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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.
Candidate: (Lindsey Elliott)
Date:
Statement 2: Statement 2: Statement 2: Statement 2:
This dissertation is the result of my own independent work/investigation except where otherwise stated.
Candidate: (Lindsey Elliott)
Date:
Statement 3: Statement 3: Statement 3: Statement 3:
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.
Candidate: (Lindsey Elliott)
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
viii
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.
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
ualities? 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 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
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 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
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 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
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
ualities? 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
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
Based on the information collected from the tree utility ranking, scoring sheets with key
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
for each of the selected tree utility attributes. This was a lengthy process requiring concentration by
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
Qualities
Early
Maturity
Type of
Food
Quantity
of Food
Shape of
Canopy
Minimum
Crop
Interference
Cow
Palatability
Quantity
of Fodder*
Ma
ng
ife
ra i
nd
ica
M
an
gif
era
in
dic
a
Eu
caly
ptu
s sp
p.
Mo
rin
ga
oli
efe
ra*
M
ori
ng
a o
lie
fera
*
Cu
sso
nia
sp
ica
ta*
Ja
tro
ph
a c
urc
as*
Leu
cae
na
leu
coce
ph
ala
*
Leu
cae
na
leu
coce
ph
ala
*
Gre
vil
lea
ro
bu
sta
P
run
us
afr
ica
na
Leu
cae
na
leu
coce
ph
ala
*
Mo
rus
alb
a*
Cy
ph
om
an
dra
be
tace
a
Ma
ng
ife
ra i
nd
ica
G
rev
ille
a r
ob
ust
a
Ca
llia
nd
ra
calo
thyr
sus*
Ca
llia
nd
ra
calo
thyr
sus*
Ma
cad
am
ia
tetr
ap
hy
lla
C
roto
n s
pp
P
ers
ea
am
eri
can
a
Mu
sa s
ap
ien
tum
M
usa
sa
pie
ntu
m
Ma
cad
am
ia
tetr
ap
hy
lla
C
ari
ca p
ap
ay
a
Mo
rin
ga
oli
efe
ra*
M
ori
ng
a o
liefe
ra*
Co
rdia
ab
yssi
nic
a/a
fric
a.
Aca
cia
me
arn
sii
Mo
rin
ga
oli
efe
ra*
C
ari
ca p
ap
ay
a
Ca
rica
pa
pa
ya
G
rev
ille
a r
ob
ust
a
Cu
sso
nia
sp
ica
ta*
M
usa
sa
pie
ntu
m
Clu
tia
ab
yssi
nic
a
Cu
pre
ssu
s
spp
.(cy
pre
ss)
Cu
sso
nia
sp
ica
ta*
G
rev
ille
a r
ob
ust
a
Pru
nu
s d
om
est
ica
P
run
us
do
me
stic
a
Cro
ton
sp
p
An
no
na
che
rim
ola
*
Mo
rus
alb
a*
M
usa
sa
pie
ntu
m
Jun
ipe
rus
pro
cera
An
no
na
che
rim
ola
*
Lan
tan
a c
am
ara
Cy
ph
om
an
dra
be
tace
a
Pe
rse
a a
me
rica
na
P
run
us
afr
ica
na
Cy
ph
om
an
dra
be
tace
a
Gre
vil
lea
ro
bu
sta
La
nta
na
ca
ma
ra
Cit
rus
au
ran
tiif
oli
a
Gre
vil
lea
ro
bu
sta
Bri
de
lia
mic
ran
tha
M
an
gif
era
in
dic
a
Ma
ng
ife
ra i
nd
ica
P
ers
ea
am
eri
can
a
Leu
cae
na
leu
coce
ph
ala
*
Ca
rica
pa
pa
ya
Aco
ka
nth
era
op
po
siti
fo.
Leu
cae
na
leu
coce
ph
ala
*
Eu
caly
ptu
s sp
p.
