After diffence

ADOPTION AND INTENSITY OF USE OF COFFEE TECHNOLOGY
PACKAGE IN YERGACHEFFE DISTRICT, GEDEO ZONE, SNNP
REGIONAL STATE, ETHIOPIA
M.Sc. Thesis
Aberham Kebedom
June, 2012
Haramaya University
ADOPTION AND INTENSITY OF USE OF COFFEE TECHNOLOGY
PACKAGE IN YERGACHEFFE DISTRICT, GEDEO ZONE, SNNP
REGIONAL STATE, ETHIOPIA
A Thesis Submitted to the College of Agriculture and Environmental
Sciences, School of Graduate Studies, Haramaya University
In Partial Fulfillment of the Requirements for the Degree of
MASTER OF SCIENCE IN RURAL DEVELOPMENT AND
AGRICULTURAL EXTENSION
(Stream: Agricultural Communication and Innovation)
Aberham Kebedom
June, 2012
Haramaya University
SCHOOL OF GRADUATE STUDIES
HARAMAYA UNIVERSITY
As Thesis Research advisors, we hereby certify that we have read and evaluated this thesis
prepared, under our guidance, by Aberham Kebedom, entitled Adoption and Intensity of
use of Coffee Technology Package in Yergacheffe, Gedeo Zone, SNNP Regional State,
Ethiopia. We recommend that it be submitted as fulfilling the Thesis requirement.
Ranjan S. Karippai (Prof.)
Major Advisor
Chinnan. K.P.M (PhD)
Co- Advisor
________________
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As members of the Board of Examiners of the M.Sc. Thesis Open Defense Examination, We
certify that we have read, evaluated the Thesis prepared by Aberham Kebedom and
examined the candidate. We recommend that the Thesis be accepted as fulfilling the Thesis
requirement for the Degree of Master of Science in Rural Development and Agricultural
Extension (Stream :Agricultural Communication and Innovation)
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ii
__________
Date
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Date
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DEDICATION
I dedicated this thesis manuscript to my beloved parents for nursing me with
affections and love and for their dedicated partnership in the success of my life.
iii
STATEMENT OF THE AUTHOR
By my signature below, I declare and affirm that this thesis is my own work. I have followed
all ethical principles of research in the preparation, data collection, data analysis and
completion of this thesis. All scholarly matter that is included in the thesis has been given
recognition through citation. I affirm that I have cited and referenced all sources used in this
document. Every serious effort has been made to avoid any plagiarism in the preparation of
this thesis.
This thesis is submitted in partial fulfillment of the requirement for a degree from the School
of Graduate Studies at Haramaya University.
The thesis is deposited in the Haramaya
University Library and is made available to borrowers under the rules of the library. I
solemnly declare that this thesis has not been submitted to any other institution anywhere for
the award of any academic degree, diploma or certificate.
Brief quotations from this thesis may be used without special permission provided that
accurate and complete acknowledgement of the source is made. Requests for permission for
extended quotations from, or reproduction of, this thesis in whole or in part may be granted
by the Head of the School or Department or the Dean of the School of Graduate Studies when
in his or her judgment the proposed use of the material is in the interest of scholarship. In all
other instances, however, permission must be obtained from the author of the thesis.
Name:
Aberham Kebedom
Signature: __________
Date:
Department: Rural Development and Agricultural Extension
iv
BIOGRAPHIC SKETCH
The author was born to his father Kebedom Darge and his mother Getenesh Mekonnen on
Nov.24, 1980, in East Shewa, Oromia Region, Ethiopia. He attended his Elementary and
Secondary Education in Kutre Sost Primary School. Then, he joined Hawass Comprehensive
Secondary School and completed his secondary education in 2000, and then he joined
Haramaya University, College of Agriculture in 1993 and graduated with B.Sc. degree in
Rural Development and Agricultural Extension in July 2006.
After his graduation, he worked as Extension Communication Expert in SNNP Regional State
Bureau of Agriculture (South-West Bench Maje, Guraferda woreda) for one year (20062007). Later, he worked in Dilla Agricultural Technical Vocational Education and Training
College serving as an instructor for three years until he joined Haramaya University for his
postgraduate study in June 2010.
v
ACKNOWLEDGEMENTS
Above all I would like to thank the Almighty God for His divine help and free gifts in all
aspect of my life not only in this world but for His eternal kingdom too.
I feel great to express my thanks to Prof. Ranjan S. Karippai who put me in the right track of
the research. Successful completion of this study would have been very difficult without his
generous time devotion from the inception of the research idea till the final write up of the
thesis through adding his constructive and extremely useful comments. I am again thankful to
my Co-advisor, Dr. Chinnan. K.P.M for his willingness to advise me as well as his valuable
comments and suggestions throughout my research work.
I would like to express my sincere gratitude to Dilla Agricultural Technical Vocational
Education and Training College funding tuition fee and my research and other expenses and
the entire staff member of Dilla ATVET College. And also remain too thankful to all the
enumerators and the members of the sample respondents for their valuable cooperation during
data collection and for sparing their precious time and hospitality of the communities the
researcher would acknowledge them heartily.
My grateful thanks go to many individuals for sharing challenges, and difficulties until I
complete the thesis. My appreciation goes to my wife families, especially Banchu Mekonnen
who cares my family and encouraged me during my absence from home. I would like to
acknowledge Ato Nigussi Zeray who devoted his time and sharing me with valuable
knowledge and materials for the completion of the research.
My special thank is given to my mother Getenesh Mekonnen and all my brothers and sisters
including my friends especially Tizazu Mekonnen for their invaluable encouragement, appeal
and support to this day.
Very exceptional thanks go to my wife Tsedey Mekonnen and my son, Eyoul Aberham,
deserve special thanks for their patience, love and encouragement to finalize my study.
vi
LIST OF ABBREVIATIONS
AI
Adoption Index
BoARD
Bureau of Agriculture and Rural Development
CBD
Coffee Berry Disease
CSA
Central Statistical Authority
CWD
Coffee Wilt Disease
D.D.T
Dichloro Diphenyltrichloroethane
DA
Development Agent
FGD
Farmer Group Discussion
HH
Household
IATP
Integrated Agricultural Training Program
Kg
Kilogram
M.A.S.L
Meters above Sea Level
MM
Millimeter
NBE
National Bank of Ethiopia
NGOs
Non Governmental Organizations
PAs
Peasant Associations
SNNPRS
Southern Nations Nationalities Peoples Regional State
TLU
Tropical Livestock Unit
USD
United States Dollar(s)
VIF
Variance Inflation Factor
WoB
vii
TABLE OF CONTENTS
STATEMENT OF THE AUTHOR
iv
BIOGRAPHIC SKETCH
v
ACKNOWLEDGEMENTS
vi
LIST OF ABBREVIATIONS
vii
LIST OF TABLES
x
LIST OF FIGURES
xi
LIST OF APPENDICES
xii
ABSTRACT
xiii
1. INTRODUCTION
1
1.1 Background
1.2 Statement of the Problem
1.3 Research Questions
1.4 Objectives of the Study
1.5 Significance of the Study
1.6 Scope and Limitations of the Study
1.7 Organization of the Thesis
1
4
5
5
6
6
6
2. REVIEW OF LITERATURE
7
2.1 Definition of Adoption and Related Concepts
2.2 Theoretical Perspectives of Adoption and Diffusion of Innovation
2.2.1 The classical five stage adoption process
2.2.2 The innovation decision process
2.3 Review of Empirical Adoption Studies
2.3.1 Personal and demographic characteristics of the household
2.3.2 Socio-economic characteristics
2.3.3 Institutional variables
2.3.4 Psychological variables
2.4 Conceptual Framework of the Study
7
8
9
9
14
14
16
17
18
20
3. RESEARCH METHODOLOGY
22
3.1 Description of the Study Area
3.2 Sample Size and Sampling methods
22
23
viii
TABLE OF CONTENTS (CONT'D)
3.3 Data type, Source and Methods of Data Collection
3.4 Definition of Variables and Working Hypothesis
3.4.1 Dependent variable
3.4.2 Independent or explanatory variables
3.5 Analytical Technique
4. RESULTS AND DISCUSSION
25
26
26
27
31
38
4.1 Current Status of Adoption and Intensity of Use of Coffee Technology Package
4.2 Current Practices of Coffee Technology Package
4.2.1 Adoption of coffee varieties
4.2.2 Seeding rate
4.2.3 Compost application
4.2.4 Spacing practice
4.2.5 Pruning practices
4.2.6 Weed management
4.3 Relationship of Independent Variables with the Dependent Variable
4.3.1 Household personal and demographic variables
4.3.2 Economical variables
4.3.3 Institutional factors
4.3.4 Social variables
4.3.5 Psychological factors
4.4 Summary of Results of Descriptive Statistics
4.5 The Results of Econometric Model
4.5.1 Determinants of adoption and intensity of use of coffee technology package
4.5.2 Effects of change in the explanatory variables on probability of change adoption
and intensity of use of coffee technology package
5. SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary
5.2 Conclusion and Recommendation
39
40
40
41
42
44
45
46
47
47
51
54
59
61
63
65
66
69
72
72
74
6. REFERENCES
77
7. APPENDICES
87
ix
LIST OF TABLES
Table
Page
1: Total number of households selected by peasant associations............................................. 24
2: list of variables included into the econometric model.......................................................... 37
3: Distribution of respondents by level of adoption of coffee technology package ................. 40
4: Intensity of adoption of improved coffee varieties .............................................................. 41
5: The average seeding rate applied by sample households (kg/ha) (n=140) ........................ 42
6: Compost application rate by adopter categories (kg/tree)
(n= 140) .................................. 43
7: Spacing practice applied by adoption category .................................................................... 44
8: Pruning practice applied by adoption category .................................................................... 45
9: Weeding frequency applied by adoption index categories
(n=140).................................. 46
10: Sex of household head by adopter category ....................................................................... 49
11: Age, education, family size and family labor of sample farmers by adopter categories
(n=160) .............................................................................................................................. 50
12: Land holding size, number of frequency, annual income and access of market by adopter
categories (n=160) ............................................................................................................ 53
13: Relationship between access to training and adoption category ........................................ 55
14: access to credit by adoption categories .............................................................................. 56
15: Relation between frequencies of contact with development agents and adoption index
categories (n=160) ............................................................................................................ 58
16: Social participation and cosmo politeness of respondents by adoption index category (n=
160) ................................................................................................................................... 60
17: Radio listening habit of sample respondents and adoption index categories ..................... 61
18: Achievement motivation and attitude to wards of technology by adopter category .......... 63
19: Summary of result of continuous /discrete explanatory variables ..................................... 64
20: Summary of result of discrete/dummy explanatory variables ............................................ 64
21: Maximum likelihood estimates of tobit model .................................................................. 66
22: Marginal effect of determinant variables ........................................................................... 70
x
LIST OF FIGURES
Figure
pages
1: The innovation decision process (Rogers, 1983) ................................................................. 10
2: Conceptual framework of the study ..................................................................................... 21
3: Location of the study area (Yirgachefee) ............................................................................. 23
4: Sampling design ................................................................................................................... 24
xi
LIST OF APPENDICES
Appendix
page
1: Improved coffee varieties ..................................................................................................... 87
2: Conversion factor used to compute Man- Equivalent (labor force) ..................................... 87
3: Conversion Factor Used to estimate Tropical Livestock Unit ............................................. 88
4: Variance Inflation Factors (VIF) of continuous explanatory variables................................ 88
5: Interview schedule and check list question .......................................................................... 89
6: Check lists for collecting data from FGD and key informants .......................................... 100
xii
ADOPTION AND INTENSITY OF USE OF COFFEE TECHNOLOGY
PACKAGE IN YERGACHEFFE DISTRICT, GEDEO ZONE, SNNP
REGIONAL STATE, ETHIOPIA
ABSTRACT
Arabica coffee (Coffee arabica L.) is an economically important crop, which is contributing
the highest of all export revenues in Ethiopia. It is also the major cash crop of Gedeo Zone
and produced in all the six woredas. In this area production and productivity of coffee is poor
due to traditional poor pre and post harvest practices. Efforts were made so far in areas of
fermentation time, drying depth, time of storage and extension support, training for coffee
expertise and coffee farmers on recommended technologies. Therefore, this study was
conducted with the objectives of analyzing adoption and intensity of use of coffee technology
package in study area; and investigating demographic, socio-economic and institutional
factors related to coffee technology package and problems in the area. For this study, a multi
stage sampling procedure was employed to select kebeles and sample respondents. In the first
stage, one district was selected purposively from 6 districts based on their potential
production and productivity of coffee and four kebeles were selected randomly. Following
that 160 household heads were selected using systematic random sampling procedure with
sample size allocation procedures of probability proportional to size method. Structured
interview schedule was prepared and administered on the sample. In addition, focus group
discussion was conducted and secondary data sources were consulted for related
information. Data were analyzed, and presented quantitatively using different statistical
methods such as percentage, mean, frequency, Chi-square (categorical variables ) and (F-test
for continuous variables), F-test and Chi-square test were employed to test the variation of
the sample respondents they have towards adoption and intensity of coffee technology
package and also used to describe the patterns of the sample data. The data collected from
the field survey were analyzed by employing the statistical procedures of STATA version 10.0.
The results of the econometric model indicated that respondent’s level of education, social
participation, access to credit, labor availability, farm size and achievement motivation were
important variables which had positively and significantly influenced adoption and intensity
of use of coffee technology package. On the other hand, market distance had shown negative
and significantly affected adoption and intensity of use of coffee technology package.
Therefore, the overall finding of the study underlined the high importance of economical and
institutional support, social participation and distance of market to enhance adoption and
intensity of use of coffee technology package. Therefore, policy and development
interventions should give emphasis to improvement of such economical and institutional
support system so as to achieve wider adoption, increased productivity and income to small
scale farmers.
xiii
1.
INTRODUCTION
1.1 Background
Ethiopia is the home of land and cradle of biodiversity of Arabica coffee. More generally
diverse cultivators of C.arabica exist in Ethiopia than anywhere else in the world, which has
lead botanists and scientists to agree that Ethiopia is the center of origin, diversification and
dissemination of the coffee plant (Fernie, 1966; Bayetta, 2001).
Ethiopian coffee is rightly known as highland coffee by consumers. The diversified types of
C. arabica in the country, growing in an ideal environment has allowed Ethiopia to be
attractive to the world coffee market. It has a great deal to offer in the way of gourmet,
specialty and organic coffees. Ethiopian coffee is rich in acidity and body. It possesses an
aromatic and sweet flavor and is characterized by winey, spicy notes and the world famous
mocha tastes so highly prized by connoisseurs. Because it has so much to offer, it can be
enjoyed as a single varietal and it can also be blended with coffees from other origins to
upgrade them(ESC, 2011).
The coffee sub-sector is very important to the Ethiopian economy, and generated about 528.3
million USD or 26.4% of the foreign exchange earnings in 2009 (NBE, 2010). Moreover, the
sub-sector is also important in terms of providing income for a large number of households: it
is estimated that over 25% of population of Ethiopia, representing 15 million people, are
dependent on coffee for their livelihoods (LMC, 2000). This includes 8 million people
directly involved in coffee cultivation and 7 million in processing, trading, transport and
financial sectors (Charveriat, 2001; Oxfam, 2002 cited by Richard et,al. 2007)
In Ethiopia, coffee as the major export crop and driving force of the economy. It is the
ecological, socio-cultural and spiritual life of the people. The relationship between Ethiopians
and coffee is deep-rooted and multifaceted and coffee production and consumption is closely
intertwined with Ethiopia history, culture and economy (Till, 2008).
1
In Ethiopia, coffee is primarily cultivated by smallholders, either cultivating coffee on their
own farms or picking semi-wild/wild coffee. Of the estimated 600,000 hectares of land
cropped with coffee, over half is semi-forest/forest, or semi-wild/wild land. Approximately
235,600 hectares are under smallholder cultivation, ('garden' or 'cottage' coffee), which is
generally inter-cropped with food staples. Smallholder coffee accounts for approximately
95% of total coffee production. There are about 20,000 hectares of plantation coffee,
consisting mainly of state farms, but increasingly also of plantations under private ownership
(McMillan et al., 2003).
Coffee growers in Ethiopia have been exposed to price fluctuations and impacts of
unpredictable and uncontrollable shocks. Despite some improvement of producer prices in the
past two years, domestic and world coffee prices have declined and remained very low for
much of the late 1990s and early 2000s. The effect of this price decline was manifested in
increasing poverty among coffee growers, who previously were able to reap good benefits
from their coffee sales. At household level the impact of depressed prices has been
considerable, leading to distress sales of assets such as cattle, or to uprooting coffee plants and
replacing them with annual food crops (Oxfam, 2002) or cash crops such as Chat. Other
strategies included giving up traditional shades coffee production to create space for
intercropping and income diversification (McMillan et al., 2003)
Despite the favorable climatic conditions, variety of coffee types for quality improvement and
long history of its quality in Gedeo Zone, coffee production and productivity is declining from
time to time due to several improper pre-and post-harvest management practices. This is still
practiced by the majority of coffee farmers/traders, from which the larger portion of the
produce is obtained. These production and productivity problems are mainly associated with
poor agronomic practices like uncontrolled shade level, lack of stumping, pruning and
weeding; poor harvesting practices, such as stripping and collecting dropped fruits from the
ground; improper post harvest processing and handling practices such as drying on bare
ground, improper storage and transportation (Desse, 2008). In addition to this, natural
2
impediment such as prolonged rainy weather, particularly during harvesting and drying
season can also contribute to reduced coffee quality (Desse, 2008).
Jimma Research center has devoted considerable effort and resources and developed several
coffee technology packages. Arabica coffee cultivars are categorized into three broad canopy
classes, identified as open, intermediate and compact types these combine that combine high
yield, disease resistance, and quality characters were developed (Bayetta et al., 2000). In
addition, recommendations have been developed on pest and disease management. The
improved cultivars produce 12-24 and 6-16 Q/ha clean coffee on station and on farmers’
fields, respectively (Bayetta et al., 2000). To facilitate the transfer and utilization of these
research outputs, Ethiopia has adopted and experimented with different forms and approaches
of coffee extension interventions. However, the small-holder coffee sector still suffers from
lack of effective and efficient support services such as extension, credit, input supply and the
likes. In this aspect, the effort and resources committed to technology development would be
of little significance unless and otherwise they are accessed, accepted, and used by intended
users. In this aspect, the communication media and public agricultural research extension and
advisory services have played a large part in introducing the new technologies and farming
practices to farmers.
