Scores 111111111111

ix

List of Figures

Figure 1 Figure 1 Figure 1 Figure 1 –––– 1. 1. 1. 1. A diagram showing the different coffee management systems. 2

Figure 1 Figure 1 Figure 1 Figure 1 –––– 2222. . . . A map showing the coffee production areas of Kenya. 4

Figure 1 Figure 1 Figure 1 Figure 1 –––– 3333. . . . The general elements of sustainable agriculture. 6

Figure 3 Figure 3 Figure 3 Figure 3 –––– 1. 1. 1. 1. Map of Murang’a District and its agro-ecological zones. 14

Figure 3 Figure 3 Figure 3 Figure 3 –––– 2222. . . . The four stages of the knowledge based systems approach. 16

Figure 3 Figure 3 Figure 3 Figure 3 –––– 3333.... Photograph of the ranking of tree utility importance by pairwise comparison. 22

Figure 4 Figure 4 Figure 4 Figure 4 –––– 1.1.1.1. A map showing the location of interviews and group discussions. 31

Figure 4 Figure 4 Figure 4 Figure 4 –––– 2.2.2.2. Causal diagram of coffee factory rules. 34

Figure 4 Figure 4 Figure 4 Figure 4 –––– 3.3.3.3. Causal diagram of the impacts of coffee shade trees on the coffee plant. 38

Figure Figure Figure Figure 4 4 4 4 –––– 4.4.4.4. Causal diagram of the shade amount appropriate for coffee. 39

Figure 4 Figure 4 Figure 4 Figure 4 –––– 5.5.5.5. Comparison of the object hierarchies: ‘coffee shade trees’ and 40

‘coffee incompatible trees’.

Figure 4 Figure 4 Figure 4 Figure 4 –––– 6.6.6.6. Comparison of the object hierarchies: ‘coffee compatible’ and 41

‘incompatible intercrops’.

Figure 4 Figure 4 Figure 4 Figure 4 –––– 7.7.7.7. Photographs of zero grazing cattle and resulting dung mixed with green waste. 43

Figure 4 Figure 4 Figure 4 Figure 4 –––– 8.8.8.8. Causal diagram of the factors affecting farm profitability. 45

Figure 4 Figure 4 Figure 4 Figure 4 –––– 9.9.9.9. Causal diagram of the factors affecting coffee price and impacts on 47

farm activities.

Figure 4 Figure 4 Figure 4 Figure 4 –––– 10.10.10.10. Causal diagram of the impacts of increasing fertiliser cost. 48

Figure 4 Figure 4 Figure 4 Figure 4 –––– 11.11.11.11. Causal diagram of the influence of coffee price on dairy farming. 49

Figure 4 Figure 4 Figure 4 Figure 4 –––– 12.12.12.12. Object hierarchy lists of profitable tree species. 51

Figure 4 Figure 4 Figure 4 Figure 4 –––– 13.13.13.13. Photographs of farmers participating in the second ranking/scoring exercise. 63

Figure 5 Figure 5 Figure 5 Figure 5 –––– 1. 1. 1. 1. Diagrammatic representation of the problems and potential positive 65

tree influences.

Figure 5 Figure 5 Figure 5 Figure 5 –––– 2.2.2.2. The potential natural vegetation types surrounding Mt. Kenya. 73

x

List of Tables

Table 3 Table 3 Table 3 Table 3 –––– 1.1.1.1. The 10 trees selected to test the ranking/scoring method 2. 25

Table 4 Table 4 Table 4 Table 4 –––– 1.1.1.1. A table of the monthly timing of farm activities/processes identified by farmers. 45

Table 4 Table 4 Table 4 Table 4 –––– 2.2.2.2. The range of prices given by farmers for some commodities sold from farms. 49

TableTableTableTable 4 4 4 4 –––– 3.3.3.3. The number of times that trees were unknown by farmers who reviewed 54

the tree list.

Table 4 Table 4 Table 4 Table 4 –––– 4.4.4.4. The tree species identified by farmers as being removed or desired. 56

Table 4 Table 4 Table 4 Table 4 –––– 5.5.5.5. The tree species identified by farmers as compatible and incompatible 57

with coffee.

Table 4 Table 4 Table 4 Table 4 –––– 6.6.6.6. The order of tree preference by utility from ranking/scoring exercise 1. 62

Table 4 Table 4 Table 4 Table 4 –––– 7.7.7.7. A comparison of the findings from ranking/scoring approach 1 and 2. 64

List of Boxes

Box 4 Box 4 Box 4 Box 4 –––– 1.1.1.1. Quotation from a farmer at the Muruka feedback session (low elevation). 36

Box 4 Box 4 Box 4 Box 4 –––– 2222. Positive and negative statements about ‘shade’ at different elevations. 37

Box 4 Box 4 Box 4 Box 4 –––– 3.3.3.3. Quotation from a farmer during an interview on their farm. 42

Box 4 Box 4 Box 4 Box 4 –––– 4.4.4.4. Quotation from a farmer at the Ngutu feedback session. 52

Box 4 Box 4 Box 4 Box 4 –––– 5.5.5.5. Quotation from a farmer at the Muruka factory feedback session. 59

Box 4 Box 4 Box 4 Box 4 –––– 6.6.6.6. Quotation from a farmer at the Ngutu factory feedback session. 60

1

1. Background: Literature Review

1.1 Coffee Agroforestry Systems

Agroforestry can generally be defined as, “the practice of integrating trees with crop

production and other farm activities in order to provide products and services previously obtained

from wild resources,” (Dawson et al., 2009 p.970). It has come to include, “the role of trees in

landscape level interactions, such as nutrient flows from forest to farm, or community reliance on

fuel, timber, or biomass available within agricultural landscapes.” (Zomer et al., 2009 p.1).

Agroforestry is a traditional land use which over the last 40 years has been extensively researched

and improved to support rural people’s livelihoods and environmental sustainability. Over one

billion hectares of land (or 46% of agricultural land) has a tree cover of over 10%, and this land

supports 558 million people (ibid).

Coffee is one of a group of perennial tree crops; “plant species with a woody support system

that periodically produce a valuable crop (for food, income or environmental benefit) other than, or

in addition to, timber.” (Omont and Nicolas, 2006). These crops play a fundamental role in the

economies of developing countries from which they are exported, and they are mostly grown on

small-scale farms (ibid).

Traditionally, coffee worldwide was grown under a diverse canopy of native tree species as

agroforest which provided a number of ecosystem services; however, starting in the 1950s coffee

systems were intensified by reducing shade cover and incorporating agrochemical use (see

Figure 1 – 1) (Perfecto et al., 2005). While intensification increased yield and revenue in many cases,

it also increased the costs of inputs (fertilisers and pesticides), indirect costs of decreased

biodiversity, and the vulnerability of farmers to fluctuations in coffee prices (ibid). In northern Latin

America alone, 50% of the 1 million ha of coffee has been converted to intensified unshaded systems

and the resulting negative impacts have included loss of biodiversity, soil erosion, and the costs of

heavy fertiliser and pesticide application (Albertin and Nair, 2004).

2

Figure 1 Figure 1 Figure 1 Figure 1 –––– 1.1.1.1. A diagram showing the different coffee management systems and how they range in

percent shade cover and shade tree richness (Perfecto et al., 2005 p.228; CBK, 2009).

1.1.1 World Coffee Trends and Outlooks

The production of coffee has historically evolved through three periods: the free market

period before the 1950s dominated by production in Brazil, the controlled market period of new

techniques for intensive production from the 1960s through the 1980s, and the current free market

period from 1989 until present (Seudieu, 2008). The International Coffee Organization (ICO) was

established during the second of these periods to regulate coffee prices, and since the return to a

free market deals with trade and movement of coffee globally (Chanakya and De Alwis, 2004).

There are two species of coffee grown commercially worldwide: Coffea robusta and C.

arabica, and a third species C. liberica which is not produced commercially (Chanakya and De

Alwis, 2004). C. robusta is high yielding (1 – 1.5 kg green coffee per plant per year), disease

resistant, and grown at lower elevation, while C. arabica fetches a higher price but yields less (0.5 –

0.8 kg green coffee per plant per year), is susceptible to drought, frost, and disease, and is best

grown at higher elevations (ibid). Optimal conditions for the growth of coffee include mean annual

temperature between 17 – 23oC, mean annual precipitation between 1500 – 2800 mm and fertile

volcanic or alluvial soils (Albertin and Nair, 2004).

Coffee is an important global commodity; 55 predominantly low-income countries

worldwide produce coffee as their primary agricultural product (Chanakya and De Alwis, 2004). The

total global coffee production in 2008 was estimated to be 127 million bags, and world consumption

3

of coffee, estimated at 128 million bags, has been growing steadily due in part to the increased

domestic consumption in exporting countries (ICO, 2009a). “It is estimated that over 125 million

people worldwide are dependent on coffee for their livelihoods,” and these people are highly

vulnerable to falling prices as was shown during the ‘coffee crisis’ in the late 1990s (Osorio, 2002).

World market coffee prices dropped from 132 US cents per kg (in the 1980s) to around 50

US cents per kg (in 2002) in what was deemed the ‘coffee crisis’ due to, “major imbalances between

supply (production) and demand (consumption).” (Karanja and Nyoro, 2002 p.4). Most recently,

coffee prices have been high due to problems in mild Arabica availability, however the dominance

of Brazilian exports and high prices for Columbian coffee have fostered uncertainty in the world

coffee market (ICO, 2009a).

1.1.2 Coffee in Kenya

The Coffee Board of Kenya (CBK) was established in 1931 after the Great Depression and

since this time there have been many alterations to coffee policy in Kenya (Condliffe et al., 2008).

Coffee was grown by Europeans on large estates in Kenya until 1934, after which time Kenyans were

finally allowed to farm the commodity (ibid). In 1937 the Kenya Planters Cooperative Union (KPCU)

was formed in the interest of small-scale farmers but it subsequently became a private company in

1941. By 1944 smallholders were forced by law to join government run cooperatives (ibid). Coffee

was introduced as a cash crop on over 80% of the farms in Muranga’a District between the late 1960s

and early 1970s during the end of the colonial period1(Ovuka, 2000). It was not until 1993 that three

commercial millers were licensed ending the long held monopoly of the KPCU (Karanja and Nyoro,

2002). In 1998, the government released control over cooperatives through the enactment of the new

Cooperative Act and since this time cooperatives have been trying to regain strength (ibid).

In Kenya, coffee is the fourth largest earner after tourism, tea and horticulture (Karanja and

Nyoro, 2002). Kenyan Arabica is grown in the highlands between 1400 – 2000 m where rainfall under

1000 mm is distributed throughout the year (Figure 1 - 2) (CBK, 2009). In 2008, Kenya produced a

total of 950 thousand bags2 of Arabica coffee of which it exported approximately 840 thousand bags

(ICO, 2009b). From 2007 to 2008, the total production of coffee in Kenya increased an impressive

35% (ICO, 2009a).

1 The colonial period in Kenya was from 1888 - 1963 2 a bag is 60 kg of green coffee

Figure Figure Figure Figure 1 1 1 1 –––– 2222.... A map showing the coffee production areas of Kenya

A map showing the coffee production areas of Kenya (CBK, 2009)

4

(CBK, 2009).

5

1.2 Ecosystem Services

Ecosystems, which can be defined as, “dynamic complex[es] of plant, animal, and

microorganism communities and the nonliving environment interacting as functional unit[s],” (MA,

2005 p.V) have undergone unprecedented change globally over the past 50 years. In order to meet

demands for food, water, fuelwood, timber and fiber, humanity has managed natural ecosystems to

increase their productive potential, and in doing so has degraded them through land use change,

overexploitation, pollution, and other unsustainable management practices (ibid). This has affected

the natural ability of such ecosystems to recover and function properly.

As research increasingly focuses on this alarming trend, the concept of ecosystem services

has grown exponentially in importance (Fisher et al., 2009). The term ‘ecosystem services’ has been

defined in many different ways, however common to these definitions is the notion that such

services link ecosystem function with human welfare (ibid). This research will adopt the definition of

Fisher et al. (2009 p.645) which states that, “ecosystem services are the aspects of ecosystems

utilized (actively or passively) to produce human well-being.”. According to this definition,

ecosystem services encompass ecosystem processes, functions as well as structures and

organizations in their own right so long as they are utilized. Humanity is completely dependent on

functioning ecosystems and the services they provide for survival (Diamond, 2005; Adams, 2008;

Fisher et al., 2009). The focus of the present research will be the utilization of trees in the ecosystem

services of coffee agroecosystems.

There have been many attempts to classify ecosystem services. In the Millennium Ecosystem

Assessment, ecosystem services are classified into four broad categories: provisioning servicesprovisioning servicesprovisioning servicesprovisioning services

(such as food, water, medicines, genetic resources…), regulating servicesregulating servicesregulating servicesregulating services (such as air quality

regulation, water purification, disease regulation, pollination…), cultural servicescultural servicescultural servicescultural services (such as recreation

values, spiritual values, aesthetic values…), and supporsupporsupporsupporting servicesting servicesting servicesting services (such as photosynthesis,

nutrient cycling, soil formation…); all of which affect human wellbeing either directly or indirectly

(MA, 2005). Fisher et al. (2009) argue that the appropriate classification of ecosystem services is

dependent on characteristics of the ecosystem(s) being investigated, and the context in which they

are being considered.

For the purposes of this research, ecosystem services relevant to coffee agroforestry and

involving trees will be classified according to the perceptions of coffee farmers in terms of their

different utilities (referred to as ‘tree utilities’). These are predominantly classified as provisioning

and regulating in nature.

6

1.2.1 Ecosystem Services and Agroforestry

Over the course of the second half of the 20th Century, global population has doubled (from

2.5 billion in 1950 to 6.1 billion in 2000) and world grain production has tripled (from 640 million

tons in 1950 to 1,855 million tons in 2000) (Nair, 2008). This rapid growth was made possible

primarily due to the intensification of agriculture which maximizes the yield from a unit of land by

adding high amounts of inputs such as water and fertilizer (ibid). In many cases intensification has

proven to be unsustainable and has damaged the ecological foundation of ecosystems and caused

vast deforestation, desertification and degradation of resources (MA, 2005). On the other hand, it

can be argued that agricultural intensification (rather than extensification) helps, “allocate

destructive pressure on habitats by meeting agricultural production needs on existing farmland,”

(Srivastava et al., 1999 p.4). Sustainability of these systems however needs to be considered.

Agriculture may be sustainably practiced only through the simultaneous consideration of

economic, ecological, and social elements (see Figure 1 – 3)(Thrupp, 2004). Agroforestry is an

approach

Figure 1 Figure 1 Figure 1 Figure 1 ---- 3333.... The general elements of sustainable agriculture. Taken directly from: (Thrupp, 2004 p.328)

which has the potential to sustainably and profitably produce products and services in an

environmentally sound manner. According to Nair (2008 p.6),

Agroforestry is based on the premise that land-use systems that are

structurally and functionally more complex than either crop or tree

monocultures result in greater efficiency of resource capture and utilization

(nutrients, light, water), and greater structural diversity that entails a tighter

coupling of nutrient cycles.

The combined goals of agroforestry are to sustain local livelihoods while decreasing pressure

on surrounding forests and protected areas, and supporting conservation on agricultural land itself

7

(Boffa et al., 2005). Through the incorporation of trees in agricultural landscapes, many ecosystem

services are supported. Market mechanisms such as payment for environmental services3 (PES) and

certification schemes4 have the potential to act as added incentives to farmers to promote

ecosystem services on their farms.

When considering ecosystem services, it is important to define the scale of investigation and

the boundaries of the system involved (Clements and Shrestha, 2004). Because, “emergent

properties are revealed only if we study agroecosystems holistically, within their landscape and

human contexts,” (ibid, p.7) ecosystem services will be classified at a landscape scale.

In the context of a landscape mosaic of multiple land uses (which includes agroforestry),

ecosystem services are not confined to single farm plots but instead flow throughout the landscape.

For this reason a definition of ecosystem services at the landscape scale is appropriate. Fisher et al.

(2009 p.650) suggest a landscape classification of ecosystem services into three spatial categories:

1. In-situ – where the services are provided and the benefits are realized in

the same location [for example soil formation]

2. Omni-directional – where the services are provided in one location, but

benefit the surrounding landscape without directional bias [for example

pollination]

3. Directional – where the service provision benefits a specific location due to

the flow direction [for example water regulation on forested slope]

It is especially important to identify omni-directional and directional ecosystem services

when considering payment for environmental services, as agreement must be reached between

service providers and benefit receivers (ibid). The temporal nature of ecosystem services may also

be important, for example the phenology of a food source supporting biodiversity or farmer

livelihoods.

There are five main agroforestry ecosystem services identified by Nair (2008). 1) Soil

protection and productivity is maintained by increased nutrient availability of trees (nitrogen

fixation, deep root systems), prevention of soil erosion, increased microbial activity and

improvement of physical soil properties (ibid). These soil ecosystem services could be in-situ (for

example nitrogen fixation), or (omni)-directional (for example prevention of soil erosion and

siltation of a river down slope). 2) Water quality maintenance and environmental amelioration due to

the reduction of non-point source pollution to streams and rivers (deep root systems) and better

retention of water (ibid). Again, these ecosystem services could be either in-situ (crop water

3 PES is the use of market mechanisms to conserve natural resources 4 Certification schemes (like Rainforest Alliance, Fair Trade, and Café Practices) ensure that coffee growing and/or processing meet defined standards to improve social and environmental conditions. The higher price of certified products theoretically compensates farmers for these improvements.

8

availability) or (omni)-directional. 3) Biological diversity is supported in the working landscape by

increasing species diversity, increasing connectivity and decreasing pressure on the remaining forest

patches (ibid). The spatial benefits of biological diversity are difficult to define but include in-situ

benefits of ecosystem stability, resilience, and resistance (Fisher et al., 2009). 4) Carbon storage and

mitigation of green house gases is achieved through sequestration in biomass and the soil, through

carbon substitution (use of wood in place of more fossil fuel dependent materials) and conservation

(preventing further deforestation) (Nair, 2008). This ecosystem service could be considered omni-

directional as the entire atmosphere benefits. Finally, 5) food and nutrition provision is sustained

either directly or indirectly by increasing system productivity (ibid). In most cases this would be

considered an in-situ ecosystem service.

Of these agroforestry ecosystem services, biodiversity is most commonly researched and

reported. The importance of this ecosystem service cannot be underestimated; agriculture and

forestry depend on ecosystem services, which in turn depend on diversity at genetic, species, and

ecological scales (Fischer et al., 2006). The next section covers biodiversity as an ecosystem service

in more detail.

1.2.2 Biodiversity in Working Landscapes

Conservation strategies must be crafted that create a biodiverse world that

includes people, not a world of biodiverse enclaves in lifeless human

landscape. It is widely recognised that protected areas cannot achieve

conservation’s aims as small high biodiversity islands.”

(Adams, 2008 p.470)

Global biodiversity is changing at an unprecedented rate due to anthropogenic use of natural

resources; specifically land-use change, climate change, nitrogen deposition, acid rain, and biotic

exchange (Sala et al., 2000). Agricultural intensification, genetic improvement and the prevalence of

monocultures has drastically reduced the genetic diversity of crops, and forestry and fishing have

also contributed towards global decline of biodiversity (Nair, 2008).

It is now widely recognised that conservation of biodiversity in protected areas (covering

only 12% of land globally) is not feasible (Boffa et al., 2005; Fischer et al., 2006; Adams, 2008);

protected areas are too small, isolated, frequently exploited, and not always managed to conserve

biodiversity.

Agrobiodiversity5 includes genetic resources, edible plants and crops, livestock, soil

organisms, insects, bacteria, fungi, wildlife and wild resources of natural habitat, and

agroecosystems themselves (Thrupp, 2004). “Biodiversity is fundamental to agricultural production,

food technology innovations, and food security, as well as being an ingredient of environmental

5 Agricultural biodiversity

9

conservation.” (ibid, p.316). As such, a paradigm shift towards the integration of ecosystem goods

and services into production landscapes is necessary (Nair, 2008). Fisher et al. (2006 p.80) identify

10 strategies to increase biodiversity in production landscapes:

1.2.3 Other Ecosystem Services

As mentioned in the above section, soil protection and productivity are other important

ecosystem services possible through agroforestry. In developing nations 1.9 billion ha of land

(comprising one third of total farmland) is degraded by erosion, salinity and decreased fertility (Nair,

2008). Trees can be used to prevent erosion, restore degraded and contaminated sites, improve

nitrogen availability, and increase soil calcium and potassium and cation exchange capacity

(Perfecto et al., 2005).

In coffee production systems in the central highlands of Kenya, high rainfall, steep slopes

(from 15-55%) and intensive continuous cultivation cause severe soil erosion (Tamubula and Sinden,

2000; Okoba and De Graaff, 2005). Recommendations to help control the problem have included

establishment of napier grass strips and Calliandra spp. hedgerows creating a soil-and-nutrient

replacement systems (ibid). Although farmers are aware of the problem of erosion and that it is

caused in part by runoff of rain and steep slopes, according to Okoba and De Graaff (2005), they did

not understand the usefulness of trees in stabilizing soil.

Water retention and improved water quality by agroforestry systems provide other valuable

ecosystem services. Over two thirds of the water used by humans is used for agricultural purposes,

and livestock and crop production creates nitrates, phosphates and pesticides that pollute water

supplies (Nair, 2008). Agroforestry practices such as riparian buffers and silvopasture reduce the

amount of non-point source pollution escaping agricultural systems, while the incorporation of trees

generally increases the amount of water retained in a system due to the extensive root systems

(ibid).

There are of course countless other important ecosystem services provided by healthy

ecosystems, however the focus of the present research is on those involving trees in coffee

landscapes. This may include for example: pollination, pest regulation, and habitat provision

amongst many others.

1. Maintain and create large, structurally complex patches of native vegetation

2. Maintain structural complexity throughout the landscape

3. Create buffers around sensitive areas 4. Maintain or create corridors and stepping stones 5. Maintain landscape heterogeneity and capture environmental gradients

6. Maintain key species interactions and functional diversity 7. Apply appropriate disturbance regimes

8. Control aggressive, over-abundant, and invasive species

9. Minimize threatening ecosystem-specific processes 10. Maintain species of particular concern

10

1.3 Local Knowledge

It can be argued that anchoring research in the needs and opportunities of

farmers is as important as it is to anchor the research in the international

scientific literature. (Roling et al.,

2004 p.214)

For the present purpose, local knowledge (LK) may be defined as an, “understanding of the

world that can be articulated by an informant.” (Sinclair and Walker, 1999 p.246). LK differs from

familiar definitions of indigenous knowledge in that it does not reflect cultural values and beliefs, but

focuses on general explanatory ecological knowledge (Walker and Sinclair, 1998). In this way,

disaggregation of LK from its cultural context is justified, however it is important to capture

contextual information6 when acquiring LK and storing it into a knowledge base (KB). LK is not

simply information, but information that is interpreted and understood.

Another important distinction to be made is the difference between knowledge and practice

(Sinclair and Walker, 1999). The primary interest of this research is to acquire knowledge that

underpins decision making since decisions themselves are affected by many other factors (such as

politics and economics). Farmers may know that a given practice is more sustainable in the long

term, but may not practice it due to economic limitations or social pressure.

The content of LK in published literature has increased in recent times, nevertheless a meta-

analysis by Brook and McClachlan (2008) of the articles from 360 environmental, conservation and

ecology journals published from 1980 – 2004 found that only 0.01% involved LK. In this light LK is an,

“important but underutilized resource”(Walker et al., 1999), which should be incorporated into

projects and research to encourage participation, and to promote relevant and appropriate

objectives within the local context (Sinclair and Walker, 1999; Roling et al., 2004; Silvano and Valbo-

Jorgensen, 2008).