Eri
ob
otr
ya
jap
on
ica
Ma
cad
am
ia
tetr
ap
hy
lla
Ma
cad
am
ia
tetr
ap
hy
lla
M
oru
s a
lba
*
Cit
rus
au
ran
tiif
oli
a
Pe
rse
a a
me
rica
na
T
rem
a o
rie
nta
lis
Cy
ph
om
an
dra
be
tace
a
Ha
run
ga
na
ma
da
ga
.
Ole
a a
fric
an
a
Pe
rse
a a
me
rica
na
Cit
rus
au
ran
tiif
oli
a
Psi
diu
m g
ua
jav
a
Co
mm
iph
ora
zim
me
rm.
Ma
ng
ife
ra i
nd
ica
M
usa
sa
pie
ntu
m
Fic
us
lute
a
Ma
ng
ife
ra i
nd
ica
Cit
rus
au
ran
tiif
oli
a
Mo
rus
alb
a*
An
no
na
che
rim
ola
*
(mu
tow
ero
)
Ca
rica
pa
pa
ya
Ma
cad
am
ia
tetr
ap
hy
lla
Ma
cad
am
ia
tetr
ap
hy
lla
An
no
na
che
rim
ola
*
An
tho
cle
ista
gra
nd
iflo
.*
Ery
thri
na
ab
yssi
nic
a
Jatr
op
ha
cu
rca
s*
Co
rdia
ab
yssi
nic
a/a
fric
a.
Cro
ton
sp
p
Po
do
carp
us
falc
atu
s*
Ric
inu
s co
mm
un
is
Mo
rus
alb
a*
Cu
pre
ssu
s
spp
.(cy
pre
ss)
Ma
cara
ng
a
kil
ima
nd
sch
*
Mu
sa s
ap
ien
tum
Pru
nu
s a
fric
an
a
Pe
rse
a a
me
rica
na
Ca
llis
tem
on
citr
inu
s
Cro
ton
sp
p
Sp
ath
od
ea
nil
oti
ca
Jatr
op
ha
cu
rca
s*
A
caci
a m
ea
rnsi
i P
inu
s p
atu
la(?
)
E
uca
lyp
tus
spp
. P
sid
ium
gu
aja
va
Sp
ath
od
ea
nil
oti
ca
Pe
rse
a a
me
rica
na
Ne
ob
ou
ton
ia
ma
cro
caly
x
Oco
tea
usa
mb
are
nsi
s
Tab
leT
able
Tab
leT
able
4
4 4
4 – –––
6 666. ... T
he o
rder
of
tree p
refe
ren
ce b
y u
tili
ty a
cc
ord
ing t
o a
vera
ge
sco
res
fro
m
ran
kin
g/s
co
rin
g e
xerc
ise 1
. S
had
ing i
nd
icat
es t
ies
betw
een
sim
ilar
ly s
had
ed
tre
es.
* in
dic
ates
trees
on
ly s
co
red
by
1 re
spo
nd
en
t (t
here
fore
hig
hly
un
cer
tain
).
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
Ra
nk
ing
/Sco
rin
g A
pp
roa
ch 2
:
Fir
ew
oo
d^
B
urn
Tim
e
Wo
od
Gro
wth
S
ha
de
^
Cro
wn
Co
ve
r C
row
n D
en
sity
Tre
e
Kik
uy
u n
am
e
Av
g R
an
k
St
De
v
Av
g R
an
k
St
De
v
Av
g R
an
k
St
De
v
Av
g R
an
k
St
De
v
Av
g R
an
k
St
De
v
Av
g R
an
k
St
De
v
Ca
rica
pa
pa
ya
m
ub
ab
ai
9.2
5
0.5
0
9.0
0
0.0
0
7.0
0
2.8
3
5.5
0
1.9
1
8.0
0
2.1
6
6.5
0
1.9
1
Co
mm
iph
ora
zim
me
rma
nn
ii
mu
kun
gu
gu
8
.00
0
.82
7
.75
1
.26
5
.25
2
.99
7
.00
1
.41
7
.75
0
.96
7
.25
1
.50
Cro
ton
me
ga
loca
rpu
s
mu
kin
du
ri
1.0
0
0.0
0
1.0
0
0.0
0
6.0
0
2.3
1
7.7
5
0.9
6
6.2
5
2.5
0
4.7
5
2.2
2
Eu
caly
ptu
s sp
p.