In spite of its importance, the coffee production is characterized by traditional method of
production and the low level of technology use. As a result, despite its importance, role in the
national economy and the wealth of genetic diversity and climatic suitability, the national
average yield is 450- 472 kg per hectare of clean coffee (Workafes and Kassu, 2000). The
technological packages of coffee is coffee management practices, including hoeing, weeding,
stamping, mulching, pruning and shade regulation are not seriously considered at the grass
root level. In addition, lack of effective extension approach in the farmers’ condition also
contributes to low productivity of coffee. The decline of production is also attributed by
prevalence of coffee berry disease (CBD) and coffee wilt disease (CWD) as well.
In general, the huge efforts and resources devoted so far on the development and transfer of
coffee technology, the national average productivity of coffee is still far below the potential.
3
1.2 Statement of the Problem
In Ethiopia where coffee is grown, majority of the small holders’ livelihood dependent on
coffee cultivation, In spite of its importance, traditional method of production and the low
level of technology characterize the coffee production. As a result, role in the national
economy and the wealth of genetic diversity and climatic suitability, the national average
yield is 450-472 kg per hectare of clean coffee (Workafes and Kassu, 2000). Adoption
decision of farmers influenced by different factors associated with socio-economic,
institutional, demographic and physical characteristics can influence farmers’ decision on
adoption of agricultural innovation. Factors such as, characteristics of household (education,
age and family size), farm characteristics, technology characteristics, wealth (economic
status), contact with extension workers, farmers’ knowledge of the specific technology, price,
access to credit, position of farmers in farmers’ organization would be important determinants
of adoption of innovations (Mekonnen, 2007; Minyahel, 2008; Ahmed, 2010; Yemane, 2010).
Several empirical studies (for instance, Abrhaley, 2006; Tahe, 2007; Ahmed, 2010; Yemane,
2010) have confirmed that adoption decision of farmers are influenced by different factors
associated with socio- economic, institutional, demographic and physical characteristics can
influence farmers’ decision on adoption of agricultural innovation. Factors such as
characteristics of household(education, age and family size), farm characteristics, technology
characteristics, wealth(economic status), contact with extension workers, farmers’ knowledge
of the specific technology, price, access to credit, position of farmers in farmers’ organization
were important determinants of adoption of innovations.
Although involving farmers in the research process has increased adoption rate of improved
coffee varieties, it can never be a substitute for adoption study, as it does not show the actual
rate and intensity of adoption of the varieties and factors influencing adoption. Moreover,
adoption of improved varieties alone is not enough to achieve the desired level of yield unless
farmers adopt the different components of the recommended coffee production technology
package altogether. Empirical observations indicate that most farmers have adopted only the
varieties, but not used the other essential components of the package as per recommendation.
4
For instance, only 15% of farmers in east and west Shewa zone adopted all the three Teff
technologies namely, variety, fertilizer and herbicide as a package while the rest used none,
one or two components of the package at different level of intensity (Legesse, 1992).
Reasons for such diversity among farmers in adoption and intensity of use of coffee
technology package in the study area has not been yet studied. On top of this, the study area is
one of the districts in the SNNP region which is specialized on coffee production since long
period especially for organic coffee for export. Hence, in such a situation, studying adoption
of coffee technology package in district is worth to fill the knowledge gap.
1.3 Research Questions
1. What is the status of adoption and intensity of use of coffee technology package by
farmers in the study area?
2. What are the factors influencing adoption and intensity use of coffee technology
package by farmers in the study area?
1.4 Objectives of the Study
The general objective of this to study is to assess the adoption and intensity of use of coffee
technology package in Yergacheffe district.
Specific objectives of the study
The specific objectives of this study are:
 to assess the status of adoption and intensity of use of coffee technology package in
Yergacheffe district;
 to identify major factors influencing adoption and intensity of use of coffee technology
package in the study area.
5
1.5 Significance of the Study
The district where the study was conducted is known for coffee production in the SNNP
region, where coffee technology packages have been introduced, since long and well known
for organic coffee for export. Moreover, the study area is one of the districts in the region that
was selected to be specializing on coffee production, and the concerned bodies have been
striving for this end. In such a situation, studying adoption of coffee technology packages in
this district provides concerned stakeholders with valuable information for better designing or
redesigning the specialization program underway and for its successful implementation. The
result of this study would also be used as base-line information for future assessment studies.
It is expected to give feedback to researchers with regard to the extent of adoption and the
performance of the package.
1.6 Scope and Limitations of the Study
This study is only a piece of huge effort to unfold realities regarding agricultural technology
adoption and factors affecting it. Therefore, its scope is limited in terms of coverage and depth
owing to financial and time resources available. It is limited to only coffee technology
package and to Yirgacheffe district in terms of area coverage. Nevertheless, the result of this
study can be used as a reference for other similar areas.
1.7 Organization of the Thesis
This thesis consists of five major chapters. Chapter one presents the background, statement of
the problem, objectives of the study, research questions, significance of the study and scope
and limitations of the study. Chapter two discusses the theoretical and empirical literature
related to the research. This is followed by the discussion of the methodology used in the
research in chapter three. Chapter four presents the results and discussion part of the study.
Finally, the conclusion and recommendation of the study are presented in chapter five.
6
2.
REVIEW OF LITERATURE
2.1 Definition of Adoption and Related Concepts
A clear definition of adoption is important before going further into other parts of literature.
In view of this, some important concepts related to adoption will be defined under this
section.
Many authors have defined the term adoption at different times. According to Feder et al.
(1985), adoption may be defined as the integration of an innovation into farmers’ normal
farming activities over an extended period of time. The author also noted that, adoption is not
a permanent behavior. This implies that an individual may decide to discontinue the use of an
innovation for a variety of personal, institutional and social reasons one of which might be the
availability of another practice that is better in farmers’ fields.
Dasgupta (1989) defines adoption as a continued use by individuals or groups of the
recommended idea or practice over a reasonably long period of time. Van den Ban and
Hawkins (1996) define technology adoption as a decision to apply an innovation and continue
to use it.
Innovation is defined as an idea, practice, or object perceived as a new by an individual. It
matters little, so far as human behavior is concerned, whether or not an idea is “objectively”
new or the idea seems new to the individual, it is an innovation (Rogers and Shoemaker,
1975).
Adams (1992) defines innovations as new methods, practices or techniques that provide the
means of achieving sustained increase in farm productivity and income. The innovation may
not be new to people in general, but if has not yet been accepted by an individual, to that
person it is an innovation.
7
2.2
Theoretical Perspectives of Adoption and Diffusion of Innovation
Rogers (1962) defined the adoption process as the mental process an individual passes from
the first hearing of about an innovation or technology to a final adoption. In addition,
according to the definition by Van den Ban and Hawkins (1996), the adoption process refers
to changes that take place within the minds of an individual with regard to an innovation from
the moment he/she becomes aware of the innovation to the final decision to continuously use
it or not.
Adoption of a new innovation being one of the possible outcomes of behavioral change
process, involves decision making which implies cognitive engagement (Koch, 1986:19).
Duvel (1975) identified psychological related variables, need, perception and knowledge
which according to him are the most important and direct determinants of behavior change.
However, largely because of the complexity of adoption behavior, as well as the impermeable
boundaries and perspectives of the traditions and disciplines involved in this research, there is
no generally accepted theory available to guide the professionals in research of the factors
affecting the adoption behavior (Düvel, 1991). In line with this, Botha (1986) and Duvel
(1991) distinguished the following theories/models of behavioral change;
1) The traditional approaches,
2) The classical five-stage adoption process,
3) The Campbell model,
4) The innovation decision- making process of Rogers and Shoemaker,
5) The psychological field theory of Lewin,
6) The Tolman model, and
7) Duvel’s behavior analysis model
Among these, the classical five-stage adoption process and the innovation decision process of
Rogers and Shoemaker behavioral analysis model will be elaborated in subsequent sections
8
simply to provide highlights of the adoption processes as well as various personal, socioeconomic and institutional factors related to adoption.
2.2.1 The classical five stage adoption process
The classical five-stage adoption process model which was formulated by the North Central
Rural Sociology Committee (1961) was the dominant model until it was modified by Rogers
and shoemaker (1971). According to Campbell (1966) the classical five –stage adoption
process model was developed from the recognition that adoption of an innovation often is not
an instantaneous act. Rather it is a process that develops over a period of time and influenced
by a series of actions. The model composed of the following five stages adoption process:
A. Awareness Stage :- first hear about the innovation
B. Interest Stage:- seek further information about an innovation
C. Evaluation Stage:- weigh up the advantages and disadvantages of using it
D. Trial Stage:- test the innovation on a small scale and,
E. Adoption Stage: - apply the innovation on a large scale in preference to old methods.
2.2.2 The innovation decision process
According to Rogers (1983) there is no sufficient evidence to prove that these stages exist in
the classical five stages theory. Decisions in practice often may be made in a less rational and
systematic manner than the stages outlined above. The adoption process does not always
follow the above sequence in practice. This indicates that adoption is not a sudden event but a
process. Farmers don’t accept technology immediately; they need time to think over things
before reaching a decision. Due to the above criticism against the classical five stage theory,
Rogers and Shoemaker (1971) designed the innovation decision process which was later
revised by Rogers (1983) and is presented as follows:
9
Communication channels
…………………...…………………………………………………
Prior conditions
1. Previous practices
Knowledge
Implementation
Decision
Persuasion
Confirmation
2. Felt need/problem
3. Innovativeness
4. Norms of the social System
adoption
Adoption
Continued
Later adoption
Characteristics of
Perceived character
Decision making unit
of innovation
Rejection Discontinuance
Continued rejection
1. Socio-economic Characteristics
1. Relative advantage
2. Personality variables
2. Compatibility
3. Communication Behavior
3. Complexity
4. Observability
5. Trialability
Figure 1: The innovation decision process (Rogers, 1983)
10
The innovation decision is thus the process through which an individual or other decision
making unit, extension organization, for example, passes from first knowledge of an
innovation to forming an attitude towards the innovation, to decision to adopt or reject, to
implementation of the new idea, and to the confirmation of the decision (Rogers, 1983:163).
This model has the following five stages:
A. Knowledge occurs when an individual (or other decision-making unit) is exposed to
the innovation's existence and gains some understanding of how it functions.
B. Persuasion occurs when an individual (or other decision-making unit) forms a
favorable or unfavorable attitude towards the innovation.
C. Decision occurs when an individual (or other decision-making unit) engages in
activities that lead to a choice to adopt or reject the innovation.
D. Implementation occurs when an individual (or other decision-making unit) puts an
innovation into use.
E. Confirmation occurs when an individual (or other decision-making unit) seeks
reinforcement of an innovation-decision already made, but he or she may reverse this
previous decision if exposed to conflicting messages about the innovation.
The innovation-diffusion model, following from the work of Rogers, holds that access to
information about an innovation is the key factor determining adoption decisions. The
appropriateness of the innovation is taken as given, and the problem of technology adoption is
reduced to communicating information on the technology to the potential end users (Adesina
and Zinnah, 1993).
However, Rogers (1983) has explained that the diffusion of innovations attracted many
scholars in that though an innovation is found having an obvious advantage; it is often very
difficult to be adopted by farmers. There is a gap between what is known and the actual
practice /use of the object. He moreover indicated that the innovation diffusion involves
considerable deliberation by most adopters even in the case of an innovation with spectacular
results. His statement indicates that there is a need to approach the adoption decision process
with other additional paradigms.
11
With regard to the relationship of technological attributes, it is difficult to attach attributes of
innovation in to definite categories. The innovation is characterized by indefinite variables
and the interwoven behavior of the many variables. But for simplicity we need a standard
classification scheme to describe the perceived attributes of innovations in universal terms.
Rogers (1983) has classified the attributes in to five. These characteristics are by which an
innovation may be described and individuals' perceptions of which predict their rate of
adoption. These characteristics of innovations are: Relative-Advantage, Compatibility,
Complexity, Triability and Observability.
A. Relative advantage is the degree to which an innovation is perceived as being better
than
the idea it supersedes. The degree of relative advantage is often expressed in economic
profitability, in status giving, or in other ways. The nature of the innovation
largely
determines what specific type of relative advantage (such as economic, social,
and the
like) important to adopters, although the characteristics of the potential adopters also
affect which dimensions of relative are most important.
B. Compatibility is the degree to which an innovation is perceived as consistent with the
existing values, past experiences, and needs of potential adopters. An idea that is more
compatible is less uncertain to the potential adopter. An innovation can be compatible or
incompatible (1) with socio-cultural values and, (2) with previously introduced ideas, or
(3) with client needs for innovations.
C. Complexity is the degree to which an innovation is perceived as relatively difficult to
understand and use. Any new idea may be classified on the complexity-simplicity
continuum. Some innovations are clear in their meaning to potential adopters while
others are not.
D. Triability is the degree to which an innovation may be experimented with in a limited
basis. New ideas that can be tried on the installment plan will generally be adopted more
rapidly than innovations that are not divisible. An innovation that is trialable is less
uncertain for the adopter. Some innovations are more difficult to divide for trial than
others.
12
E. Observability is the degree to which the results of an innovation are visible to others.
The results of some innovations are easily observed and communicated to others, where as
some innovations are difficult to describe to others.
With regard to the concepts related to diffusion, it refers to the spread of an innovation among
the members of the social system. The dissemination of agricultural innovations to users is
one of the priority areas that deserve attention in agricultural and rural development. The
application of improved techniques (innovations) whether it is introduced from within or
outside is important for framers to achieve increased production or productivity. A
technological innovation consists of both the idea component and the object component
(Rogers and Shoemaker, 1971). Both the hard ware and software components of agricultural
innovation are important.
Diffusion of an innovation is a multidisciplinary concept of planned social change that is
brought about by the spread of new ideas or new technologies throughout the social system.
Communication among the change agency and the client system, and further communication
within that system results in individuals or groups making a decision whether to adopt or
reject the innovation (Gross, 1979 as cited by Kidane, 2001:21).
Rogers and Shoemaker (1971) categorized social change in to two broad categories as
immanent and contact change based on the sources of the change. The former is a kind of
change where the source of the change is from within the social system under analysis while
the later is where the sources of the new idea are outside the social system. Direct contact
change or planned change, which is one component of contact change, is caused by outsiders
who on their own or as representatives of change agencies, intentionally seek to introduce
new ideas in order to achieve goals they have defined. Diffusion of a new innovation is
considered as a planned social change.
According to the study made by Ryan and Gross (1943), research on diffusion process by
rural sociologist’s dates back to the Iowa state hybrid corn studies of 1940s. The intent was to
better understand the diffusion of a particular innovation, hybrid corn, and later other
13
innovations among Iowa farmers. The then finding inspired a large volume of rural
sociological research on the diffusion of agricultural innovations which grew rapidly in the
1950s and 1960s in the United States, and influenced the beginning of similar studies in other
countries (Dasgupta, 1989). According to Rogers (1995), after Ryan and Gross’s hybrid corn
study, about 5000 papers on diffusion study were published in 1994.
However, largely because of the complexity of adoption and diffusion behavior, as well as the
impermeable boundaries and perspectives of the traditions and disciplines involved in this
research, there is no generally accepted theory available to guide the professionals in research
of the factors affecting the adoption behavior (Düvel, 1991).
2.3
Review of Empirical Adoption Studies
A number of empirical studies have been conducted by different people and institutions on
farmers’ adoption behavior both outside and inside Ethiopia. For instance, Mahdi (2005),
Abrhaley (2006) and Taha, (2007) have confirmed that adoption decisions of farmers are
influenced by different factors. Factors associated with socio- economic, institutional,
demographic and physical characteristics can influence farmers’ decisions on adoption of
agricultural innovations. Factors such as, characteristics, wealth (economic status), contact
with extension workers, farmers’ knowledge of the specific technology, price, access to
credit, prostitution farmers in farmers’ organization were important determinants of adoption
of innovations.
2.3.1 Personal and demographic characteristics of the household
Household’s personal and demographic variables are among the most common household
characteristics which are mostly associated with farmers' adoption behavior. From this
category of variables sex, education, family size and age of household were reviewed in this
study.
14
Sex differentials are one of the important factors influencing adoption of improved
agricultural technologies. Due to long lasted cultural and social grounds in many societies of
developing countries, women have less access to household resources and also have less
access to institutional services. Regarding the relationship of household’s sex with adoption of
agricultural technologies, many previous studies reported that household’s sex has positive
effect on adoption in favor of males. For instance, Yemane (2010), in his study on farmers’
Evaluation and determinants of adoption of upland rice varieties in Fogera district revealed
that there is significant relationship between sex and the adoption of upland rice variety.
Similarly, Tadesse (2008) in his study on farmers’ evaluation and adoption of improved onion
production package in Fogera district, found significant relationship between sex and
adoption.
With regard to education, there is a general agreement that education is associated with
adoption because education is believed to increase farmers’ ability to obtain, and analyze
information that helps him to make appropriate decision. Several studies for example the one
conducted by Daniel (2007), Minyahe (2008), Tadesse (2008) and Ahmed (2010) have
reported that education had positive and significant relationship with adoption. On the other
hand, study conducted by Mekonnen (2007) and Gebresenbet (2008) on assessment of factors
influencing adoption of integrated striga management technologies of sorghum in habro and
fedis woredas and determinants of adoption and intensity of use improved soil and water
conservation practice in sodo district revealed that there is no variation between literacy and
illiteracy rates in terms of striga management technologies soil and water conservation
practices.
Family size is one of the other important household demographic variables which have
influence on farmers’ adoption behavior. Large family size usually implies availability of
labor provided that majority or all of the family members are within the age range of active
labor force (15-50 years). In most studies family size had positive relationship with adoption
of improved agricultural technologies. For instance, Amsalu (2008), Gebresenbet (2008) and
Yemane (2010) indicated that family size is positively affects adoption of improved
technology. On the other side, the study conducted by Mubarak (2009), Tadesse (2008) and
15
Ahmed (2010) indicate that family size had no significant effect on adoption of packages of
agricultural technologies.
2.3.2 Socio-economic characteristics
Farm size is the other important variable, which in most cases has an effect on household’s
decision to adopt new technologies. Several studies reported the positive effect of household
farm size on adoption of improved agricultural technology. For instance, Minyahil (2008)
Tadesse (2008), Ahmed (2010) and Yemane (2010) have found positive effect of household’s
labor availability on adoption of soil conservation measures.