1.3.1 Local Knowledge in Combination with Scientific Knowledge

Scientific and locally derived knowledge are inherently different in that scientific knowledge

aims to objectively explain natural variation and be generally applicable, while local knowledge aims

to explain local observations and experience (Sinclair and Walker, 1999). While Sillitoe (1998)

believes that these forms of knowledge cannot successfully be combined without critically losing

accuracy, it has convincingly been shown in the literature that it is both possible and meaningful to

6 Contextual information includes: source information, conditionality, and hierarchy of relationships (Walker and Sinclair, 1998)

11

do so (Sinclair and Walker, 1999; Walker et al., 1999; Bart, 2006; Silvano and Valbo-Jorgensen, 2008).

In the words of Berkes (1999 p.11):

The worlds of the shaman and the scientist are two parallel modes of

acquiring knowledge about the universe… the philosophical differences

between the two kinds of science are not sharply defined; rather it is our

reductionist analysis that tends to exaggerate the differences.

Precise acquisition and documentation of local knowledge is necessary in order to make

comparison with existing scientific knowledge feasible. The ways in which these different types of

knowledge complement and contradict one another provide meaningful insights and highlight areas

for further consideration and exploration (Waliszewski et al., 2005).

12

2. Research Objectives

2.1 Organisational Setting

This research was part of the Coffee Agroforestry Network (CAFNET) Project in association

with the World Agroforestry Centre (ICRAF)7 and the Mugama Farmers’ Cooperative Union (Mugama

Union) in Kenya. The CAFNET Project, which operates in Central America, India, and East Africa,

aims to, (1) “link sustainable management and environmental benefits of coffee agroforests with

appropriate remuneration for producers,” and (2) “improve livelihoods for coffee farming

communities while conserving natural resources” (CAFNET, unpub.). Relevant activities of the

CAFNET Project are to, “document traditional agroforestry knowledge and the value of native trees”,

and to, “train staff and build capacity of local organizations to manage sustainable, market-oriented

agroforests.” (ibid). This research would not have been possible without the financial and

institutional support provided by the CAFNET Project and ICRAF.

In accordance with the goals of the CAFNET Project, the Mugama Union seeks to encourage

farmers to increase tree cover and diversity on coffee farms. One of the activities they are

undertaking to achieve this goal is to update and expand tree nurseries on the farms owned by the

Union. The present research will help to inform the Mugama Union about which trees coffee farmers

in Central Province are most interested to plant on their farms, and which trees are highly valued for

a number of different utilities. By making a diversity of trees available to farmers at subsidized

prices, it is hoped that farmers will be encouraged to plant them on their farms both inside coffee

plots, and elsewhere on the farm.

2.2 Rationale

Due to the recent drop in coffee prices in Kenya and the increasing cost of pesticide and

fertiliser inputs, many coffee farmers have been forced to diversify from coffee to other activities on

their farms. In some cases the situation has reached a point where farmers have completely

neglected or even uprooted their coffee and intercropped or replaced it with subsistence food

crops for farm consumption or other profitable activities such as dairy farming and macadamia nut

production.

Trees have a multitude of important utilities on farms in Kenya. By encouraging tree diversity

and abundance on coffee farms, both inside and outside coffee plots, it is believed that farm

7 The World Agroforestry Centre was previously known as the International Center for Research in Agroforestry (ICRAF) which is where its accronim was derived.

13

sustainability will be increased while diversifying farm production and decreasing the vulnerability

of farmers to cash crop market fluctuations.

By acquiring local knowledge from coffee farmers about the utilities of trees and the many

factors limiting the presence of trees on farms, it is possible to make informed and context

appropriate recommendations about the diversity of trees that can be used for each utility in Central

Province, Kenya. Access to such information will facilitate farmers’ decision making about the variety

of trees meeting their requirements. The information can simultaneously be used for the

development of tree nurseries to make a diversity of seedlings available and affordable to farmers,

encouraging them to benefit from increased tree diversity on their coffee farms.

2.3 Objectives

The primary objective of the research was to acquire an understanding of farmers’

knowledge about tree utilities and to develop participatory tools to encourage tree diversity and

abundance in addition to improving tree productivity on coffee farms.

Additionally, the research aims to identify key areas where farmer knowledge can be

expanded by increasing access to information and training.

2.4 Research Topics

The main research topics were the following:

· WHY do farmers have trees on their farms?

· WHAT are the most important utilities of trees on coffee farms?

· WHAT trees can be used for each utility?

· WHAT factors limit the distribution of trees in coffee plots? On farms? In the landscape?

· WHERE are trees located on farms? In the landscape?

· WHAT impacts coffee productivity and profitability?

· HOW are farmers managing their coffee?

· WHAT do farmers understand about coffee quality and price?

· HOW do trees influence coffee productivity and profitability?

· WHAT other activities are coffee farmers implementing on their farms?

14

3. Methods

3.1 Study Area

The study took place across Murang’a District and in the upper part of Maragua District8 in

Central Province, Kenya from June – September, 2009 (see Figure 3 – 1, top right). The main ethnic

group inhabiting this region is the Kikuyu and the most common languages spoken are Kikuyu,

Kiswahili, and English. The population of Murang’a District was 1,056,000 in 1997 with a population

growth of 2.5% per year, thus the area experiences a high population density of approximately 450

people/km2 (Ovuka and Lindqvist, 2000).

8 Because the farms visited in Maragua District were so close to boarder with Murang’a District, the description of the latter is sufficient for the description as the sites are not believed to differ dramatically.

Figure 3 Figure 3 Figure 3 Figure 3 –––– 1111.... Maps locating

Murang’a District in Kenya (top)

and of the agro-ecological

zones of Murang’a District (left).

Taken directly from: (Ovuka and

Lindqvist, 2000 p.108,111)

15

The vast majority of this population practices small-scale subsistence farming, and the average farm

size is 1.5 ha and decreasing due to modes of inheritance (ibid)

According to Ovuka and Lindqvist (2000),who have conducted extensive research in the

area, “agricultural potential for Murang’a District generally decreases from the northwestern to the

southwestern side, mainly because of decreasing rainfall and decreasing soil fertility.” (p.109). The

altitude in the District ranges from 900 – 3,300 m a.s.l. and temperature is closely related to altitude.

There are two rainy seasons from mid March – the end of May, and from October – December (ibid).

Agro-ecological zones, based on climatic and altitudinal information, show where the dominant

crops are grown in the District (see Figure 3 – 1, left). The traditional staple crops of the area which

included millet, sorghum, peas, and yams have been in decline and there has been a shift towards

the cultivation of banana, Irish potato, maize and cabbage (Ovuka, 2000).

Coffee and tea are the main cash crops in the region. Andosol is the dominant soil type, and

is generally well-draining and highly weathered (ibid). Although these soils are naturally high in

organic matter, fertilizer application is generally necessary to sustain soil fertility (ibid). Only Arabica

coffee is grown in the area, however there are many varieties, including SL28, SL34, Blue mountain,

and Ruiru 11 which have different characteristics (Lamond, 2007).

Located between the Aberdares National Park and Mt. Kenya National Park, the study area is

a hotspot for biodiversity (CAFNET, unpub.), and an area of priority for ecosystem services provision

as the benefits of these services (like improved water quality, soil fertility and biodiversity) reach the

surrounding area.

16

3.2 Local Knowledge Acquisition

The knowledge based systems (KBS) approach developed at Bangor University was used for

the acquisition of local knowledge (Sinclair and Walker, 1998; Walker and Sinclair, 1998). The KBS

approach may involve 4 stages (see Figure 3 – 2), however for the purpose of this research the first

three stages were deemed most essential.

Figure 3 Figure 3 Figure 3 Figure 3 –––– 2222.... The four stages of the knowledge based systems approach including the objectives,

informants and activities of each stage. Taken directly from: (Walker and Sinclair, 1998 p.374)

3.2.1 Scoping Stage

The scoping stage was a period of familiarization and orientation. Attempts were made

during this first stage to meet with coffee cooperative members, coffee factory employees, and

influential members of surrounding communities to raise awareness about the research and to

determine the interests of different stakeholders. These key informants identified through purposive

snowball sampling9999 (Laws et al., 2003) were asked to suggest possible informants for the

compilation stage of the research and to identify what factors they believed may affect the

9 Purposive snowball sampling is a technique that involves asking key respondents to refer researchers to other appropriate informants, who may then refer to other informants, and so on (Laws et al., 2003).

17

knowledge held by members of the coffee farming community. Factors deemed to influence the

knowledge of informants during previous research in the area included: occupation, age, farm size,

and coffee plot size (Lamond, 2007). In addition to these factors it was decided that farm elevation

could have an important impact on the management of coffee as suggested by Albertin and Nair

(2004).

During the scoping stage of the methodology, interviews were conducted with the following:

the warden at Wanjerere Forest Station near the Aberdares National Park and the Community

Forestry Association representative there; the Divisional Forestry Officer for Kangama; and the staff

at an agro-chemical and fertilizer shop in Murang’a. It should also be noted that conversations with

many other people at the ICRAF in Nairobi and the Mugama Farmers’ Cooperative Union in Murang’a,

and its affiliated societies and coffee factories contributed to this stage of the research.

A knowledgeable translator named Vickpreston Mbugua Njoroge was identified during the

second week of the data collection period and he was present as the research assistant for all of the

field work from this point on. His assistance in translation when necessary and with logistical matters

in the field was invaluable to the progress of the research.

By the end of the scoping stage a pamphlet of information was designed and translated (by

the research assistant) into Kikuyu for distribution to farmers during upcoming interviews (Appendix

A). The information it contained outlined the aims of the research and provided the contact

information of the researcher and research assistant (collectively the ‘researchers’). In this way all

inquiries about the research could be answered in either Kikuyu or English.

3.2.2 Definition Stage

The objective of this stage of the research was to define terminology used by local

communities related to coffee farming and tree utilities on farms. It became apparent early on that

the order and way in which questions were asked greatly impacted the ability of farmers to

understand. In this way, interviewing was a learning process for the researchers who improved with

practice. It was especially critical during this time to become familiarized with the local names of the

trees present on farms as farmers often pointed during interviews to trees in the surrounding

landscape.

An interview with the high school biology laboratory technician at Gitugi Girls High School

was particularly important to learn the accurate Kikuyu and scientific identification of the many trees

on the school compound. Also a book entitled ‘Kikuyu Botanical Dictionary of Plant Names and

Uses’(Gachathi, 1989) was lent to the researchers by the Rwaikamba Society Chairman and proved

to be incredibly useful throughout the research for identifying the scientific name of trees that

farmers only knew in Kikuyu.

18

During this stage of the research it was also important to fully discuss the aims and

methodology of the research with the research assistant. It was agreed that when translation was

necessary it would be accomplished as literally as possible on the spot such that interviews could

be conducted accurately and be understood by all involved in an informal conversational style

which farmers seemed to be most comfortable with. It was also decided that all interviews would be

recorded10101010 using a digital recording device so that conversations could be more accurately

translated for quotations upon review if necessary and to supplement the notes taken by hand

during interviews. The existing research conducted by Lamond (2007) and Gathoni (2007) in Central

Province acted as an important additional source of information providing a basis of understanding

about the terminology, landscape, and knowledge of farmers in the area.

3.2.3 Compilation Stage

The majority of the research timetable was devoted to this stage of the methodology which

predominantly involved repeated semi-structured interviews with purposely selected informants and

formal representation of the acquired knowledge into a knowledge base (see next section). Potential

informants were, “stratified according to the variables that were identified as likely to influence

knowledge held by people in the scoping stage” (Walker and Sinclair, 1998 p.375-376). Ideally the

methodology calls for five informants from each strata, however due to the short time period for the

study the primary criteria for purposely selecting informants was their willingness to participate and

their level of experience with coffee farming and trees on farms. It was found that identifying farmer

informants through coffee factories and cooperative society management was an effective strategy

as factory or society managers often selected farmers with high experience whom they predicted

would be interested to participate. Using this technique it was also possible to save valuable time in

selecting informants.

The study targeted coffee farmers and specifically coffee farm owners as they were generally

the most knowledgeable about the management of coffee and tree utilities. Once the information

pamphlets were circulated there was great interest by farmers to be involved in the research. A total

of 33 respondents11111111 were interviewed including 27 farmer respondents of which 24 were

interviewed while walking around their farms (Appendix B). Ten of the respondents were

interviewed in person a second time, and five respondents were asked specific follow-up questions

over the telephone after the first interview.

Different techniques of knowledge elicitation were employed during this stage to triangulate

the knowledge acquired (Laws et al., 2003). It is well established that using an interdisciplinary

approach with multiple methods is the best means to acquire an in-depth understanding and a

10 Interviews were only recorded when given permission by the interviewee (which occurred in all cases). 11 An individual or two people (group discussions and feedback sessions involved a greater number of people)

19

holistic view (Den Biggelaar and Gold, 1995; Franzel et al., 1996; Vabi, 1996; Gausset, 2004). The

overall combination of methods employed was similar to that used by Vabi (1996) to elicit

community knowledge about tree uses which included, “techniques such as semi-structured and key

informant interviews, institutional analyses, transect walks, matrix scoring and ranking, participatory

mapping and diagramming…” (p.31). Second interviews were held with respondents who were

found to be very knowledgeable about trees during first interviews and also afforded an opportunity

to clarify areas of uncertainty, draw farm sketches, rank and score trees, and discuss tree utilities.

The research followed an iterative process of alternation between interviewing and formal

knowledge representation and knowledge exploration (ibid).

3.2.3.1 Informal Semi-Structured Interviews

The dominant research method used to acquire information during the scoping stage was

informal semi-structured interviewing12121212. A list of important interview topics was prepared in

advance, however interviews did not follow any formal structure and were more conversational in

nature which is why they have been called ‘informal’ semi-structured interviews. This approach

allowed farmers to feel more comfortable to share their knowledge during interviews, unlike being

interrogated in a formal interview style, and also focused primarily on the topics that farmers were

most knowledgeable about.

Whenever possible the interviews were held in farmers’ fields since the ability to see features

of discussion and examples in the surrounding landscape significantly added to the quality and

understanding of knowledge acquired and added context to the information. In countless instances

farmers pointed out examples of what they were describing, and without this opportunity a great

deal of knowledge (and even tree species) would have been overlooked. Through farm observation,

researchers could inquire about observed features that were not previously discussed; this was the

case with many tree species that did not have direct economic benefits.

Interviews were always initiated by fully describing the purpose of the research. It was also

important to inform farmers that the report compiled from the information that they shared would

be given to Bangor University, ICRAF, and the Mugama Farmers’ Cooperative Union and that the key

information would be made available to farmers at the coffee factory level. Every interviewee gave

permission when asked for the interview to be digitally recorded, and review of the audio

recordings proved to be very useful.

12 According to Laws et al. (2003), semi-structured interviews are flexible in what questions are included, the types of questions used, and the ways in which questions are asked.

20

3.2.3.2 Group Discussions

Three group discussions13131313 (GDs) were held at coffee factories at different times during the

research period. The first GD at Ngutu Factory involved approximately 35 participants (including

coffee farmers and coffee factory employees) who participated voluntarily in discussions about

coffee farming and tree utilities. This meeting provided an excellent opportunity to get early

feedback on the topics discussed during interviews up until that point, and to build on the tree

species lists for different utilities. It was also used to undertake a group pairwise ranking of tree

utilities (see section 3.2.3.6) to determine the priority if tree utilities on coffee farms.

The other two GDs were held during the last week of data collection as feedback sessions

involving interviewed farmers, interested farmers, and factory and society employees (see section

3.2.4).

3.2.3.3 Telephone Interviews

Five telephone interviews were conducted with farmers previously interviewed in person.

These brief telephone interviews served the purpose of posing specific questions about the rules

and regulations imposed by their respective coffee factory, and about the sources from which they

had received information about coffee shade and coffee quality. The interview questions were

prepared in advance, and the interviews were carried out in Kikuyu (for ease of understanding over

the telephone) by the research assistant, and transcribed after the conversation in English.

Conducting such an interview over the telephone saved time and transport funds, and was

successful in all cases since the researchers had previously met with interviewees and had already

fully described the aims of the research.

3.2.3.4 Farm Sketches

During second interviews some farmers were asked to sketch their farm. Many farmers were

reluctant to sketch for fear that the result would not be accurate or look nice. Six farmers agreed to

represent their farms on paper with a sketch, and one farmer even drew two sketches; one

representing the farm at present and another representing what he would like the farm to look like in

the near future.

Farmers rarely added trees to their farm sketches despite having discussed the trees on their

farm prior to sketching. For this reason farmers were asked with the help of researchers to add the

13 The term ‘focus group discussion’ describes, “a group interview where 6 to 12 people are brought together for a discussion” (Laws, et al., 2003, p.298) while ‘group discussion’ is used here to describe a discussion focused on a limited number of topics but involving over 18 (and up to 100) participants.

21

trees to the sketch after it was completed. After this point any major features either within sight or

previously seen on the farm that were not included in the diagram were inquired about and usually

added to the sketch. According to Gausset (2004), “studying the spatial and tenure distribution of

trees provides information about what type of trees are found in the landscape and who owns

them.” (p.4). Such information demonstrates which trees are grown in practice, which may differ

from the trees that farmers know about or intend to have on farms. In reality there are constraints

which affect the type and number of trees which are present on farms such as land and labour

availability, local institutions, finances, etc (ibid).

3.2.3.5 Tree Spreadsheet

Throughout the research period a spreadsheet of trees was compiled and regularly updated

(see Appendix C). The spreadsheet includes information about: tree names (Kikuyu, English, and

scientific), location on farms, origin, establishment, and utilities (noting which can be sold).

Compilation of this spreadsheet helped to organize the information about trees from a variety of

different sources (indicated by codes), and served as a tool which was used during later interviews

with farmers.

Three interviewees14141414 (including 2 farmers during second interviews and a Divisional Forestry

Officer) were asked to review the entire tree spreadsheet and each added a great deal of important

information in doing so.

3.2.3.6 Tree Utility Ranking

To determine from farmers the importance priority of tree utilities, ranking by pairwise

comparison (Gausset, 2004) was carried out with two individual farmers during second interviews

and with a group of farmers during the first GD15151515 (see Appendix D). This technique, “allows one to

transform a multi-class classification problem… into a number of binary problems,” (Hüllermeier et

al., 2008) by independently comparing each pair combination of tree utilities in turn and storing the

results in a matrix (see Figure 3 – 3). In each case, farmers were first asked to identify all the utilities

of trees on coffee farms and were then asked to compare them two at a time. The results from each

of the three pairwise comparisons were scored (from 9 being the most important utility to 1 being

the least important utility) and the overall ranking of utility importance was determined by the sum

of scores from the three pairwise rankings.

14 Interviewees who reviewed the tree spreadsheet are marked with ‘~’ in Appendix B. 15 Those who completed pairwise ranking of tree utilities are marked with ‘%’ in Appendix B

Farmer interviewees were also asked to identify which specific attributes were desirable

under each utility. For example, what attributes about a tree make it a good firewood tree? Burning

qualities? Amount of wood produced? Early maturity? This information was needed so that key

utility attributes could be independently scored during ranking and scoring exercises. It was most

appropriate to have farmers identify relevant utility attributes t

choosing which attributes they deemed most important.

Figure Figure Figure Figure 3 3 3 3 –––– 3333.... Photograph taken by researchers of thecomparison from the first GD on 08/07/09understanding and to simplify translation from English to Kikuyu during the activity (tree utility symbols from left to right across the top represent: timber, firewood, mulch, shade, environmental services (bringing the rain), medicine, food/fruit, income, and animal fodder).

3.2.3.7 Ranking/Scoring

Based on the information collected from the tree utility ranking, scoring sheets with key

attributes of the most important utilities and the complete list of

on farms (from the tree spreadsheet) were prepared (

very good (VG), good (G), average (A), bad (B), very bad (VB), not used (N/A), and uncertain (?)

two farmers and one high school teacher (also a farmer himself) were asked to score all of the trees

for each of the selected tree utility attributes. This was a lengthy process requiring concentration by

the informants for up to 2 hours (depending on the level of translation requir

the informants asked to complete this exercise patiently scored all

16 The choice of scoring scale was based on a similar approach by Gausset (2004) however the ‘not used’ (N/A) category was added to accommodate the fact the not all trees were present in all locations across the research area.



ualities? Amount of wood produced? Early maturity? This information was needed so that key


appropriate to have farmers identify relevant utility attributes themselves rather than researchers

choosing which attributes they deemed most important.

Photograph taken by researchers of the ranking of tree utility importance by pairwise on 08/07/09. Utilities were represented by drawings to facilitate farmer

understanding and to simplify translation from English to Kikuyu during the activity (tree utility symbols from left to right across the top represent: timber, firewood, mulch, shade, environmental

he rain), medicine, food/fruit, income, and animal fodder).

Ranking/Scoring – Method 1


attributes of the most important utilities and the complete list of tree species discussed and found

on farms (from the tree spreadsheet) were prepared (see Appendix E). Using a priority scale from

very good (VG), good (G), average (A), bad (B), very bad (VB), not used (N/A), and uncertain (?)

hool teacher (also a farmer himself) were asked to score all of the trees


the informants for up to 2 hours (depending on the level of translation required), however each of

the informants asked to complete this exercise patiently scored all the species they knew. Prior to

The choice of scoring scale was based on a similar approach by Gausset (2004) however the ‘not used’ (N/A) category was added to accommodate the fact the not all trees were present in all locations across the

22



ualities? Amount of wood produced? Early maturity? This information was needed so that key


hemselves rather than researchers

ranking of tree utility importance by pairwise nted by drawings to facilitate farmer

understanding and to simplify translation from English to Kikuyu during the activity (tree utility symbols from left to right across the top represent: timber, firewood, mulch, shade, environmental


tree species discussed and found

Using a priority scale from

very good (VG), good (G), average (A), bad (B), very bad (VB), not used (N/A), and uncertain (?)16,

hool teacher (also a farmer himself) were asked to score all of the trees


ed), however each of

species they knew. Prior to

The choice of scoring scale was based on a similar approach by Gausset (2004) however the ‘not used’ (N/A) category was added to accommodate the fact the not all trees were present in all locations across the

23

scoring, the exercise and scoring scale were fully explained and it was stressed that the scores

should reflect potential tree utilities (quality) on coffee farms generally, rather than the actual utilities

(frequency) experienced (Gausset, 2004). A visual list of the scoring scale was provided throughout

the exercise for reference.

The scores were later converted to numerical values where: VG=5, G=4, A=3, B=2, VB=1,

N/A=0 and ?=were not included as data (although noted as unknown, see section 4.6.1). Average

scores and standard deviation were calculated for each tree species to demonstrate how data could

be analysed but it is acknowledge that no conclusions can be drawn from such a small sample of

respondents. For trees which were unknown by one or more informants, averages were calculated

from the data of those who knew the tree; trees only known by one informant were highlighted as

highly uncertain. Using this approach, the tree species were ordered from best to worst under each

utility attribute. This method is one possible approach to rank multipurpose trees (MTPs) on coffee

farms and should only be considered in combination with the other results (including qualitative

information such as factors limiting tree occurrence). The drawbacks to this scoring approach

include:

· Long time needed to carry out this method for all tree species and required lengthy

conversations about ‘why’ trees were good or bad for each utility because attributes

remained ambiguous.

· The ‘environmental’ utility, which was added to the scoring form after further consideration,

encompassed numerous regulatory environmental services provided by trees, but especially

the ability of trees to ‘bring the rain’. Further exploration and breakdown of this utility into

different attributes is necessary and should be one focus of future research in the area.

· Each utility was scored against the same pre-determined scale which may not have been

appropriate

· Although trends arose from this small sample, further scoring is needed to statistically

represent the preferences of coffee farmers for trees under each utility.

Having acknowledged these major limitations, it was believed that this method was useful for

the acquisition of additional important information about the multiple purposes of trees on coffee

farms in Central Province, Kenya.