m
ub
au
6
.00
1
.63
5
.75
2
.06
2
.00
0
.82
8
.00
1
.41
7
.25
0
.96
7
.75
0
.96
Gre
vil
lea
ro
bu
sta
m
ub
ari
ti
3.7
5
0.9
6
4.5
0
0.5
8
1.5
0
0.5
8
1.2
5
0.5
0
4.0
0
1.4
1
8.0
0
1.8
3
Ma
cad
am
ia t
etr
ap
hy
lla
m
uka
nd
an
ia
3.7
5
1.8
9
3.7
5
0.9
6
6.0
0
0.8
2
2.0
0
0.8
2
4.0
0
1.4
1
2.7
5
2.0
6
Pe
rse
a A
me
rica
na
m
uko
nd
o
4.5
0
1.2
9
4.5
0
1.2
9
3.2
5
0.5
0
4.0
0
1.1
5
3.0
0
2.0
0
2.2
5
0.5
0
Pru
nu
s A
fric
an
a
mu
iri
1.7
5
0.5
0
1.7
5
0.5
0
7.7
5
1.8
9
4.0
0
2.1
6
4.2
5
0.9
6
3.0
0
0.8
2
Sy
zyg
ium
gu
ine
en
se
mu
koe
7
.00
*
7
.00
*
5
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1.0
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(a
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leT
able
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4
4 4
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7 777. ... A
co
mp
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on
of
the f
ind
ings
fro
m r
anki
ng/s
co
rin
g a
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h 1
(b
elo
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and
2 (
abo
ve).
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ks a
re f
rom
1 (
best
) to
10
(wo
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fo
r p
rio
rity
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kin
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(in
dic
ated
wit
h ^
) an
d
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(lo
nge
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to 1
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t) f
or
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m 1
(fa
stest
) to
10 (
slo
west
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th,
fro
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(la
rgest
are
a) t
o 1
0 (
smal
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are
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red
by
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) to
10 (
leas
t d
en
se –
mo
st lig
ht
pas
sin
g t
hro
ugh
) cro
wn
.
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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1
Pe
ter
Mb
uru
Ru
be
n
+ ^
1
1
Sa
mu
el
Mw
au
ra K
aru
ru &
Ja
ne
Ka
ruru
+
@ %
1
1
Sta
nle
y G
aku
re
1
Te
risa
Wa
ng
eci
+
1
1
Wils
on
Mw
an
gi K
ari
uki
+
^
1
1
Rw
aik
am
ba
So
cie
ty F
arm
ers
%
Fe
ed
ba
ck S
ess
ion
Ng
utu
Fa
cto
ry
Fe
ed
ba
ck S
ess
ion
Mu
ruka
Fa
cto
ry
TO
TA
L
2
9
13
3
6
9
8
1
2
Tab
le L
eg
end
+
farm
vis
ited
*
tr
ee r
anki
ng
#
tree
sco
rin
g
@
farm
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
ati
on
Co
de
<
10
0
10
0-3
99
4
00
-69
9
70
0-9
99
1
00
0+
o
wn
er
wo
rke
r fa
mil
y
oth
er
Be
rna
rd N
gu
gi K
ara
nu
+
^
1
1
Be
th W
an
ga
ri K
iru
bi
~
1
Ce
cilia
Wa
rim
u a
nd
Fra
nci
s N
go
ne
+
1
1
1
Ch
air
ma
n G
ate
mb
ura
1
Da
nie
l Bu
rug
ath
ii +
1
1
Em
ily W
an
jiku
Ma
ina
+
@ ^
1
1
Est
er
Kim
an
i +
~
1
1
Eu
nic
e N
ya
gu
thii
an
d E
lisp
he
r W
an
jiru
+
1
Fra
nci
s M
uh
ihi M
bir
a
+ ^
1
1
Ida
h W
an
jira
Ka
ma
u
+
1
1
Isa
ack
G M
wa
ng
i +
@
1
1
Jam
es
(an
d M
rs.)