Social participation contributed positive and significant influence on the adoption of crossbred cows as Ebrahim (2006) indicated. Similarly, Dereje (2006) and Rahmeto (2007)
reported that social participation had significant and positive relationship with adoption.
Cosmopolteness is the degree of contact that a farmer has with external situations of the social
system. This assumed to influence access to information on improved farming practices as
compared to other members of the group and influence adoption positively. According to
Rolling (1988), 23% of the most innovative farmers were found to be cosmopolitans have
negative and significant influence on adoption of decision process.
Livestock holding is an important indicator of household wealth position and an important
source of income. Livestock ownership of a household influences the adoption of improved
agricultural technologies differently by different area. In most cases, livestock holding has
positive contribution to household’s adoption of agricultural technologies. In this line, many
authors (Yishak, 2005; Tesfaye, 2006; Mekonnen, 2007; Gebresenbet, 2008; Mubarak, 2009
and Ahmed, 2010) have found that livestock holding has positive and significant influence on
adoption of improved agricultural technologies in different part of the country. Contrary to the
above findings, Minyahil (2008) and Yemane (2010) reported that livestock holding not
influenced on the adoption of improved technologies.
16
Households’ income position is one of the important factors determining adoption of
improved technologies. In the context of rural households, annual farm income obtained from
sale of crop, livestock, and livestock products income are important income sources.
Regardless of income types, almost all empirical studies reviewed shows the effect of annual
income on household’s adoption decision is positive and significant. For example, Taha
(2007), Gebresembet (2008), Tadesse (2008) and Mubark (2009) reported that positive
influence of household’s farm income on adoption of improved technologies.
Other sources of information such as mass media in the area are also important in diffusion of
agricultural innovations. Radio also plays the greatest role in provision of information in
shortest possible time over large area of coverage. Many studies reported the positive and
significant relationship of mass media with adoption of agricultural technologies. In line with
this, Yishak, (2005) in his study on determinants of adoption of improved maize technology
in Damote-Galewereda, Wolaita, Ethiopia indicated that ownership of radio and participation
in demonstration had positive influence on adoption of improved maize technologies.
2.3.3 Institutional variables
Contact with extension or development agents is another variable which is reported in many
adoption studies as one of the variable that influences the probability of adoption of
agricultural innovations. With this regard, studies conducted by Abadi (2006), Abrahaley
(2006), Minyahil (2008), Gebresembet (2008), Tadesse (2008) and Mubark (2009) revealed
that contact with extension/ development agents positively and significantly related to the
adoption decision of farmers. In the same way, the studies carried out by Ahmed (2010)
reported that there is significant association between access to extension services and
adoption of improved durum wheat varieties in the highland of Bale: the case of Agarf
district.
In this study, access to market in using fertilizer was also one of the factors affecting the
adoption rate negatively. Similarly, the results of research by Legesse et al., (2001) showed
that it is structural factors, in particular distance to market, that is determining the adoption
17
and intensity of use of technologies was found to be significant with negative effects. In
addition, Ibrahim (2006) on his study on the Adoption of dairy innovations in Adami Tulu
found out significant and negative relationship between adoption and distance to market. The
results of many other researchers who reported that market distance is negatively and
significantly associated with the adoption of crop technologies include Mesfin (2005), Mehdi
(2005), Yishak (2005) and Ahamed (2010). On the other hand, there are a number of studies
conducted by Minyahil, 2008 on analysis of factors influencing adoption and intensity of
adoption of improved bread wheat production package in jamma district, south wollo have
found significant with positive effects on the adoption of improved bread wheat production
package.
The other institutional support that farmers need to get to improve production and
productivity is, credit service and other inputs. Financial and risk constraints are key factors
that limit the adoption of most improved agricultural technologies by small scale farmers.
Access to credit can relax the financial constraints of farmers and enable farmers to buy input
and hence increase the probability of adopting improved technology package. The result of
several adoption and diffusion studies conducted so far (Yishak, 2005; Ebrahim, 2006;
Rahmato, 2007; Zelalem, 2007; Tadesse, 2008) indicated that access to credit services
positively and significantly affected the adoption of improved agricultural innovations.
2.3.4 Psychological variables
Behavioral change process involves decision- making, which implies cognitive engagement in
deciding whether to adopt or reject a given innovation (Koch, 1986). Under this category,
psychological variables such as, achievement motivation, information seeking behavior and
level of aspiration were included.
Achievement motivation is also one of the factors that determine farmers’ decision behavior.
It is the desire to accomplish something, to reach standard of excellence, and expand effort to
excel. Achievement motivation is based on reaching success and achieving all of our
aspiration in life. Achievement goals can affect the way a person performs a task and
18
represent a desire to show competence (Harackiewicz et al., 1997). A study made by
psychologists tells that an individual with higher achievement motivation has strong hope of
success than fear of failure (McClelend, 1955, 1978) and Yemane (2010), reported that
achievement motivation had insignificant relation with technology adoption. Hence, it is
hypothesized to influence adoption positively. Moreover, Asres (2005), Deribe (2007) and
Mokonnen (2007) reported that achievement motivation had insignificant relation with
technology adoption.
Positive attitude towards change in agricultural is one of the factors that can speed up the
change process. Positive attitude formation is also a prerequisite for behavioral change to
occur and it influences adoption positively. For instance, Rogers and Shoemaker (1983)
identified that adoptions behavior is positively related with favorable attitude towards change.
In addition, Ahmed (2010) indicated that Farmers attitude toward upland rice in the Fogera
district was found too positively and significantly affects the adoption of improved upland
rice varieties. Similarly, Rahmato (2007) found that attitude toward haricot been productions
again positively and significantly related with adoption of the technology package at less than
1% probability level.
Studies conducted by Ethiopian Agricultural Research organizations shows that production of
improved sorghum varieties have decreased in western Wollega due to bird susceptibility. To
minimize bird damage, the varieties (Bakomash, IS9302, D. 1057) were demonstrated on
large area, still bird damage was not minimized and the yield was very low. In general it was
observed that improved varieties of Sorghum require good land preparation, sufficient soil
moisture at planting, shallow planting and regular bird scaring (Beyene and Abera, 1998).
Research conducted by Kolawole et al., (2003) stated that lack of input require for the
implementation of the technology package may lead to the rejection of innovation. Oladele
and Kareem (2003) also reported that 60% of arable farmers in Oya state, fertilizer due to the
Nigeria had stopped using unavailability, and the untimely and high cost of the input.
19
According to Dasgupta, 1989, cited in Yemane, 2010, adoption is not a permanent behavior.
An individual may decide to discontinue the use of any innovation for a variety of reasons,
which might be the availability of another practice that is better in satisfying farmers’ needs.
Generally, most of adoption studies reviewed so far considered adoption of only a particular
technology (E.g. adoption of improved varieties, of fertilizer, of practices, etc…). While such
an analysis remains essential for identification of determinants of a particular technology
adoption, it does not provide adequate bases for defining adopters when the new technologies
are provided as a package and farmers use the whole or some components of the package.
According to Bezabih (2000), if the aim of a study is evaluation of impacts of technologies
adoption on the livelihood of farmers, the whole set of technologies, as a package should be
considered in the definition of adapters.
2.4
Conceptual Framework of the Study
Adoption decision of different technologies across space and time are influence by different
factors and their association. Factors such as personal, socio- economic, institutional and
psychological factors determine the probability of adoption and intensity of use of agricultural
technologies. It is obvious that different studies have been conducted to look into the direction
and magnitude of the influence of different factors on farmers’ adoption decision of
agricultural technology in one locality at one time was found to hinder it or to be irrelevant to
adoption of the same technology in another locality. Although some known, determinants
tend to have general applicability; it is difficult to adopt a universal model of the process of
technology adoption with defined determinants and hypotheses that hold to everywhere. The
dynamic nature of the determinants and the distinctive nature of the areas make it difficult to
generalize what factors influence which technology adoption.
However, based on the theoretical background and empirical adoption studies reviewed so far,
the following analytical framework is developed for this study. In the analytical framework,
the different factors supposed to affect farmers’ adoption behavior particularly those, which
20
contribute to the variations in adoption and intensity of use of adoption of coffee technology,
package among farmers in the study area were considered (figure 2). The framework
emphasized mainly on the relationship of the explanatory variables with the dependent
variable. This does not mean that there is no relationship between explanatory variables, but
simply to concentrate on their relationship with the dependent variable rather than relationship
among themselves.
Variables in adoption/decision making
Economic variables
Personal/Demographic
characteristics
 Household income
Psychological factors
 Achievement
motivation
 Age of HH
 Land holding
 Attitude towards
 Education level
 Livestock ownership
 Family size
improved coffee
technology package
Sex of HH
Institutional factors
 Access to training
Adoption and Intensity of
Use of Coffee Technological
Package
Social variables
 Social participation
 Contact with DAs
 Cosmopoliteness
 Access to credit
 Listening to radio
agricultural programs
 Access to market
Source: Researcher’s own design
Figure 2: Conceptual framework of the study
21
3.
3.1
RESEARCH METHODOLOGY
Description of the Study Area
The study was conducted in Yirgacheffe district found in Gedeo zone of the Southern
Regional State located to South of the country at 395km from Addis Ababa and 35km from
the zonal town, Dilla. It has common boundaries with Kochere in the south, Sidama zone in
the East, Abaya/oromiya in the west. It has a total area of 29158 hectare and a total population
of 199, 077 out of which, 15,919 are urban dwellers and 183,158 are rural dwellers (CSA,
2007). The district is comprised of 31 rural kebeles, and the average land holding per
household estimated 0.5 ha (Gedeo Zone, BoA, 2011G.C).
Generally, Yirgacheffe district is classified into two traditional agro climate zones: Dega
(high altitude) covers 8% of the area and range between 2300 - 2500 (m.a.l.s) and Weynadega
cover 92% of the area and range between 1750 - 2300 (m.a.s.l). The minimum and maximum
temperature in Yirgacheffe district is 12 and 25-degree cent grade respectively. The annual
mean rainfall range is between 1800-2000mm.
Crop- livestock mixed farming system characterizes agriculture in the district. Cattle, goats,
sheep and chickens are important livestock species reared by farmers. Coffee, maize, and
wheat are major cereal crops in the area. In addition, there are number of types of fruits like
mango, lemon, avocado and bananas and there are root /tuber products.
22
Figure 3: Location of the study area (Yirgachefee)
3.2
Sample Size and Sampling methods
An important decision that has to be taken while adopting a sampling technique is about the
size of the sample. Appropriate sample size depends on various factors relating to the subject
under investigation like the time, cost, degree of accuracy desired, etc (Rangaswamy, 1995;
Gupta and Gupta, 2002). In this study, to determine sample size, different factors such as
research cost, time and accessibility were taken into consideration.
23
A multi stage sampling procedure was employed to select peasant associations and sample
respondents. In the first stage, the district was selected purposively from six districts which
are found in Gedeo zone based on its potential of coffee production (in terms of area
coverage). In the second stage, four PAs were selected randomly following simple random
sampling technique. The list of households in each PA was used to prepare the sampling
frame for this study. Accordingly, with coffee cultivators, four sampling frames were
prepared, one for each PAs. Finally, 160 farmers were selected using systematic random
sampling with probability proportional to size from the selected PAs. Since the numbers of
farmers in each sample PA was differentiated, specific numbers of respondents were selected
with proportionate to size to ensure representativeness of the sample (Table1).
Table 1 Total number of households selected by peasant associations
No
1
2
3
4
Sample peasant association
Konga
Chelba
H/Werabi
H/Betela
Total
Source: own computation
Number of total household
805
844
855
736
3240
Total sample size
40
42
42
36
160
Yirgacheffe districts
Four kebeles
PA1
PA2
PA3
One hundred sixty (160) sample households
Figure 4: Sampling design
24
PA4
3.3
Data type, Source and Methods of Data Collection
Prior to data collection, the respondents were carefully informed about the objective of the
survey so that farmers would be convinced and get trust to cooperate. Both primary and
secondary data were gathered and used for this study.
Primary data were collected from the sample respondents on different issues such as
household characteristics and all other variables hypothesized to influence adoption and
intensity of use of coffee technology package using structured interview schedule. In addition,
data on the amount of coffee technology packages used vis-a-vis the recommendation were
also collected to know the intensity of use sample respondents.
Interview schedule was pre tested before the execution of the field survey on randomly
selected farm households and the necessary amendments as ordering, wording of questions,
elimination and inclusion were made.
Enumerators who were familiar with the existing social settings and can communicate with
the local language were recruited. Then, training was organized to enumerators on the content
and interviewing technique. Next, field survey was conducted by enumerators under close
supervision of the researcher in October 2011G.C. Focus Group Discussions (FGD) were
conducted with groups of selected farmers and key informant interviews by using cheek list
from each sample kebeles so as to cross check the information obtained during field survey.
Finally, Primary data was supplemented with secondary data which were obtained from
different sources particularly from district Bureau of Agriculture (BoA), Research center,
cooperative union and others who are supposed to have relevant information for this particular
study.
25
3.4 Definition of Variables and Working Hypothesis
3.4.1 Dependent variable
One key issue that major studies bring out is the question of what is meant by an “adopter” of
a technology. The definition of adopter varies across different studies. This proved to be
complicated issue with no obvious answer. Considering adoption as a discrete measure is
most appropriate when farmers typically grow either local varieties or improved variety.
However, if the interesting aspects of adoption are such that farmers are increasingly
allocating more land to improved varieties while continuing to grow some local varieties, then
a continuous measure of adoption is more appropriate (Doss, C.R. 2003). For example,
Nkonya et al. (1997) used the proportion of area planted with improved seed and area
receiving fertilizer as a proportion of recommended rate as continuous dependent variables.
Adoption is thus defined as the status of use of one of coffee technology package such as
improved variety, seeding rate, fertilizer rate, pruning, spacing, compost application rate, and
stumping, recommended frequency of weeding during the last three years. Accordingly, a
farmer who has been using one of these coffee technology packages at least once in the last three
years was considered as an “adopter” and “non-adopters” otherwise. Still, this might be a
narrower definition for the reason that it may exclude those who have discontinued the use of the
technology for some reason and those who have mentally adopted it. On the other hand intensity
of adoption refers to adoption index indicating farmers’ level of use of multiple practices from the
recommended coffee technology package. Adoption index is one of the techniques that are used in
the case of adoption study of multiple practices (package) and measures adoption and intensity of
adoption of coffee technology package. Adoption index in this case is a continuous dependent
variable.
26
3.4.2 Independent or explanatory variables
The explanatory variables of importance in this study are those variables, which were thought
to have influence on adoption and intensity of use of coffee technology package are explained
as follows.
Household’s personal and demographic characteristics
Age of the household head (AGE): Age is a continuous variable that represents age of the
household head measured in years. In traditional societies, age serves as an important
indicator of the individuals’ position in the society. Elder farmers were assumed in a position
to experience much with their traditional farming practices and are expected to be less
responsive to newly introduced agricultural technologies. Therefore, in this study, it was
hypothesized that farmer’s age and adoption and intensity of use of coffee technology
package has inverse relationship.
Sex of household head (SEX): Sex of the household head influences the adoption of
improved technologies. In most cases, males have better access to information, credit and
agricultural inputs and are more likely to adopt new technologies than females. Therefore,
maleness was hypothesized positively influence adoption and extent of use of coffee
technology packages. This variable takes a value of 1 if the household head is male and 0,
otherwise.
Education level of household head (EDUCHH): It represents the level of formal schooling
completed by the household head at the time of the survey. Education enhances farmers’
ability to practice, interpret and make the right decision to adopt or reject new technologies.
Therefore, in a study the education level of the household head was expected to be positively
related with level of coffee technology packages adoption.
Family size (FAMSIZE): Family size refers to the number of members who are currently
living within the family. Large farm family needs to produce more food to feed its members
27
and thus more likely to invest on high productive technologies than those of small family do.
It was hypothesized adoption and intensity of use of coffee technology package related
positively.
Economic variables
Household income (ANINCOME): This refers to annual income in Birr obtained from the
farm, off-farm and non-farm activities by the household. The higher the household’s income,
the higher was the probability of acquiring farm inputs leading to higher adoption of new
technologies. Therefore, income was hypothesized to affect adoption and extent of use of
coffee technology package positively.
Livestock Ownership (LVSTOCK): This is the total number of livestock holding by the
farm household measured in tropical livestock unit. Livestock are important sources of
income, which can enhance the purchasing power of farmers. Therefore, a household who has
more livestock may take more risk of trying new technologies than others. As result of this,
the effect of the size of livestock holding was hypothesized to influence adoption and
intensity of use of coffee technology package positively.
Labor availability (LABAVAIL): It refers to the active labor force that the household owns
and is measure in terms of man equivalent. As coffee cultivation is much more tedious and
laborious than the cultivation of other grain crops, availability of labor in the household is
very important factor for adoption of coffee technologies. Therefore, availability of labor in
the household was hypothesized to influence adoption and intensity of use of coffee
technology package positively.
Farm size (FARMSIZE): This variable refers to land holding size of a household measured
in hectares. Many empirical studies argue that farmers with large area of land are more likely
to adopt new technology than those with small land area; and large farm size provide good
opportunity for investment on agricultural production. It was hypothesized that farm size is
28
directly associated with the probability of adoption and intensity of use of coffee technology
package.
Institutional factors
Access to training (PEXTION): this is dummy variable, which takes a value 1 if the
household has access to extension service such as training, demonstration and field days,
workshop, etc and 0 otherwise. The variable representing training as a source of information
and knowledge has influence on farm household’s adoption of coffee technology package.
Therefore, it was assumed that this variable has positive influence on adoption and intensity
of use of coffee technology package.
Access to market (DISMARKT): It refers to distance between the household’s residence
and the market centre measure in kilometres. The closer the farmer to the market, the more
likely he/she was received valuable information, buy farm inputs and sale his/ her farm
produces easily, and hence more likely adopt improved technologies. Therefore, distance to
the market canter was expected to negatively affect adoption and intensity of use of coffee
technology package.
Access to credit (CREDIT): Differential access to credit or capital is often cited as a factor
in differential rate of technology adoption. It is a dummy variable, which takes the value 1 if
the farm household uses credit and 0 otherwise. Therefore, access to credit was hypothesized
to positively influence adoption and intensity of use of coffee technology package.