3.2.3.8 Ranking/Scoring – Method 2

As an alternative to the first ranking/scoring approach, a second approach was tested to

determine if it would be feasible for replication with a large sample of farmers17. This approach does

17 Acknowledgement is given to Dr. Fergus Sinclair for his ideas and inputs for this ranking approach

24

not attempt to cover all the tree species with each respondent, but instead 10 species are randomly

selected to be ranked within a manageable amount of time. For every tree species a small card

containing a picture of the tree and its Kikuyu name(s) would need to be prepared. Assuming

farmers were familiar with all the tree species, implementation of this approach of 10 trees at a time

with a large sample of respondents would result in a dataset of information about all tree species. In

reality however, some trees are less commonly known by farmers, and for this reason it would be

necessary to form two groups of trees: the ‘commonly known trees’ and the ‘lesser known trees’. In

this way, a larger proportion of the 10 trees randomly selected would come from the ‘lesser known

trees’ group following the assumption that it will be more difficult to obtain information about these

trees because they are less commonly known.

To conduct this method with farmers, the 10 randomly selected trees are first review

together by the researchers and respondents18 to ensure that they are familiar with the species. For

any unknown species a note is made and replacement species would be randomly chosen until the

farmer is familiar with all 10 trees. The researchers would then fully describe the exercise to the

farmer until they are confident that they are comfortable and understanding. On flip-chart paper a

line is drawn from the top to the bottom along the left-hand side and it is indicated that the top

represents one extreme and the bottom represents the opposite extreme. Farmers are then asked to

arrange the 10 cards along the continuum between extremes for each tree utility attribute

independently and once they are happy with the order researchers mark the position of the cards on

the flip chart paper for later analysis. For ease of recording the back of each card is marked with a

random letter or number which can later be converted to the tree name.

This method was initially intended to simultaneously gather a ranking (order of the tree

cards) and scoring (distance of card placement from the bottom of the page – capturing the

distance between cards) data, however the concept of relative distance was difficult for farmers to

understand. Among the four farmers that this method was tested with, it was believed that three of

them understood to varying degrees that the distance between cards was relevant. As a result, it was

decided that the scoring information acquired based on the distance of card placement would be

too unreliable and the method was simplified to a ranking based on tree card order.

To test this method, two of the most important utilities were selected: firewood provision,

and coffee shade provision. The researchers then purposely selected 10 trees (half indigenous and

half exotic) that they believed would represent the range between extremes for each utility (see

Table 3 – 1). Both utilities (firewood and coffee shade) were first ranked generally from ‘best’ to

‘worst’ and farmers were then asked which attributes made trees ‘best’ and ‘worst’ for each utility in

order to confirm that appropriate attributes had been selected (based on previous interview

information). For firewood it was decided to then rank the 10 trees for ‘length of burning time’ (from

longest burn to shortest burn), and for ‘speed of wood growth’ (from fastest wood growth to

18 The language most comfortable for the farmer is used for communication in this and all methods.

25

slowest wood growth); selection of these attributes was supported by farmers descriptions for why

trees were good as firewood. For coffee shade the attributes chosen were ‘area of crown cover’

(from largest crown cover to smallest crown cover), and ‘light penetration of crown cover’ (from

least light coming through crown to most light coming through crown). These were only two

possible attributes from a list of relevant measures for shade, but farmers supported that light (vs.

temperature, moisture, etc.) was the chief measure of shade from their perspective.

Table Table Table Table 3 3 3 3 –––– 1111.... The 10 trees selected to test the ranking/scoring method 2. * hypothesized qualities based on the results from the ranking/scoring method 1 exercise.

TreeTreeTreeTree KikuyuKikuyuKikuyuKikuyu OriginOriginOriginOrigin Firewood Firewood Firewood Firewood Qualities*Qualities*Qualities*Qualities*

Coffee Shade Coffee Shade Coffee Shade Coffee Shade QualiQualiQualiQualities*ties*ties*ties*

Carica papaya mubabai exotic bad medium

Commiphora zimmermannii mukungugu indigenous bad bad

Croton megalocarpus mukinduri indigenous good good

Eucalyptus spp. mubau exotic good good

Grevillea robusta mubariti exotic good good

Macadamia tetraphylla mukandania exotic good good

Persea Americana mukondo exotic good medium

Prunus africana muiri indigenous good good

Syzygium guineense mukoe indigenous medium medium

Trichilia emetica mururi indigenous medium medium

By ranking the 10 trees generally for each utility first, a comparison of the general utility

ranking with the ranking of its specific attributes provides an indication of which attribute (or

combination) is likely the most important consideration for that utility.

3.2.4 Feedback Sessions

A critically important (but all too often neglected) part of research is feeding back the

acquired information to local communities in the area. To do so it was decided to hold two

feedback meetings at coffee factories located near farmers who participated in the research. These

group discussions were also useful to confirm the knowledge acquired and to provide clarity on

areas of uncertainty.

The first feedback session was held on July 28th, 2009 at Muruka Coffee Factory in Kandara

Division, Murang’a District. The group discussion was attended by 18 coffee farmers (8 of which had

been previously interviewed and 2 of which were female) and later by coffee factory employees and

society members. A farmer from neighbouring Gatanga Division was invited to join the feedback

session and travel costs were provided for him to do so. This manageable number of participants

allowed farmers to discuss freely the information that was presented (Appendix F).

26

The second feedback session was held on July 30th, 2009 at Ngutu Coffee Factory in

Murang’a District. The group discussion began with 22 farmers participating and finished with

approximately 75 coffee farmers, factory employees, and society members present (15 of which

were female). The high attendance of this feedback session did not impede discussion and many

important points were validated and debated. It should be noted that women participated far less in

the discussions at both factories and separate group meetings with women would be interesting and

useful in the future, unfortunately there was not enough time.

Although it was not possible to hold feedback sessions in each of the areas covered, all

farmers will have access to the major research findings through a short report which will be

circulated to all Mugama Union Coffee Factories.

27

3.3 Representation of Local Knowledge

The knowledge based system (KBS) approach involves an iterative process of alternation

between all the methods of knowledge acquisition presented in section 3.2 and storage of this

information in a variety of forms. Storage and reflection of the knowledge being acquired throughout

the compilation stage helped to guide further questioning and as Walker and Sinclair (1998 p.378)

state, “this iterative evaluation proved a very powerful means of keeping the knowledge acquisition

process focused, thereby facilitating collection of more precise and consistent knowledge than had

been previously been obtained.”

3.3.1 Agroecological Knowledge Toolkit (AKT5)

The Agroecological Knowledge Toolkit (AKT5) for Windows Version 4.65 (Dixon et al., 2001)

was utilized to record, manage and represent the knowledge acquired throughout the research in a

knowledge base (KB) (Walker and Sinclair, 1998). Formal representation in AKT5 involves

disaggregation of knowledge into unitary statements (which cannot be further broken down) and

translation into formal grammar (ibid). This approach captures definitions, contextual information,

and the relationships between formal terms and statements (including causal linkages, comparisons,

values, etc.), and facilitates the organization of formal terms into hierarchies. Knowledge can then be

diagrammatically represented as nodes and links. Such visual representations of knowledge can

improve clarity and understanding and facilitates simultaneous consideration of many related

statements from different informants. Diagrams are also extremely useful as participatory tools and

in extension work. Continuous evaluation of acquired knowledge with AKT5 throughout the

collection process helped to identify gaps in understanding and to organize further questioning

(Waliszewski et al., 2005).

The completed KB (entitled ‘murang’a_kb’) contains a total of 686 formal terms forming 393

statements from 32 sources including the group discussion and feedback sessions (41 sources if

second interviews are considered independently). The information from 4 sources19 was not stored

in the kb but through other techniques.

The vast majority of the statements (73%) were ‘causal’ in nature, while the remainder were

‘attribute’ (20%), ‘comparison’ (7%), and ‘link’ (<1%) statements. The high proportion of ‘causal’

statements indicates that the knowledge shared by respondents was predominantly explanatory in

nature; farmers indicated not only that they knew something, but how this affected other things on

19 Two of these respondents participated solely in the ranking/scoring approach 2 exercise, one respondent reviewed the tree spreadsheet and another assisted with tree identification and the information from these respondents is captured in the tree spreadsheet.

28

farms. A total of 110 KB statements are accompanied with conditions adding further context to these

statements.

3.3.2 Other Methods

As previously described in section 3.2.3, a variety of methods was utilized during the

compilation stage of the research to triangulate the knowledge acquired. This included visual

methods such as: farm sketching to capture spatial information about trees on coffee farms, digital

photography of farm features, on-farm examples, and compilation of a monthly calendar based on

the temporal knowledge provided by farmers during interviews (see Table 4 – 1). The tree

spreadsheet also acted as a key tool for organizing and representing all of the tree related

information.

29

3.4 Limitations of the Methodology

Time was the major factor which limited the scope and extent of this research. It was

however possible to make use of previous research about LK in coffee farming in Central Province

(Gathoni, 2007; Lamond, 2007) adding significantly to the scope of the research. The two feedback

sessions were also important to confirm research findings since there was not sufficient time to

conduct the generalization stage of the KBS approach.

Every effort was made to efficiently make use of the available time, however ranking and

scoring of trees for different utility attributes proved to be a lengthy process and as a result the

activities were restricted to a testing stage. Also, the approach taken to identify tree species on farms

was not exhaustive and as such not all tree species were discovered. Additionally it is possible that

there were some points of confusion about Kikuyu tree names which may have resulted in multiple

species identified under one name (for example it was subsequently realized that Croton

megalocarpus in the tree spreadsheet actually likely represents different Croton species together). A

more exhaustive survey of trees on farms with a botanical expert was not possible during the

present study, and researchers did their best with the Kikuyu names.

Due to the purposive sampling of a small number of farmers, it is not possible to make

conclusions about the distribution of tree species across the region. Before recommending specific

trees to farmers it is recommended that tree distribution be determined through an ethno-botanical

survey (see section 5.3.2).

30

4. Results

4.1 Respondent Stratification

It was decided based on previous research from the area and information from scoping

interviews that farm owners (and their family members) would be the target for the study and that

they would be stratified according to: farm elevation, total farm size, and coffee tree number

(Appendix B).

Of the 27 farms visited during the scoping stage, 81% were between the elevation 1400 –

1799 m.a.s.l., two farms were below 1400 m.a.s.l., and three farms were above 1799 m.a.s.l (see

Figure 4 – 120). The unevenness of this distribution reflects that at lower elevation there were

relatively fewer coffee farms (due to the suitability of the area to other food crops, and poorer

quality of coffee produced), and at higher elevations farms were less accessible from Murang’a town

(where researchers were based) and also within the tea growing area. Although it is not suitable to

make sweeping generalizations about the information attained from farmers at different elevations,

one trend was that statements from farms at an elevation between 1800 – 1999 m.a.s.l. were

generally negative about the impacts of shade on coffee. For example farmers told researchers that

shade trees with dense crowns decreased coffee productivity and others caused root competition.

These farmers did not identify any of the positive effects of coffee shade which could reflect that at

higher elevations less shade is tolerated, although further research would be needed to prove so.

A second elevation-specific distinction made by farmers (and supported by statements from

a Boolean search of ‘harvest and coffee’ in the kb) was the difference in coffee season. At the highest

elevations (typically above 1799 m.a.s.l.21) farmers generally have only one coffee harvest yearly from

approximately October until the end of December, while in lower areas they have two coffee

harvests per year: the early harvest season from approximately April until June, and the late harvest

season from approximately October until December.

The size of respondents’ farms was relatively evenly distributed among ‘small-scale’

categories (from less than 1.5 acres to 9.9 acres) with 88% of the farms visited lying within this range.

Only 3 of the visited farms were over 9.9 acres in size; one of which was a large farm owned and

operated by Mugama Union. No clear farm size trends were evident upon review of the kb, however

it was stated during group discussions that smaller farms are more limited in the number and size of

trees that they have due to limited land availability (see section 4.6.2).

With respect to the number of coffee trees on farms, only one farmer visited had fewer than

100 coffee plants, while eight of the respondents had over 1000 coffee trees. There was a general

20 Thank you to Sanjeeb for his assistance in preparing the map. 21 On farmer between 1600 – 1799 m.a.s.l. identified a single coffee harvest season between October and December.

31

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32

relationship between the overall size of farm and the number of coffee trees with few exceptions. In

addition to the number of coffee trees, the variety of these trees was an important determinant of

coffee management (especially pesticide application).

It was determined during the ranking/scoring approach testing exercises that gender is also

an important influence on a respondent’s knowledge about tree utilities due to different gender roles

on farms. This resulted in different specialized knowledge about the trees on farms. For example,

women were asked by their husbands to participate during the ranking of trees for firewood utility

since they were most familiar with wood burning qualities from cooking activities, while men had

more specialized knowledge about the strength and durability qualities of wood for building. For

this reason it would be appropriate in future research to also stratify respondents by gender.

33

4.2 Knowledge Derivation

Farmers derive knowledge from different sources. This information was captured as a

derivation associated to each statement in the kb. There were three main derivation groups that

farmers repeatedly acknowledged during interviews. The great majority of statements (84%) were

either explicitly identified or implied as being derived from the respondent’s own experience and

observation (denoted in the kb as ‘observed’). This includes knowledge introduced by another

person or source and subsequently confirmed through observation. In many cases farmers

demonstrated their knowledge by providing an example on the farm or by describing historical

events supporting what they were saying. For example, one farmer told researchers that although

agricultural officers advised that he space macadamia trees 30 m apart in his coffee plot, he found

through his own experience that a spacing of 60 to 80 m was more appropriate.

Many farmers discussed their previous participation in farmer field days, seminars, and

trainings. At these events, trained extension workers from the Coffee Research Foundation (CRF),

Ministry of Agriculture, etc. provided advice to farmers about agricultural techniques and coffee

management. Farmer field days periodically held throughout the region act as an arena where

agricultural input companies can inform farmers about the benefits and use of their products

alongside extension workers. Although only 5% of the kb statements were derived from extension

advice, it is believed that such information initiated many of the practices now common on farms22

and is thus an important tool for the communication of new information to farmers. Unfortunately,

extension advice does not reach all farmers in the area, and efforts are needed to make this

information more widely available.

Coffee factory staff and society management often advise farmers about agricultural

techniques. These sources were identified as the derivation of 6% of the statements in the kb. Coffee

factories also stipulate rules and regulations that member coffee farmers should obey. These rules

vary slightly from factory to factory but generally cover restrictions concerning the application of

coffee inputs, intercropping, and coffee harvest (see Figure 4 – 2). At some factories farmers are

advised to use specific pesticide and fertilizer inputs, which is restrictive for farmers as these inputs

are increasingly expensive and rarely available from the factory directly as they have been in the

past. Most factories have rules which specify which vegetable crops must not be intercropped with

coffee. According to coffee factory staff these rules are put in place to ensure that intercropped

vegetables do not negatively impact coffee quality, however farmers often neglect these rules

because they need to supplement poor coffee revenues with profitable and subsistence food crops.

Many farmers acknowledged that coffee rules are rarely monitored on the farm, and that they are

even formally relaxed at some factories when the price of coffee decreases. Coffee factories were

22 information initially obtained through extension advice but subsequently practiced on farm (and in many cases modified through practice) was included in the KB as ‘observed’.

34

not found to advise farmers about coffee shade, nor do they have restrictions about the type or

amount of coffee shade (see Figure 4 – 2).

Figure Figure Figure Figure 4 4 4 4 –––– 2222.... Causal diagram representing respondents’ knowledge of the rules of coffee factories. Nodes represent human actions (box with rounded corners) or attributes of objects, processes or actions (boxes with straight edges). Arrows connecting nodes denote the direction of causal influence. Numbers indicate whether the relationship is two-way (2), in which case ↑A causing ↓B also implies ↓A causing ↑B, or one-way (1), which indicates that this reversibility does not apply. Words below the numbers denote a value of the node other than increase or decrease (e.g. coffee factory rules cause the spraying of coffee pesticides in_coffee_plot). A black dot on a causal arrow indicates a negation of the node it is coming from or going to (e.g. coffee factory rules do notnotnotnot provide advice about coffee_shade_trees).

35

Two farmers (from different societies) explained that information sessions organized through

their coffee factory in association with agricultural extension officers acted as a successful approach

to educate farmers, however this was uncommon. Such an approach could be a useful means to

increase farmer understanding about coffee shade and quality; two areas where farmer knowledge is

limited.

The derivation ‘employment’, associated with 5% of the statements, refers to statements that

were derived from a respondent’s current or past employment experience. For example, Johnson

Gichoya is a factory manager and therefore had more specialized knowledge about coffee factory

rules and regulations.

36

4.3 Factors Affecting Coffee Productivity

The issue of coffee productivity was prominent during interviews with coffee farmers due to

its great influence on farm profitability and livelihood security. Shade, intercropping, and input

application were identified by farmers as the factors having the most major impact on coffee

productivity.

4.3.1 Shade

The knowledge farmers demonstrated about coffee shade was inconsistent, and farmers

could be broadly categorized into those having knowledge about the use of shade in coffee, those

who did not believe in shade or those who did not have knowledge about the use of shade in

coffee. There were no clear characteristics which explained whether farmers had knowledge on this

subject. One farmer stated that coffee farms at high elevation require less shade for their coffee due

to cooler temperatures and less need to prevent damage from the sun (see Box 4 – 1). This view was

supported by a comparison of statements about shade at different elevations (see Box 4 – 2). Farms

at high elevation were still however found to have shade trees in coffee plots during farms visits.

BoxBoxBoxBox 4 4 4 4 –––– 1111.... Quotation from a farmer at the Muruka feedback session (low elevation) on 28/07/09.

A comparison of the negative shade statements from high (1600 – 1999 m a.s.l) and low

(1200 – 1599 m a.s.l) elevation demonstrated that dense shade and the resulting decrease in coffee

plot temperature were identified as being more problematic at higher elevations (see Box 4 – 2). A

comparison of positive shade statements demonstrated that at lower elevations shade is

acknowledged for its importance in protecting coffee from high temperatures and sun damage and it

was farmers at lower elevations that suggested that the presence of shade trees decreases coffee

pest abundance (thrips and leaf miner).

“This area is a bit hot. There are areas which are cool. Those farmers from cool areas

would say that shade is not good, because they do not need it because it is cool, but

in our area we need it! It’s very important!”

37

Box Box Box Box 4 4 4 4 –––– 2222. Positive and negative statements about ‘shade’ resulting from Boolean searches at different elevations.

High ElevationHigh ElevationHigh ElevationHigh Elevation:

Negative shade statements from Boolean search of ‘shade’ from farms ‘1600 – 1999m’

124: water dropping coffee_shade_trees location is in_coffee_plot causes an increase in damage of coffee_berry

125: weather temperature is cool causes a decrease in amount of coffee_shade_trees

142: the level of shade is very_high causes a decrease in productivity of growth of coffee_plant coffee_berry

148: macadamia shade coffee_plant amount is too_high causes cutting of macadamia location is in_coffee_plot

154: shade of coffee_plant amount is too_high causes a decrease in temperature of coffee_plot

174: coffee_shade_trees shade coffee_plant causes a decrease in temperature of coffee_plot air

177: macadamia canopy density is thick causes shade of coffee_plant amount is too_high

206: coffee_plot location is high_elevation causes a decrease in need of coffee_shade_trees

244: shade of coffee_plot level is none causes the quality of coffee_plant coffee_berry is high

Positive shade statements from Boolean search of ‘shade’ from farms ‘1600 – 1999m’

133: intercropping of banana location is in_coffee_plot causes shade of coffee_plant amount is good

173: grevillea density is 20 trees per acre causes shade of coffee_plant amount is good

283: planting of grevillea location is in_coffee_plot causes an increase in quality of coffee_plant coffee_berry

284: shade of coffee_plot causes an increase in productivity of growth of coffee_plant coffee_berry

305: coffee_shade_trees shade coffee_plant causes coffee_plant leaves colour is more_green

310: coffee_shade_trees shade coffee_plant causes an increase in moisture of coffee_plot

Low ElevationLow ElevationLow ElevationLow Elevation:

Negative shade statements from Boolean search of ‘shade’ from farms ‘1200 – 1599m’

52: shade of coffee_plant knowledge_level is none

142: the level of shade is very_high causes a decrease in productivity of growth of coffee_plant coffee_berry

244: shade of coffee_plot level is none causes the quality of coffee_plant coffee_berry is high

267: boundary_trees shade neighbours_farm causes complaining of neighbours

340: coffee_shade_trees location is in_coffee_plot causes a decrease in amount of nutruents feeding coffee_plant

Positive shade statements from Boolean search of ‘shade’ from farms ‘1200 – 1599m’

81: the position of coffee_shade_trees branches is low causes a decrease in temperature of coffee_plant

133: intercropping of banana location is in_coffee_plot causes shade of coffee_plant amount is good

135: coffee_shade_trees shade coffee_plant causes an increase in moisture of coffee_plant

168: coffee_shade_trees shade coffee_plant causes an increase in protection_from_sun of coffee_plant

169: coffee_shade_trees shade coffee_plant causes a decrease in abundance of thrips

170: coffee_shade_trees shade coffee_plant causes a decrease in abundance of leaf_miner

209: coffee_shade_trees shade coffee_plant causes an increase in productivity of growth of coffee_plant

coffee_berry

210: coffee_shade_trees shade coffee_plant causes an increase in size of coffee_plant coffee_berry

305: coffee_shade_trees shade coffee_plant causes coffee_plant leaves colour is more_green

38

The main benefits of shading coffee identified by farmers included: decreased coffee pest

abundance, increased moisture of coffee plot and plant, protection from the sun, and increased (or

no change) in coffee berry size and productivity (see Figure 4 – 3). Many farmers were interested to

increase their understanding about the effects of shade on coffee and were seeking

recommendations about appropriate tree species to shade coffee in their area.

Figure Figure Figure Figure 4 4 4 4 –––– 3333.... Causal diagram representing respondents’ knowledge of the impacts of coffee shade trees on the coffee plant. Diagrammatical symbols are the same as described in Figure 4 – 2 above. Additionally, oval nodes represent natural processes and small arrows above links refer to an increase (↑) or decrease (↓) in the effect node.

Farmers also identified negative effects of shade on coffee. Damage can occur to coffee

plants from falling branches, falling debris during shade tree pruning, and from water falling from

shade trees. Additionally, certain shade trees were identified as attracting coffee pests; for example

Bridelia micrantha and Kigelia africana attract boring insects to coffee if planted in coffee plots (see

section 4.6.2).

Even among farmers that agreed about the potential benefits of shade for their coffee there

was disagreement about the appropriate amount of shade and shade species (see Figure 4 – 4). The

appropriate level of shade will depend on the site (elevation, soil type, moisture content, etc.) and

the species, spacing and management (pruning amount and frequency) of shade trees. For this

reason advice about shade tree management must include consideration of biophysical and

ecological suitability.

39

Figure Figure Figure Figure 4 4 4 4 –––– 4444.... Causal diagram representing respondents’ knowledge of the shade amount appropriate for coffee. Diagrammatical symbols are the same as described in Figures 4 – 2 and 4 – 3 above.

By comparing the trees species identified by farmers as ‘coffee shade trees’ and ‘coffee

incompatible trees’ it is evident that farmers disagree (Figure 4 – 5). There are many reasons

explaining why farmers were inconsistent about which trees can or cannot be grown with coffee.

First of all a difference of site conditions across the study area may impact the suitability of shade

tree species (for example elevation as presented above). Also, farmers disagree on what level of

shade (with respect to crown cover, crown density, and crown height) is best for coffee, and this

may also vary according to site. The dominant reason for inconsistency however is likely the

different management strategies implemented by farmers. For the species: Persea americana, Musa

sapientum, and Mangifera indica the discrepancy was due to differences in management of shade

trees or choice of tree species variety; for example tall banana varieties such as ‘Isreal’ or ‘giant’ are

good for coffee shade while shorter varieties are not according to some farmers. Farmers identified

that these trees could be used for shade so long as they were pruned frequently and/or widely

spaced. Many of the other inconsistent tree species were identified as being fine with coffee by

some farmers while limiting factors (see section 4.6.2) prevented other farmers from planting them

with coffee. This included: Ficus natalensis and Markhamia lutea which grow too large and slowly;

Eucalyptus spp. and Croton megalocarpus which compete with coffee plants; and Bridelia

micrantha, Psidium guajava, Neoboutonia macrocalyx, Carica papaya, and Prunus Africana (also

growing slowly) which attract coffee pests.