Mb
uru
Kim
an
i +
*
1
1
1
Jere
mia
Ka
rug
a M
ita
mb
o
+ @
1
1
Jess
e M
wa
ng
i K
an
yi
+
@ #
1
1
Joh
n M
uch
ek
e C
he
ge
+
1
1
Joh
n M
wa
ng
i +
1
1
Joh
n N
go
yan
ae
1
Joh
n (
an
d M
rs.)
Wa
nyo
ike
*
1
1
Tab
le L
egen
d
+
farm
vis
ited
*
tr
ee
ran
kin
g
#
tree
sco
rin
g
@
farm
ske
tch
%
pai
rwis
e
uti
lity
ran
kin
g
^
tele
ph
on
e
inte
rvie
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
e t
ree
nu
mb
er
occ
up
ati
on
Co
de
<
10
0
10
0-3
99
4
00
-69
9
70
0-9
99
1
00
0+
o
wn
er
wo
rke
r fa
mil
y
oth
er
Joh
n W
aw
eru
Ka
riu
ki
+
#
1
1
Joh
nso
n G
ich
oya
1
Jose
ph
Kim
an
i
*
1
Jose
ph
Mu
kuri
a
+
1
1
Josh
ua
Wa
mb
a
+ ~
1
1
Juli
us
Mo
ng
ai
Mu
ku
ha
+
@ %
1
1
Ka
ran
i M
uro
ro
#
1
Mr.
Njo
roge
~
1
Nju
gu
na
+
1
1
Pa
ul (
an
d J
an
e)
Ka
riu
ki
+ *
1
1
1
Pe
ter
Mb
uru
Ru
be
n
+ ^
1
1
Sa
mu
el
Mw
au
ra K
aru
ru &
Ja
ne
Ka
ruru
+
@ %
1
1
Sta
nle
y G
aku
re
1
1
Te
risa
Wa
ng
eci
+
1
1
Wils
on
Mw
an
gi K
ari
uki
+
^
1
1
Rw
aik
am
ba
So
cie
ty F
arm
ers
%
1
1
1
Fe
ed
ba
ck S
ess
ion
Ng
utu
Fa
cto
ry
1
1
1
Fe
ed
ba
ck S
ess
ion
Mu
ruka
Fa
cto
ry
1
1
1
TO
TA
L
1
7
4
4
8
27
4
6
9
Tab
le L
egen
d
+
farm
vis
ited
*
tree
ran
kin
g
#
tree
sco
rin
g
@
farm
ske
tch
%
pai
rwis
e
uti
lity
ran
kin
g
^
tele
ph
on
e
inte
rvie
w
~
spre
adsh
eet
re
view
ed
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
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
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
) ?
*
x
x
x
ith
uth
i p
alm
??
(it
hu
thi)
?
#
* @
?
?
x
jatr
op
ha
ja
tro
ph
a
Jatr
op
ha
cu
rca
s +
*
x
x
x
kaiy
ab
a
key
ap
ple
D
ov
yali
s ca
ffra
^
^ *
@
x
x
kan
yon
do
re ~
/tre
e t
om
ato
tr
ee
to
ma
to
Cy
ph
om
an
dra
be
tace
a
^ *
@
x
x
leu
cae
na
le
uca
en
a
Leu
cae
na
le
uco
cep
ha
la
+ *
@
x
x
x
ma
rig
u
ba
na
na
(ir
igu
) M
usa
sa
pie
ntu
m
+ *
@
x
x
x x
x
x x
ma
ruru
/kiu
ruru
/mu
ruru
(a
coka
nth
era
) A
cok
an
the
ra o
pp
osi
tifo
lia
^
^ *
@
x
x
x
x
mb
ari
ki/b
ari
ki/m
wa
riki
ca
sto
r R
icin
us
com
mu
nis
+
^^
* @
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
+ ^
^ *
@
~ x
x
x -
x
x
x
x
mu
ba
ba
i p
ap
aya
C
ari
ca p
ap
ay
a
* @
x
-
x
x
x
x x
mu
ba
riti
/mu
kim
a
gre
ville
a
Gre
vil
lea
ro
bu
sta
+
*
x
x x
x -
x
x
x
x x
mu
ba
u
blu
eg
um
E
uca
lyp
tus
spp
. +
* @
~
-
~
-
x
x
x x
x
x x
mu
be
ra/m
be
ra
gu
ava
P
sid
ium
gu
aja
va
*
@
~ -
x
x
x
x
x
mu
bu
ra
(rh
amn
us)
R
ha
mn
us
sta
dd
o
* ^
^
x
mu
bu
thi
(Ca
esa
lpin
ia)
Ca
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
/mu
tuku
yu^
^
elg
on
te
ak
Ole
a h
och
ste
tte
ri/w
elw
itsc
hii
^
^ $
x
x
mu
cin
da
-nu
gu
p
ine
P
inu
s sp
p.