Development agent contact (CONTACTDAs): This is also dummy variable indicating
whether the respondent has contact with development agents or not. It takes the value 1 if a
farmer has contact with DAs, and 0 otherwise. Contact with development agent is believed to
be the main source of information, knowledge, and advice to smallholder farmers in the
country. Farmers who have a frequent contact with development agents are expected to accept
and practice new ideas faster than those farmers who made few contacts. It is, therefore,
hypothesized that development agent contact had positive relationship with adoption and
intensity of use of coffee technology package.
29
Social variables
Social participation (SSTATUS): This refers to household’s leadership positions assumed in
community, organizations such as farmers’ cooperatives and unions, rural kebeles
administration, Ider, Equip and yelemat Buden. These organizations are important sources of
information, credit, labour and technology exchange to the farming communities. Hence,
participation in these organizations as leaders was hypothesized to have positive influence on
adoption and intensity of use of coffee technology package. It was measured by the number of
social organizations in which respondents were assumed leadership position.
Cosmopolitans (COSMOPLT): This refers to the degree of mobility of individuals. If a
person has opportunities to visit other places outside his/ her locality, he/she can learn many
things from the new environment and become interested to try new ideas and technologies in
his/her locality. Therefore, the mobility of a respondent was hypothesized to influence
adoption and intensity of use of coffee technology package positively. This variable measures
by the number of time that the respondent has been outside his/her district.
Listening agricultural program (RALIST): This is refers to access of information and on
the willingness and ability of farmers to use information channels available to them. It is a
dummy variable taking a value 1 if the farmer has radio listening habit and 0 otherwise. At
present, radio is popular means of mass communication. So that, farmers who have radio
listening habit can have better access to agricultural information than farmers who don’t have
radio listening habit and make decision to adopt agricultural innovations. Therefore, Radio
listening habit was assumed to increase the probability of adoption and intensity of use of
coffee technology package.
Psychological factors
Achievement motivation (ACHIMMOT): This is defined as the need of an individual to
perform different roles with some degree of excellence. This variable was measured using the
scale suggested by Pareek and Rao (1992) with slight modification as 1= disagree, 2=
30
undecided and 3= agree. Achievement motivation had positive relationship with dependent
variable.
Attitude towards coffee technology package (ATTITUDE): Farmers attitude towards the
technology affects its adoption. To measure farmers degree of liking or disliking the varieties,
those who are like adopted technology have five point scale was employed (1 = strongly
dislike, 2- disagree, 3= undecided, 4= agree and 5= strongly agree), with a reverse scoring for
negative statement. Therefore, it was hypothesized that favourable attitude towards coffee
technology package influence the interest of adopting and intensity of use of coffee
technology package.
3.5
Analytical Technique
Quantitative data analysis and presentation involves the use of descriptive and inferential statistics
such as frequency distribution, measures of central tendency, percentage and Chi-square test and
F-test respectively. This helps to assess and analyze farmers' adoption and intensity of adoption
behavior of coffee technology package. Pearson correlation was used to see the direction of
association between variables. Moreover, Tobit model was used to determine the relative
influence of various explanatory variables on the dependent variable.
Adoption is a decision to make full use of an innovation at best appropriate course of action
available (Rogers, 1983:176). For multiple practices (package), there are two options of
measuring adoption;
i.
Adoption index: measures the extent of adoption at the time of the survey. It is
used in the case of adoption study of multiple practices to measure adoption and
intensity of adoption of improved bread wheat production package at the time of
the survey.
ii.
Adoption quotient: measures the degree or extent of use with reference to the
optimum possible without taking time into consideration. In this study, the first
option was employed. Accordingly, adoption index which shows to what extent
31
the respondent farmer has adopted the whole set of package will be calculated
using the following formula.
In this study, the first option was employed. Accordingly, adoption index which shows to
what extent the respondent farmer has adopted the whole set of package was calculated using
the following formula. In order to know the intensity of adoption of coffee technology
package, adoption index of individual farmer was calculated as follows:
AIi = adoption index of the ith farmer
NP = Number components of coffee technology package
AHi = Area allocated for improved coffee verities by the ith farmer
ATi = Area allocated to coffee (improved + locals) by the ith farmer
PPAi = Actual rate of pruning practice (tree/ha) applied by the ith farmer on coffee varieties
PPR = Recommended rate of pruning practice (tree/ha) for the cultivation of coffee varieties
SPAi = Actual rate of spacing practice between coffee trees (centimeter) applied by the ith
farmer on coffee varieties
SPR = Recommended rate of spacing practice between coffee trees (centimeter) for the
cultivation of coffee varieties
SAi = Actual seed rate (Kg/ha) applied by the ith farmer on improved coffee varieties
SRi = Recommended seed rate (Kg/ha) for the cultivation of improved coffee varieties
WAi= Actual weed rate (No_/year) applied on improved coffee varieties
WRi= Recommended weed rate (No_/year) applied on improved coffee varieties
CRi = Actual rate of compost (ha) applied by the ith farmer on coffee varieties
CRi = Recommended rate of compost (ha) applied by the ith farmer on coffee varieties
On the basis of adoption index score, adopter farmers were classified in to three categories as
low, medium, and high adopter. Adoption index is thus a continuous dependent variable
which is affected by different factors to be investigated. Tobit model was used to identify
factors affecting farmers’ adoption and intensity of use of coffee technology package.
32
The Tobit Model
Tobit model is an extension of Probit model and it is one of the approaches dealing with the
problem of censored data (Johnston and Dandiro, 1997). Some authors call such model
limited dependent variable model, because of the restrictions put on the values taken by the
regressand (Gujarati, 1995). Tobit model is superior over the other dichotomous regression
models in that the later only attempts to explain the probability of adoption of agricultural
technologies by the farm households rather than the extent of adoption. However, adoption of
improved technology alone is not sufficient enough since improvement in production and
productivity of farm households depends not only on adoption but also on the intensity of use
of the technology. Strictly dichotomous variable often is not sufficient for examining intensity
of adoption (Feder et al., 1985). In such cases, Tobit model, which has both discrete and
continuous part, is appropriate as it handles both the probability and intensity of adoption at
the same time.
Many researchers have used Tobit model to identify factors affecting adoption and intensity
of adoption of improved agricultural technologies. To mention some, for instance, Nkonya et
al. (1997) used Tobit model to identify factors affecting adoption of improved maize seed and
fertilizer in Northern Tanzania. He used area planted with improved seed and area receiving
fertilizer as continuous dependent variables for running Tobit model.
From adoption studies conducted in Ethiopia, Legesse (1992) and Chilot (1994) used Probit
and Tobit model to identify factors affecting adoption of improved varieties, fertilizer and
herbicide. Both of them used Probit model to identify factors affecting adoption of improved
variety and Tobit model to identify factors affecting intensity of fertilizer and herbicide use.
On the other hand, Techane (2002) used Tobit model to identify determinants of adoption and
intensity of use of fertilizer in Ethiopia. In the same line, Endrias (2003) and Getahun (2004)
used Tobit model to assess factors affecting adoption and intensity of adoption of sweet
potato varieties and wheat technologies respectively.
Specification of the Tobit Model
33
The econometric model applied for analyzing factors influencing adoption and intensity of
technology use is the Tobit model shown in equation (1). This model is chosen because it has
an advantage over other adoption models (LPM, Logistic, and Probit) in that it reveals both
the probability of adoption of new technology and intensity of its use. Following Amemiya
(1985), Maddala (1992) and Johnston and Dinardo (1997), the Tobit model can be defined as:
AIi* = βXi + ui
i = 1.2,………n
AIi = AIi* if AIi* > 0
(1)
= 0 if AIi* ≤ 0
Where
AIi = is adoption index for ith farmer
AIi* = is the latent variable and the solution to utility maximization problem of intensity of
adoption subjected to a set of constraints per household and conditional on being above
certain limit,
Xi = Vector of factors affecting adoption and intensity or level of use of coffee technology
package,
βi = Vector of unknown parameters, and
ui = is the error term normally distributed with mean 0 and variance  2.
The model parameters are estimated by maximizing the Tobit likelihood function of the
following form (Maddala, 1992 and Amemiya, 1985).
 AI   i X i
i
L= 
 
AI i * 0 



1





AI i * 0
  i X i 
F

  
(2)
Where  and F are respectively, the density function and cumulative distribution function of
AIi*.  Means the product over those i for which AIi*  0, and

AI
AI i  0
over those i for which AIi*>0.
34
i
0
means the product
A computer software known as “STATA” was employed to run the Tobit model. It may not
be sensible to interpret the coefficients of a Tobit in the same way as one interprets
coefficients in an uncensored linear model (Johnston and Dinardo, 1997). Hence, one has to
compute the derivatives of the estimated Tobit model to predict the effects of changes in the
explanatory variables.
As cited in Maddala (1997), Johnston and Dinardo (1997) and Nkonya et al. (1997),
McDonald and Moffit (1980) proposed the following techniques to decompose the effects of
explanatory variables into adoption and intensity effects. Thus; change in Xi (explanatory
variables) has two effects. It affects the conditional mean of AIi* in the positive part of the
distribution, and it affects the probability that the observation will fall in that part of the
distribution. Similarly, in this study, the marginal effect of explanatory variables was
estimated as follows.
1. The marginal effect of an explanatory variable on the expected value of the dependent
variable is:
( AI i )
 F ( z) i
X i
Where,
(3)
i X i
is denoted by z, following Maddala, (1997)

2. The change in the probability of adopting a technology as independent variable Xi changes
is:

F ( Z )
  (z) i

X i
(4)
3. The change in the intensity of adoption with respect to a change in an explanatory variable
among accessed (utilized) is:
35
2

f ( z)  f ( z)  
E ( AI i / AIii*  0)
 

=  i 1  Z
F ( z )  F ( z )  
X i


(5)
Where,
F (z) is the cumulative normal distribution of Z,
(z)
is the value of the derivative of the normal curve at a given point (i.e., unit normal
density),
Z
is the Z score for the area under normal curve,

is a vector of Tobit maximum likelihood estimates and

is the standard error of the error term.
Before running the Tobit model all the hypothesized explanatory variables were checked for
the existence of multi-co linearity problem. Two measures often suggested testing the
existence of multi-colinearity. These are: Variance Inflation Factor (VIF) for association
among the continuous/discrete explanatory variables and contingency coefficients for
dummy/categorical variables. In this study, variance inflation factor (VIF) was to test multi-co
linearity problem for continuous/discrete and dummy/categorical variables.
According to Maddala (1992), VIF can be defined as:
VIF (Xi) =
1
1  Ri2
Where Ri2 is the squared multiple-correlation coefficient between Xi and the other
explanatory variables? The larger the value of VIF, the more would be the problem, as a rule
of thumb, if the VIF of a variable exceeds 10 (this will happen if Ri2 exceeds 0.95), that
variable is said to be highly collinear (Gujarati, 1995). Tobit using computer software
(STATA) and insignificant variable was dropped and all significant variables were included
in to the Tobit model. List of variables with their description is presented in Table 2 below:
36
Table 2: List of variables included into the econometric model
Variable name
SEXHH
Description
Sex of HH
Variable types
Values
Continuous
Measured by assigning
score value
AGEHH
Age of HH
Continuous
Measured in years
EDUCHH
Education of HH
Continuous
Measured in years of
schooling
FAMSIZE
Family size of HH
Continuous
RADIOHB
Radio listening habit Continuous
Measured in number
Measured by assigning
score value
LABORAVAL
Labor availability
Continuous
Measured in number
SOCIALPT
Social participation
Continuous
Measured by a
assigning score value
COSMOPLT
Cosmopoliteness
Continuous
The number of times
that a HH had been
outside the district
LIVESTOCK
Livestock owned
Continuous
Measured in tropical
livestock unit
ANINCOME
Total annual income
Continuous
Measured in birr
FARMSIZE
Land holding size
Continuous
Size of farm land
measured in hectare
ACCESSTRA
Access to training
Continuous
Measured by a
assigning score value
ACCESSCR
Access of credit
Continuous
Measured by Birr
DISMARKET
Access of Market
Continuous
Measured by kilo meter
DACONTACT
Contact with DAs
Continuous
Frequency of DAs
contact with HH per years
ACHIMMOT
Achievement
Continuous
Measured by a
assigning score value
ATTITUDE
Attitude of HH
Continuous
technology package
Measured by
assigning score value
37
4. RESULTS AND DISCUSSION
This chapter is the nucleus of the thesis work and it is consist of the overall findings of the
study under adoption and intensity of use of coffee technology package. In this chapter the
status of adoption and intensity of use of coffee production package, current practices of
coffee technology package are discussed in detail. Subsequently, the influence of different
personal, demographic, socio-economic, institutional and psychological factors on the
adoption and intensity of use of coffee technology package were discussed consecutively.
Coffee technology package involves the use of different practices recommended by the
research system and being promoted by extension. These include the use of improved variety,
seeding rate, fertilizer rate, pruning, spacing, compost application rate, and stumping,
recommended frequency of weeding. In any case significant improvement of a farm
household in coffee production and productivity depends on his/her adoption and level of
adoption of these packages.
In this study, out of the recommended coffee technology package mentioned above, only
variety use, seeding rate, compost rate, pruning, spacing and frequency of weeding were
included for calculating the adoption index. Due to absence of variation among farmers and
difficulty in getting reliable data, the remaining package components were excluded from
adoption index calculation.
The categories as non adopter, low adopter, medium adopter and high adopter were identified
based on the result of adoption index score. Adoption index score was calculated by adding
the adoption index of each practice and dividing it by the number of practices applicable. This
helps to know the level of adoption of each farm households.
The adoption index of each practice was also calculated by taking the ratio of actual rate
applied to the recommended rate, which indicates the extent to which an individual farmer has
adopted the package of practices. The final adoption index scores of sample adopter groups
were categorized into three as low, medium and high. The non-adopter group was given a
38
score of 0 and kept as separate category to investigate factors influencing adoption and
intensity of use of coffee technology package. Towards this end, for ease of comparison and
identification of determinants of adoption and intensity of adoption, respondents were
categorized into four adopter categories.
The adoption index score ranges used to classify respondents as non-adopter, low, medium
and high adopter were 0, 0.35–0.57, 0.58–0.78, 0.79–1 respectively. Classifying the adopters
based on equal ranges majority of adopters are belonging to high adopters followed by
medium and low adopters respectively. The percentage of non adopters, low adopters,
medium adopters and high adopters are 12.5, 06.89, 26.88 and 53.75 respectively. Table 2:
shows the distribution of sample respondents by the level of adoption of coffee technology
package. The actual adoption index score ranges from 0 to 1. Adoption index score of 0 point
implies non-adopter of the overall coffee technology package and 1 implies as per the
recommendation of adoption. Distribution of sample respondents by adoption category is
illustrated in Table 3.
4.1
Current Status of Adoption and Intensity of Use of Coffee Technology Package
Technologies are usually recommended in a set or package form for use to farmers. However,
for several reasons farmers usually adopt only certain components of the package. Moreover,
in most cases there is variation in intensity or level of use of a given technology or practice.
Diversity among farmers in their level of adoption could be related to many factors.
Understanding why farmers adopt one component of the package while rejecting the other as
well as the underlying reasons for their variation is of paramount importance.
With regard to the study area, the finding revealed that 12.5% of the sample respondents were
found to be non adopters. The mean adoption index score of the sample respondents was
found to be 0.69. One way analysis of variance indicated that there is significant mean
difference (F=1232, P=.000) among the adoption index score of the four adoption categories
at 1% significance level which indicated variation in level of adoption among sample farmers
(Table 3).
39
Table 3: Distribution of respondents by level of adoption of coffee technology package
Adopter
Index
Category
N
Percent
Score
Mean
S.D
F
Non
20
12.5
0
0
0
Low
11
6.87
0.35-0.57
0.48
0.059
Medium
43
26.88
0.58-0.78
0.69
0.064
High
86
53.75
0.79-1.00
0.88
0.063
Total
160
100
0.00-1.00
0.69
0.186
1232***
Source: own survey data, 2011; ***the mean difference is significant at 1% level.
In addition, from the total sample respondents, 12.5% had adoption index score of 0 which
indicates their overall package non-adoption while the remaining proportion (87.5% of
adopters) had adoption index score between 0.35-1 indicating adoptions at different levels.
Farmers’ deviation from the recommended rates could be associated with several factors to be
discussed in the next sections.
4.2
Current Practices of Coffee Technology Package
Farmers' current practices of the six components of coffee technology package are discussed
here below.
4.2.1 Adoption of coffee varieties
Among improved coffee varieties, 744, 74112, 741,754 and 74158 are widely grown in the
study area. The intensity of variety adoption is measured in terms of area covered by the three
improved coffee variety. The area coverage varies among coffee growing sample households.
As indicated in Table 4, the total sample households’ average area coverage was 0.47 hectare.
The minimum and maximum area coverage by adopter sample households ranged from 0.17
to 1.00. One way analysis of variance (F=6.408, P=0.002) revealed that there is significant
mean difference among adopter categories at 1% significance level. The difference in area
coverage under improved coffee variety may be attributed to varying land holding and stage
of an individual in the adoption process.
40
Table 4: Intensity of adoption of improved coffee varieties
Adopter Category
N
Mean
S.D
F
Low
11
0.35
0.15
Medium
43
0.41
0.18
High
86
0.52
0.22
Total
140
0.47
0.21
6.408***
Source: own survey data, 2011; ***the mean difference is significant at 1% level
4.2.2 Seed rate
Seed is considered as a critical input contributing significantly to agricultural production.
Using quality seed, proper seeding rate and appropriate time of planting are the most
important practices in improved coffee production. Excessive or under utilization of seed
would result in poor plant population and leading to low production.
The amount of seed required to plant out 1ha (10,000m2) at a final field of 3000seedling /ha:
including 5%, 10%, 10% and 20% are losses in transit from nursery to field, losses in the
field, losses at planting and estimated loss from poor germination respectively; generally,
farmers will require 4687 seeds/ha. Therefore, the methods of calculating the amount of seed
requires are count out the seeds in piles of 500 and check the number. As alternative, weight
the seed. In a kilogram of coffee seeds there are between 4,000 and 4,500 seeds/kg. To be on
the save side, the base of calculation is 4000 seeds/kg then farmers required 1.17kg/ha.
As a rule, research recommends specified level of seeding rate, seed quality and time of
planting for a given variety of crop. Hence, in the study area extension workers are
recommended 1.17kg/ha for the three coffee varieties that are mention above. Technology
promoters are also advice farmers to adopt the recommendation as they are. Farmers' use of
the recommended seeding rate however depends on several factors including their own
criteria such as seed selection, seed preparation, seed viability and other household related
socio-economic factors also influence adoption decisions. Farmers in the study area were
found to use varying seeding rates ranging from 0.75 to 1.75 kg per ha.