40

Figure Figure Figure Figure 4 4 4 4 –––– 5555.... A comparison of the object hierarchy lists (common names) of coffee shade trees (left) and coffee incompatible trees (right). * indicates trees that occur in both object hierarchies (inconsistencies).

4.3.2 Intercropping

A similar comparison of the intercrops deemed by farmers to be compatible and

incompatible with coffee shows far fewer overlapped items compared to shade trees (see Figure 4 –

6). Farmers receive advice and regulations about intercropping regularly from coffee factories,

during farmer field days, and through their own experience (see section 4.2 and Figure 4 – 2).

Although farmers had much knowledge and generally agreed about which crops should not be

intercropped with coffee, they were often found to be intercropping these crops in practice out of

necessity for the food or income provided – especially when the price of coffee was low.

Napier grass was utilised by many to stabilize the soil along bench terraces and as cow

fodder yet farmers identified that it is a strong competitor with coffee for available nutrients thus

decreasing coffee productivity. In this way the decision whether to intercrop with napier grass may

be seen as a tradeoff; it is possible that this practice is more common in steep coffee plots prone to

erosion or where dairy farming has become a priority. Farmers disagreed about intercropping coffee

*

*

*

*

*

*

*

*

* *

*

*

*

* *

*

*

*

*

*

*

*

*

*

*

*

*

*

41

with pumpkins, although some specified that butternut squash (often identified as ‘pumpkin’) was

fine with coffee while pumpkins were not.

Figure Figure Figure Figure 4 4 4 4 –––– 6666.... A comparison of the object hierarchy lists (common names) of coffee compatible (left) and incompatible (right) intercrops. * indicates crops that occur in both object hierarchies (inconsistencies).

Farmers described to researchers that maize cannot be grown with coffee due to the

negative effect of maize pollen on coffee leaves and therefore production (kb: 97)23. Onion is not

planted with coffee because it impacts the flavour of the coffee while cassava, sugar cane, and

sweet potato were said to compete with coffee for nutrients and water (kb: 96, 172). Beans and

desmodian were identified as crops that improve soil fertility and are therefore beneficial to coffee

in addition to providing food and fodder (kb: 93, 129). Despite its many benefits as a nutritious

fodder crop and soil fertility improver, few farmers were intercropping desmodian and this is

believed to be due to a limitation of knowledge about this crop.

4.3.3 Input Application

While it was widely accepted that pesticide and fertilizer application improves coffee

productivity, farmers were rarely able to afford these expensive inputs. This is a perfect example of a

case where farmers have knowledge about the benefits of certain practices, but are limited in the

application of this knowledge. Alternatives to synthetic pesticides and fertilizers were identified by

farmers during interviews.

To decrease the need to spray coffee trees with expensive pesticides, many farmers have

changed (or are interested to change) their coffee from ‘SL’ varieties to ‘Ruiru 11’ which has

increased resistance to coffee berry disease (CBD) (kb: 186, 200, 366, 382). It was found however

23 Indicating the statement number(s) from the kb which support the information presented (format ‘kb: ###’)

*

*

*

*

42

that few farmers understood the ways in which the quality of these coffee varieties differed. Only

two farmers told researchers that coffee berries from SL varieties were preferred in terms of

boldness and density (kb: 370, 388). Also it was identified by only a small number of farmers that the

‘Ruiru 11’ coffee variety required more water and was therefore less drought resistant as compared

to ‘SL’ varieties that have roots which penetrate deeper into the soil (kb: 31,364).

As an alternative to synthetic pesticides, one farmer described to researchers how leaves of

Acokanthera oppositifolia can be used to prepare a liquid spray which when applied to coffee

plants decreases the incidence of CBD while fertilizing the coffee – doubly improving coffee

productivity (kb: 234-236, see Box 4 – 3). Another farmer told researchers that lower coffee plot

temperature, resulting from shade, decreased coffee pest presence (kb: 277); however farmers

identified many trees that attract coffee pests therefore not all shade trees would be suitable for this

purpose.

BoxBoxBoxBox 4 4 4 4 –––– 3333.... Quotation from a farmer during an interview on their farm, 29/06/09.

Farmers also identified different ways in which trees can be used to improve soil fertility in

place of expensive fertilisers. Researchers were told by farmers that the following species helped to

retain soil nutrients and/or moisture: Calliandra calothyrsus, Dovyalis caffra, Cordia Africana,

Neoboutonia macrocalyx, and Acokanthera oppositifolia (see Box 4 – 3). Farmers also identified that

tree leaves could be used to mulch coffee and that mulching helped to maintain soil moisture (kb:

113), soil quality including humous amount (kb: 109, 167), and decreased weed growth (kb: 163).

Trees identified as providing mulch were: Musa sapientum, Acokanthera oppositifolia, Macadamia

tetraphylla, Grevillea robusta, Eucalyptus spp., Mangifera indica, Ficus natalensis, Ficus sycomorus,

Cordia africana, Neoboutonia macrocalyx, and Euphorbia tirucalli.

Livestock manure is of increasing importance as a coffee input when the price of fertiliser

increases. All of the farmers having livestock that were visited practiced zero grazing24 and clearly

understood the value of livestock dung for use on the farm (see Figure 4 – 7). Farmers also

acknowledged that dung from different livestock animals has different qualities as manure and that it

is beneficial to mix them together before application (kb: 15, 223). Many farmers demonstrated how

they mix tree leaves (such as Grevellia robusta) with livestock dung to prepare nutrient rich manure

for their coffee (kb: 15, 101). When manure is of poor quality more of it needs to be applied (kb:

146). Even those farmers able to afford inorganic fertilisers added manure to their coffee if it was

available or it was purchased for this purpose. Two of the farmers visited had biogas systems on

24 Zero grazing refers to a livestock management system where livestock are kept in a restricted area (normally a shed or contained raised platform) where feed and fodder is brought to them. This way they use less space (don't graze over large distance) and the dung they produce is easily collected for use on the farm.

“You first cut the [A. oppositifolia] leaves into very small pieces, then you soak in

water for many days… about 10 days… and then you can use as manure or for CBD.”

43

their farm which allowed them to benefit from the gas produced during the decomposition of

livestock dung. This biogas product was used in both cases as cooking fuel in place of firewood.

Figure Figure Figure Figure 4 4 4 4 –––– 7777.... Photographs of zero grazing cattle (left) and the resulting dung which is mixed with green waste from the farm and used as manure (right). Taken during a farm interview on 29/06/09 by researchers.

44

4.4 Factors Affecting Farm Profitability

Even when farm activities such as food crops, cash crops, and livestock are managed

independently (although they often do interact directly), they are rarely independent as financial

components of the total farm as a business. Revenue from one farm activity may be invested into

inputs for another. It is also essential to acknowledge the importance of non-monetary farm assets

(for example food and firewood) which do not necessarily have a market, but are critical for farm

livelihood. These products and services do not directly generate income but may decrease farm

expenditure.

Coffee which has the potential to be highly valuable has historically proven to be vulnerable

as a cash crop. It dominates the income potential of farm activities on many coffee farms. Unlike

some other cash crops, coffee is not directly utilizable on the farm which further increases the

vulnerability of coffee farming families to fluctuating prices. Additionally, due to the complex value

addition process and marketing chain for this commodity, farmers have little to no influence on the

price that they receive.

In addition to coffee, tree products including: honey (indirectly a tree product), firewood,

charcoal, timber, and fruit were identified by farmers as being profitable (see Figure 4 – 8). During

the ranking/scoring exercises farmers also identified that some trees could be sold as seedlings or

seeds from farm tree nurseries. A complete listing of the profitable species for each of these utilities

is shown in Appendix C (marked with ‘o’ to indicate profitability).

Another result of interest was that indigenous trees were not seen to be directly profitable

and this was one explanation for why indigenous trees were cut and removed from farms (kb: 150,

376; see Figure 4 – 8 at bottom); especially since they were also believed to take up more space and

grow slower as compared to exotic trees (kb: 272, 306, 385).

45

Figure Figure Figure Figure 4 4 4 4 –––– 8888.... Causal diagram representing respondents’ knowledge of the factors affecting farm profitability. Diagrammatical symbols are the same as described in Figures 4 – 2 and 4 – 3 above.

It is also important to consider the profitability of different farm activities throughout the

year. Coffee farming is financially challenging in that it pays only once or twice a year (depending on

farm elevation) but requires investment in the form of inputs year-round (see Table 4 – 1). Sale of

other products such as mango, banana, and milk at times during the year when coffee is not sold

supplements farm capital during these financially difficult times.

TableTableTableTable 4 4 4 4 –––– 1111.... A table of the monthly timing of farm activities and processes identified by farmers.

Activity/Process Jan

Fe

b

Ma

r

Ap

r

Ma

y

Jun

Jul

Au

g

Se

p

Oct

No

v

De

c

early lowland coffee harvest

late lowland coffee harvest

highland coffee harvest

thinning of coffee stems

rainy season (highland)

macadamia nut harvest

mango fruit harvest

banana fruit harvest

dairy milk harvest

application 17/17

fertiliser application

foliar feed spray time

CEN (fertiliser) application

46

4.4.1 Coffee Price Instability

Researchers were told that the price of coffee ranged between 14 – 32 KSH/kg25 (kb: 62). The

price realized by farmers depended on the society and factory with which they were a member and

the qualities and grade of their coffee.

4.4.1.1 Factors Affecting the Price of Coffee

Respondents had a far better understanding about the impacts that changes in coffee price

had on their farms than they did about the factors affecting coffee price (see Figure 4 – 9). Few

farmers had any knowledge at all about coffee quality let alone how this impacts the price they

receive for their coffee (kb: 53).

Two of the farmer respondents had their own coffee processing units on their farms, and one

farmer had begun the process of applying for a permit to build one26. It was these farmers who best

understood how coffee berry size, weight, and boldness affect the price of their coffee (kb: 336,

337). Having this knowledge allowed them to adopt management practices to improve these

qualities and in doing so improve the price of their coffee. They also benefitted from having

complete control over the operational costs of processing their coffee and avoided the chance that

some of the coffee proceeds would be lost to coffee society mismanagement, as was found to be

the case in many societies. Furthermore, these farmers had control over when to sell their coffee and

had the option to wait for an adequate price.

25 As a reference the exchange on 02/09/09 was approximately 1 KSH = 0.0131926 US Dollars 26 According to one farmer, the regulations for application to process coffee on-farm include having 5 acres of land with at least 2500 coffee trees that are well managed. The application process to the District CBK representative is a lengthy process taking over a year.

47

Figure Figure Figure Figure 4 4 4 4 –––– 9999.... Causal diagram representing respondents’ knowledge of the factors affecting the price of coffee and the impact of coffee price on other farm activities. Diagrammatical symbols are the same as described in Figures 4 – 2 and 4 – 3 above.

The increasing cost of coffee fertilizers (and other inputs) also affects the profitability of

coffee farming. When the cost is high enough that farmers cannot apply adequate amounts, the

growth of the coffee suffers as a consequence and this is a downward cycle which many of the

interviewed farmers identified (see Figure 4 – 10).

48

Figure Figure Figure Figure 4 4 4 4 –––– 10101010.... Causal diagram representing respondents’ knowledge of the impacts of increasing fertiliser cost. Diagrammatical symbols are the same as described in Figures 4 – 2 and 4 – 3 above.

Due to this limitation, even when the price of coffee increases it takes some time before

farmers are able to realize the benefits as they first need to build capital for inputs before they can

produce high yields and sell at an improved price. Researchers were told that fertilizer which used

to cost 1500 KSH/50kg bag has now risen to 2800 – 3000 KSH/50kg bag. Such an increase makes

these products out of reach for the average farmer, who must make do with whatever manure they

can get.

4.4.1.2 The Impact of Changing Prices on Farm Activities

In response to low coffee prices and high input costs, many coffee farmers have altered the

management of their coffee and some have diversified or changed to other activities on their farms.

To increase food an income generation many farmers have increased the amount of intercropping

within their coffee plots despite factory regulations and negative impacts to coffee.

Many farmers have increased their efforts in dairy farming since the price of milk has been

high (23 KSH/Kg) and is perceived to be more stable than the coffee price (kb: 375, see Figure 4 –

11). Also milk can be consumed on farm if the price drops unlike coffee. As such, farmers were

increasing the amount of napier grass that they are growing, which in some cases meant

intercropping it into coffee plots or replacing coffee altogether (see Appendix F – 6). The

occurrence of desmodian on farms was also increasing as it is a great cow fodder which increases

the production of milk (kb: 131, 132). Other farmers have uprooted areas of their coffee to plant

subsistence food crops for food security.

During interviews farmers were also asked what they would do to the trees on their farms if

the price of coffee changed. If the price decreased (as it had during the coffee crisis) many farmers

said they would uproot their coffee and switch to other profitable crops (kb: 151, 158 see Table 4 –

49

2) or that shade trees would be pruned more heavily to be used as building wood, firewood, or sold

(kb: 175). If the price of coffee increased, some farmers said that they would cut the shade trees

(showing that they did not understand the benefits of shade), or maintain the shade at the same level

(kb: 127, 144). No farmers explicitly said that they would increase the amount of shade trees when

asked this question.

Figure Figure Figure Figure 4 4 4 4 –––– 11111111.... Causal diagram representing respondents’ knowledge of the influence of coffee price on dairy farming. Diagrammatical symbols are the same as described in Figures 4 – 2 and 4 – 3 above.

Table 4 Table 4 Table 4 Table 4 –––– 2222.... The range of prices given by farmers during interviews for some commodities sold from farms.

Farm CommodityFarm CommodityFarm CommodityFarm Commodity Current PriceCurrent PriceCurrent PriceCurrent Price Past Price (time agoPast Price (time agoPast Price (time agoPast Price (time ago in yrsin yrsin yrsin yrs))))

papaya fruit 10-20 KSH/fruit

banana fruit 200 KSH/bunch

macadamia nuts 20-40 KSH/kg 70 - 100 KSH/kg (2 yrs ago)

cow's milk 23 KSH/kg 7 - 15 KSH/kg (2 yrs ago)

coffee (green, dried) 14 - 32 KSH/kg

4.4.2 Trees as a Source of Income

Farmers identified a number of ways in which it is possible for them to profit financially from

having trees on their farms. High quality timber species act like bank accounts or insurance policies

for farmers in that they can be cut for cash whenever needed (so long as a buyer for the wood exists,

see Figure 4 – 12, a). Building wood, which is smaller in diameter and/or not strong enough to be

sold as timber is mostly used on farm but can also be sold (see Figure 4 – 12, b). Trees with good

burning qualities such as Acacia mearnsii and Cupressus spp. are also mostly used on farm but can

50

be sold if surplus firewood is available (see Figure 4 – 12, c). Trees from which charcoal can be

made such as Croton megalocarpus, and Syzygium guineense can be especially profitable (see

Figure 4 – 12, d).

Fruit trees act as another important source of income for farmers (see Figure 4 – 12, e).

Unlike coffee, the products from these trees have the added benefit of contributing to food security

on farms as they can be eaten if they are not sold. Fruits such as banana (Musa sapientum) and

avocado (Persea Americana) are typically sold at a low cost locally, while other fruits such as

macadamia nuts (Macadamia tetraphylla) can fetch high prices as they are sold internationally.

Some desirable tree species can be raised in tree nurseries on farms or at coffee factories

and sold to neighbouring farmers. Seeds from trees which are less common or difficult to obtain can

also be sold to farmers interested to plant them themselves (see Figure 4 – 12, f). Farmers did not

identify fodder as being a profitable tree product nor did they explicitly identify any medicinal trees

as being profitable although it is believed that there is potential in these areas for farmers to profit

from these tree products. The average farmer does not know about the potential of tree services

(such as soil fertility improvement or maintenance of biodiversity) in generating income through PES

or coffee certification schemes as these have not widely been established in Central Province.

51

Figure 4 Figure 4 Figure 4 Figure 4 –––– 12.12.12.12. Object hierarchy lists of profitable tree species from a) timber, b) building wood, c)

seedlings/seeds, d) firewood, e) fruit, and f) charcoal.

a) b)

c)

d)

e)

f)

52

4.5 Tree Utilities on Coffee Farms

Box Box Box Box 4 4 4 4 –––– 4444.... Quotation from a farmer at the Ngutu feedback session on 30/07/09.

A total of 84 species (plus 3 species identified at the end of the data collection period) were

identified during the study (Appendix C). The majority of these trees were identified by farmers

during interviews and/or seen on farms during visits. Others were identified and discussed during

the group discussion and feedback sessions. A few species, generally not as commonly known by

farmers, were identified by John Ngoyanae at the Wanjerere Forest Station near the Aberdares

National Park, and by Mr. Njoroge the Kangema Divisional Forestry Officer (see Section 4.6.1).

Two species, Thunbergia alata and Aloe spp., which were identified by farmers were not

included in the list because they are not trees (although Musa sapientum was included as it was

widely regarded as a tree by farmers).

During interviews, group discussions, ranking/scoring exercises and especially during the

tree utility ranking exercise, farmers identified and described the many different utilities of trees. The

most commonly identified utilities were provisioning services that directly provided farm income

such as timber and fruit provision, or that contributed to farm subsistence such as firewood,

medicine and fodder provision. The ecosystem services provided by trees were relatively less

frequently understood and discussed.

Indigenous trees were identified as having superior shade qualities, ability to stabilize soil,

and maintain and attract water as compared to exotic trees (kb: 372-374, 381), but they are

outnumbered by exotics due to the many limitations which restricts their abundance in practice as

shown in section 4.5.2.

4.5.1 Occurrence of trees on Farms

Qualitatively speaking, the most commonly observed trees on coffee farms during the

research period included (in no relevant order): Grevillea robusta, Macadamia tetraphylla, Mangifera

indica, Persea americana, Commiphora zimmermannii, Eucalyptus spp, Acacia mearnsii, Croton

spp., Cupressus spp., Juniperus procera, Euphorbia tirucalli, Psidium guava, Carica papaya,

Markhamia lutea, Pinus spp., Erythrina abyssinica, Cordia africana, Neoboutonia macrocalyx,

Macaranga kilimandscharica, Jacaranda mimosifolia, Spathodea nilotica, Acokanthera oppositifolia,

Musa spientum, and Bridelia micrantha, amongst others. Ficus spp were commonly discussed

although not often observed on the farms visited. An ethno-botanical study (which was beyond the

scope of this research) is needed to objectively determine the frequency and distribution of trees on

coffee farms across the region (see section 5.3.2).

“In general…tree is life for humans, because you cannot life without all these things,

you must have all of them, not one particular thing.”

53

To get a sense of which of the listed trees were less commonly known by farmers in the area,

a tally was taken of the number of respondents who reviewed the tree list27 that did not know each

tree (see Table 4 – 3). It is important to note that the tree list changed throughout the research

period as additional trees were being identified, however this tally gives some sense of which trees

are less known, and from where these trees were identified to researchers.

Many of the lesser known species had been identified to researchers at Wanjarere Forest

Station near the Aberdares National Park or by the Kangama Divisional Forestry Officer, and these

trees may not frequently exist on coffee farms. Other trees not commonly known were those which

have been recently introduced to coffee farms by Agricultural Officers, seminars, and research

projects. As such, these trees are not yet common knowledge to all farmers.

The other factor influencing farmer tree familiarity was the local name used to describe

them. It was found that many of the trees had numerous names in Kikuyu depending on where

within the research area farmers were located. Also, the ‘Kikuyu Botanical Dictionary’ (Gachathi,

1989) was used to determine the Kikuyu names of many trees (12 of the 44 trees unknown by one or

more of the five respondents who went through the tree list, see Table 4 – 3) as they were first

identified to researchers in English or scientific nomenclature and it is possible that some of these

Kikuyu names are outdated and not commonly used by farmers.

27 A total of 5 respondents reviewed the tree list and indicated the trees they did not know: 2 respondents reviewed the tree spreadsheet during second interviews and 3 respondents went through the trees during the ranking/scoring approach 1 exercise.

54

TTTTable able able able 4 4 4 4 –––– 3333.... The number of times that trees were unknown by fiver farmers who reviewed the tree list and the sources from where trees were first identified to researchers. * indicates trees that were first identified in English of scientific nomenclature for which Kikuyu names were subsequently found. Tree IdentificationTree IdentificationTree IdentificationTree Identification RespondentsRespondentsRespondentsRespondents Information SourceInformation SourceInformation SourceInformation Source

Local NameLocal NameLocal NameLocal Name Scientific NameScientific NameScientific NameScientific Name Est

er K

iman

iE

ster

Kim

ani

Est

er K

iman

iE

ster

Kim

ani

Jess

e K

anyi

Jess

e K

anyi

Jess

e K

anyi

Jess

e K

anyi

Mr.

Kar

ani

Mr.

Kar

ani

Mr.

Kar

ani

Mr.

Kar

ani

Joh

n W

aweru

Joh

n W

aweru

Joh

n W

aweru

Joh

n W

aweru

Beth

Kar

ub

iB

eth

Kar

ub

iB

eth

Kar

ub

iB

eth

Kar

ub

i

sumsumsumsum Where tree was identified from?Where tree was identified from?Where tree was identified from?Where tree was identified from?

mubura Rhamnus staddo 1 1 1 1 4 Identified in a fence on farm - supported by book

mukuhokuho Xymalos monospora 1 1 1 1 4 Identified by a farmer

mukui Newtonia buchananni 1 1 1 1 4 Kikuyu name mentioned - supported by book

muthengeta Agauria salicifolia 1 1 1 1 4 Forest station, usually forest tree

muthigitha Lepidotrichilia volken. 1 1 1 1 4 From the forest station

mutomoko Annona cherimola* 1 1 1 1 4 From Ngutu group discussion and seen on farm

mwethia Sesbania sesban 1 1 1 1 4 In previous kb, relatively new and not known?

mulberry Morus alba 1 1 1 1 4 From one farmer who had gotten from a seminar

calliandra Calliandra calothyrsus 1 1 1 3 From farmers and seen on farms, relatively new

gituthu ? 1 1 1 3 From farmer and seen on farm

leucaena Leucaena leucoceph. 1 1 1 3 In previous kb and seen on farms (picture taken)

mucarage Olea spp. 1 1 1 3 Identified by division forest officer, book info

mucoruo Nuxia congesta*? 1 1 1 3 From Ngutu group discussion

muhuru Vitex keniensis* 1 1 1 3 From the forest station

mununga Ekebergia capensis* 1 1 1 3 From Ngutu group discussion, known by farmer

muricu Acokanthera schimp. 1 1 1 3 Identified by division forest officer, known by farmer

muthaithi Cassipourea spp.? 1 1 1 3 (unknown)

muthengera Podocarpus spp. 1 1 1 3 From forest station, but not existing in all areas

mutowero ? 1 1 1 3 From farmer and seen on farm but not well known

mutunguru Anthocleista grandifl. 1 1 1 3 From Ngutu group discussion and seen on farm

mwerere Tabernaemontana spp. 1 1 1 3 From forest station, possible confusion w diff. tree

ithuthi ? 1 1 2 From high school, not common on farms

jatropha Jatropha curcas 1 1 2 In previous kb, quite new, known by some farmers

muhathi Sapium ellipticum* 1 1 2 From Ngutu group discussion

mukoigo Bridelia micrantha 1 1 2 In previous kb and seen on farms (picture taken)

mukuhakuha Macaranga kiliman. 1 1 2 From forest station and farmers

nyanjoe Euphorbia tirucalli 1 1 2 From farmers and seen on farms

bottlebrush Callistemon citrinus 1 1 From farmers and seen on farms

kanyondore Cyphomandra betacea 1 1 From farmers and seen on farms

mucinda-nugu Pinus spp. 1 1 From farmers and seen on farms

muhethu Trema spp.* 1 1 From Ngutu group discussion

muitathua Harungana madagas.* 1 1 From Ngutu group discussion

mukindu Phoenix reclinata 1 1 From farmers and seen on farms

mukoe Syzygium guineense 1 1 From forest station and farmers

mukurue Albizia gummifera 1 1 In previous kb and seen on farms (picture taken)

mukuyu Ficus sycomorus 1 1 From farmers and seen on farms

mumbu Ficus lutea 1 1 From farmers

mururi Trichilia emetic 1 1 From farmers and seen on farms

mutathi Clausena anisata* 1 1 From Ngutu group discussion

mutero Olea europaea* 1 1 From Ngutu group discussion

muthima-mburi Clutia abyssinica* 1 1 From a farmer

muturamuthi Prunus domestica* 1 1 From Ngutu group discussion, other farmers

nandiflame Spathodea nilotica 1 1 From many farmers, high school, etc.