* @
-
x
x
mu
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
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
natural regeneration
existed when arrived
mu
coru
o/m
uco
rui^
^?
?
N
uxi
a c
on
ge
sta
^^
??
~
~
mu
em
be
/mw
iem
be
m
an
go
M
an
gif
era
in
dic
a
+ ^
^ *
@
~ x
-
>
x
x
mu
ga
ga
ti/m
ub
era
/mu
run
ga
ti ^
^
(eri
ob
otr
ya)
Eri
ob
otr
ya
ja
po
nic
a
# ^
^ *
@
-
x
x
mu
gu
mo
n
ata
l fig
F
icu
s n
ata
len
sis
+ *
x
x
-
x x
x
x
mu
ha
thi
(sa
piu
m)
Sa
piu
m e
llip
ticu
m ^
^
~
~
^
^
^^
mu
he
thu
(t
rem
a)
Tre
ma
sp
p.^
^
$ *
^^
mu
hu
ru
(vit
ex)
V
ite
x k
en
ien
sis
^^
$
*
x
mu
hu
ti
(ery
thri
na
) E
ryth
rin
a a
bys
sin
ica
^
* @
-
x
x x
x
x
x
mu
iri
pru
nu
s P
run
us
afr
ica
na
+
* @
~
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
^^
?
mu
kam
bu
ra
(do
vya
lis)
Do
vya
lis a
by
ssin
ica
^^
~
*
^^
^
^
mu
kig
i/ja
ji/k
aru
rin
a
tick
_b
err
y La
nta
na
ca
ma
ra
^^
* @
x
^
^
mu
kin
du
w
ild d
ate
pa
lm
Ph
oe
nix
re
clin
ata
^
* @
x
x
mu
kin
du
ri/m
uth
idu
ri
cro
ton
C
roto
n m
eg
alo
carp
us
+ *
@
~ x
-
-
x -
x
x
x
mu
koe
w
ate
rbe
rry
Sy
zyg
ium
gu
ine
en
se
+ $
*
x -
x
x
x ^
^
x
mu
koig
o/m
uki
gi/
mu
kun
di
mo
ng
o
(bri
de
lia)
Bri
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
$ ^
*
- x
x
x x
mu
kuh
oku
ho
/mu
ren
de
ti
lem
on
wo
od
X
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
natural regeneration
existed when arrived
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
. +
* @
~
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
?
$
^
^
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
^ *
@
x
x
x
mu
tara
mw
aka
(t
erm
ina
lia)
Te
rmin
ali
a s
pp
??
?
* @
-
x
x
mu
tara
kwa
(ce
dar
) ce
da
r Ju
nip
eru
s p
roce
ra
+ *
x
x
x x
mu
tara
kwa
(cy
pre
ss)
cyp
ress
C
up
ress
us
spp
. +
*
-
x
mu
tare
b
lack
be
rrie
s R
ub
us
spp
. ^
* @
-
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
O
lea
eu
rop
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 +
* @
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
+ *
-
x x
x x
mu
the
ng
era
/po
do
p
od
oca
rpu
s P
od
oca
rpu
s sp
p.