41
Table 5: The average seeding rate applied by sample households (kg/ha) (N=140)
Adopter Category
N
%
Mean
S.D
Low
11
7.86
1.04
0.09
Medium
43
30.71
1.11
0.03
High
86
61.43
1.17
0.02
Total
140
100
1.15
0.02
Source: own survey data, 2011; **, at 5% level of significant
F
P
2.909** .058
r
.202
On an average low, medium, and high adopters used 1.04, 1.11, 1.17 kg/ha respectively.
Except the high adopter groups the average seed rate used by sample households is below the
recommended rate. However, there was a significant variation among the sample households
in the amount of seed rate per unit area used where the minimum was 0.75 kg, while the
maximum was 1.75 kg per ha.
One way ANOVA analysis of variance revealed the existence of significant mean difference
in seeding rate applied among the three adopter categories, low, medium and high ( F=2.909,
P=.058 ) at 5% significance level (Table 5). Even though the average seed rate used by
respondents is below the recommendation rate there are more sample households who applied
more than recommended rate.
During FGD, it was revealed that they have gap on quality seed selection, preparation and
produced. So, government organization like MoA, WoA and others who are participating in
this sub sectors need to provide training on how to maintain quality seed for farmers or supply
them with quality seed regularly.
4.2.3 Compost application
Application of compost improves the activity of micro organisms and improves macro–and
micro- nutrient availability. Compost acts as a good soil conditioner and improves the
physical, chemical and biological properties of the soil. Good growth conditions usually have
a positive effect on bean size and flavor (Wintgens, 2004). Taye (1998) reported the use of
decomposed coffee husk at a rate of 10 ton/ ha -1 (4 kg tree -1 on dry weight basis) was found
42
to be superior in terms of yield performance of coffee trees. A significant improvement in
growth and yield of mature coffees was reported in response to coffee pulp and husk compost
application (Chane, 1999). On the contrary, repeated application of elephant grass or livestock
manure resulted in an increased percentage of undesirable brown colored bean and, thus, poor
roasting characteristics. This effect was associated with a magnesium deficiency induced by
the high potassium content of elephant grass as well as high concentration of potassium and
calcium in manure (Wintgens, 2004).
Table 6: Compost application rate by adopter categories (kg/tree)
(N= 140)
Adopter Category
N
%
Mean
S.D
F
Low
11
7.85
4.64
1.12
Medium
43
30.71
4.14
1.08
High
86
61.43
4.54
1.04
Total
140
100
4.42
1.07
2.284NS
Source: own survey data, 2011; NS, no significant difference
The result of this study revealed that the average rate of compost applied by sample farmers
was found to be 4.42kg/tree. The maximum amount of compost used by sample respondents
was 7.00kg/tree while the minimum was 0.4kg/tree respectively. Analysis of mean variance
indicated that there was no significant mean difference in the application rate of compost in
general (F= 2.284, p=.106) among adopter categories with respect to adoption and intensity of
use of coffee technology package (Table 6).
During the group discussion, interesting facts were brought to the focuses by the respondents.
They expressed that none of them have used inorganic fertilizer and chemical in their coffee
field due to this reason farmers in the study area were prepared organic fertilizer like compost
with close supervision and technical support by extension agent and woreda agricultural and
development personnel.
43
4.2.4 Spacing practice
Spacing is one of important practice for coffee production to avoid nutrient competition.
Sufficient spacing between plants in a row and plants within rows is vital to get maximum
yield in given plot of land. However, the level of space between coffee trees is varying and it
may be depending on different factors such as coffee varieties, farming system, farm size and
so on. Appropriate spacing enables the farmer to keep appropriate plant population in his/her
field. Hence, a farmer can avoid over and less population in a given plot of land which has
negative effect on yield. In the study area, the extension agents have recommended spacing
for coffee production as 2x2m spacing where 2m is spacing between plants and 2m between
rows.
Table 7: Spacing practice applied by adoption category
Adopter Category
Medium
High
N
%
N
%
N
Yes 6
54.5
31
72.1
80
No
5
45.5
12
27.9
6
Total 11
100
43
100
86
Source: own survey data, 2011; χ2= 16.472***, df=2, p= .000
Low
Total
%
93.0
7.0
100
N
117
23
140
%
83.6
16.4
100
From the total numbers (140) of sample respondents, about 117 (83.6%) farmers reported that
they adopted spacing practice as recommended while the rest 23(16.4%) sample respondents
did not adopt spacing practices as recommended. Table 7 shows that 54.5%, 72.1% and
93.0% of low, medium and high adopters were adopted spacing practice as recommended.
About 45.5%, 27.9% and 7.0% of low, medium and high adopters reported that they were not
adopted spacing practice as recommended. Pearson chi-square analysis revealed that spacing
practice among the adopter categories there were significant difference at 1% level (χ2=16.472,
P=.000) (Table 7) between the adoption categories in relation to spacing practice and adoption
and intensity of use of coffee technology packages.
44
During group discussion farmers pointed out that, adopted the practice as recommended in the
`field is required additional labor, time and costs as well as its reduced plant density due to all
these reasons the practice did not adopted by all sample households.
4.2.5 Pruning practices
Pruning is important cultural practice in the management of coffee. Pruning of the young
coffee plant makes sure the trees attain the desired physical framework during their formative
period. In mature plants, pruning helps reduce the incidence of pests and diseases and
regulates production when it starts to decline. However, if coffee trees are left to grow freely
without pruning, they tend to produce heavy crops during the early years. By above the fifth
cropping year this will result in serious dieback of the primary branches due to overbearing
and damage to the root system because of the death of the feeder roots.
Table 8: Pruning practice applied by adoption category
Adopter Category
Low
Medium
High
N %
N
%
N
%
Yes
5
45.5
27
62.8
77
89.5
No
6
54.5
16
37.2
9
10.5
Total
11 100
43
100
86
100
Source: own survey data, 2011; χ2= 19.164***, df=2, p= .000
Total
N
%
109
77.9
31
22.1
140
100
Accordingly, the data from field survey revealed that 77.9% of the sample respondents had
adopted pruning practices while the rest (22.1%) hadn’t used pruning practice. In the same
way, among the adopter categories 45.5%, 62.8%, and 89.5% for low, medium and high
adopter had used pruning practice respectively. The chi-square test was used to see whether
there is statistically significant difference between the adopter categories.
The result of chi square test revealed that there was statistically significant difference at 1%
level between pruning practice and adoption and intensity of use of coffee technology
package (χ2= 19.164, p= .000) (Table 8). From this result, it could be conclude that there is
45
statistically significant association between pruning practice and adoption and intensity of use
of coffee technology package in the study area.
During FGD, participants pointed out that adopting pruning practice in coffee field is very
difficult due to additional materials, skills and labor required. In generally, less number of
farmers only used this practice in the study area. So, any stakeholder should design some
kinds of training on how to prune a coffee tree.
4.2.6 Weed management
Weed management is very crucial to avoid nutrient competition. Weeds can be controlled by
uprooting, plowing or harrowing and slashing. The extension agents suggest weeding on
average 5-6 times per year.
Table 9: Weeding frequency applied by adoption index categories
(N=140)
Adopter Category
N
%
Mean
S.D
F
Low
11
7.86
3.00
0.45
Medium
43
30.71
3.65
0.72
High
86
61.43
5.23
1.10
Total
140
100
4.57
1.28
54.518***
Source: own survey data, 2011; *** significant at 1% respectively
r
.651
As indicated in Table 8, the average weeding frequency applied by the sample respondents
was 4.57 times per year with standard deviation of 1.32. The maximum weeding frequency
applied by the sample farmers was 7 times per year while the minimum was 2 times per year.
The mean weeding frequency applied by low, medium and high adopter was found to be 3.00,
3.65 and 5.23 times per years respectively. This indicted that except the high adopter groups
the average weeding frequency applied by sample households is below the recommendation
rate. The result of one- way ANOVA indicated that there was statistically significant mean
weeding frequency difference at 1 % level (F= 54.518, P= .000) between weeding frequency
and adoption and intensity of use of coffee technology package (Table 9). Moreover, result of
bivariate correlation analysis revealed positive and significant relationship of weeding rate
46
with adoption and intensity of coffee technology package. This means that there is statistically
significant association between weeding rate and adoption and intensity of use of coffee
technology package in the study area.
According to FGD, most of the participants mentioned that the main reason for weeding rate
is need for additional labor, time and cost required.
4.3 Relationship of Independent Variables with the Dependent Variable
In this study, the independent variables that are thought to have relationship with adoption of
coffee technology package were grouped as household’s personal and demographic variables,
socio-economic variables, psychological and institutional related variables. The relationship
of these variables with adoption and intensity of use of coffee technology package are
discussed under the following sub topics.
4.3.1 Household personal and demographic variables
Age of the household head (AGEOHHH)
Age is one of the demographic factors that is useful to describe households and provide clue
about the age structure of the sample and the population. Age is usually related to the risk
management nature of an individual farmer. Because of their risk averting nature, older
people are usually reluctant to adopt new technologies. Based on this assumption, age was
hypothesized to have negative relationship with adoption of coffee technology package. As
indicated in Table 11, the average age of the respondents was 47.21 years with standard
deviation of 10.48. The maximum age for the sample farmer was 75 years while the minimum
was 27 years. The mean age of non, low, medium and high adopters was found to be 44.70,
43.809, 46.02, and 48.92 years with standard deviation of 9.69, 10.85, 10.19 and 10.58
respectively. The result of one- way ANOVA indicated that there was no significant mean age
difference among the adopter categories (F= 1.927, P= .127) (Table 10). This means that there
47
is no statistically significant association between age and adoption and intensity of use of
coffee technology package in the study area.
The result of the study in agreement with the studies of Ahmed (2010) and Yemane (2010) on
farmers’ evaluation and determination of adoption of upland rice varieties in fogera district
and determinants of adoption of improved durum wheat (Triticum durum) varieties in the
highlands of bale were also reported the absence of relationship between age and adoption of
new technology.
Sex of the household head (SEXOHHH)
Sex difference is one of the factors that influence adoption of agricultural technologies. Due
to many socio-cultural values and norms, males have freedom of mobility and participation in
different meetings and consequently have access to information. Thus, sex of the household
head was hypothesized to influence adoption of coffee technology in such way that maleheaded household were expected to make a decision to adopt technologies more than femaleheaded household.
In this study, sample respondents were composed of both male and female household heads.
The survey result revealed that among the total respondents, 81.9% were male respondents
and the remaining 18.1% were female respondents as it is presented in Table 10. From the
total male headed respondents, 65%, 36.4%, 76.7% and 94.2% were found to be non, low,
medium and high adopter categories respectively, while Out of the total female-headed
respondents, 35%, 63.6%, 23.3% and 5.8% were found to be non, low, medium and high
adopter categories respectively. The chi-square test of sex distribution of male-headed and
female-headed revealed that there is statistically significant at 1% level difference between
sex of household head and adoption and intensity of use of coffee technology package. This
confirms the finding of Tadesse (2008), Ahmed (2010) and Yemane (2010) and consistent
with the prior expectation that male headed households are performing more production
activities than the female headed households.
48
Table 10: Sex of household head by adopter category
Adopter category
Total
Sex of the HH head
Non
Low
Medium
High
Male
65%
64.6%
89.7%
98.1%
Female
35%
35.4%
10.3%
1.9%
Total
100 %( 20) 100 %( 48) 100 %( 39) 100 %( 53)
Source: own survey data, 2011; χ2= 0.365***, df = 3
p=.000
81.87%
18.12%
100 %( 160)
Education of household head (EDUCLHHH)
Adoption of a given technology is a behavioral change process, which is the result of a
decision to apply that particular innovation. Farmers need enough information about the
technology to make the right decision. Education enhances the capacity of individuals to
obtain, process, and utilize information from different sources.
The maximum educational achievement for the sample farmers was 11.00 while the lowest
was 0.00. Medium and high adopter had better level of educational achievement on average
about 5.45 years of schooling than non and low adopters who on average had educational
achievement of 3.22 implying the significant role of education in adoption and intensity of use
of coffee technology package. Result of mean test showed that there was significant
difference in education level among adopter categories.
The result of this study is in agreement with most of the empirical finding reported by various
authors. For example, Rahmatu (2007), Tadesse (2008), Mubarak (2009), Ahmed (2010),
Yemane (2010). Based on their research conducted in different parts of Ethiopia have found
positive relationships between education and adoption of technologies but contradictory with
the study of Mekonnen (2007).
49
Table 11: Age, education, family size and family labor of sample farmers by adopter
categories (N=160)
Non
Age
44.70
Education
2.35
Family size 4.35
Laboraval
1.23
Source: own survey
respectively
Adopter category
Low
Medium
High
F
43.09
46.02
48.91
4.09
4.39
6.50
6.45
6.47
6.89
1.73
1.96
2.19
data, 2011; ***, * and NS significant at 1, 10%
1.927NS
21.058***
9 .734***
4.960*
and non significant
Family size (FAMLYSIZ)
Large farm family needs to produce more food to feed its members than those of small family.
As the family size increases, the household tends to invest on more productive technologies to
meet the subsistence requirement of the family. On the other hand, large family size is
indicator for availability of labor, provided that the proportion of those within the age range of
active labor force is high. Availability of labor in the household is one of the important
resources for coffee production. Based on these assumptions, family size was hypothesized to
have positive and significant relationship with adoption and intensity of adoption of coffee
technology package.
In line with these assumptions, result of statistics in Table 11 indicated the average family
size of the sample population is 6.43. It was observed that the non, low, medium and high
adopters’ categories had a family size of 4.35, 6.45, 6.47 and 6.89 respectively. Test of mean
difference using one-way ANOVA shown that there was significant mean difference at 1%
significance level (F= 9.734, p= 0.000) between family size and adoption and intensity of use
of coffee technology package. Moreover, the result of the Pearson correlation coefficient
revealed (r=.336) positive and significant relationship of family size with adoption and
intensity of coffee technology package.
50
The result of this study is in conformity with the studies of Amsalu (2008), Gebresenbet
(2008) and Yemane (2010) who reported positive relationships of family size and adoption
and intensity of use of coffee technology package.
4.3.2 Economical variables
Labor availability (LABORAVAL)
The man equivalent (ME) was calculated for the sample respondents. The average labor
availability in terms of man equivalent for sample household as it is described in Table 11
was 2.01 with standard deviation of 1.09. The average number of available labor force in
terms of man equivalent for non, low, medium and high adopters were1.23, 1.73, 1.96 and
2.19 with standard deviations of 1.20, 1.01, 0.86 and 1.07 respectively.
In this study (Table 11), significant difference was observed at 1% with regard to the size of
labor force between labor availability and adoption and intensity of use of coffee technology
package through evident from the analysis of one way ANOVA (F= 4.960 and P= .003). The
result of this study with respect to labor in ME as explanatory variable was similar to studies
that dealt with adoption of technologies by Yishak (2005), Mubarak (2009) and Ahmed
(2010).
Farm size (FARMSIZE)
Land is perhaps the single most vital resource as it is a base for any economic activities
especially in the agricultural sector. Farm activities, particularly crop production, require
primarily the availability of suitable farmland. Farm size was expected to be positively
associated with the decision to adopt coffee technology packages. This means that farmers
who have relatively large farm size will be more initiated to adopt coffee technology package
than farmers with small farm size.
The land holding of the sampled respondents vary between 0.50 and 2.00 ha. The average of
land holding of the sample respondents was 1.54 ha with a standard deviation of 0.40 ha. The
51
mean size of land for non, low, medium and high was 1.40, 1.39, 1.53 and 1.61 ha with
standard deviation 0.40, 0.47, 0.39 and 0.39 ha respectively.
Different empirical studies show that the effect of farm size on household’s adoption decision
is positive and significant. To mention some of them for example, Minyahil (2008), Tadesse
(2008), Ahmed (2010) and Yemane (2010) reported positive influence of household’s farm
income on adoption of improved technologies.
The result of one-way ANOVA analysis indicates that there is statistically significant mean
difference between adoption categories and adoption and intensity of use of coffee technology
package as far as farm size is concerned at 10% significance level (F=2.303, P=.079) (Table
12). The finding in this study provides a supportive evidence for the presence of statistically
significant association between farm size and adoption and intensity of use of coffee
technology package.
Livestock holding (LVSTOCK)
Livestock serve several purposes in rural economy. Farm animals are sources of draught,
power, food (meet, milk and milk products), cash income, means of transportation and animal
dung (as an organic fertilizer and fuel). In addition, they serve as an indicator of wealth and
prestige in rural area. Livestock production is an important component of the farming system
in the study area which includes mainly cattle, sheep, goat, chicken and honeybees. Based on
the aforementioned premises, livestock holding was hypothesized to have positive and
significant relationship with adoption and intensity of use of coffee technology package. To
help the standardization of the analysis, the livestock number was converted to tropical
livestock unit (TLU). Conversion factors used were based on storck et al. (1991), and
indicated in Appendix 3.
According to the survey result, the average livestock holding of the sample household was
1.62 TLU with standard deviation of 1.67. On the other hand, the average livestock holding in
adoption categories in terms of TLU for non, low, medium and high adopters were 1.95, 0.74,
1.05 and 1.95 with standard deviations of 1.87, 1.08, 1.08 and 1.82 respectively.
52
Test of mean difference using one-way ANOVA showed that there was significant mean
variation (F=4.337, P=.006, r= .282) among adoption categories with respect to adoption and
intensity of use of coffee technology package (Table 12). Moreover, result of bivariate
correlation also confirmed the positive and significant relationship of livestock holding with
adoption and intensity of use of coffee technology package. The implication is that those who
have high adoption index have better access to financial capital by selling their livestock to
purchase agricultural inputs and repay their debt.
The finding of this result is in conformity with the result of Ahmed (2010) on determinants of
adoption of improved durum wheat (Triticum durum) varieties in the highlands of bale but
contradictory with the study of Minyahil (2008) and Yemane (2010) on analysis of factor
influencing intensity of adoption of improved bread wheat production package in jamma
district, south wolo zone and Farmers’ Evaluation and Determinants of Adoption of Upland
Rice Varieties in Fogera District, South Gonder respectively.