55

4.5.2 Factors Limiting Tree Presence

There is a difference between the trees that farmers know have multiple utilities and

benefits, and the trees which exist on farms due to factors limiting the presence of specific trees. It is

critically important to consider these limitations alongside knowledge about tree utilities when

determining how to incorporate desired trees into the farming landscape. Limitations need to be

acknowledged and addressed so that viable recommendations can be made.

On coffee farms, one of the main criteria for tree selection is coffee compatibility. Trees can

be planted outside coffee plots on farms, however trees which can be planted directly with coffee

diversify the products and profitability coming from coffee plots. The two main ways that trees were

identified as being incompatible with coffee were direct competition with coffee plants, and

attraction of pests which negatively impact coffee plants.

A Boolean search28 of ‘competition_with_coffee’ retrieved 10 statements of which 5

pertained to trees, and 5 pertained to intercrops. The statements indicate that root competition of

Euphorbia tirucalli, Jacaranda mimosifolia, Croton megalocarpus, and Bridelia micrantha with coffee

prevents the planting of these trees in coffee plots. Competition for nutrients was another factor

limiting the occurrence of tree species with coffee. For example one farmer identified that J.

mimosifolia competes with coffee due to its high nutrient requirements and therefore should not be

planted with coffee. Farmers also recognized that species such as Acacia mearnsii and Eucalyptus

spp dry the soil and therefore compete with coffee for water.

Certain trees were identified to attract pests of different kinds and were therefore

undesirable with coffee. The trees Carica papaya, Psidium guajava, Eriobotrya japonica, and Prunus

Africana are not recommended with coffee because they attract birds which eat coffee berries.

Farmers told researchers that Psidium guajava, Harungana madagascariensis, Commiphora

zimmermannii, and Neoboutonia macrocalyx attract black ants which can harm coffee (although it

was not understood how black ants negatively affect coffee plants). Bridelia micrantha and Kigelia

Africana were said to attract boring insects and Eriobotrya japonica was generally said to attract

insects. In some areas Juniperus procera is no longer planted on farms because it suffers from

‘diseases’. Having said this, one farmer identified that decreased coffee plot temperature, a result of

having coffee shade generally, can decrease the frequency of coffee pest occurrence.

Likely the most important limitation of tree presence and distribution identified by farmers in

Central Province was the decreasing size of farms. Due to the mode of inheritance, in which farmers

divide their land among their sons, farms are rapidly getting smaller. Although farmers identified

numerous utilities and benefits of indigenous tree species (including high quality shade for coffee

28 A Boolean search is a feature of AKT5 which allows the user to search a kb for statements containing specific terms or combinations of terms.

56

and soil stabilization), they told researchers that because they grow very large in size they could not

exist on small farms.

Another limitation of indigenous trees identified by many farmers was that they grow slower

than most improved exotic species (kb: 306). Farmers want fast returns from farm components and

they are not prepared to plant trees which they will have to wait a long time to benefit from. The low

availability of many indigenous seeds in comparison with exotic seeds further limits their occurrence

on farms (kb: 368). Due to these limitations of indigenous trees there is a dominance of exotic trees

on coffee farms, especially: Grevillea robusta, Acacia mearnsii, Eucalyptus spp. and exotic fruit

trees.

Although some farmers were keen to plant trees on their farms, they lacked knowledge

about which trees were suitable in combination with other farm components. General information

about what trees are available for different utilities was also very limited and researchers found they

were often being asked by farmers for advice about tree species selection.

Farmers were asked which tree species used to be present on their farms and why they were

removed (see Table 4 – 4). They were also generally asked which trees they would like to plant on

their farms if they could. Interestingly five of the 13 ‘desired’ species were species that had also been

identified as trees that were previously removed from farms29 (see Table 4 – 4). This indicates that

the above limitations are powerful enough to prevent the presence of some of the most desired trees

on farms.

TableTableTableTable 4 4 4 4 –––– 4444.... The tree species identified by farmers as being removed from their farms indicating the reasons they were removed, and the species identified by farmers are being desired for their farms. * indicates the desired species which have also been removed from farms.

Removed Tree Spp Reason for Removal

Desired Tree Spp

Ficus natalensis

Tree too large

Persea americana

Ficus sycomorus

Musa sapientum

Markhamia lutea

Ficus natalensis *

Acacia mearnsii Tree dries the soil

Grevillea robusta

Eucalyptus spp.

Cordia africana *

Bridelia micrantha Tree attracts pests

Ficus sycomorus *

Kigelia Africana

Macadamia tetraphylla

Juniperus procera Tree is often diseased

Mangifera indica

Prunus Africana Tree grows too slowly

(not replanted)

Markhamia lutea *

Croton megalocarpus

Tree removed for unknown

reason

Spathodea nilotica

Erythrina abyssinica

Ficus spp.

Cordia africana

Neoboutonia macrocalyx

Millettia dura

Prunus Africana *

29 These results are from farmers generally, removed trees and desired trees were not necessarily identified by the same farmers.

57

4.5.3 Tree Location

Farms generally shared the same major components; coffee, maize, napier grass, banana,

livestock, homestead, vegetable patch/kitchen garden, etc. They differed greatly however in the

arrangement of these components and in the distribution of trees in and around these components

(see farm sketches in Appendix G). Some trees were consistently grown in a specific location on

farms; for example Cupressus spp. was very commonly found along the roadside as a fence and

boundary tree. The location of other trees varied greatly across farms; for example Macadamia

tetraphylla was found inside and outside coffee plots, in open areas around the homestead, with

vegetable crops, etc.

The most important distinction of tree location with respect to coffee farms was whether or

not a tree species can be grown with coffee. There were as many trees that farmers disagreed on as

there were trees that were consistently identified as either being compatible with coffee, or

incompatible with coffee (see Table 4 – 5).

TableTableTableTable 4 4 4 4 –––– 5555.... The tree species identified by farmers as compatible with coffee and incompatible with coffee, and the trees that farmers disagreed about in terms of coffee compatibility.

Coffee Compatible Trees Coffee Incompatible Trees Mixed Response Trees

Cordia africana Arundinaria spp. Persea Americana

Teclea spp. Eucalyptus spp. Musa sapientum

Sapium ellipticum Bridelia micrantha Croton megalocarpus

Grevillea robusta Commiphora zimmermannii Myrianthus holstii

Macadamia tetraphylla Eriobotrya japonica Psidium guajava

Ficus natalensis Erythrina abyssinica Mangifera indica

Ekebergia capensis Euphorbia tirucalli Markhamia lutea

Ficus sycomorus Ficus sycomorus Neoboutonia macrocalyx

Azadirachta indica Jacaranda mimosifolia Carica papaya

Pinus spp. Prunus africana

Terminalia spp? Syzygium guineense

Many of the farmers interviewed believed that Persea americana was compatible with coffee

while some regarded the shade from this tree to be too much for coffee. This discrepancy is likely

due to differences in the management of this tree in terms of spacing and pruning. Differences of

opinion among farmers about the incorporation of Musa sapientum into coffee plots is likely due to

both differences in management (spacing, pruning, thinning) and differences of plant variety. Four

farmers substantiated the kb statement (kb: 133) that, ‘the intercropping of banana in coffee plots

causes a good amount of shade [for coffee growth]’. Croton megalocarpus was identified as being

generally good for shade, however its roots can be competitive with coffee (kb: 361). At the group

discussion Myrianthus holstii was identified as a coffee shade tree, however another farmer told

58

researchers that the shape of its crown was not desirable for shade. Many of the mixed response

trees were said by farmers to attract coffee pests yet they were maintained in coffee plots by others.

Psidium guava and Neoboutonia were said to attract black ants; Prunus africana, Syzygium

guineense and Carica papaya were said to attract birds which also eat coffee (kb: 230, 357, 359,

360). Many farmers believe that Markhamia lutea and Mangifera indica are too large to be with coffee

because they take up too much room (kb: 286). It was also said that branches from M.indica could

break and damage coffee plants (kb: 276).

Another important distinction identified by farmers was whether or not trees could be

planted with crops (outside coffee plots). Few of the small-scale farms had the space for trees

outside coffee and vegetable plots, and therefore trees that are compatible with crops are desired.

Trees that were identified as incompatible with crops included: Macadamia tetraphylla (shade too

dense), Eucalyptus spp. (competition for water and allelopathy), Jacaranda mimosifolia (root

competition), Ficus natalensis (unknown), Prunus africana (unknown), Croton megalocarpus (root

competition and shade too dense), Persea americana (shade too dense), Macaranga

kilimandscharica (tree spreading?), Cupressus spp. (dries the soil and needles cover ground), Rubus

spp. (unknown), and Acacia mearnsii (dries the soil and root competition).

4.5.4 Priority of Tree Utilities

The combined results from the pairwise rankings of tree utilities with two farmers and during

the group discussion (Appendix D) indicated that the order of importance of tree utilities on coffee

farms was (from most to least important):

1. Income generation 2. Firewood provision 3. Food/fruit provision 4. Environmental services/bringing the rains 5. Shade provision 6. Medicine provision 7. Fodder provision 8. Building wood provision 9. Mulch provision 10. Timber provision 11. Soil fertility improvement 12. Prevention of insect attack

Although farmers did not agree on the exact order, the top 7 utilities were consistently

ranked as the most important utilities of trees on farms. This result was confirmed at the feedback

sessions where farmers said that the top 7 utilities were the most important at both events. The most

ambiguous utility was ‘Environmental services/bringing the rains’ which was often identified by

farmers but understood in different ways. A few farmers commented that this utility should be the

59

most important and that everything else on farms depended on adequate rain to function (see

Box 4 – 5). Farmers repeatedly talked about the ability of trees to ‘pull the rains’ to an area and said

that when trees were cut the area from which they were removed became more dry. It is believed

that much emphasis was placed on this particular utility given the recent dry conditions in the area.

Box Box Box Box 4 4 4 4 –––– 5555.... Quotation from a farmer at the Muruka factory feedback session on 28/07/09.

Farmers were asked to identify the attributes under each of the most important tree utilities

which make trees useful for that purpose. With respect to income generation, farmers simply prefer

trees that can generate the most income for the farm; therefore trees for which the products have a

market and which reliably produce sellable products. In terms of firewood farmers consider the

length of time that wood will continue to burn, how easily the wood burns, and how quickly the

trees reaches maturity. For the provision of fruit (and other edible products) farmers consider the

type of fruit produced (how good it tastes, how nutritious it is), and the quantity of fruit produced.

For ‘environmental services’, farmers identified that it was desirable for a tree to be indigenous and

large in size. With respect to shade, trees with widely spreading crowns are desired and trees which

minimally interfere with crops (minimal competition of roots and for water and nutrients). Livestock

palatability was the main criterion for fodder tree selection. According to farmers, the best trees for

building wood are those which produce strong wood, last longer (resistant to decomposition and

insect attack), and quickly reach maturity. Timber trees must produce marketable wood with

desirable qualities, minimally interfere with crops, and quickly reach maturity. Trees regarded to

improve soil fertility are those increasing nutrient availability in the surrounding soil. The desired

attributes of windbreak trees are strength (to resist wind damage) and early maturity. Finally, for

mulch farmers desire trees that shed their leaves and improve the fertility of the soil.

4.5.5 Selection of Most Important Trees

The task of determining the most valued trees from the perspective of farmers is not a simple

one especially when the trees are simultaneously utilised for so many different and important

utilities. The present study did not attempt to sample a representative group of farmers to reflect the

views of the entire area, but aimed to capture some of the variation in the knowledge of coffee

farmers about tree utilities and tree preferences. To do so, two methods were tested to acquire

“Environment as first priority… environment, it is affecting our life, so we should put

it first… otherwise the list of uses is very good…because we want to encourage

farmers to plant trees, so it is better to encourage environment first… because we

need rain, without rain you cannot grow anything.”

60

additional information and to determine what method would be most appropriate to obtain

statistically rigorous data on tree preference for all the species identified.

It was decided not to use this information to produce an overall ordered list of ‘best trees’

because this would oversimplify valuable information about the importance of trees for specific

utilities and potentially result in reduced biodiversity on farms if only the ‘best trees’ were promoted.

For example, even the best firewood tree may not produce edible fruit or valuable timber, so it is

important to consider trees suitable for each of the most important tree utilities and resist the urge to

lump them all together under one defining list.

Another important consideration is that the study covered a large area across varied

elevations and climatic conditions. While many of the trees are well suited to grow across this

landscape, it is believed that others are more suited to grow in specific areas (see Box 4 – 6). As

such, the present information about farmer knowledge and preference should be utilised in

combination with bio-physical data from the region to formulate an implementation strategy.

Box Box Box Box 4 4 4 4 –––– 6666.... Quotation from a farmer at the Ngutu factory feedback session on 30/07/09.

4.5.5.1 Ranking/Scoring Approach 1 Results

This approach took a great deal of time per respondent to complete and as a result

information was limited to three patient respondents. For this reason it is inappropriate to draw

conclusions about definitive species scores from such a limited sample, and the following results are

reported to demonstrate how analysis of a larger sample could be conducted, and to provide

speculative insight into which trees might be most valued for each utility.

One conclusion that can be drawn from this exercise was that farmers generally agreed

among themselves (with subtle differences) about which trees were best for each specific utility

(see Appendix H). This indicates that farmers share general knowledge about the utilities of trees,

making this type of exercise applicable. Also, as might be expected, it was shown that different trees

are preferred for different utilities, and that some ‘multiple purpose’ trees are highly ranked for

multiple utilities (see Table 4 – 6). For example Mangifera indica was present as one of the top

species for 7 of the 9 utilities scored.

A comparison of the list of highly scored species with the trees most commonly found on

farms (see section 4.5.1) indicates that many of these species are not common despite being highly

valued. These species are (including limitations to tree presence on farms): (* indicates trees known

by only one of three respondents)

“You see we are in different regions…the climate of Kiambu and Murang’a is not the

same, so you see some recommending that, and we are recommending the next.”

61

· Citrus Citrus Citrus Citrus aurantiifoliaaurantiifoliaaurantiifoliaaurantiifolia – (none given)

· LeuLeuLeuLeuceana ceana ceana ceana leucocephalaleucocephalaleucocephalaleucocephala**** – (none given)

· Cyphomandra Cyphomandra Cyphomandra Cyphomandra betaceabetaceabetaceabetacea – (none given)

· Morus Morus Morus Morus albaalbaalbaalba**** –––– (none given)

· Prunus Prunus Prunus Prunus AfricanaAfricanaAfricanaAfricana – grows very slowly and attracts birds to coffee (planted elsewhere on farm?)

· Ficus luteaFicus luteaFicus luteaFicus lutea –––– (none given)

· HarunganaHarunganaHarunganaHarungana madagascariensmadagascariensmadagascariensmadagascariensis is is is – attracts black ants

· AnnonaAnnonaAnnonaAnnona cherimolacherimolacherimolacherimola****– (none given) · CussoniaCussoniaCussoniaCussonia spicataspicataspicataspicata**** – (none given) · MoringaMoringaMoringaMoringa olieferaolieferaolieferaoliefera* * * * – (none given) · LantanaLantanaLantanaLantana camaracamaracamaracamara – (none given)

· Eriobotrya japonicaEriobotrya japonicaEriobotrya japonicaEriobotrya japonica – attracts insects and birds to coffee (could be planted elsewhere on farm?)

· Olea africana Olea africana Olea africana Olea africana – (none given) · Anthocleista grandifloriaAnthocleista grandifloriaAnthocleista grandifloriaAnthocleista grandifloria – (none given) · PodocarpusPodocarpusPodocarpusPodocarpus falcatusfalcatusfalcatusfalcatus**** – (none given)

· MacarangaMacarangaMacarangaMacaranga kilimandscharicakilimandscharicakilimandscharicakilimandscharica**** – wood too soft for charcoal and spreads into crops (competes) · JatrophaJatrophaJatrophaJatropha curcascurcascurcascurcas**** – (none given)

· CallistemonCallistemonCallistemonCallistemon citrinuscitrinuscitrinuscitrinus – (none given)

· TremaTremaTremaTrema orientalisorientalisorientalisorientalis – (none given) · OcoteaOcoteaOcoteaOcotea usambarensisusambarensisusambarensisusambarensis – large tree, when burned smoke is poisonous · Ricinus communisRicinus communisRicinus communisRicinus communis – seasonal shade only

· Clutia abyssinicaClutia abyssinicaClutia abyssinicaClutia abyssinica – (none given)

Because there were few limitations given for these trees it is most likely that their benefits are

not well known by farmers and these species should be promoted on farms.

62

Profitability

Burn

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63

4.5.5.2 Ranking/Scoring Approach 2 Results

The second ranking/scoring approach was only tested for 10 species. As such it is not

possible to present results about the overall ranking of tree species for different utilities, however it

is interesting to compare the two approaches for the 10 trees selected.

To do so scores from ranking/scoring approach 1 were converted to ranks and rank averages

were calculated (see Table 4 – 7). Because the tree utility attributes were refined between the two

approaches a direct comparison was not possible. Instead, the three most comparable attributes

were selected to speculatively ascertain how the results compared.

The first important finding was that ranking/scoring approach 2 provided results with fewer

ties (more specific information) than ranking/scoring approach 1. It also appears from this small

sample that the data from ranking/scoring approach 2 is more consistent among famers (smaller

standard deviation on average), although a larger data set is needed to confirm this finding. This

could be explained by the fact that the second approach was much faster to complete and required

less patience and concentration from respondents.

A comparison of the ranked order of trees for ‘burn qualities’ from approach 1 and ‘burn

time’ (identified by farmers as the most desirable characteristic for firewood) from approach 2

shows that the results are very consistent. By continuing ranking/scoring approach 2 with a large

sample of farmers it is believed that consistent reliable data could be collected for each of the tree

species.

FigureFigureFigureFigure 4 4 4 4 –––– 13131313.... Photographs taken by researchers of farmers participating in the ranking/scoring approach 2 exercise on 12/08/09.

64

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65

5. Discussion

5.1 Problems Facing Coffee Farmers

The information gathered during this research indicates that coffee farmers face a number of

problems including: unstable coffee prices, decreasing coffee yield and farm profitability, and

climate change. Trees are an important component on farms which have the potential to have a

positive influence and improve some of these areas (see Figure 5 – 1).

FigureFigureFigureFigure 5 5 5 5 –––– 1111.... A diagrammatic representation of the interaction between the problems faced by coffee farmers and the potential positive influence of trees on farms.

66

Another important problem identified repeatedly by farmers and acknowledge in other

literature about the area (Ovuka, 2000; Ovuka and Lindqvist, 2000; Roothaert and Franzel, 2001) was

that of decreasing farms size. This has important implications as trees are seen to take up limited

space and inclusion of indigenous trees, which are generally larger in size, is not deemed practical

despite their many known benefits. This issue is not a simple one to resolve, and although attempts

were made to discuss the issue during interviews and group discussions, it is unclear how this

problem can be resolved.

5.1.1 Unstable Coffee Price

Price instability, market inefficiencies, difficulties in diversifying, gaps in

commodity chain organisation, problems in renewing the means of production,

and quality demands are a few of the problems that need to be dealt with.

(Omont and Nicolas, 2006 p.27)

Fluctuation of coffee price is largely out of the control of farmers and remains a large

problem for small-scale coffee farmers worldwide. Unfortunately when the price of coffee decreases

below the subsistence level for farmers, as was the case in Kenya in the late 1990s, farmers are

forced to divert to other farm activities which are often less environmentally friendly than shaded

coffee systems (Perfecto et al., 2005). In Central Province farmers were shown to divert to dairy

farming and intensive food crop cultivation.

One of the areas for improvement identified during this research was society

mismanagement and corruption. Many coffee societies were found to have numerous coffee

factories operating well below capacity and such management is wasteful. There is potential and

need to improve financial benefits to Kenyan farmers irrespective of international coffee prices.

The promotion of trees which can provide profitable products such as timber, fruit, charcoal,

and firewood or which can increase the productivity and therefore profitability of other profitable

farm crops has the potential to buffer farmers from volatile coffee prices. Agroforestry may also

increase the flexibility of farmers with respect to the timing of tree harvest (Omont and Nicolas,

2006).

5.1.2 Decreased Coffee Yield and Profitability

Coffee inputs, which are needed to replenish soil fertility and prevent pest damage, are

increasing in cost in part due to poor infrastructure and high transportation costs (Jama et al., 2006).

As such, practical and affordable substitutes are needed to maintain productivity. Farmers identified

different ways in which trees and intercrops can be utilized to replenish soil nutrients, maintain soil

moisture, improve farmyard manure, and decrease pest disturbance. The tree Acokanthera

67

oppositifolia, known by only some of the farmers interviewed, should be encouraged on farms for its

fertilization and pesticidal qualities.

5.1.3 Climate Change

Farmers perceived the climate to be changing in that conditions are getting dryer and

warmer. It was especially dry during the research period which lead to many comments about the

utility of trees to maintain soil moisture. The ICO acknowledges the climatic changes which are

affecting coffee production worldwide. “The crop year 2008/09 has been significantly affected by

climatic problems and constraints linked to high fertilizer prices and labour costs in many exporting

countries.” (ICO, 2009a p.4). Trees such as Eucalyptus spp. which utilise large amounts of water are

not recommended on farms for they compete heavily for limited water resources, and many farmers

said that they will be cutting these trees shortly.

It is also important to consider that the ecological suitability of trees will change under an

altered climate. Not all of the trees that once thrived in the area will be appropriate in the future, and

this needs to be considered when determining tree species to promote.

Finally, there is future potential for farmers to benefit financially from the trees they have on

farms (agroforestry) through the Clean Development Mechanism (CDM; certified emission

reductions) and Voluntary carbon markets (voluntary emission reductions) (Brown, 2002; Corbera et

al., 2007). A few farmers had heard about this possibility, and there is potential through the

organization of cooperative societies, that the marketing of C from groups of small-scale coffee

farmers could act as an added incentive for farmers to increase and maintain tree abundance on

farms.

68

5.2 Farmers’ Knowledge Limitations

Coffee farmers in Central Province have demonstrated that they have extensive knowledge

about tree utilities in general, but there were three areas where their knowledge seemed to be most

limited or inconsistent.

5.2.1 Shade

The potential use of shade trees in coffee was not uniformly understood across the study

area, nor did farmers agree on the species or amount of shade that is appropriate for coffee. Farmers

disagreed farm more about the tree species which could be used to shade coffee as compared to

the intercrops planted within coffee plots. The most common shade tree by far was Grevillea

robusta which many farmers believed was the only tree that they could plant with their coffee. Other

desired shade trees were not planted with coffee due to limitation of size, competition, and pests. It

is believed that a better understanding of shade tree management would help to alleviate some of

these limitations. One important distinction raised by farmers was that the management of shade will

differ depending on farm elevation, and this would need to be taken into account.

The limitation of specific knowledge about coffee shade is likely in part due to the fact that

there are more sources providing information about intercropping to farmers than about shade, and

this is an area that can be improved as farmers were keen to learn more about shade.

5.2.2 Coffee Quality

Very few of the farmers interviewed during this research had an understanding about the

quality of their coffee or how this impacted the price they receive for it. The long history of coffee

cooperative societies in Kenya has resulted in farmers being excluded from the processing and

marketing of their coffee. Few farmers understood what happens to their coffee after they bring it to

the factory. Increasing farmers’ understanding about coffee quality is important so that coffee quality

and grade may be improved through better management thus fetching a higher price.

5.2.3 Regulating tree utilities

Farmers have a greater understanding about the provisioning utilities of trees than they do of

the regulating utilities. This is likely because provisioning utilities are more directly profitable to

farmers, however it is the regulating utilities of trees which help to sustain and support productivity

on farms.