^^
$ *
@
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
natural regeneration
existed when arrived
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
^^
~
*
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 ^
^
^^
* @
~
^^
mu
ton
gu
so
do
m a
pp
le
So
lan
um
sp
p.
^^
~
*
?
?
mu
tow
ero
(m
uto
we
ro)
?
* @
x
?
?
mu
tun
du
(n
eo
bo
uto
nia
) N
eo
bo
uto
nia
ma
cro
caly
x +
# *
@
~ x
-
x
x x
x
x
mu
tun
gu
ru
cab
ba
ge
tre
e
An
tho
cle
ista
gra
nd
iflo
ra
^ ^
^ *
~
x
x
x
mu
tura
mu
thi
plu
m
Pru
nu
s d
om
est
ica
^^
~
^
^
mu
tuya
g
ian
t ye
llow
mu
lbe
rr
Myr
ian
thu
s h
ols
tii
^^
~
~
-
^^
^^
mu
u
ma
rkh
am
ia
Ma
rkh
am
ia lu
tea
+
*
- x
x
x
x x
x
mw
aro
ba
ine
n
ee
m
Aza
dir
ach
ta i
nd
ica
+
* @
~
x
x
mw
en
yere
(c
uss
on
ia)
Cu
sso
nia
sp
ica
ta ^
^
$ ~
?
?
mw
ere
re/m
ue
rere
(t
ab
ern
ae
mo
nta
na
)
Ta
be
rna
em
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
soil fertility/moisture
shade
environmt/bring rains
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
soil fertility/moisture
shade
environmt/bring rains
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
soil fertility/moisture
shade
environmt/bring rains
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
vera
ge
Tre
e U
tili
ty S
co
res
Tre
e I
de
nti
fica
tio
n
Inco
me
F
ire
wo
od
F
oo
d/F
ruit
S
ha
de
F
od
de
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
B
C
a
vg
A
B
C
a
vg
A
B
C
a
vg
A
B
C
a
vg
A
B
C
a
vg
A
B
C
a
vg
A
B
C
a
vg
A
B
C
a
vg
A
B
C
a
vg
Ca
llist
em
on
cit
rin
us
4
0
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4
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2
2
4
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0
0
0
0
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3
5
4
4
3
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4
2
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llia
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us
pa
tula
(?)
4
5
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4
2
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tale
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s 0
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cum
0
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112
Tre
ma
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3
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mm
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3
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3
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4
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2
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4
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rdia
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yss
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4
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rus
pro
cera
5
5
5
5
5
4
3
4
3
3
3
3
0
0
0
0
0
0
0
0
4
3
3
3
.33
2
3
2
2
.33
0
0
0
0
0
0
0
0
Cu
pre
ssu
s sp
p.
5
5
5
5
5
4
4
4.3
3
3
3
3
3
0
0
0
0
0
0
0
0
4
3
2
3
2
3
2
2.3
3
0
0
0
0
0
0
0
0
Ru
bu
s sp
p.
2
3
4
3
2
0
0
0.6
7
3
4
0
2.3
3
4
2
4
3.3
3
4
2
3
3
2
0
2
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
.5
3
0
1
.5
0
0
0
0
0
0
3
0
1
.5
3
0
1
.5
0
0
0
0
0
0
Ole
a A
fric
an
a
3
0
1
.5
3
4
3
.5
3
4
3
.5
0
0
0
0
0
0
4
0
2
3
0
1.5
0
0
0
0
0
0
Oco
tea
usa
mb
are
nsi
s 4
5
5
4
.67
3
5
4
4
3
1
4
2
.67
0
0
0
0
0
0
0
0
4
5
2
3
.67
3
3
2
2
.67
0
0
0
0
0
0
0
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
0
0
0
0
4
4
2
2
0
0
0
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
0
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
.5
3
2
2
.5
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
.33
4
5
4
4
.33
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
3
4
2
.33
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
.67
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.
114