Table 12: Land holding size, number of frequency, annual income and access of market by
adopter categories (N=160)
Non
LANDSIZE 1.40
LIVESTOCK 1.96
ANINCOME 7601
DISMARKET 2.43
Source: own survey
respectively
Adopter category
Low Medium
High
F
1.39 1.53
1.61
2.303*
0.74 1.05
1.95
4.337***
11455 12870
16127
11 .304***
2.23 1.58
0.69
26.143***
data, 2011; ***, * and NS significant at 1, 10%
P
r
.079
.161
.006
.282
.000
.420
.000
-.506
and non significant
Household income (ANINCOME)
The income sources of the rural farm households can be farm income or income from some
additional means. Both non-farm and off-farm activities were taken as additional sources of
income generating activities in the household total expense in addition to agricultural
activities. In this study summation of income from additional sources with farm income was
tested whether it had significant relationship with adoption and intensity of use of coffee
53
technology package at household. Based on income from crop production, livestock
production and additional sources, analysis was computed as of hypothesized that predicts the
total annual income correlates with adoption and intensity of adoption positively.
Accordingly, the mean annual income of sample respondents and result obtained using one
way ANOVA are presented in Table 12.
As it is indicated in Table 12, the mean annual incomes of the sample respondents were 13864
Birr with standard deviation of 6846.99 Birr. The mean annual income distribution among the
adoption categories represents 7601, 11455, 12870 and 16127 for non, low, medium and high
adoption index respectively. One-way ANOVA analysis of variance was conducted to test the
association of total household income and adoption and intensity of use of coffee technology
package and the test result shown a significant mean (F=11.304 and p=.000) difference
between the adoption categories in relation to income from different sources at less than 1%
probability level. The Pearson correlation test result (r=.420) also reveals that existence of
positive and significant relation between adoption index of household and total annual
income.
4.3.3 Institutional factors
Farmers make decision within a broader environment or context (Tesfaye, 2003:41).
Institutional factors are part of such broader environment, which affects farmers’ adoption
decision of agricultural technologies. Institutional factors in the context of this study include
the degree of farmers’ access to market, credit, training and contact with DAs.
Access to training
Exposure for training on coffee production related issues was hypothesized to correlate
positively with adoption index (adoption and intensity of use of coffee technology package)
practices. Cross tabulation result of Chi- square test in the Table 12 indicates these relations.
54
Table 13: Relationship between access to training and adoption category
Adopter Category
Non
Low
Medium
High
N
%
N
%
N
%
N
%
Yes
16
80
9
81.2
36
83.7
79
91.9
No
4
20
2
18.2 7
16.3
7
8.1
Total
20
100
11
100
43
100
86
100
Source: own survey data, 2011; χ2= 3.410NS, df=3,
p= .333
Total
N
140
20
160
%
87.5
12.5
100
From the total number (160) of sample respondents, about 140 (87.5%) farmers reported that
they had access of training in coffee production and related aspects at least once in years while
the rest 20 (12.5%) of sample respondents had not access of training in coffee production.
Table 13 shows that 80%, 81.8%, 83.7% and 91.9% of none, low, medium and high adopters
had access of training in coffee production respectively. About 20%, 18.2%, 16.3% and 8.1%
of non, low, medium and high adopters reported that they had not access of training in coffee
production. In general, 87.5% of the sample respondents had access of training in coffee
production and 12.5% had not access of training in coffee production. Training variation
among the adopter categories didn’t bring significant difference (χ2=3.410, P=.333) (Table
13) between the adoption categories in relation to access of training and adoption and
intensity of use of coffee technology packages. Probable reason might be that large majority
of the respondents had chance to attend trainings in some or other occasion. In the case of
non-adopters, other reasons might have influenced their adoption behavior.
Access to market (DISMARKET)
Farmers` decision to adopt coffee technology package was hypothesized to correlate with
market distance negatively. Access to market was measured in kilometer that the household
has to travel to local market where almost all farmers in the district buy farm input and sale
their farm produces.
Regarding the distance taken to travel from home to the nearest market place, sample farmers
reported that they had to travel an average of 1.25 km with standard deviation of 1.15 km. The
minimum and maximum distances were 0.03km and 6km respectively. Mean distance to
55
travel to the nearest market center among adopter categories was 2.43, 2.23, 1.59 km and 0.67
for non, low, medium and high adopter respectively. The result of one-way ANOVA analysis
revealed that there was significant mean difference at 1% level (F= 26.145, p= .000, r= -.506)
between distance to market and adoption and intensity of use of coffee technology package.
Moreover, bivariate correlation analysis also showed that there was negatively and significant
relationship between distance to make and adoption and intensity of use of coffee technology
package (Table 12)
In confirmation with the findings of these study different studies conducted by Mesfin (2005),
Mehdi (2005), Yishak (2005) and Ahamed, (2010) revealed that there is negative and
significant relationships between access to market and adoption and intensity of use of coffee
technology package.
Access to credit (ACCESSCR)
Financial limitation is one of the common problems facing farmers in rural areas. Credit
service is another component of institutional variables that influences adoption of agricultural
technologies especially to resource poor farmers to relax the limited finance for purchasing
agricultural inputs from their own saving particularly at the early stage of adoption. Beside
availability of credit, farmers’ level of credit use also matters. Based on this argument, access
of credit was hypothesized to have positive and significant relationship with adoption and
intensity of use of coffee technology package
Table 14: Access to credit by adoption categories
Adopter Category
Non
Low
Medium
High
N
%
N
%
N
%
N
%
Yes 2
10
5
45.5 37
86.0 82
95.3
No
18
90
6
55.5 6
14.0 4
4.7
Total 20
100
11
100 43
100
86
100
Source: own survey data, 2011; χ2= 79.304***, df=3, p= .000
56
Total
N
%
126
78.8
34
21.2
160
100
The survey result indicated in Table 13, from the total respondent 78.8% (126) of sample
respondents had access to credit while the rest 21.2% (34) had not access to credit. Moreover,
among adopter categories 10.0%, 45.5%, 86.0% and 95.3% for non, low, medium and high
adopter had access to credit respectively. The result of chi-square test showed that there was
statistically significant difference at 1% level between adopter categories with respect to
access to credit and adoption and intensity of use of coffee technology package(χ2= 79.303, p
= .000) (Table 14). The probable reason in the study area, the amount of down payment and
fear of indebtedness were found to be the most problems in credit provision system in that
farmers could not afford to pay half of the price of input.
In the study area, currently credit is provided by different organizations or institutions. In
general, micro- finance institution and cooperatives served as a source of credit for 63.8% and
13.8% of the sample household respondents respectively. While informal organization credit
facilities provided credit for 6.2% of sample respondents.
The result of this study is in agreement with the finding by Minyahil (2008) and Yemane
(2010) on analysis of factor influencing intensity of adoption of improved bread wheat
production package in jamma district, south wolo zone and Farmers’ Evaluation and
Determinants of Adoption of Upland Rice Varieties in Fogera District, South Gonder
respectively.
Contact with extension agent (DACONTACT)
The major sources of agricultural information for farmers are frontier extension agents.
Contact with development agents alone may not bring about the required behavioral changes.
The frequency of visit is the single variable that emerged significantly in most of the research
work on technology transfer and adoption. In this study too its correlation was hypothesized
to influence adoption and intensity of use of coffee technology package positively and the
result on frequency of contact with extension agent is presented in Table 15.
57
Table 15: Relation between frequencies of contact with development agents and adoption
index categories (N=160)
Adopter Category
Mean
S.D
F
Non
1.10
0.64
Low
0.91
0.30
Medium
1.05
0.49
High
1.22
0.42
Total
1.14
0.47
2.422*
Source: own survey data, 2011; * significant at 10% level
P
r
.068
.236
The score for frequency of contact with the extension agent was given on the month basis
such as scores, zero for having no contact with extension agent, 1 for only once, 2 For twice
contact etc. Table 13 shows that the average score of sample respondents’ Contact with DAs
was 1.14, with a standard deviation of 0.47. The mean of sample respondent’s contact with
DAs among adoption categories was 1.10, 0.91, 1.05 and 1.22 for non, low, medium and high
adopter respectively. Test of mean using one-way ANOVA showed that there was significant
mean difference at 10% level (F=2.422, p=.068) between frequencies of contact with
development agents and adoption and intensity of use of coffee technology package.
Moreover, results of Pearson correlation coefficient also showed that positive and significant
relationship at 1% level (r=.236, p=.005) between adoption index category with frequency of
extension agent contact.
This result confirms the findings of Abadi (2006), Abrahaley (2006), Minyahil (2008),
Gebresembet (2008), Tadesse (2008) and Mubark (2009) who revealed that contact with
extension/ development agents was positively and significantly related to the adoption
decision of farmers.
58
4.3.4 Social variables
Social participation (SOCIALPT)
In the realm of rural and agricultural development, the importance of social capital is
perceived as a willingness and ability to work together. The very assumption on which the
relationship between social capital and adoption is anchored is that neighboring agricultural
households are, de facto, members of a social structure who exchange information about
improved agricultural practices. Rogers (1995) concluded that: “the heart of the diffusion
process consists of interpersonal network exchange ….Between those individuals who have
already adopted an innovation and those who are then influenced to do so.” In this study,
social participation refers to participation of the respondents in formal and informal
organizations as a member or office bearer. Farmers who have some position in different
formal and informal associations and organizations are more likely to be aware of new
practices as they are easily exposed to information. Therefore, it was hypothesized to
positively and significant relationship with adoption and intensity of use of coffee technology
package.
As indicated in Table 16, the average social participation score for sample household
respondents was 14.03 with standard deviation 5.00, while the mean score of social
participation among adoption categories was 10.45, 9.82, 11.69 and 16.57 and with the
standard deviation 3.07, 9.82, 11.69 and 5.11 for non, low, medium and high adopter
respectively. The result of one-way ANOVA analysis indicates that there was statistically
significant mean difference at 1% level (F=23.546, P=.000) between social participation and
adoption and intensity of use of coffee technology package. Moreover, bivariate correlation
analysis also showed that there was positive and significant relationship between social
participation and adoption and intensity of use of coffee technology package. This study is in
line with Yemane (2010) where he found a positive relationship between adoption and
intensity of use of coffee technology package and social participation.
59
Cosmopoliteness (COSMOPLT)
Cosmopoliteness is the degree of orientation of the respondents towards outside the social
system to which he/she belongs. It can be measured by frequencies of visits to outside his/her
area of residence for several reasons. Cosmopoliteness as independent variable was expected
to have positive relationship with adoption and intensity of use of coffee technology package.
It provides more chance of exposure to external information and environment. As expected,
Cosmopoliteness had significant relationship with adoption and intensity of use of coffee
technology package. There was also significant difference at 1 % level among adopter
categories with respect to this variable (Table 16).
Table 16: Social participation and Cosmo politeness of respondents by adoption index
category (N= 160)
Adopter Category
Variables
Non Low Medium
High
F
SOCIPAR
10.45 9.82 11.69
16.57
23.546***
COSMOP
1.70 0.64 1.58
2.71
23.204 ***
Source: own survey data, 2011; ***significant at 1% level
P
.000
.000
r
.507
.590
Listening to radio agricultural programs (RADIOLHBT)
The adoption process of agricultural technologies depends primarily on access to information
and on the willingness and ability of farmers to use information channels available to them.
The role of information in decision-making process is to reduce risk and uncertainties to
enable farm households to make the right decision on adoption and intensity of use of
technology package. In this regard, radio plays the greatest role in providing information in
shortest possible time over large area of coverage than other communication channels.
Farmers who have radio listening habit can have better access to agricultural technology.
Radio listening habit was hypothesized to have positive influence on the adoption and
intensity of use of coffee technology package.
60
Accordingly, the data from field survey revealed that 93.1% of the sample respondent had
radio listening habit while the rest (6.9%) of the respondents had not radio listening habit. In
the same way, among the adopter categories 90%, 81.8%, 95.3% and 94.2% for non low,
medium and high adopter had radio listening habit respectively. The chi-square test was used
to see whether there is statistically significant difference between the adopter categories. The
result of test statistic revealed that there was no statistically significant difference between
radio listening habit and adoption and intensity of use of coffee technology package (χ2=
2.985, p= .394) (Table 17). From this result, it could be conclude that there is no statistically
significant association between radio listening habit and adoption and intensity of use of
coffee technology package in the study area. The probable reason may be the absence/ lack of
radio program broadcast in relation to coffee technology package.
Table 17: Radio listening habit of sample respondents and adoption index categories
Adoption Index Categories
Non
Low
Medium
N
%
N
%
N
%
Yes
18
90
9
81.8
41
95.3
No
2
10
2
18.2
2
4.7
Total
20
100
11
100
43
100
NS
Source: own survey data, 2011; χ2= 2.985 , df=3, p= .394
N
81
5
86
High
%
94.2
5.8
100
Total
N
149
11
160
%
93.1
6.9
100
4.3.5 Psychological factors
Psychological variable refer to those variables which are related to human mind or mental
processes. In this study, achievement motivation and level of attitude of farmers toward coffee
technology package were included under psychological variable
Achievement motivation (ACHIMMOT)
Achievement motivation can be defined as the need to an individual to perform different roles
with some degree of excellence. It refers to the need for success or the attainment of
excellence. Individuals will satisfy their needs through different means, and are driven to
succeed for varying reasons both internal and external. Individual’s motivations for
achievement can range from biological needs to satisfying creative desires or realizing
61
success in competitive ventures. In this study, achievement motivation was hypothesized to
influence adoption and intensity of use of coffee technology package.
Achievement motivation of the respondents was measured using three scales (agree,
undecided and disagree) and six questions each having a maximum score of three. This makes
the total score of achievement motivation eighteen. The summary result of the study (Table
18) indicates that sample farmers had an average achievement motivation score of 15.81 with
a standard deviation of 2.29. The study also shows that the mean score for non, low, medium
and high adopter categories were 12.50, 14.73, 15.86 and 16.69 with standard deviation 4.29,
1.10, 1.17 and 1.21 respectively. Test of mean variance using one-way ANOVA showed that
there was significant mean difference (F=28.557, p=.000) between adoption categories at 1%
significance level. This means that there was statistically significant mean difference between
achievement motivation and adoption and intensity of use of coffee technology package.
Additionally, bivariate correlation analysis also showed that there was positive and significant
relationship between achievement motivation and adoption and intensity of use of coffee
technology package.
The result of this study is in agreement with the studies conducted by Yemane (2010) who
reported that achievement motivation had significant relation with technology adoption
Attitude of respondents to coffee technology package (ATTITUDE)
Farmers’ attitude in this study was defined as disposition to respond favorably or unfavorably
to an object, a person or an institution, or an event. In this study, attitude of farmers towards
c6ffee technology was measured with help of five point Likert scale. In this scale, for
favorable statements, the scores are allotted on the following orders: 5/strongly agree (SAG),
4/agree (AG), 3/ neutral (NEWTR), 2/disagree (DA) and 1/strongly disagree (SDA). On the
contrary, for unfavorable statements the scoring was reversed.
As it is presented in Table 18, the average score for sample household respondents was 34.67
with standard deviation 3.67, while the mean score of attitude of sample respondents among
62
adoption categories was 33.75, 30.46, 33.42 and 36.05 and with the standard deviation 4.48,
2.34, 3.32 and 3.11 for non, low, medium and high adopter respectively.
The result of one-way ANOVA analysis indicates that there was statistically significant mean
difference at 1% level (F=13.378, P=.000) between attitude toward coffee technology package
and adoption and intensity of use of coffee technology package. In the same way, Pearson
correlation coefficient indicated that the relation between adoption categories and attitude of
sample respondents towards coffee technology package were found to be positive and
significant at 1 % level (r=.490). Maximum value that can be obtained from Pearson bivariate
correlation coefficient is 1. Therefore according to the result obtained it can be concluded that
the relation is strong enough. The result of this study confirms the findings of adoption study
by Roger and shoemaker (1983), Rahmato (2007) and Ahmed (2007).
Table 18: Achievement motivation and Attitude to wards of technology by adopter category
Adopter Category
Variables
Non
Low
Medium
High
F
ACHIMMOT 12.50
14.73
15.86
16.69
28.557***
ATTITUDE 33.75
30.46
33.42
36.05
13.378***
Source: own survey data, 2011; ***significant at 1% level
P
.000
.000
r
.444
.490
4.4 Summary of Results of Descriptive Statistics
Before passing to the econometric part of the analysis it is important to summarize the results
of the descriptive statistics. The overall respondent’s personal and demographic, Socioeconomic, institutional and psychological variables were discussed using descriptive
statistical techniques. The results on each variable were demonstrated using tables and
percentage. In doing so, respondents were treated in four adoption categories. The difference
between adoption categories were assessed using F-test and Chi-square test statistics for
continues and discrete variables respectively. The mean and SD were used to discriminate the
four adoption categories for continuous variables. Out of the hypothesized continues
variables, education level of household, family size, cosmopolitans, availability of labor in
man equivalent, social participation, and livestock holdings in TLU, frequency of contact with
DAs, achievement motivation and attitude towards technology were found to significantly
63
difference across households at less than 1% level. Similarly, out of the five discrete/dummy
variables considered, sex of the household head and access of credit were found to be
significant at less than 1%. Age of household, listening to radio agricultural program and
access to training were also variables in both continue and discrete variables which fail to
discriminate between adoption categories. Therefore, summary of the overall findings is
presented in the subsequent tables 19.
Table 19: Summary of result of continuous /discrete explanatory variables
Mean across adopter Category
Non
Low
Medium
High
F
AGEOHHH
44.70
43.09
46.02
48.92
EDUCHHH
2.35
4.09
4.39
6.50
LABOURAV 1.22
1.73
1.96
2.19
FAMILYSIZE 4.35
6.45
6.47
6.89
SOCIALPT
10.45
9.81
11.69
16.57
COSMOPOT
0.80
1.46
1.58
2.71
LIVESTOCK 1.08
1.67
1.55
2.19
ANINCOME
7601
11455
12870
16127
FARMSIZE
1.40
1.39
1.53
1.61
DACONTACT 1.10
0.91
1.05
1.22
DISTAMKT
2.43
2.23
1.58
0.69
ACHIMMOT 12.50
14.73
15.86
16.69
ATTITUDE
30.15
33.36
33.74
36.05
Source: own survey data, 2011. *** And * significant at 1, 10% and
respectively.