69

Farmers frequently identified the ability of mainly large indigenous trees to ‘pull the rains’

which hints at an understanding of the value of trees in water conservation, however understanding

is confused and limited. The need of trees to maintain water availability was exasperated given the

recent dry conditions this past year. Farmers also identified some different ways that trees and their

products are used to improve soil fertility including mulch and green manure but it is believed that a

better understanding of these benefits could be developed especially given that fertilizer application

is not possible by most farmers due to high costs.

5.2.1 Extension Approaches

Scaling up is a communication process, and change agents have to understand

how farmers receive, analyse, and disseminate information in order to facilitate it.

(Franzel et al., 2006 p.62)

The present research provides information about the sources and derivation of knowledge

which is extremely useful as it may act as the basis of future extension efforts. Coffee farmers in

Central Province, Kenya appeared to respond positively to informal extension efforts jointly

coordinated by coffee cooperative societies and agricultural officers and this approach could be

expanded. Farmers are keen to learn new techniques and practices and it is important that extension

efforts reach more people, not only the elite few. A similar approach through cooperatives was

shown to be successful with coffee farmers in El Salvador (Mendez et al., 2009). Additionally they

found that, “working with farmer cooperatives, rather than with individual farms, may facilitate

achieving landscape-scale results in terms of ecosystem services conservation and management.”

(p.4).

During feedback sessions farmers also indicated that demonstration plots and farmers’ field

days have been useful tools to disseminate information in the area. These techniques have proven to

be successful in many other agroforestry projects in Africa (Chivinge, 2006; Pye-Smith, 2008).

Farmers also identified that loans are difficult to access and interest rates are too high. As such, a

further incentive to farmers would be the provision of materials such as tree seeds or seedlings at

subsidized rates (Wambugu et al., 2006), which is the intention of the Mugama Union through tree

nursery development.

The current World Agroforestry Centre strategy (2008) document states that knowledge

extension must match the problems of the recipients; in this way training about shade (especially in

lowland areas), coffee quality and regulating tree utilities should be the priority to address the major

problems facing coffee farmers. Furthermore, the combined consideration of local knowledge with

the ecological and biophysical suitability of tree species across the region is warranted, as discussed

in the section 5.3.2.

70

5.3 Important Tree Utilities

From the perception of farmers, the most important utilities of trees respectively are: income

generation (including timber, building wood, fruit, firewood, and seeds/seedlings), firewood

provision, food/fruit provision, environmental services (‘bringing the rains’), shade provision,

medicine provision, and fodder provision. Multipurpose trees which provide multiple utilities are

generally preferred by farmers over specialized trees providing only one utility, especially given the

limitation of space due to decreasing farm size.

Firewood remains the main source of fuel on farms and is especially important for cooking.

According to Puri et al. (1994), “indigenous tree species are better suited as fuelwood species as

they contain high density wood, low ash content and low N percentage.” (p.123). There was no clear

preference of indigenous trees for fuelwood in Central Province, Kenya. The qualities that farmers

desire for firewood is long burning time, early maturity and fast wood growth.

Fruit provision was one of the most important tree utilities because it increases food

production and nutrition with numerous vitamins and can be sold for income (Pye-Smith, 2008). The

most important fruit crops are those producing large quantities of desirable and sellable fruit

including: Musa sapientum, Carica papaya, Persea avocado, Mangifera indica, Prunus domestica,

Citrus aurantifolia, Cyphomandra betacea, Morus alba, and Macadamia tetraphylla. Many farmers are

increasing macadamia nut production as a result of low coffee prices and market availability, and

Kenya is now responsible for 10% of the world production of this commodity (Gitonga et al., 2008).

Macadamia and other fruit production could be expanded through increased availability of

improved cultivars and planting materials (ibid). Much research has stressed the domestication

potential for indigenous fruit trees based on the preferences of local people (Styger et al., 1999;

Tchoundjen et al., 2006), however the fruit trees preferred by farmers were all exotic. The utility of

Moringa oliefera for fruit and edible greens production (among many other utilities) was emphasized

by a few farmers but was not yet widely understood. Encouragement of this species is thus

recommended.

Kenya is well known for having a diversity of medicinally important trees (Njoroge and

Bussmann, 2006) and farmers demonstrated that they had extensive knowledge about the use of

such trees. Interestingly some farmers were even aware of more recent medicinal applications of

indigenous trees. For example extract from Prunus africana was recently found to have application in

combination therapy drugs to treat HIV (Kanyara and Njagi, 2005) and one farmer acknowledged this

use of the tree during an interview. P. africana is listed as a CITES endangered species due to

overexploitation for medicinal products from the wild (Stewart, 2003), and given its many other

identified utilities it could be encouraged for planting on farms. Generally speaking, the medicinal

uses of trees identified by farmers are well documented in the scientific and other literature, for

71

example the anti-malarial qualities Caesalpinia volkensii (Gachathi, 1989; Njoroge and Bussmann,

2006).

In terms of fodder, many of the well researched species of potential were not widely used

on farms and napier grass, which was found to be the dominant fodder crop. Well researched trees

preferred by some farmers but not widely known included: Calliandra calothyrsus, Leucaena spp.,

Morus alba (Wambugu et al., 2006). Results from a study by Roothaert and Franzel (2001) support

research findings in that, “exotic fodder trees have been introduced in central Kenya but most have

not been adopted by farmers.” (p.240). Explanations that they provide include unfamiliarity of the

species and pest infections. Of the local species they found to be preferred by famers for fodder

only Latana camara and Commiphora zimmermanii were found to be used in the present study and

there is potential for Triumfetta tomentosa and Aspilia spp. to be encouraged as fodder crops.

5.3.1 Ranking and Scoring – the Way Forward

Much insight was gained by testing the two different ranking/scoring approaches. While the

first approach was useful to acquire species specific information under each utility, it was not found

to be a practical method for determining the overall priority of tree species given the time needed

per respondent. The second approach improved this limitation by covering only 10 randomly

selected trees per respondent and it is believed that with a few improvements this method could be

used to attain a rigorous dataset about all of the tree species, therefore avoiding restriction of tree

selection to the few highest ranked and thus encouraging biodiversity on farms.

It is proposed that 7 out of the 10 species first proposed to each respondent could be from

the ‘lesser known trees’ list. It could be possible to reduce the tree list for each specific utility to only

those trees identified as useful for that purpose, however it was decided to maintain the full list in

case not all utilities were identified by the limited sample of farmer respondents and because having

different tree lists for each utility would overcomplicate the exercise. Additionally, personal

information about the respondent could be collected for analysis about what characteristics impact

tree presence.

To attain sufficient information it is believed that obtaining ranking information from 20

respondents per tree would be appropriate. The list of trees contains 87 species therefore a

minimum of 174 respondents would need to rank 10 trees at a time. In practice though, a larger

number will be needed given that some trees are not well known and that trees will be randomly

selected.

The resulting information needs to be utilised in combination with findings about the

ecological suitability of these species and information about additional trees which are suitable to

the area but which are not known by farmers (see section 5.3.2).

72

5.3.2 Tree Diversification

Diversification of trees on farms is an important objective given the numerous benefits of

diversity including increased agroecosystem stability and productivity and income diversification (as

established in section 1.2.2.2)(Thrupp, 2004; Kindt et al., 2006; Oginosako et al., 2006). In practice,

farm tree diversity is limited by factors such as restricted space (tree size), competition between

farm components, and pest problems. Many studies have highlighted the potential to overcome

some of these limitations through the domestication and improvement of indigenous trees.

The present research supports the position of Wambugu et al. (2006) that it is better to

provide list of suitable tree species for each desired utility rather than a few ‘best’ trees. In this way

the different needs of individual farmers can be accommodated and the benefits of increased

biodiversity may be realized.

Encouragement of indigenous trees is especially important. An excellent study conducted by

Kindt et al. (2007) compared original potential natural vegetation types (PNVTs) surrounding

Mt.Kenya from 1960 to current indigenous species composition surveyed from 1999 – 2004. They

have identified that at least 30% of the current indigenous vegetation overlaps with original potential

natural vegetation in the most frequent vegetation types, and that the species no longer present

could be selected for promotion given their ecological suitability. Kindt et al. also state that, “to

promote agroecosystem diversification, ecological and socio-economic reasons for low current

frequencies of most indigenous tree species need to be better understood.”(2007 p.633). According

to their PNVT map, the present research was conducted within ‘moist intermediate forest’ (MI) and

‘moist montane forest’ (MM) PNVTs (see Figure 5 – 2). A comparison of the most frequent

indigenous species present in each PNVT from their study with the indigenous trees identified and

discussed with farmers indicates two main things: that the most common indigenous species

coincide with those found on farms during the present research (with the exceptions of Vangueria

infausta, and Clerodendrum johnstonii which were not identified, and Croton macrostachyus which

was likely identified under Croton spp generally) and that there are many suitable species that are

not present on farms.

Kindt et al. (2007) highlight the need to promote the slower-growing primary forest

species: Olea europaea, Podocarpus falcatus, Cassipourea malosana, and Ocotea usambarensis.

These species, while present on the tree spreadsheet, were among those not commonly well known

by farmers. Their main utilities as identified by farmers were for timber (O.usambarensis and

P.falcatus ) and firewood (O.europaea and O.usambarensis). Incentives would likely be needed to

encourage C.malosana which had no identified utilities, and raising awareness about these trees is

warranted.

73

FigureFigureFigureFigure 5 5 5 5 –––– 2222.... The potential natural vegetation types surrounding Mt. Kenya taken directly from: (Kindt et al., 2007 p.634). The present study area is approximately indicated by the box.

Information from the present study, in combination with continued ranking of trees for each

utility, and the findings from other research (such as species historically shown to be present in the

area) should form the basis of an accurate and appropriate list of species suitable for each utility.

74

6. Conclusions

Coffee farmers in Central Province, Kenya have a wealth of knowledge about the utility of

trees on coffee farms. The practical limitations of available finance (due primarily to vulnerability to

changing coffee prices) and space due to decreasing farm size hinder the application of some of this

knowledge about trees. Limitations to the incorporation of desired trees may also be prevented by

the size of the tree, competition with other farm components (such as coffee or crops) for water,

light or nutrients, and pest problems. This is especially true of many indigenous trees which used to

be present on farms. To encourage the trees with limitations incentives need to be put in place and

the issue of decreasing farm size needs to be addressed.

Some of the most useful tree species that should be encouraged more widely on farms

include: Acokanthera oppositifolia, Prunus Africana, Morus alba, and Moringa oliefera. Also,

desmodian should be widely encouraged to farmers for its recognised benefits as a fodder and soil

improvement crop. Another potential area of focus is genetic improvement and general promotion

of underrepresented and desired trees such as: Ficus spp., Cordia Africana, and Markhamia lutea.

Increasing the diversity of trees on coffee farms will diversify the products produced while

improving regulating ecosystem services and therefore the sustainability of production.

Farmer’s knowledge about the use of shade for coffee, the quality of coffee, and the

regulating utilities of trees appears to be limited and should be the focus of extension efforts.

Information about the sources where farmers receive information indicated that informal trainings

organized jointly by farmers, agricultural officers, and coffee societies may be the most effective

approach for future farmer training in these highlighted areas. Farmers were keen to increase their

understanding about these topics and organizing further trainings should be a priority alongside

nursery development of suitable and desirable tree species.

6.1 Recommendations

Further research is needed to compare the eco-physiological suitability of trees with utilities

deemed most important by farmers to identify useful trees which farmers may not yet have

knowledge about. Additional exploration into what is meant by ‘bringing the rains’ would also be

insightful and future research in the area about tree utilities should aim to stratify respondents by

gender to determine the differences in knowledge between these groups.

Information from the present research in combination with that of continued tree ranking

and existing eco-physiological data should be utilized to design tools to encourage tree

diversification and abundance on coffee farms. Such tools need to be practical and utilizable by

coffee farmers and the desired trees need to be made available at affordable prices. One suggestion

is to assemble a booklet or poster listing the top 20 trees (based on the data acquired from

75

continued tree ranking) suggested for each different utility and include characteristics about each

tree, management information and price, and where to get them. In this way farmers would have a

list of reliable and available trees that they could choose from to meet their specific needs.

76

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3

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Tab

le L

eg

end

+

farm

vis

ited

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tr

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anki

ng

#

tree

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rin

g

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ske

tch

%

pai

rwis

e

uti

lity

ran

kin

g

^

tele

ph

on

e

inte

rvie

w

~

spre

adsh

eet

revi

ew

ed

Bo

ldB

old

Bo

ldB

old

se

co

nd

in

terv

iew

85

Inte

rvie

we

e N

am

e

co

ffe

e t

ree

nu

mb

er

occ

up

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on

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de

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10

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er

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rd N

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gi K

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th W

an

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cilia

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u a

nd

Fra

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s N

go

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+

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air

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n G

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ura

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l Bu

rug

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ina

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(an

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rs.)

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Kim

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egen

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inte

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w

~

spre

adsh

eet

re

view

ed

B

old

Bo

ldB

old

Bo

ld

seco

nd

in

terv

iew

86

. .

Inte

rvie

we

e N

am

e C

on

tin

ue

d

co

ffe

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ree

nu

mb

er

occ

up

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n W

aw

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n G

ich

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ph

Kim

an

i

*

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ph

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kuri

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+

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ua

Wa

mb

a

+ ~

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us

Mo

ng

ai

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ku

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ran

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uro

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#

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an

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ng

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ph

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adsh

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re

view

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B

old

Bo

ldB

old

Bo

ld

seco

nd

in

terv

iew

87

Ap

pe

nd

ix C

– T

ree S

pre

adsh

ee

t (l

egen

d o

n p

g.

97)

Tre

e I

de

nti

fica

tio

n

Lo

cati

on

O

rig

in

Est

ab

lish

me

nt

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l N

am

e

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gli

sh N

am

e

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en

tifi

c N

am

e

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de

s

with coffee

with tea

with crops

forest / woodlot

open area/near farm

boundary/boarder

riparian

removed from farm

exotic

indigenous

planted

natural regeneration

existed when arrived

bo

ttle

bru

sh t

ree

b

ott

leb

rush

C

all

iste

mo

n c

itri

nu

s *

x

x

x

calli

an

dra

ca

llia

nd

ra

Ca

llia

nd

ra c

alo

thy

rsu

s +

*

x

x

x

git

uth

u

(git

hu

thu

) ?

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x

x

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uth

i p

alm

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hu

thi)

?

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?

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op

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ja

tro

ph

a

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cu

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s +

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x

kaiy

ab

a

key

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ple

D

ov

yali

s ca

ffra

^

^ *

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kan

yon

do

re ~

/tre

e t

om

ato

tr

ee

to

ma

to

Cy

ph

om

an

dra

be

tace

a

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le

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cep

ha

la

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x

x

x

ma

rig

u

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na

na

(ir

igu

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usa

sa

pie

ntu

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@

x

x

x x

x

x x

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/kiu

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/mu

ruru

(a

coka

nth

era

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cok

an

the

ra o

pp

osi

tifo

lia

^

^ *

@

x

x

x

x

mb

ari

ki/b

ari

ki/m

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riki

ca

sto

r R

icin

us

com

mu

nis

+

^^

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x

x

x

mb

eg

u c

ia m

ag

uta

/mu

kan

da

nia

m

aca

da

mia

M

aca

da

mia

te

tra

ph

yll

a

+ ^

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x -

x

x

x

x

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ba

ba

i p

ap

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ari

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ay

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x

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ba

riti

/mu

kim

a

gre

ville

a

Gre

vil

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bu

sta

+

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x

x x

x -

x

x

x

x x

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ba

u

blu

eg

um

E

uca

lyp

tus

spp

. +

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~

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~

-

x

x

x x

x

x x

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be

ra/m

be

ra

gu

ava

P

sid

ium

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aja

va

*

@

~ -

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x

x

x

x

mu

bu

ra

(rh

amn

us)

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ha

mn

us

sta

dd

o

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^

x

mu

bu

thi

(Ca

esa

lpin

ia)

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esa

lpin

ia v

olk

en

sii

^^

~

x

?

?

mu

caka

ran

da

ja

cara

nd

a

Jaca

ran

da

mim

osi

foli

a

+ ^

^ *

@

-

-

x

x

mu

cara

ge

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tuku

yu^

^

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on

te

ak

Ole

a h

och

ste

tte

ri/w

elw

itsc

hii

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^ $

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-nu

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p

ine

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inu

s sp

p.

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-

x

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coro

rom

a

(mu

coro

rom

a)

?

*

x

x

88

Tre

e I

de

nti

fica

tio

n

Lo

cati

on

O

rig

in

Est

ab

lish

me

nt

Loca

l N

am

e

En

gli

sh N

am

e

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en

tifi

c N

am

e

Co

de

s

with coffee

with tea

with crops

forest / woodlot

open area/near farm

boundary/boarder

riparian

removed from farm

exotic

indigenous

planted



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coru

o/m

uco

rui^

^?

?

N

uxi

a c

on

ge

sta

^^

??

~

~

mu

em

be

/mw

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be

m

an

go

M

an

gif

era

in

dic

a

+ ^

^ *

@

~ x

-

>

x

x

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ga

ga

ti/m

ub

era

/mu

run

ga

ti ^

^

(eri

ob

otr

ya)

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ob

otr

ya

ja

po

nic

a

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^ *

@

-

x

x

mu

gu

mo

n

ata

l fig

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s n

ata

len

sis

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x

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(sa

piu

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piu

m e

llip

ticu

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^

~

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^

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he

thu

(t

rem

a)

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ma

sp

p.^

^

$ *

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hu

ru

(vit

ex)

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ite

x k

en

ien

sis

^^

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*

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hu

ti

(ery

thri

na

) E

ryth

rin

a a

bys

sin

ica

^

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-

x

x x

x

x

x

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iri

pru

nu

s P

run

us

afr

ica

na

+

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~

x -

-

x x

x

x

mu

ita

thu

a/m

uit

a-h

uth

a (h

aru

ng

an

a)

Ha

run

ga

na

ma

da

ga

sca

rie

nsi

s ^

^

~ *

x

^^

?

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kam

bu

ra

(do

vya

lis)

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vya

lis a

by

ssin

ica

^^

~

*

^^

^

^

mu

kig

i/ja

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aru

rin

a

tick

_b

err

y La

nta

na

ca

ma

ra

^^

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x

^

^

mu

kin

du

w

ild d

ate

pa

lm

Ph

oe

nix

re

clin

ata

^

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x

x

mu

kin

du

ri/m

uth

idu

ri

cro

ton

C

roto

n m

eg

alo

carp

us

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@

~ x

-

-

x -

x

x

x

mu

koe

w

ate

rbe

rry

Sy

zyg

ium

gu

ine

en

se

+ $

*

x -

x

x

x ^

^

x

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koig

o/m

uki

gi/

mu

kun

di

mo

ng

o

(bri

de

lia)

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de

lia

mic

ran

tha

+

@

~ x

-

x

x

x

mu

kon

do

/mu

koro

be

a

avo

cad

o

Pe

rse

a a

me

rica

na

+

*

x -

x -

x

-

x

x x

mu

kuh

aku

ha

/mu

kwa

kwa

(m

aca

ran

ga

) M

aca

ran

ga

kil

ima

nd

sch

ari

ca

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*

- x

x

x x

mu

kuh

oku

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de

ti

lem

on

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od

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ym

alo

s m

on

osp

ora

*

x

^^

x

mu

kui

(ne

wto

nia

) N

ew

ton

ia b

uch

an

an

ni

^^

*

x

x

mu

kun

gu

gu

(c

om

mip

ho

ra)

Co

mm

iph

ora

zim

me

rma

nn

ii

^^

*

> -

x

x x

x

x

mu

kuru

e

alb

izia

A

lbiz

ia g

um

mif

era

+

*

x

x

89

Tre

e I

de

nti

fica

tio

n

Lo

cati

on

O

rig

in

Est

ab

lish

me

nt

Loca

l N

am

e

En

gli

sh N

am

e

Sci

en

tifi

c N

am

e

Co

de

s

with coffee

with tea

with crops

forest / woodlot

open area/near farm

boundary/boarder

riparian

removed from farm

exotic

indigenous

planted



mu

kuyu

fi

g F

icu

s sy

com

oru

s *

@

~ x

- >

x x

x x

x

x x

mu

lbe

rry

mu

lbe

rry

Mo

rus

alb

a

*

?

x

mu

mb

u

(lu

tea

fig

) F

icu

s lu

tea

*

x

mu

nd

ere

nd

u/m

ud

ere

nd

u

(te

cle

a)

Te

cle

a s

pp

. +

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~

x

x

x

x

mu

nu

ng

a

(eke

be

rgia

) E

ke

be

rgia

ca

pe

nsi

s ^

^

~

~ -

^

^

mu

ran

gi

ba

mb

oo

A

run

din

ari

a s

pp

. ^

^ *

@

-

x

x x

x

x x

x x

mu

rati

na

/mu

rati

na

^^

sa

usa

ge

tre

e?

Kig

eli

a a

fric

an

a^

^?

~

*

x

x

^

^

mu

ricu

^^

sc

him

pe

ri

Aco

ka

nth

era

sch

imp

eri

?

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^

^

mu

rin

ga

la

rge

lea

ved

co

rdia

C

ord

ia a

fric

an

a

+ *

@

~ x

>

x

x

x

mu

ruri

(t

rich

ilia

) T

rich

ilia

em

eti

ca

^ *

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x

x

x

mu

tara

mw

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(t

erm

ina

lia)

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rmin

ali

a s

pp

??

?

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x

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tara

kwa

(ce

dar

) ce

da

r Ju

nip

eru

s p

roce

ra

+ *

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x

x x

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tara

kwa

(cy

pre

ss)

cyp

ress

C

up

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us

spp

. +

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tare

b

lack

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rrie

s R

ub

us

spp

. ^

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x

x

x x

x

mu

tath

i (c

lau

sen

a)

Cla

use

na

an

isa

ta ^

^

~ *

^^

mu

tero

b

row

n o

live

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lea

eu

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ae

a v

ar.

afr

ica

na

^^

~

*

mu

tha

ith

i (c

ass

ipo

ure

a)

Ca

ssip

ou

rea

sp

p.?

^

^

x

mu

tha

iti

cam

ph

or

Oco

tea

usa

mb

are

nsi

s +

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x

x

mu

tha

kwa

(v

ern

on

ia)

Ve

rno

nia

au

ricu

life

ra

^ ^

^ *

@

x x

x

- x

mu

tha

nd

uk

u

bla

ck w

att

le

Aca

cia

me

arn

sii

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mu

the

ng

era

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p

od

oca

rpu

s P

od

oca

rpu

s sp

p.

^^

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@

x

x

mu

the

ng

eta

(a

ga

uri

a)

Ag

au

ria

sa

lici

foli

a

^ ^

^ $

x

x

x

90

Tre

e I

de

nti

fica

tio

n

Lo

cati

on

O

rig

in

Est

ab

lish

me

nt

Loca

l N

am

e

En

gli

sh N

am

e

Sci

en

tifi

c N

am

e

Co

de

s

with coffee

with tea

with crops

forest / woodlot

open area/near farm

boundary/boarder

riparian

removed from farm

exotic

indigenous

planted



mu

thig

ith

a

(le

pid

otr

ich

ilia

) Le

pid

otr

ich

ilia

vo

lke

nsi

i ^

^^

$

x

x

x

mu

thim

a-m

bu

ri

(Clu

tia

) C

luti

a a

bys

sin

ica

^^

~

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x

^

^

mu

tim

u

lime

C

itru

s a

ura

nti

ifo

lia

^^

~

*

x

^^

mu

tom

oko

/mu

ton

do

me

cu

sta

rd a

pp

le

An

no

na

ch

eri

mo

la ^

^

^^

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~

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mu

ton

gu

so

do

m a

pp

le

So

lan

um

sp

p.

^^

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tow

ero

(m

uto

we

ro)

?

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x

?

?

mu

tun

du

(n

eo

bo

uto

nia

) N

eo

bo

uto

nia

ma

cro

caly

x +

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@

~ x

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x

x x

x

x

mu

tun

gu

ru

cab

ba

ge

tre

e

An

tho

cle

ista

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nd

iflo

ra

^ ^

^ *

~

x

x

x

mu

tura

mu

thi

plu

m

Pru

nu

s d

om

est

ica

^^

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^

mu

tuya

g

ian

t ye

llow

mu

lbe

rr

Myr

ian

thu

s h

ols

tii

^^

~

~

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^^

^^

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u

ma

rkh

am

ia

Ma

rkh

am

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tea

+

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x

x

x x

x

mw

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ba

ine

n

ee

m

Aza

dir

ach

ta i

nd

ica

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~

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x

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yere

(c

uss

on

ia)

Cu

sso

nia

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ica

ta ^

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re/m

ue

rere

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na

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be

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on

tan

a s

tap

fia

na

/ R

au

vo

lfia

caff

ra

^

x

x

x

mw

eth

ia

sesb

an

ia

Se

sba

nia

se

sba

n

+

x

^^

^

^

na

nd

ifla

me

na

nd

ifla

me

/ f

lam

e

tre

e

Sp

ath

od

ea

nil

oti

ca

^ *

@

x

x

x

x

nya

njo

e/k

ari

ari

a^

^

eu

ph

orb

ia

Eu

ph

orb

ia t

iru

call

i *

@

x

x

x

mo

rin

ga

(m

ori

ng

a)

Mo

rin

ga

oli

efe

ra

*

x

x

mu

riru

(c

ord

atu

m)

Sy

zyg

ium

co

rda

tum

*

^^

x

?

mu

ha

tia

(m

ille

ttia

) M

ille

ttia

du

ra

* ^

^

x

?

mu

ba

ge

m

au

riti

us

tho

rn

Ca

esa

lpin

ia d

eca

pe

tala

*

^^

x

91

Tre

e I

de

nti

fica

tio

n

Uti

liti

es

Sci

en

tifi

c N

am

e

timber

building wood/poles

firewood

charcoal

edible fruit/nuts

seeds/seedlings

mulch/manure

cow fodder

goat fodder

sheep fodder

medicines

fence/boundary mark

crop support

soil stabilization

soil fertility/moisture

shade

environmt/bring rains

wildlife

cultural/spiritual

Ca

llis

tem

on

cit

rin

us

o

x

Ca

llia

nd

ra c

alo

thy

rsu

s

x

x

x

? (

git

uth

u)

x

x

? (

ith

uth

i)

o

Jatr

op

ha

cu

rca

s

o

x

Do

vya

lis

caff

ra

x o

x

x

Cyp

ho

ma

nd

ra b

eta

cea

o

x

Leu

cae

na

le

uco

cep

ha

la

x

o

x x

x

x

Mu

sa s

ap

ien

tum

o

x x

x

x

Aco

ka

nth

era

op

po

siti

foli

a

x -

x

x

x

x

x

Ric

inu

s co

mm

un

is

o

x

x

x

Ma

cad

am

ia t

etr

ap

hy

lla

x

o

x -

-

-

x

x -

x

Ca

rica

pa

pa

ya

o

x

x

x

x

Gre

vil

lea

ro

bu

sta

o

o

x

~x

~ -

x

x

x x

Eu

caly

ptu

s sp

p.

o

o

o

~

x

x

- -

x

Psi

diu

m g

ua

jav

a

x

o

x

x

x x

~

Rh

am

nu

s st

ad

do

x

x

Ca

esa

lpin

ia v

olk

en

sii

^^

~

x

x

Jaca

ran

da

mim

osi

foli

a

o

x

x x

Ole

a h

och

ste

tte

ri/w

el.

x

Pin

us

spp

. o

o

-

- x

? (

mu

coro

rom

a)

-

o

x

x

x x

92

Tre

e I

de

nti

fica

tio

n

Uti

liti

es

Sci

en

tifi

c N

am

e

timber

building wood/poles

firewood

charcoal

edible fruit/nuts

seeds/seedlings

mulch/manure

cow fodder

goat fodder

sheep fodder

medicines

fence/boundary mark

crop support

soil stabilization


shade


wildlife

cultural/spiritual

Nu

xia

co

ng

est

a^

^?

?

Ma

ng

ife

ra i

nd

ica

x

x o

x x

x

Eri

ob

otr

ya

ja

po

nic

a

x

o

x

Fic

us

na

tale

nsi

s o

o -

o

x

x

x ~

x x

x

Sa

piu

m e

llip

ticu

m ^

^

~

~

~

Tre

ma

sp

p.^

^

o

x ~

x

~x

x ~

x

Vit

ex

ke

nie

nsi

s ^

^

o

Ery

thri

na

ab

yssi

nic

a

-

o

x

x x

-

Pru

nu

s a

fric

an

a

o

~

o

~

x

x

x

Ha

run

ga

na

ma

da

ga

. ^

^

x x?

~

x

x

~

Do

vya

lis

ab

yss

inic

a ^

^

x

x

~

Lan

tan

a c

am

ara

x

x

x -

x

x

~

Ph

oe

nix

re

clin

ata

o

x

Cro

ton

me

ga

loca

rpu

s

o

~ o

o

~ -

~

-

~ -

~

x x

x

x

~

Sy

zyg

ium

gu

ine

en

se

x

~o

o

x

x

x ~

~

Bri

de

lia

mic

ran

tha

x

o

~x

o?

~

x ~

x

x

x

~

~

Pe

rse

a a

me

rica

na

x

x o

x

x

Ma

cara

ng

a k

ilim

an

dsc

ha

rica

x

~

o

-

x ~

~

Xy

ma

los

mo

no

spo

ra

Ne

wto

nia

bu

cha

na

nn

i

Co

mm

iph

ora

zim

me

rma

nn

ii

x

x

x

Alb

izia

gu

mm

ife

ra

x

x -

?

?

93

Tre

e I

de

nti

fica

tio

n

Uti

liti

es

Sci

en

tifi

c N

am

e

timber

building wood/poles

firewood

charcoal

edible fruit/nuts

seeds/seedlings

mulch/manure

cow fodder

goat fodder

sheep fodder

medicines

fence/boundary mark

crop support

soil stabilization


shade


wildlife

cultural/spiritual

Fic

us

syco

mo

rus

x

x

~

~

x

~ x

~

Mo

rus

alb

a

o

x

Fic

us

lute

a

o

~

x

~ x

Te

cle

a s

pp

.

~

x

~ x

Ek

eb

erg

ia c

ap

en

sis

^^

~

~

Aru

nd

ina

ria

sp

p.

o?

o

x

x

Kig

eli

a a

fric

an

a^

^?

~

x

Aco

ka

nth

era

sch

imp

eri

?

o

Co

rdia

afr

ica

na

o

o

~

x

x

~

x

x x

~ x

~

Tri

chil

ia e

me

tica

x

x

~

~

~

x

~ x

Te

rmin

alia

sp

p?

??

o

x

- x

Jun

ipe

rus

pro

cera

o

x

x

? -

x

Cu

pre

ssu

s sp

p.

x

o

x

?

-

x

Ru

bu

s sp

p.

o

x

Cla

use

na

an

isa

ta ^

^

~x

Ole

a e

uro

pa

ea

va

r. A

fric

.^^

~

x

~

Ca

ssip

ou

rea

sp

p.?

Oco

tea

usa

mb

are

nsi

s o

o -

x

x

Ve

rno

nia

au

ricu

life

ra

x

x

Aca

cia

me

arn

sii

x o

~

o

~

x

x

-

x

Po

do

carp

us

spp

. o

x

Ag

au

ria

sa

lici

folia

x

-

?

94

Tre

e I

de

nti

fica

tio

n

Uti

liti

es

Sci

en

tifi

c N

am

e

timber

building wood/poles

firewood

charcoal

edible fruit/nuts

seeds/seedlings

mulch/manure

cow fodder

goat fodder

sheep fodder

medicines

fence/boundary mark

crop support

soil stabilization


shade


wildlife

cultural/spiritual

Lep

ido

tric

hil

ia v

olk

en

sii

x

Clu

tia

ab

yssi

nic

a ^

^

o

x

~

x

x

Cit

rus

au

ran

tiif

oli

a ^

^

x

o

x

x

An

no

na

ch

eri

mo

la ^

^

o

o

x

So

lan

um

sp

p.

^^

~

x

?

? (

mu

tow

ero

)

Ne

ob

ou

ton

ia m

acr

oca

lyx

o x

~o

x -

~x

x

x

x

x ~

x

~

An

tho

cle

ista

gra

nd

iflo

ra

~

?

x

~

Pru

nu

s d

om

est

ica

^^

x

o

Myr

ian

thu

s h

ols

tii

^^

o

o

x

~

x

x

~ x

~

Ma

rkh

am

ia lu

tea

x

x ~

o

~

~

x ~

x

Aza

dir

ach

ta i

nd

ica

~

x

Cu

sso

nia

sp

ica

ta ^

^

x

x

x

~ x

Ta

be

rna

em

on

tan

a s

tap

fia

na

/Ra

uv

olf

ia c

a.

x

Se

sba

nia

se

sba

n

Sp

ath

od

ea

nil

oti

ca

o

o

x

x

- x

x

Eu

ph

orb

ia t

iru

call

i

x

x

x

Mo

rin

ga

oli

efe

ra

x x

x

x ?

o

x

Sy

zyg

ium

co

rda

tum

x

x

Mil

lett

ia d

ura

Ca

esa

lpin

ia d

eca

pe

tala

x

95

Sci

en

tifi

c N

am

e

com

me

nts

Ca

llis

tem

on

cit

rin

us

oft

en

an

orn

am

en

tal t

ree

alo

ng

th

e r

oa

dsi

de

, so

ld a

s se

ed

lin

gs

fro

m f

arm

nu

rse

rie

s

Ca

llia

nd

ra c

alo

thy

rsu

s ca

n b

e p

lan

ted

wit

h n

ap

ier

gra

ss

? (

git

uth

u)

use

d f

or

tea

pe

gs

? (

ith

uth

i)

see

dlin

gs

cam

be

so

ld

Jatr

op

ha

cu

rca

s re

sea

rch

ed

tre

e,

can

ma

ke b

iod

iese

l, s

ee

ds

sold

at

30

0ks

h/k

g,

ha

rd t

o f

ind

se

ed

lin

gs -

afr

aid

th

at

ne

igh

bo

urs

will

up

roo

t h

is t

ree

s

Do

vya

lis

caff

ra

see

dlin

gs

can

be

so

ld f

or

fen

ces

(liv

e?

)

Cy

ph

om

an

dra

be

tace

a

Leu

cae

na

le

uco

cep

ha

la

can

als

o b

e f

ed

to

pig

s

Mu

sa s

ap

ien

tum

loca

ted

an

ywh

ere

wh

ere

wa

rm e

no

ugh

, st

em

s ca

n b

e c

ut

an

d f

ed

as

fod

de

r to

co

ws,

wit

hst

an

ds

dro

ug

ht,

lea

ves

can

be

bu

rnt

as

fue

l (b

ut

no

t g

oo

d f

ue

l)

Aco

ka

nth

era

op

po

siti

foli

a

can

cu

t sm

all

an

d c

ove

r/so

ak

for

(10

) d

ays

fo

r liq

uid

fe

rtili

zer/

ma

nu

re a

nd

vs

CB

D,

take

n li

ke t

ob

acc

o (

snu

ff in

no

se),

lea

ves

are

go

od

fo

r th

e s

oil,

bu

rns

very

fa

st,

can

fe

ed

to

co

ws

if m

ixe

d w

ith

oth

er

fod

de

r

Ric

inu

s co

mm

un

is

pro

du

ces

oil

wh

ich

fa

rme

rs u

sed

to

be

ab

le t

o s

ell,

ap

pli

ed

to

ski

n a

nd

use

d b

y d

oct

ors

, se

ed

s a

ttra

ck p

ige

on

s a

nd

use

d t

o t

rap

the

m,

sha

de

on

ly s

ea

son

al

Ma

cad

am

ia t

etr

ap

hy

lla

can

ma

ke c

oo

kin

g f

at,

loca

ted

on

up

pe

r p

art

of

farm

?,

are

a s

urr

ou

nd

ing

is n

ot

pro

du

ctiv

e,

sha

de

no

t g

oo

d f

or

foo

d c

rop

s, p

ole

s

use

d t

o p

rop

up

co

ffe

e b

ran

che

s, t

ake

s 5

ye

ars

to

gro

w f

or

fire

wo

od

Ca

rica

pa

pa

ya

st

em

/le

af

juic

e is

use

d a

s m

ed

icin

e f

or

wo

un

ds,

gro

ws

very

fa

st,

fru

it a

ttra

cts

an

d e

ate

n b

y b

ird

s

Gre

vil

lea

ro

bu

sta

inte

rcro

pp

ed

wit

h m

aiz

e,

no

t n

ea

r h

ou

se b

c le

ave

s b

ad

? Le

ave

s a

s co

w f

od

de

r b

ut

on

ly w

he

n d

ry,

lea

ves

mix

ed

wit

h c

ow

ma

nu

re f

or

coff

ee

fe

rtili

zati

on

, m

an

y b

ird

s in

th

is t

ree

Eu

caly

ptu

s sp

p.

farm

ers

ad

vise

d t

o r

em

ove

b/c

hig

h w

ate

r co

nsu

mp

tio

n,

ma

kes

soil

dry

, le

ave

s a

cid

ify

the

so

il, m

ed

icin

e t

o t

rea

t co

lds

in s

om

e

spp

, ca

n b

rea

k a

nd

hu

rt c

off

ee

/cro

ps,

on

ly t

ype

of

wo

od

so

ld t

o b

urn

at

tea

fa

cto

rie

s, w

oo

d u

sed

to

bu

ild

ca

ttle

sta

lls,

he

ron

s

see

n in

th

is t

ree

Psi

diu

m g

ua

jav

a

can

aff

ect

co

ffe

e b

eca

use

ha

s b

lack

an

ts,

an

d if

sp

rayi

ng

co

ffe

e d

on

't w

an

t to

sp

ray

fru

its,

att

ract

s b

ird

s w

hic

h e

at

coff

ee

Rh

am

nu

s st

ad

do

Ca

esa

lpin

ia v

olk

en

sii

^^

ro

ots

of

this

sh

rub

use

d in

so

up

fo

r n

urs

ing

mo

the

rs,

use

d t

o t

rea

t m

ala

ria

, p

ote

nti

al t

o b

e s

old

Jaca

ran

da

mim

osi

foli

a

roo

tin

g s

yste

m b

rin

gs

com

pe

titi

on

so

ca

n in

terf

ere

wit

h c

rop

s, t

imb

er

of

go

od

qu

alit

y ca

n b

e s

old

, u

sed

to

ma

ke s

culp

ture

s

Ole

a

ho

chst

ett

eri

/we

lwit

sch

ii

Pin

us

spp

.

no

t p

lan

ted

wit

h c

off

ee

be

cau

se m

ake

s a

ir c

oo

l, n

oth

ing

gro

ws

aro

un

d,

ne

ed

les

ma

ke g

rou

nd

slip

pe

ry,

go

od

fu

elw

oo

d b

ut

no

t

for

coo

kin

g b

c sm

ell

? (

mu

coro

rom

a)

use

d t

o m

ark

bo

un

da

ry,

for

fen

cin

g, m

ed

icin

e t

o m

ake

blo

od

clo

t, w

oo

d v

ery

so

ft s

o n

ot

no

rma

lly b

urn

ed

, se

ed

lin

gs

sold

fo

r

fen

cin

g

96

Sci

en

tifi

c N

am

e

com

me

nts

Nu

xia

co

ng

est

a^

^?

?

Ma

ng

ife

ra i

nd

ica

too

larg

e t

o b

e w

ith

co

ffe

e,

sha

de

ta

kes

mu

ch r

oo

m,

bre

aks

an

d h

arm

s co

ffe

e,

can

ma

ke g

oo

d c

ha

rco

al,

wo

od

bu

rns

we

ll,

lea

ves

fed

to

co

ws

du

rin

g d

ry s

ea

son

Eri

ob

otr

ya

ja

po

nic

a

no

t p

lan

ted

wit

h c

off

ee

bc

bri

ng

s in

sect

s a

nd

no

t e

no

ug

h s

ha

de

, a

ttra

cts

bir

ds

wh

ich

ea

t co

ffe

e

Fic

us

na

tale

nsi

s

no

rma

lly a

lon

g r

ive

r b

an

ks,

tra

dit

ion

all

y w

ith

re

ligio

us

imp

lica

tio

ns,

ve

ry g

oo

d w

ith

co

ffe

e,

rais

e w

ate

r ta

ble

, w

ith

ma

ny

bir

ds,

ba

rk b

oile

d t

o t

rea

t co

ld,

sacr

ed

tre

e,

orn

am

en

tal (

be

au

tifu

l),

do

esn

't in

terf

ere

mu

ch w

ith

cro

ps

bu

t ve

ry la

rge

tre

e

Sa

piu

m e

llip

ticu

m ^

^

cut

do

wn

his

tori

cally

be

cau

se h

as

few

use

s

Tre

ma

sp

p.^

^

occ

ati

on

ally

so

ld a

s fi

rew

oo

d,

fed

to

go

ats

, ca

n f

ee

d t

o c

ow

s if

mix

ed

wit

h o

the

r fo

dd

er

Vit

ex

ke

nie

nsi

s ^

^

tim

be

r so

ld

Ery

thri

na

ab

yssi

nic

a

as

sup

po

rt t

o s

ug

ar

can

e,

me

dic

ine

ma

de

fro

m b

ark

, u

sed

to

co

nst

ruct

be

e h

ive

s, t

rap

ma

teri

al,

use

d t

o m

ake

scu

lptu

res,

ha

s

sho

rt r

oo

ts,

dro

ps

lea

ves,

see

dlin

gs

sold

Pru

nu

s a

fric

an

a

gro

ws

very

slo

wly

, sh

ad

e t

ree

, u

sed

to

co

nst

ruct

be

e h

ive

s, t

imb

er

very

ha

rd a

nd

va

lua

ble

, u

sed

to

ma

ke b

rid

ges,

ext

ract

s to

ma

ke A

RV

s a

nd

to

tre

at

sto

ma

ch u

lce

rs,

att

ract

s b

ird

s w

hic

h e

at

coff

ee

Ha

run

ga

na

ma

da

ga

sca

rie

nsi

s

^^

a

ttra

cts

bla

ck a

nts

Do

vya

lis

ab

yssi

nic

a ^

^

very

sim

ilar

wit

h k

aiy

ab

a (

rela

ted

) a

nd

ha

ve s

am

e p

rop

ert

ies,

ha

s fr

uit

s th

at

are

hig

h in

vit

am

ins

Lan

tan

a c

am

ara

d

rou

ght

resi

sta

nt,

wo

od

use

d t

o m

ake

tra

dit

ion

al g

ran

ery

, ca

n b

e f

ed

to

co

ws

an

d m

ake

s th

en

pro

du

ce m

ore

milk

Ph

oe

nix

re

clin

ata

u

sed

fo

r P

alm

Su

nd

ay,

se

ed

lings

so

ld

Cro

ton

me

ga

loca

rpu

s

to s

up

po

rt y

am

s, m

an

y b

ird

s n

est

s, s

ha

de

is n

ot

go

od

? C

an

ge

t b

iod

iese

l fro

m s

ee

ds,

inte

refe

ren

ce w

ith

cro

ps

be

cau

se s

ha

de

an

d r

oo

ts c

om

pe

te

Sy

zyg

ium

gu

ine

en

se

pro

vid

e s

ha

de

, lo

cate

d a

lon

g r

oa

ds,

se

ed

s a

re e

ate

n 'm

ati

nd

a'?

Bri

de

lia

mic

ran

tha

pro

vid

e s

ha

de

, re

sist

an

t to

te

rmit

e a

tta

ck,

go

od

bu

ildin

g w

oo

d a

nd

fo

r fe

nce

po

les

bc

last

s lo

ng,

re

mo

ved

fro

m f

arm

bc

att

ract

ing

bo

rin

g i

nse

ct,

no

t g

oo

d w

ith

co

ffe

e b

c to

o b

ig a

nd

ro

ots

co

mp

ete

, d

rie

s th

e s

oil

Pe

rse

a a

me

rica

na

ha

s th

orn

s, n

ot

ne

ar

ho

use

be

cau

se le

ave

s ru

st r

oo

f, t

oo

mu

ch s

ha

din

g f

or

cro

ps,

lea

ves

an

d f

ruit

ca

n b

e f

ed

to

co

ws,

ca

n b

e

ba

d f

or

coff

ee

un

less

pro

pe

rly

spa

ced

an

d m

an

ag

ed

Ma

cara

ng

a k

ilim

an

dsc

ha

rica

o

nly

in

fo

rest

s, (

no

t o

n f

arm

s?),

wo

od

to

o s

oft

fo

r ch

arc

oa

l pro

du

ctio

n,

tre

e s

pre

ad

s in

to n

ea

rby

cro

ps

Xy

ma

los

mo

no

spo

ra

lea

ves

use

d a

s sa

nd

pa

pe

r

Ne

wto

nia

bu

cha

na

nn

i

Co

mm

iph

ora

zim

me

rma

nn

ii

to s

up

po

rt y

am

s (p

lan

ted

ne

ar

pla

nts

to

be

su

pp

ort

ed

) b

ut

no

t e

no

ug

h s

ha

de

^^

fo

r m

ole

tra

ps,

no

t p

lan

ted

wit

h c

off

ee

bc

att

ract

s b

lack

an

ts

Alb

izia

gu

mm

ife

ra

Wo

od

to

o l

igh

t fo

r ch

arc

oa

l pro

du

ctio

n,

use

d f

or

tim

be

r a

nd

ba

rk u

sed

fo

r w

ash

ing

be

cau

se f

oa

ms,

inte

rfe

res

wit

h c

rop

s

97

Sci

en

tifi

c N

am

e

com

me

nts

Fic

us

syco

mo

rus

very

go

od

wit

h c

off

ee

or

too

big

to

be

wit

h c

off

ee

, d

oe

sn't

bre

ak

on

co

ffe

e,

she

ds

lea

ves

as

mu

lch

, re

tain

s w

ate

r in

so

il, j

uic

e

pro

du

ced

is

wh

ite

an

d t

urn

s re

d,

use

d a

s p

ain

kill

er

for

de

nta

l wo

rk,

be

st a

s w

ind

bre

ak

bc

spre

ad

ing

Mo

rus

alb

a

gre

en

s a

re a

lso

ed

ible

Fic

us

lute

a

fire

wo

od

ma

y b

e s

old

aft

er

a lo

ng

tim

e,

gro

ws

we

ll w

ith

ma

ize

, g

oo

d a

s w

ind

bre

ak

Te

cle

a s

pp

. fo

rest

tre

e,

no

t o

n f

arm

s

Ek

eb

erg

ia c

ap

en

sis

^^

n

ot

go

od

wit

h c

off

ee

, g

row

s to

o s

low

ly

Aru

nd

ina

ria

sp

p.

use

d f

or

bu

ild

ing

/fe

nci

ng

an

d s

pre

ad

s ve

ry f

ar,

no

t w

ith

co

ffe

e b

c sh

ad

e t

oo

mu

ch,

use

d t

o c

lea

n t

ee

th,

gro

ws

very

qu

ickl

y

Kig

eli

a a

fric

an

a^

^?

for

me

dic

ine

an

d f

or

loca

l bre

w (

alc

oh

ol)

wh

ich

ca

n b

e s

old

, h

as

few

lea

ves,

fru

it is

po

iso

no

us,

re

mo

ved

fro

m f

arm

bc

att

ract

s

bo

rin

g i

nse

ct

Aco

ka

nth

era

sch

imp

eri

?

tim

be

r so

ld,

a v

ery

go

od

tre

e in

ge

ne

ral

Co

rdia

afr

ica

na

rais

es

wat

er

tab

le,

tim

be

r so

ld,

a v

ery

go

od

tre

e in

ge

ne

ral,

ve

ry g

oo

d w

ith

co

ffe

e>

, co

ffe

e a

rou

nd

th

is t

ree

ha

s le

ss t

hri

ps

an

d

lea

f ru

st

Tri

chil

ia e

me

tica

ve

ry b

ig a

nd

wid

e,

no

t n

ea

r th

e h

ou

se

Te

rmin

ali

a s

pp

??