1.927NS
21.058***
4.960***
9 .734***
23.546***
29.520***
4.064***
11.304***
2.303*
24.22*
26.145***
28.557***
22.169***
NS non significant
Table 20: Summary of result of discrete/dummy explanatory variables
Adopter Category
Non
Low
Medium
High  -valu
ACCESSCR Yes
10
45.5
86.0
95.3 79.304***
No
90
50.5
14.0
4.7
RADIO
Yes
90
81.8
95.3
94.2 2.985NS
NO
10
18.2
4.7
5.8
ACCESSTRA Yes
80
81.8
83.7
91.9 3.410NS
N0
20
18.2
16.3
8.1
SEXHH
Male
65
36.4
76.7
94.2 28.737***
Female
35
63.6
23.3
5.8
Source: own survey data, 2011;*** significant at 1% and NS non significant respectively.
2
64
4.5
The Results of Econometric Model
Descriptions of the sample population and test of the existence of association between the
dependent and explanatory variables to identify factors affecting adoption of coffee
production package have been discussed thoroughly in the previous section. Identification of
these factors alone is however not enough to stimulate policy actions unless the relative
influence of each factor is known for priority based intervention. In this section, the
econometric (Tobit) model was used to see the relative influence of different personal,
demographic, socio-economic, institutional, and psychological variables on adoption and
intensity of use of coffee technology package. Before running the model, it was also found
necessary to see the problem of muti-collinearity or association among the variables. In this
case the VIF (Variance inflation Factor) was applied. VIF was used for testing the association
between the hypothesized continuous variables.
According to Degnet et al., (2005), variables which are significant at descriptive statistics
term need to be checked for problem of multi-co linearity. Gebresenbet (2008) and Rahmato
(2007) too followed the same procedure before running of econometric model. Only VIF
(variance inflation factor) was used for testing the association between the hypothesized
continuous and discrete variables using the formula,
VIF ( ) =
Where,
.
is the squared multiple correlation coefficient between Xi and the other
explanatory variables. A statistical package known as STATA was employed to compute the
VIF values. To avoid the problem of multi-collinearity, it is essential to exclude the variables
with the high VIF value (10) which usually happen when
Once VIF values were obtained the
exceeds 0.9 or highly correlated.
values can be computed using the formula. The VIF
values displayed in appendix 3 have shown that all the continuous and discrete explanatory
variables have no serious multi-collineartity problem.
65
4.5.1 Determinants of adoption and intensity of use of coffee technology package
This section deals with the discussion of the empirical findings of the econometric model. As
it is showed in Table 21 and 22, all explanatory variables were included into the econometric
model and out of which only seven variables were found to significantly influence adoption
and intensity of adoption of coffee technology packags. These include households’
educational level, farm size, labor availability, social participation, access to market, and
access to credit and achievement motivation. (Table 21):
Table 21: Maximum Likelihood Estimates of Tobit Model
Variable
Estimated
Standard
T-ratio
P-value
Coefficient
Error
Constant
-.1429164
.1482623
-0.96
0.337
SEXHH
.044499
.0309099
1.44
0.152
AGEHH
-.0015256
.0010122
-1.51
0.134
EDUCHH
.0181349
.0045043
4.03***
0.000
SOCIALPT
.0079997
.0028954
2.76***
0.006
RADIOHB
.0120422
.0415431
0.29
0.772
COSMOPLT
.0021348
.011757
0.18
0.856
LIVESTOCK
.0006401
.0083282
0.08
0.939
FAMSIZE
.0032141
.0044655
0.72
0.473
LABORAVAL
.0226507
.0100345
2.26**
0.026
ANINCOME
-2.37e-07
1.83e-06
-0.13
0.897
FARMSIZE
.0541394
.022678
2.39**
0.018
PARTRAIN
-.0172658
.0328496
-0.53
0.600
ACCESSCR
.0721198
.0295913
2.44**
0.016
DACONTACT
-.0172296
.0248723
-0.69
0.490
DISMARKET
-.0335533
.0129702
-2.59**
0.011
ACHIMMOT
.0343556
.0065478
5.25***
0.000
ATTITUDE
.000018
.0000381
0.47
0.637
Sigma
.1992136
.012357
Log likelihood = 76.507161
ANOVA based fit measure (R2) = 0.6387
Source: Model output, ** and *** represents significance at 5% and 1% level respectively.
Educational level of the household head: As expected, education was positively and
significantly influencing the probability of adoption and intensity use of coffee technology
package at 1% significant level. Generally education is thought to create a favorable mental
attitude for the acceptance of new innovation and practices. It enhances farmers’ ability to
66
acquire, analyze, interpret and use information relevant to the adoption of agricultural
innovations. The result of the analysis indicated that an increase in years of schooling
increases the probability of adoption and intensity use of coffee technology package by 1.8%.
This suggests that farmers with higher educational background would have better opportunity
to access information and can easily understand the benefit of improved coffee use.
This result supports the findings of earlier researches on technology adoption (Kebede, 2006;
Minyahel, 2008 and Ahmed, 2010) who reported positive and significant influence of
household heads’ educational level on adoption and intensity use of technology package.
Social participation: The core of technology diffusion process consists of interpersonal
networks of information exchange between those individuals who have already adopted an
innovation and those who are then influenced to do so (Rogers, 1995; cited in Ebrahim,
2006). In this study also, social participation was considered to influence adoption positively.
It is a social asset that creates an opportunity to share experience and exchange information on
innovations in the farming community.
The model indicated that farmers who have participation in social activities were
hypothesized to have more opportunity of getting access to information and adopting
technologies better than the non-participants. The marginal effect from the model result shows
that a one unit variation of social participation increases the adoption and intensity of use of
coffee technology package by 0.79%. This is imply that strong social participation lead to
have better access of information and technologies then lead to adopt technology.
Labour availability (LABORAVA): Labor availability was found statistically significant at
less than 5% probability level with the expected value and positively related with adoption
and intensity of use of coffee technology package. The model result confirms that households
with high labor availability in man equivalent are more likely to adopt adoption and intensity
of use of coffee technology package than households with low labor availability in adult
equivalent. The likelihood estimation indicates that the probability of using adoption and
intensity of use of coffee technology package increases by 2.2% as labor availability increases
67
by one man equivalent unit. The result of this study was consistent with the finding of many
other researches which were conducted in different parts of the world, as well as agrees with
the ideas mentioned in the hypothesis part of this thesis. Molla, (2005) and Tesfaye, (2006)
mentioned that availability of labor as an important factor for adoption of technology.
Farm size (FARMSIZE): The model output revealed that farm size had positive and
significant influence to the probability of adoption and intensity of use of coffee technology
package at less than 5% significant level. The finding in this study supports the hypothesis
that farmers with large farm size are more likely to adopt coffee technology
practices/packages than those farmers who have small land holding. The marginal effect from
the model result shows that an increase in unit measure of the landholding increases the
probability of adoption and extent of use of coffee technology package by 5.4 %. This
research supports the finding of earlier researches on technology adoption (Yishak, 2005 and
Ahmed, 2010).
Market distance: Distance from nearest market center was assumed to influence coffee
technology package. The finding in Table 21 agrees with the hypothesis in that market
distance to market is negatively and significantly associated the probability of adoption and
intensity of use of coffee technology package at less than 5% significance level. The negative
association suggests that the likelihood of adopting coffee technology package declines as the
distance from market center increases. The possible reason might be farmers nearer to market
center have access to production inputs and the incentive to output market than those at far
distant. As market distance increases, farmers may incur more costs for transport, spend time
and energy. Consequently, farmer initiation for adoption of new coffee technology package
would diminish. For a unit increase in market distance, the probability of adopting and use
extent of improved coffee technology package will decrease by 3.4 %. This result is
contradictory with the finding of Yishak (2005) and Abrhaley (2006).
Access of credit (ACCESSCR): Access of credit was also another institutional variable that
was found to influence significantly the probability of adoption and intensity of use of coffee
production packages less than at 5%. The model result shows that credit use was found to
68
have larger contribution compared to other independent variables i.e. the variable accounted
for a 7.2% of the variation in adoption and intensity of adoption of coffee production package.
This has an implication that credit use helps farmers to relax their limited resources for
purchasing agricultural inputs. Service cooperatives distribute various types of agricultural
inputs on credit basis that requires 50% down payment. In this case, only those farmers who
possess cash at hand can benefit from formal credit. On the other hand, farmers who have no
cash at hand will be devoid of the opportunity. Therefore improving performance efficiency
of actors which are dealing with credit services is pertinent and looks for solutions to correct
the defects associated with credit system. Earlier study made by different researchers also
reveals the same result (Getahun, 2004 and Million and Belay, 2004; Minyahel, 2008).
Achievement motivation (ACHIMMOT): Achievement motivation is something that causes
a person to make an effort to become successful and be goal oriented and influence
significantly the probability of adoption and intensity of use of coffee production packages
less than at 1%. The model output result revealed that a one unit variation in the achievement
motivation increases the adoption and intensity of use of coffee technology package by
3.43%. This means that a strong need for achievement leads for technology adoption.
Similarly, the result of this study is conceded with Yitayal, (2004) and Yemane, (2010).
4.5.2 Effects of change in the explanatory variables on probability of change adoption
and intensity of use of coffee technology package
Estimates of the parameters of the variables expected to have effect on adoption and intensity
of use of coffee technology package are displayed in Table 22. A total of seventeen
explanatory variables were included into the Tobit model. Out of which seven variables were
found significantly influence on adoption and intensity of use of coffee technology package.
These included household level of education, labor availability, farm size and access to
market, access to credit, social participation and achievement motivation. Hence, using a
decomposition procedure suggested by McDonald and Mofffitt (1980), the results of Tobit
model can be used to assess the effects of changes in the explanatory variables into adoption
and intensity of use of coffee technologies. Based on this fact, the effect of changes in the
69
explanatory variables on the probability of adoption and intensity of use of coffee production
package was computed and the results are summarized in Table 22.
Table 22: Marginal effect of determinant variables
Variable
Change in
Probability of
Adoption
EDUCHH
.0004
SOCIALPA
.0002
FARMSIZE
.0012
LOBORAVA
.0005
ACCESSCR
.0034
DISMARKET
-.0008
ACHIMMOT
.0009
Source: Model output, 2011
Change in
Intensity of
Adoption
.0181
.0079
.0541
.0226
.0719
-.0335
.0343
Change
Among the
whole
.0181
.0079
.0541
.0227
.0721
-.0336
.0344
The results computed in Table 22, indicate that the estimated increase in the probability of
change on adoption and intensity of use of coffee technology package resulting from a unit
change in schooling level is about 0.04% and 1.81% respectively. The overall change from
this variable is .0181.
The model result confirms that households with high participation in social activities are more
likely to change on adoption and intensity of use of coffee technology package was .0079
with the probability of change on adoption and intensity of using coffee technology package
increases by0.02% and 0.79% as social participation increases by one unit respectively (Table
22).
The marginal effect of Tobit model analysis showed that the effect of farm size on adoption
and intensity of use of coffee technology package was .0541; a unit increase the farm size of
the household head increases the probability of change on adoption and intensity of use of
coffee production package by0.123% and 5.41% respectively.
Labor availability was found statistically significant at less than 5% probability level with the
expected value and positively related with adoption coffee technology package. The model
70
result confirms that households with high labor availability in man equivalent are more likely
to change on adoption and intensity of use of coffee technology package was .0227 with the
probability of change on adoption and intensity of using coffee technology package increases
by 0.052% and 2.26% as labor availability increases by one man equivalent unit respectively.
The marginal effect of access of credit on the overall coffee production package adoption was
.0721. Table 22 above showed that access of credit increased the probability of adoption and
intensity of adoption of coffee technology package by0.34% and 7.19% respectively. The
effect is very immense as compared to the changes resulting from other significant variables
implying that priority should be given to improving credit service provision system.
The result of this study also showed that distance from market center had negative significant
effect on adoption and intensity of use of coffee technology package (Table 21). As show in
this table, the overall effect of this variable on adoption and intensity of use of coffee
technology package was -.0336, and a unit increase in distance away from the market center
decreases the probability of change on adoption and intensity of use of coffee technology
package by -0.078% and -3.35% respectively.
The model result showed (Table 22) that the marginal effect of achievement motivation on
adoption and intensity of use of coffee technology package was .0344, and if farmers have
positive motivation toward coffee technology package, the probability and intensity of use of
coffee technology package are increased by 0.078% and 3.43% respectively.
71
5.
5.1
SUMMARY, CONCLUSION AND RECOMMENDATIONS
Summary
This study was conducted in order to assess adoption and intensity of use of coffee technology
package. Coffee technology package consider in this study consisted of improved coffee
varieties, seed rate, spacing, pruning, rate of compost application and weed management.
Based on the level of adoption of such package components, adoption index was calculated
for each respondent.
Variation in the adoption among the sample households was assessed in view of various
factors that are theoretically known to influence farmers’ adoption behavior of new
technologies and on the bases of the empirical studies particularly on adoption decision of
coffee technology package. These variables were categorized as household personal and
demographic variables, economic or resource ownership, institutional, social and
psychological factors. Result of descriptive statistics using were used to analyze the
quantitative data. Inferential statistics such as one–way ANOVA and chi-square were used to
evaluate the significance of the relationship between dependent and explanatory variables
Pearson correlation was used to see the direction of association between variables. Moreover,
Tobit model was used to determine the relative influence of various explanatory variables on
the dependent variable.
Variation in adoption decision among the sample household respondents was assessed with
respect to various factors theoretically known to influence farmers’ adoption decision of new
technologies. These variables were categorized as household personal and demographic,
socio-economic, institutional and psychological factors. Result of inferential statistics using
Chi-square test and one way ANOVA analysis tests indicated that most of the variables
hypothesized to influence farmers’ adoption behavior were significantly related with adoption and
intensity of use of coffee technology package.
72
Among households’ economic related variables which were hypothesized to influence
adoption and intensity of use of coffee technology package total annual income, livestock
ownership in TLU, labor availability and farm size were found to be significantly related with
adoption and intensity of adoption.
Sex of household head, family size and education level of household head as demographic
and household related variables were significantly related with adoption and intensity of uses
of coffee technology package.
Regarding institutional variables, access to credit and frequency of contact with extension
agents were found to have positive and significant relationship with adoption and intensity of
adoption in which access of market associated negatively and significantly with the rest of the
independent variables.
Moreover, two psychological variables were taken and tested for their association with
adoption index category and all of them have shown statistical significant association. These
include achievement motivation and attitude towards coffee technology package.
On the other hand, results of the econometric model indicated the relative influence of
different variables on adoption and intensity of use of coffee technology package. A total of
seventeen explanatory variables were included into the model of which seven of them had
shown statistically significant influence on the adoption and intensity of use of coffee
technology package. Accordingly, level of education, social participation, farm size, labor
availability, access of credit and achievement motivation were found to have positive and
significant effect on the adoption and intensity of use of coffee technology package. Contrary
to this, access to market had shown negatively and significant influence on the adoption and
intensity of use of coffee technology package. However, all these seven variables were found
to have relatively more effect on intensity than probability of adoption.
73
5.2
Conclusion and Recommendation
Arabica coffee (Coffea arabica L.) is an economically important crop, which is serves as a
major means of income for the livelihood of coffee farming families in the study area and
contributing the highest of all export revenues in Ethiopia. So that, institutional supports
should be given to the sector, such as credit service, research and extension were not to the
expected level. These factors coupled with other household personal, demographic, socioeconomic and psychological factors greatly affected the adoption and intensity of use of
coffee production packages and consequently production and productivity of the sector.
Therefore, as per the research findings of this study, the following points are recommended to
improve farmers’ adoption and intensity of the same thereby enhancing production and
productivity.
This study has identified key factors that influencing adoption and intensity of use of coffee
technology package in the study area. This insight is also useful to rethink about the barriers
of adoption of new technologies. Therefore, the result can be used by policy makers to
promote technological change that is directly needed for economic development of the
country.
Education was found to have positive and significant influence on adoption and intensity of
use of coffee technology package as it enhances farmers’ ability to acquire and use
information required for production and marketing. Therefore, due emphasis has to give
towards strengthening rural informal education at different levels for youth and adults.
Farmers’ deviation from the recommended coffee technology package was partly due to low
participation in social activities. Therefore, participation of farmers in different formal and
informal organization like peasant association, informal associations( Ider, Ekub, Mahber and
others, farmers cooperatives and women’s association has to be strengthened so as to improve
farmers’ access to information and adoption of technologies.
74
Labour availability of the family was another key factor that influenced adoption of coffee
technology package positively. Coffee technology practice demands labour for different
activities from pre-harvesting up to post-harvesting management. Households with high labor
availability in man equivalent were found to adopt coffee technology package than
households with low labor force. Hence, different coffee technology package with relatively
less labor requirements should be designed.
Distance to the input and output market from residence of the household head was found to be
the determinant of adoption and intensity of use of coffee technology package in the study
area. One way forward to increase market access of rural people is to improve the
infrastructures and to increase the number of roads in each PAs to close average distance gap
and result vehicles could be easily accessible so that rural people visit to nearby market.
Therefore, attention should be given to developing infrastructure and transportation
availability. Moreover, organizing and strengthening producers’, co-operatives will ease
procurement of inputs and sale of outputs in collective basis and will help to overcome market
barrier to some extent.
The study also revealed that technological change among smallholder’s farmers requires an
external financial source through credit. Farmers who have access to credit tend to adopt
coffee technology package more than those who do not have access to credit. Therefore,
barriers on the supply-side of credits (high interest rate, down payment, etc.) should be
overcome if a genuine major means of income for the livelihood of coffee farming families is
to be achieved in the study area. Generally, it is enhancing and promoting the overall national
economy.
Result of adoption and intensity of use of coffee technology package indicated that farmers
with strong need for achievement require encouragement and special support in terms of
rewards and financial aspects. So the concerned bodies should be formulating a strategy for
rewarding and recognition like green certificate, financial and material support for those
farmers who are genuinely successful and be goal oriented.
75
In general, the result of this study indicated that adoption and intensity of use of coffee
technology package was the result of many interplay of several factors, which needs much
due attention by the stockholders in the provision of shade, pruning, fertilizing (such as
compost, manure, etc) spacing, seed preparation, weed management and soil and water
conservation need to be integrated to achieve a sustainable production system.