?

dri

es

the

so

il, s

ee

dlin

gs

sold

Jun

ipe

rus

pro

cera

in

fest

ed

by

dis

ea

ses

(no

t re

pla

nte

d in

so

me

ca

ses)

, u

sed

fo

r fe

nce

po

sts

bu

t n

ot

live

fe

nce

, e

xpe

nsi

ve t

imb

er,

dri

es

lan

d?

Cu

pre

ssu

s sp

p.

Use

d f

or

live

fe

nce

, e

xpe

nsi

ve t

imb

er,

dri

es

lan

d?

, ca

n b

e b

urn

ed

as

fue

l wo

od

Ru

bu

s sp

p.

ind

on

e t

ho

rny,

are

ind

ige

no

us

(re

pro

) a

nd

an

exo

tic

(pla

nte

d)

vari

eti

es,

fru

it c

an

be

so

ld if

exo

tic

vari

ety

Cla

use

na

an

isa

ta ^

^

wo

od

use

d t

o b

ea

t ch

ildre

n a

t sc

ho

ol!

use

d t

rad

itio

na

lly t

o m

ake

wa

lkin

g st

icks

, le

ave

s u

sed

as

ha

nke

rch

iefs

Ole

a e

uro

pa

ea

va

r. a

fric

an

a

^^

w

oo

d u

sed

to

cle

an

co

nta

ine

rs,

smo

ke u

sed

to

so

ur

milk

Ca

ssip

ou

rea

sp

p.?

Oco

tea

usa

mb

are

nsi

s n

ot

bu

rne

d b

eca

use

sm

oke

po

iso

no

us,

fo

rest

tre

e n

ot

on

fa

rms,

pro

du

ces

ha

rdw

oo

d,

very

larg

e t

ree

Ve

rno

nia

au

ricu

life

ra

use

d in

ce

rem

on

y fo

r gi

rls'

cir

cum

cisi

on

Aca

cia

me

arn

sii

use

d f

or

bu

ild

ing

, d

rie

s th

e s

oil,

ro

oti

ng

sys

tem

co

mp

ete

s, p

ole

s u

sed

to

pro

p u

p c

off

ee

bra

nch

es,

pu

lls t

he

ra

in

Po

do

carp

us

spp

. e

xpe

nsi

ve t

imb

er

Ag

au

ria

sa

lici

foli

a

po

iso

no

us

pla

nt,

if g

oa

ts e

at

it t

he

y d

ie

98

Sci

en

tifi

c N

am

e

com

me

nts

Lep

ido

tric

hil

ia v

olk

en

sii

can

no

t b

urn

be

cau

se s

mo

ke p

ois

on

ou

s

Clu

tia

ab

yssi

nic

a ^

^

Cit

rus

au

ran

tiif

oli

a ^

^

fru

it s

old

An

no

na

ch

eri

mo

la ^

^

the

sm

ell

of

the

flo

we

rs r

ep

els

flie

s, s

old

as

fire

wo

od

, fr

uit

no

t so

ld m

uch

wh

ich

is a

mis

sed

op

po

rtu

nit

y b

c fr

uit

swe

et

So

lan

um

sp

p.

^^

a

me

dic

ina

l sh

rub

, u

sed

to

so

oth

ach

es,

on

ly o

ccu

rs w

he

re s

oil

fert

ile,

sme

lls g

oo

d li

ke m

ato

ke

? (

mu

tow

ero

) h

as

a s

we

et

sme

ll

Ne

ob

ou

ton

ia m

acr

oca

lyx

no

t u

sed

as

tim

be

r b

eca

use

ho

llow

, ti

mb

er

som

eti

me

s so

ld?

, fr

uit

use

d a

s m

ed

icin

e,

pio

ne

er

spp

, la

te m

atu

rity

,

fod

de

r fo

r g

oa

ts o

nly

, m

ed

icin

al i

n t

ha

t is

clo

ts b

loo

d,

att

ract

s b

lack

an

ts s

o n

ot

pla

nte

d w

ith

co

ffe

e

An

tho

cle

ista

gra

nd

iflo

ra

ea

rly

ma

turi

ty

Pru

nu

s d

om

est

ica

^^

Myr

ian

thu

s h

ols

tii

^^

n

ot

a g

oo

d s

ha

pe

fo

r sh

ad

e,

can

be

fe

d t

o g

oa

ts d

uri

ng

dry

se

aso

n

Ma

rkh

am

ia lu

tea

to

o la

rge

to

be

gro

wn

wit

h c

off

ee

>

Aza

dir

ach

ta i

nd

ica

Cu

sso

nia

sp

ica

ta ^

^

fed

to

go

ats

wh

en

dry

Ta

be

rna

em

on

tan

a s

tap

fia

na

/Ra

uv

olf

ia

caff

ra

roo

ts u

sed

fo

r lo

cal b

ee

r, h

as

a m

ilky

po

iso

n a

nd

pe

op

le f

ea

r u

sin

g i

t, t

wo

tre

es

wit

h t

he

sa

me

na

me

^^

Se

sba

nia

se

sba

n

Sp

ath

od

ea

nil

oti

ca

dis

tro

ys t

he

so

il? t

imb

er

can

be

so

ld,

go

ats

will

ea

t th

e b

ark

, co

ws

will

ea

t le

ave

s w

he

n it

s d

ry,

wa

nt

to p

lan

t n

ea

r

rive

r

Eu

ph

orb

ia t

iru

call

i

Mo

rin

ga

oli

efe

ra

ext

rem

ely

go

od

nu

trit

ion

ally

, g

rea

t a

s fo

dd

er,

gro

ws

qu

ickl

y, le

ave

s ca

n b

e d

rie

d a

nd

fe

d t

o r

ab

bit

s

Sy

zyg

ium

co

rda

tum

ca

n g

row

rig

ht

ne

ar

wa

ter

an

d d

oe

sn't

wa

sh a

wa

y, le

ave

s lo

ok

like

eu

caly

ptu

s a

nd

ca

n b

e f

ed

to

co

ws

Mill

ett

ia d

ura

Ca

esa

lpin

ia d

eca

pe

tala

se

en

in f

en

ce a

nd

ha

d v

ery

lon

g p

od

s

99

Appendix D – Pairwise Ranking of Tree Utilities

* Farmers assumed that we were only talking about potential utilities of indigenous trees on farms

** When the findings of the first utility ranking were explained the farmer added fodder and said it

would be at the bottom

*** In the case of the three-way tie, a score of 7 was given to each (even if the information from the

Ngutu factory was omitted, the order of the top 5 would be the same)

Utility score a score b score c Sum Rank

income 7 8 9 24 1

firewood 7 6 7 20 2

food/fruit 4 7 8 19 3

env/rains 8 9 17 4

shade 2 2 6 10 5

medicine 9 9 6

fodder 7 1 8 7

building 5 3 8 7

mulch 3 2 5 9

timber 1 4 5 9

windbreak 5 5 9

soil fert 4 4 12

prev insec 3 3 13

RankRankRankRank Ngutu Factory Ngutu Factory Ngutu Factory Ngutu Factory

FGD*FGD*FGD*FGD* (a)(a)(a)(a) Julius Mongai Julius Mongai Julius Mongai Julius Mongai MukuhaMukuhaMukuhaMukuha (b)(b)(b)(b)

Samuel & Jane Samuel & Jane Samuel & Jane Samuel & Jane KaruruKaruruKaruruKaruru (c)(c)(c)(c) SSSScorecorecorecore

1 Medicine Environment/Rains Income 9

2 Environment/Rains Income Food/Fruit 8

3 Fodder (3) Food/Fruit Firewood 7 ***

4 Firewood (3) Firewood Shade 6

5 Income (3) Building Wood Windbreak 5

6 Food/Fruit Soil Fertility Timber 4

7 Mulch Preventing Insects Building Wood 3

8 Shade Shade Mulch 2

9 Timber - Fodder ** 1

Farmers were asked to

identify WHY they have

trees on farms, and then

they were asked to rank

the identified utilities

through pairwise ranking

in a table.

The scores from the three sources in

the previous table were summed

and the resulting overall scores

were ranked.

100

Appendix E – Ranking/Scoring Sheets (sample)

Scale: VG G A B VB N/A ?

Tree Identification Income Firewood Food/Fruit Shade Fodder Env

Local Name Scientific Name

Pro

fita

bil

ity

Bu

rn Q

ua

liti

es

Ea

rly

Ma

turi

ty

Ty

pe

of

Fo

od

Qu

an

tity

of

Fo

od

Sh

ap

e o

f C

an

op

y

Min

imu

m C

rop

In

terf

ere

nce

Co

w P

ala

tab

ilit

y

Qu

an

tity

of

Fo

dd

er

En

vir

on

me

nt

/ b

rin

gin

g t

he

ra

in

bottlebrush tree Callistemon citrinus

calliandra Calliandra calothyrsus

gituthu (gituthu)

ithuthi (ithuthi)

jatropha Jatropha curcas

kaiyaba Dovyalis caffra

kanyanja Thunbergia alata

kanyondore ~/tree tomato Cyphomandra betacea

kiruma Aloe spp.

leucaena Leucaena leucocephal.

marigu Musa sapientum

maruru/kiururu/mururu Acokanthera oppositif.

mbariki/bariki/mwariki Ricinus communis

mbegu cia maguta/mukand. Macadamia tetraphyl.

mubabai Carica papaya

mubariti/mukima Grevillea robusta

mubau Eucalyptus spp.

mubera/mbera Psidium guajava

mubura Rhamnus staddo

mubuthi Caesalpinia volkensii

mucakaranda Jacaranda mimosifolia

mucharage/mutukuyu^^ Olea welwitschii

mucinda-nugu Pinus patula(?)

mucororoma (mucororoma)

mucoruo/mucorui^^? Nuxia congesta^^??

muembe/mwiembe Mangifera indica

mugagati/mubera/murungati Eriobotrya japonica

mugumo Ficus natalensis

muhathi Sapium ellipticum ^^

muhethu Trema orientalis ^^

muhuru Vitex keniensis?

muhuti Erythrina abyssinica

muiri Prunus africana

muitathua Harungana madagasc.

mukambura Dovyalis abyssinica ^^

mukigi/jaji/karurina Lantana camara

mukindu Phoenix reclinata

mukinduri/muthiduri Croton spp

101

Appendix F – Feedback Session Outline

Introduction to Topics:

· What will be covered

· Welcome farmers to tell us if they disagree/agree

· Interviewee stats:

· Interviewed 31 people (24 with farmers on their farm) · 10 people re-interviewed · 5 people re-interviewed by telephone

· 1 FGD at Ngutu Factory with about 30-40 farmers · 2 Feedback sessions: Muruka and Ngutu factory · Information booklet (with the info from today) will be distributed to all factories

Main Problems:

· Unstable price of coffee (out of farmers’ control) · Increasing cost of coffee inputs and decreasing availability which decreases coffee prod. and quality · Increasingly small size of farms (inheritance system) · Changing climate: more dry · Poor administration in some societies

Impacts on Coffee Productivity:

· Improving productivity: · Proper pruning, improved soil fertility, amount of fertilizer/manure application, shade trees

· Decreasing productivity: · Insufficient rain, dew from shade trees, shade too high, maize with coffee, cold temperature · Decreased farm productivity � can afford fewer inputs � less coffee � less profit to farm

· Inputs – increasing in cost · Diff between Ruiru 11 and SL varieties

· Ruiru 11 more resistant to CBD, but less dense and less bold flavour · Alternatives: some farmers using manure (but need livestock) and planting/grafting Ruiru 11,

mulching… · Fertilizers: increase coffee growth rate and rate of ripening, can make soil acidic after long

· Intercropping · Factory rules often limit what is planted with coffee but don’t monitor · Good for intercropping: beans, desmodian, potato, pigweed, spinach, sukumawiki, swiss

chard, tomatoes, yams · Bad for intercropping: cassava, maize, onions , sugar cane · Ask about crops (these crop were identified as both good and bad for intercropping): napier

grass, pumpkins, banana, sweet potato · Shade

· The majority of farmers believed that shade helps coffee plant and improves coffee · Information from: seminars, field days, agricultural officers, factories, eachother · Some don’t believe in shade and think it is bad

· Benefits: increased coffee plant/plot moisture, protection from sun, increased coffee berry size, increased greenness of plant, increased/no change to coffee productivity, decreased leaf miner abundance, decreased coffee plot air temperature, less thrips

· Problems: if shade too high decreases productivity of coffee and decreases coffee plot temperature, dew from shade trees affects coffee plants,

· Shade amount: maintained with pruning of lower branches, grevillea good at 20 trees/acre or spacing 30m x 30m, other trees 50-60m spacing needed

· Good for Shade: mukondo, mukoigo, mununga, mubariti, muringa, muu, mugumo, mwarobaine, mutundu, muhathi, munderendu

· Bad for shade: murangi, muhuti, mugagati, mucakaranda, mucinda-nugu, mutara mwaka

102

· Ask about shade trees (these trees were identified as both good and bad for shade): marigu, mbegu cia maguta, mubabai, mubau, mubera, muembe, muiri, mukinduri, mukoe, mukuyu, mutuya, mukinduri, mukungugu

Farm Profitability:

· Effects of changing coffee price to farm activities · Dairy gaining popularity on farms since price (26ksh/L) is good

· Ask about dairy cooperatives? · If price of coffee goes down � many would abandon coffee for other crops, milk, profitable

· In past led to increased intercropping, tree planting, subsistence crops · More banana, macadamia, napier grass, maize

· If price increases � some said they would keep shade trees, others said they would cut trees · Trees as source of income

· Beekeeping: need diversity of flowers for good honey �sold · Firewood sold: muthanduku, mubariti, mubau, mucinda_nugu, (mugumo), muhethu, muiri,

mukinduri, mukoe, mukuhakuha, mumbu, mutarakwa, muthaiti, muthima-mburi, mutomoko, mutundu, mutuya, muu, nandiflame,

· Charcoal sold: mukoigo, mukoe, mukinduri, (mugumo) · Timber sold: mubariti, mubau, mucakaranda, mucinda-nugu, nuiri, muringa, mutarakwa,

muthaiti, muthengera, mutundu, nandiflame · Building wood: murangi, muthanduku, muringa, mukoigo, mukinduri, mubau

· Fruit sold: kanyondore, marigu, mbariki, mbegu cia maguta, mubabai, mubera, muembe, mugagati, mukondo, mulberry, mutare, mutimu, mutomoko, muturamuthi, mutuya

· Potential for medicine to be sold

* SODA BREAKSODA BREAKSODA BREAKSODA BREAK [soda and cakes provided][soda and cakes provided][soda and cakes provided][soda and cakes provided]

Tree Utilities on Farms:

· Distribute spreadsheet of all utilities · Most important tree utilities (agreement?)

· All uses listed: shade, soil fertility, preventing insects, firewood, building wood, environmental/bring rains, income, food/fruit, beauty, windbreak, timber, mulching, fodder, medicines

· Most important: income>firewood>food/fruits>env/rains>shade>meds>fodder · Asked which qualities make trees good for each utility

· Scoring of trees for the most important utilities � multipurpose ranking (description of scoring/ranking approach 1)

Recommendations:

· Farmers have a great deal of knowledge about coffee management and the utilities of trees · It is important to learn what farmers know before attempting to improve situation · Find where there are gaps in knowledge and how might be best to improve

· Key areas of disagreement and confusion � organize trainings either through factory or self help gps · Shade of coffee, which trees can be used, effects of shade on coffee, other benefits of trees · Quality of coffee: what impacts it and how price depends on it! Market chain of coffee · Alternatives to expensive inputs? Manure improvement, mulching… · Diversification on farms to safeguard vs. price changes (other options)

· Limited by size of farms – discussion about this problem · Eg) training with agricultural officers through factories – have been successful in some areas

or where self help groups hire agricultural officers for specific trainings · Once farmers have learned about utilities of trees – make trees available through nurseries

· Mugama nurseries – but first trainings! · Farm nursery development for interested farmers (training made available??)

103

Appendix G – Farm Sketches

Figure Figure Figure Figure FFFF –––– 1111.... Legend indicating the meaning of symbols in the farm sketches. (Any other symbols

included in farm sketches are clearly labeled).

104

Figure Figure Figure Figure FFFF –––– 2222.... An electronic representation of the farm sketch by Jeremia Karuga Mitambo of his farm

in Kahuro Division (drawn on 02/07/09).

105

Figure Figure Figure Figure FFFF –––– 3333.... An electronic representation of the farm sketch by Isaack G. Mwangi of his farm in

Kahuro Division (drawn on 02/07/09).

106

Figure Figure Figure Figure FFFF –––– 4. 4. 4. 4. An electronic representation of the farm sketch by Jesse Mwangi Kanyi of the current

state of his farm in Kahuro Division (drawn on 20/07/09).

107

Figure Figure Figure Figure FFFF –––– 5555.... An electronic representation of the farm sketch by Jesse Mwangi Kanyi of the desired

future state of his farm in Kahuro Division (drawn on 20/07/09).

108

Figure Figure Figure Figure FFFF –––– 6666.... An electronic representation of the farm sketch by Julius Mongai Mukuha of his farm in

Kandara Division (drawn on 09/07/09).

109

Figure Figure Figure Figure FFFF –––– 7777.... An electronic representation of the farm sketch by Samuel Mwaura Karuru and Jane

Karuru of their farm in Gatanga Division (drawn on 09/07/09).

110

Figure Figure Figure Figure FFFF –––– 8888.... An electronic representation of the farm sketch by Emily Wanjiku Maina of her father-in-

law’s farm in Mathioya Division. (drawn on 30/06/09).

111

Ap

pe

nd

ix H

– A

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ire

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oo

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ruit

S

ha

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F

od

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r

Sci

en

tifi

c N

am

e

Profitability

Burn Qualities

Early Maturity

Type of Food

Quantity of Food

Shape of Canopy

Minimum Crop Interference

Cow Palatability

Quantity of Fodder*

A

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1.3

3

2

1

2

1.6

7

0

0

0

0

0

0

0

0

Cla

use

na

an

isa

ta

3

0

1

.5

3

0

1

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3

0

1

.5

0

0

0

0

0

0

3

0

1

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3

0

1

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0

0

0

0

0

0

Ole

a A

fric

an

a

3

0

1

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3

4

3

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3

4

3

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0

0

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0

0

0

4

0

2

3

0

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0

0

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0

0

0

Oco

tea

usa

mb

are

nsi

s 4

5

5

4

.67

3

5

4

4

3

1

4

2

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0

0

0

0

0

0

0

0

4

5

2

3

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3

3

2

2

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0

0

0

0

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0

Ve

rno

nia

au

ricu

life

ra

2

0

0

0.6

7

3

2

0

1.6

7

3

5

0

2.6

7

0

0

0

0

0

0

0

0

3

3

2

2.6

7

2

5

2

3

0

0

0

0

0

0

0

0

Aca

cia

me

arn

sii

4

5

5

4.6

7

5

5

5

5

4

1

4

3

0

0

0

0

0

0

0

0

4

3

2

3

1

1

2

1.3

3

0

2

0

0.6

7

3

1

0

1.3

3

Po

do

carp

us

falc

atu

s 4

4

4

4

2

2

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4

4

2

2

0

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0

Clu

tia

ab

yssi

nic

a

2

2

2

2

5

3.5

4

1

2.5

0

0

0

0

0

0

0

5

2.5

1

5

3

0

0

0

4

5

4.5

Cit

rus

au

ran

tiif

oli

a

5

5

5

5

4

3

4

3.6

7

4

1

4

3

4

3

5

4

5

3

4

4

0

4

4

2.6

7

4

4

4

4

0

3

0

1

0

2

0

0.6

7

113

A

nn

on

a c

he

rim

ola

2

2

5

5

1

1

4

4

3

3

4

4

4

4

0

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0

0

Sola

nu

m s

pp

.

0

0

0

0

3

2

0

1.6

7

4

3

0

2.3

3

0

0

0

0

0

0

0

0

1

0

0

0.3

3

1

1

0

0.6

7

0

0

0

0

3

0

0

1

(mu

tow

ero

) 0

0

0

3

2

2

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3

2

2

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0

0

0

0

0

0

4

0

2

3

5

4

0

0

0

0

0

0

Ne

ob

ou

ton

ia m

acr

oc.

4

3

0

2

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4

5

4

4

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3

1

4

2

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0

0

0

0

0

0

0

0

4

5

2

3

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2

5

2

3

0

0

0

0

0

3

4

2

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An

tho

cle

ista

gra

nd

ifl.

1

1

2

2

3

3

0

0

0

0

4

4

3

3

0

0

0

0

Pru

nu

s d

om

est

ica

4

4

4

3

4

3

.5

4

2

3

4

5

4.5

4

5

4.5

4

2

3

3

3

3

0

0

0

0

0

0

My

ria

nth

us

ho

lsti

i

4

4

3

3.6

7

4

4

3

3.6

7

2

1

3

2

3

3

4

3.3

3

3

3

4

3.3

3

4

5

2

3.6

7

2

4

2

2.6

7

0

0

0

0

0

2

0

0.6

7

Ma

rkh

am

ia lu

tea

3

3

0

2

4

4

4

4

3

1

4

2

.67

0

0

0

0

0

0

0

0

4

5

2

3

.67

2

5

2

3

0

0

0

0

0

2

0

0

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Aza

dir

ach

ta i

nd

ica

4

0

0

1

.33

4

2

0

2

3

2

0

1

.67

0

0

0

0

0

0

0

0

4

3

0

2

.33

3

3

0

2

0

0

0

0

0

0

0

0

Cu

sso

nia

sp

ica

ta

3

3

5

5

1

1

0

0

0

0

5

5

4

4

0

0

2

2

Spa

tho

de

a n

iloti

ca

4

5

5

4.6

7

4

5

4

4.3

3

3

2

3

2.6

7

0

0

0

0

0

0

0

0

4

4

3

3.6

7

2

3

3

2.6

7

0

0

0

0

0

2

0

0.6

7

Eu

ph

orb

ia t

iru

call

i

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

Mo

rin

ga

olie

fera

4

4

4

4

5

5

5

5

5

5

5

5

Tre

es

un

kn

ow

n b

y a

ll

3 r

esp

on

de

nts

:

Rh

am

nu

s st

ad

do

Ole

a w

elw

itsc

hii

Nu

xia

co

ng

est

a

Xy

ma

los

mo

no

spo

ra

Ne

wto

nia

bu

cha

na

nn

i

Ek

eb

erg

ia c

ap

en

sis

Aco

ka

nth

era

sch

imp

eri

?

Ca

ssip

ou

rea

sp

p.

Ag

au

ria

sa

lici

foli

a

Lep

ido

tric

hil

ia v

olk

en

sii

Ta

be

rna

em

on

tan

a s

tap

f.

Se

sba

nia

se

sba

n

A t

able

sh

ow

ing t

he a

vera

ge

sco

res

(sh

aded

gre

y) o

f th

e tr

ee s

pecie

s fo

r eac

h u

tili

ty.

Sp

eci

es

shad

ed

in

pin

k ar

e t

ho

se o

nly

kn

ow

n b

y o

ne r

esp

on

de

nt

and

are

th

ere

fore

hig

hly

un

cer

tain

. T

he e

nvi

ron

men

tal u

tili

ty w

as o

mit

ted

becau

se i

t w

as b

eli

eved

to

be t

oo

am

big

uo

us

and

th

ere

fore

in

co

nsi

ste

nt

amo

ng t

he r

esp

on

den

ts.

Th

e t

rees

list

ed t

o t

he left

wer

e o

mit

ted

fro

m t

he

list

as

they

were

un

kno

wn

by

all

thre

e re

spo

nd

en

ts.

Farmers’ Perceptions about the Utilities of Trees Associated with...

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Transcript of Farmers’ Perceptions about the Utilities of Trees Associated with...