76
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86
7. APPENDICES
Tables in the appendices
Appendix 1: Improved coffee varieties
Cultivars
741
7487
7492
744
754
7454
74165
74158
74148
74110
74112
74140
7440
Growth habit
Yield (q/ha)
Open type
Medium open type
Compact
11.4
19.1
15.1
11.3
12.0
16.0
17.0
17.0
16.7
22.6
18.3
20.2
14.0
Sources: CTMA and IATP, 1995
Appendix 2: Conversion factor used to compute Man- Equivalent (labor force)
Less than 10 (years)
Male
Female
<10
0
0
10-13
0.2
0.2
14-16
0.5
0.4
17-50
1
0.8
>50
0.7
0.7
Sources: Storck, et al.(1991)
87
Appendix 3: Conversion Factor Used to estimate Tropical Livestock Unit
Animal category
TTLU
TTTLU
Animal category
Cows and oxen
Heifers
Calves
Goat and sheep (adult)
Goat and sheep (young)
Sources: Storck, et al., (1991)
1.00
0.75
0.25
0.13
0.06
Donkey (adult)
Donkey (young)
Horses
Camel
Chicken
Appendix 4: Variance Inflation Factors (VIF) of continuous explanatory variables
Variable
SEXHHH
FAMILYSIZE
EDUCT
SOCIALPT
COSMOPLT
LIVESTOCK
ANINCOME
FARMSIZE
ACCESSTR
DACONTACT
ACHIMMOT
ATTITUDE
DISMARKET
LABORAVAL
Mean VIF
1.47
Source: Model output
Co linearity diagnoses
VIF
1.34
1.08
1.56
2.20
1.59
1.48
1.96
1.35
1.16
1.14
1.52
1.07
1.91
1.16
Tolerance
0.743696
0.923813
0.641651
0.453536
0.628768
0.677425
0.510345
0.738379
0.861214
0.874151
0.656500
0.933484
0.523020
0.864074
88
0.70
0.35
1.10
1.25
0.013
Appendix 5: Interview schedule and check list question
Interview schedule
Serial No_ of the interview schedule …………………………
Dear respondents,
This instrument is designed for the purpose of gathering information regarding Adoption and
intensity of use of coffee technology package in yerga chefe district, Gedeo zone. The final
paper that will be written based on information you provide is intended to be significant in a
variety of ways. For doing so, the information you provide is quite essential. Therefore, you
are kindly requested to provide accurate information as much as possible. The researcher
confirms you that all date will be treated confidentially and only aggregated and average
information will be published.
Thank you in advance
Instruction: circle (use tick mark) or write the answer as may be necessary to indicate your
appropriate response.
Household address and interview results
Address: Woreda _____________Kebele __________ Village (Got)____________
Results of interview
 Complete ______________________
 Not complete____________________
 Name of interviewer______________
 Date of interview_________________
 Signature____
89
1. Household characteristics
1.1 Sex of the household head:
1= Male
0= Female
1.2 Age of the household head:______________________________
1.3 Educational level of household head in years of schooling _________________
1.4 Marital status:
1= married
2= single
3= divorce
4= widowed
2. Social Characteristics
A. social participation
2.1
Have you participated in any activities of formal and informal institutions/
organizations in your area?
0= No
1= Yes
2.2
If yes, type of institutions/ organizations and type of membership
No
Organization/ institution
1
2
3
4
5
6
7
8
*office bearer , member or **Frequency
non member
participation
Peasant association
Farmers cooperatives
Parent committee in school
Religious organization (mosque/church )
Informal associations( Ider, Ekub,
Mahber and others)
Women’s association
Saving and credit
Others (specify)
* 0= Non- Member
1= Member
** 0= Never
1= sometimes
2=office bear
2= whenever conducted
2.3 What services do you get from the formal and informal organizations you belong to?
1= Education/ information
inputs 5= labour
2=loans/ credit
3= seeds/seedling
4=agricultural
6= others (specify)
B. Cosmo politeness
2.4 Do you visit the nearby village/town? 1= Yes 2=No
2.5
If yes, how often?1= daily (4) 2= Most often(3) 3= Once a week(2) 4= sometimes (1)
2.6 What is the purpose of visit?
90
of
1= To discuss on farming issue(5) 2= To purchase agricultural related input(4)
public meetings and mediation issues (3)
4= to visit friends/ relatives(2)
3=for
5= for
recreation (1) 6= any other purpose (specify)=(0)………………
C. Listening to radio agricultural programs
2.7
Do you have a radio?
0= No
1= Yes
2.8
If yes, do you listen to any agricultural education program? 0= No
2.9
If No for 2.8, why? ......................................
1= Yes
2. Economic characteristics
A. Livestock ownership
3.1
Do you have livestock?
0= No
1= Yes
3.2
If yes, specify the type and number of livestock you have
SN
1
2
3
5
6
7
8
9
11
12
Livestock type
Cows
Calves
Heifers
Oxen
Sheep
Goat
Donkeys
Horses
Chicken
Camel
3.3
Are yours livestock important for your coffee production?
Number
Remark
0= No
1= Yes
3.4 If yes, please specify for what purpose and activities are used in coffee production?
.......................................
B.
Labor availability
3.4
What are the sources of labor availability?
1. Family
3. Relatives
2. Hired labor
4. Others, specify
91
3.5
Household Labor Availability and coffee production activities
No _
Age category
Number (#)
Male
Female
1
2
3
4
4
<10 years
10-13years
14-16years
17-50 years
>50 years
3.6
Do you face labor shortage in coffee production activities? 0= No
3.7
For which activities do you demand more Labor?
1= Yes
1.Land preparation
3. Planting
5. Harvesting
7. Marketing
2.Weed management
4. Pruning
6. Compost preparation
8. Others, specify
3.8
Is labor available in your area/ the market if you want to hire?
3.9
If yes, How much is the per man per day currently?.....................(Birr)
3.10
Is the labor cost affordable?
C.
Source of income
a.
On- farm income
3.12
What are your main sources of income (in order of importance)?
0= No
0= No 1= Yes
1= Yes
2= livestock rearing 3= off- farm/ non –farm activities
1= crop production
4= Others
3.13
Estimated cash income from crop sales during last year’s?..................(Birr)
3.14
Estimated
cash
income
from
livestock
and
livestock
products
in
last
year’s?...........(Birr)
b.
3.15
Involvement in off- farm/ non- farm activities
Have you or your family member been engaged in off/non- farm activities?
0= No
3.16
1= Yes
If yes, please mention types of work?
1= hand craft
2= trading
3=causal labor
4= wood and charcoal selling
5= others (specify) …………………….
3.17 Cash income from off/non- farm activities during the last one years?.................(Birr)
92
D.
Farm characteristics
3.18
Total land holding __________ hectares
3.19
What is the total amount of area covered by coffee plant from your total size of
land_______ hectares ?
4. Adoption of Coffee Technology Package
4.1 Among the improved coffee variety/ ies that you already know, which one/s did you try
once and continue to use or discontinue using? And which one/s you have not tried at all? Fill
the following table)
Variety Amount of initial seed Area
A mount of If no tried at all,
(Kg) and who were the covered in yield per qt
**Why?
source for it
“Eka”
Amount
744
74112
71158
Others
*1. DAs
4.Seed Enterprise
*source
2. Research center
3. farmers
5. Cooperative
6. Others (specify)……
** 1= the improved coffee variety have no advantage over the local
2. Short supply of improved seed
3= Have no access for improved varieties
4= improved seed not available on time
6. Others (specify)……………
5= poor quality of improved seeds
4.2 In addition to improved coffee variety/ies, do you grow local variety/ies?
0 = No
1 = Yes
4.3 If yes for 4.4, mention the local coffee variety/ies, you have grown, area covered by it
and how many quintal of grain yield obtained from it since 2001E.C?
93
No_
Name of local Area covered in “Eka”
coffee varieties
Yield per qt.
1
2
3
4
4.4 What are your major criteria for selecting coffee varieties and give rank to the criteria?
Rank (1,2,3,4…n: 1= the
most preferred criterion)
No_
Criteria
1
2
3
5
6
7
Grain yield
Diseases resistant
Agro-ecological condition
Earliness
Market demand
Others
4.5 If you know about the improved coffee varieties, please make comparison between the
improved and local coffee varieties against your own selection criteria? (Give the score of 3
if the farmer prefers improved variety to the local against each criteria; Give 2 if the farmer
perceive no difference between the improved and the local; Give 1if the farmer prefer the
local to the improved one )
No_
Improved
variety
Name of farmer’s most preferred local coffee variety……..
AgroGrain yield Diseases
Earliness Market
ecological
resistant
demand
Others
condition
1
2
3
4
744
74112
71158
Others
4.6
From where do you usually get improved coffee varieties?
1. Office of Agriculture and development
Market
5. Cooperatives
2. By own preparation
3.NGOs4.
6. Others
4.9
Have you ever selected coffee mother trees from your farm? 1. Yes
4.10
If yes for 4.9, how many kilo grams of seed are you prepared for one hectare……?
94
2. No
A.
Information on adoption of the recommended production package for coffee
production.
4.11
Do you use chemical fertilizer for coffee production? 0 = No
4.12
If No for 4.11, why? Because:
1. Chemical fertilizers are too expensive
2. My plots are fertile
4. Others (specify)……
3. I use organic fertilizer
4.13
1 =Yes
If yes for 4.11, when did you start to use chemical fertilizers for coffee
Production........?
4.14
If yes for 4.11, what type, rate and time of application of fertilizers do you use for
coffee production?
Fertilizer
type
Rate for black soil(Kg/ha)
Rate for red soil(Kg/ha)
Time of application
At planting At
tillering
Time of application
At
At
planting tillering
Other
stage
(specify)
Remark
Other
stage
(specify)
DAP
UREA
4.15
Do you prepare and use organic fertilizer/compost for your coffee production?
0= No
4.16
If yes for 4.15, please fill the following table
No Year
1
2
3
4
1= Yes
*How many times A mount/
apply compost /years ha(kg)
A mount per Remark
tree(kg)
for
young trees
2001E.C
2002E.C
2003E.C
2004E.C
4.17
Do you have coffee nursery site?
0 = No
4.18
Is weed is a major problem for coffee production? 0 = No
4.19
How frequently did you weed your coffee farm per year……………?
4.20
How frequently did cultivate your coffee field per year…………..?
4.21
Do you applying pruning practices on your coffee production? 0. No
95
1= Yes
1= Yes
1. Yes
4.22
If yes for 4.21, which types of pruning practice did you usually adopted in your coffee
trees?
1. Formative pruning
3. Maintenance pruning
2. Rehabilitation by side pruning
4.Rehabilitation by topping pruning
4.23
Do you applying stumping practice on your coffee production? 0= No
1. Yes
4.24
If No for 4.23, why...................................?
4.25
Do you applying spacing practice on your coffee production? 0. No
4.26
If yes for 4.25, what is the amount of space you left between plant to plant ____ (cm)
1. Yes
and row to raw_______(cm)
4.27
If No for 4.25, why? -------------------------------
5. Institutional factors
A.
Access to training/Research and/or Extension
5.1 Have you ever participated in any types of coffee training program? 0. No 1.Yes
5.2 If yes for 5.1, please list out/ mention the types, duration and training organizer of
training you participated
No_
Types of training
Duration
training
of Training
organizer
Remark
1
2
3
4
5.3 Have you ever participated in research and extension activities? 0. No
1. Yes
5.4 Please, indicate your participation in the following research and/or extension activities
related to coffee production in the last four years
No
1
2
3
4
5
Research
and/or 1= participated
Extension activities
2=Not participated
Field day/visit
Workshop
Host on farm trial
Farmers training
Others(specify)
96
Number of times
participated in
the last four
years
*who
arranged
activity
you?
Remark
the
for
*1= DAs 2= Research center
3= Seed Enterprise agency
4= cooperatives
5= others (specify)
B.
Access to credit
5.5
Is credit service available in your area?
0= No
1= Yes
5.6
If yes for 5.5, have you ever used credit? 0= No
1= Yes
5.7
If you used credit, what is your source of credit?
1= ACSI/MFI
2= cooperative
3=Local organization
4. Friends and neighbor
5.8
Did you take credit last cropping season? 0= No
5.9
If yes for 5.8, please fill the following table
No
Credit institution
1
2
3
4
4
ACSI/MFI
Cooperative
Local organization
Friends and neighbor
Others (specify)
5.10
Amount many
received
1= Yes
*Purpose of credit
If No for 5.8, why?
1= I have enough money
Payment
5.11
5. Others (specify)
2= lack of awareness
4= lack of collateral
3= No money for down
5= high interest rate
6 others (specify)
Which kinds of credit institution play important role in improving the life of rural
people..............................................?
C.
Contact with DAs and/or others
5.12
Have you ever consult development agent/ Extension worker? 0= No
5.13
If no, why?
5.14
1=Yes
1. No EA nearby
3. No need for service
2. Possessed the required information
4. EA office is far from my residence
5. I am not happy with the EA
6. Others
If yes, did you get advice about improved wheat production? 0= No
97
1. Yes
5.15
If yes, to Q 5.14, how frequently did you make contact with the EA last year? _____
D. Access to market
5.16
No
Where and to whom you sell your coffee product?
Market place
Distance to the *to whom you Remark
market
sale
1
Village market
2
Yirgachefa district
3
Dilla town
4
Others
*1= traders
2= cooperative
5.17
Do you think you have received fair price for coffee in relation with input price?
0= No
5.18
3= consumers 4= others (specify)
1= Yes
In general, what are the major problems hindering coffee production in order of
importance and what possible solutions would you suggest to overcome such
problems?
No
1
2
3
4
5
Problem
Rank (1= most severe problem)
Suggested solution
6
Psychological issues
6.1
Achievement motivation (how is your felling to achieve something?) (Total score)
No
1
2
3
4
5
6
Achievement motivation
Success brings relief or further determination
and not just pleasant feeling
How true it is to say that your efforts are
directed towards success?
How often do you seek opportunity to excel
(to perform better than other)
Would you hesitate to undertake something
difficult?
In how many occasion that might lead to
your failures? (you anticipate failure)
In how many situations do you think you
will succeed in doing things? (anticipate
success)
98
Response
Agree (3)
Undecided (2)
Disagree (1)
True (3)
Not sure (2)
Not true (1)
Always (3)
Some times(2)
Never (1)
Never (3)
Some times (2)
Always (1)
Mostly(3)
Some time (2)
Never (1)
Mostly(3)
Some time (2)
Never (1)
6.2
SN
Attitude towards improved coffee technology package
Statement
Response
Strongly
agree
1
2
3
4
5
6
7
8
Improved coffee technology package
give only extra work load
Improved coffee variety/ies are able to
give good yield than local variety/ies
It is highly costly to implement all
coffee technology package
Use of coffee technology package will
certainly reduce production problem
in the area.
Coffee technology package may be
good for other places, but not in our
place.
Coffee technology package shows the
progressiveness of the farmers
Coffee technology package use more
risk for coffee producer
Use of coffee technology package is
the only solution to achieve the best
yield and income
99
Agree
Undecid
ed
Disagree Strongly
disagree
Appendix 6: Check lists for collecting data from FGD and key informants
For farmers
1. What are the common crops grown in your area?
2. Mention improved coffee varieties that are grown in your area?
3. List out coffee technology packages that are used/ adopted in your area?
4. How do you judge the trend of coffee production in your area?(decreasing, stable or
increasing)
5. What are the conditions that facilitate/ hamper the expansion of coffee production in your
area?
6. According to you, how serious are the following constraints in limiting the increase of
coffee production in your area?
Constraints
Very
Moderately
No
serious
serious
problem
Difficult access to seed
Low yield
Lack of coordination with other stockholder
Lack of management knowledge
Lack of well organized market
Others (specify)
7. What are the sources of improved coffee variety/ies in your area?
8. What are the problems related with coffee production process?
9. What should be done to solve the problem of coffee production at farmers, woreda and
policy levels?
10. Farmers’ attitude towards each of the coffee technology packages with regarded coffee
production?
100
11. What are the common constraints faced by farmers in adopting the different package
components?
For Development agents
1. How do you see the relationship between DAs, coffee producer farmers and Experts in the
area?
2. What kinds of technical support and package are you delivered to coffee producer farmers?
And what about your attitude towards coffee technology packages?
3. Have you ever getting training on coffee technology packages for the last three years?
4. Which types/kinds of coffee technology packages are mostly implemented as
recommended by the coffee producer farmers? And why? In the area
5. How do you explain the adoption rate of coffee technology packages in production process
in the area for the last three years?
6. What are the constraints associated with coffee technology packages, production and
marketing of coffee product?
7. What measures recommended in order to solve the problem associated with coffee
technology packages, production and marketing of coffee of product in country general and in
the study area in particularly?
8. Other recommendations and comments with regard to coffee technology packages,
production and marketing of coffee product?
For Researchers and Expert
1. What kinds of coffee technology packages are you recommended for coffee producer
farmers?
2. How do you explain the application of coffee technology packages in Gedeo zone?
3. What are the constraints associated with coffee technology package in Gedeo zone?
4. What are the opportunities of coffee technology packages in the country in general and in
Gedeo zone in particular
101
5. Lack of effective linkage among stakeholders is said to be the main bottle neck for
effective production and marketing of coffee product in the country in general and in Gedeo
zone in particular. In your opinion what should be done to improve this situation?
6. In my opinion, the objective of coffee technology packages in coffee production to
increase coffee product in the country and at the same time it enhancing foreign currency.
However, this objective is not fulfilled until currently what are the possible reasons for this
failure?
a. What should be done at different levels to solve the aforementioned problems?
b.At farmers level
c. At zone/district level
d.At policy level
7. Issues of quality and quantity (low quality and small quantity) are the main points raised by
exporter in association with coffee production in the area.
a. What are the reasons behind these problems?
b.What measures should be taken to alleviate the problem at:
1. Farmers levels
2. At zone/district level
3. At policy level
4. At exporter level
8. What are the enabling environments/conditions for the adoption of coffee technology
packages in the area?
9. Other recommendations and comments with regard to coffee technology packages,
production and marketing of coffee product so that farmers will benefit from this coffee in the
area
Cooperatives organization
1. coffee variety/ies is/ are more recommended by cooperative organization to the members
2. How do you explain the coffee production potential of Gedeo zone?
3. What kinds of services and technical support are delivered to coffee producer by your
organization in the area?
4. What kinds/ types of coffee technology packages are utilized by the primary coffee farmer
societies
5. How do you see the adoption rate of coffee technology package in coffee production
process in the area for the last three years?
102
6. How many primary coffee farmer societies are involved in the cooperative organization?
7. As you know that there are number of coffee variety/ies like 744, 74112, 74158, and others
so that which you say about the quality of coffee produced in the area?
8. What are the constraints associated with technology package, production and marketing of
coffee product?
9. What measures do you recommended so that the problem associated with production and
marketing of coffee product in the country in general and in the study area in particularly can
be solved?
10. Other comments and suggestions……
103