ASSESSMENT OF FARMERS’ EVALUATION CRITERIA AND

ADOPTION OF IMPROVED BREAD WHEAT VARIETIES

M. Sc. Thesis

DEREJE HAMZA MUSSA

December 2005

Alemaya University

ASSESSMENT OF FARMERS’ EVALUATION CRITERIA AND

ADOPTION OF IMPROVED BREAD WHEAT VARIETIES

A Thesis Submitted to the Department of

Rural Development and Agricultural Extension, School of Graduate Studies

ALEMAYA UNIVERSITY

In Partial Fulfillment of the Requirements for the Degree of

MASTER OF SCIENCE IN AGRICULTURE

(AGRICULTURAL EXTENSION)

By

Dereje Hamza Mussa

December 2005

Alemaya University

ii

APPROVAL SHEET OF THESIS

SCHOOL OF GRADUATE STUDIES

ALEMAYA UNIVERSITY

As members of the Examining Board of the Final M. Sc. Open Defense, We certify that we

have read and evaluated the thesis prepared by DEREJE HAMZA MUSSA and recommend

that it be accepted as fulfilling the thesis requirement for the degree of MASTER OF

SCIENCE IN AGRICULTURE (AGRICULTURAL EXTENSION)

…………………………………. ………………… ……………………

Name of Chairman Signature Date

……………………………………. ……………………… ………………

Name of Internal Examiner Signature Date

………………………………. ……………………………… …………………

Name of External Examiner Signature Date

Final approval and acceptance of the thesis is contingent up on the submission of the final

copy of the thesis to the council of the Graduate Studies (CGS) through the Departmental

Graduate Committee (DGC) of the candidate’s Major Department.

I here by certify that I have read this thesis prepared under my direction and recommend that it

be accepted as fulfilling the thesis requirement.

Ranjan S. Karippai (Ph.D ) ………………….. ……………….

Name of Thesis Advisor Signature Date

iii

DEDICATION

I dedicate this thesis manuscript to my wife, TIRUWORK ABATE and my son, SOLOMON

DEREJE, for their love and untold-enormous partnership effort in my academic success.

iv

STATEMENT OF AUTHOR

First, I declare that this thesis is my bona fide work and that all sources of materials used for

this thesis have been duly acknowledged. This thesis has been submitted in partial fulfillment

of the requirements for an advanced M. Sc. degree at Alemaya University and is deposited at

the University Library to be made available to borrowers under rules of the Library. I

solemnly declare that this thesis is not submitted to any other institution anywhere for the

award of any academic degree, diploma, or certificate.

Brief quotations from this thesis are allowable without special permission, provided that

accurate acknowledgement of source is made. Requests for permission for extended quotation

from or reproduction of this manuscript in whole or in part may be granted by the head of the

major 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 interests of scholarship. In all other instances,

however, permission must be obtained from the author.

Name: DEREJE HAMZA MUSSA Signature: ……………………

Place: Alemaya University, Alemaya

Date of Submission: December 2005.

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BIOGRAPHICAL SKETCH

Dereje Hamza Mussa was born in Jamma District (Sora-Micha village), South Wollo Zone,

and Amhara region on August 6, 1964. He attended his elementary and junior education at

Boren -Teklehaimanot and Jamma-Degollo elementary and junior schools (both found in my

district) respectively. He also attended his High-school education at Woreilu secondary Senior

–high school. After completion of his high school education, he joined Awassa Junior

Agricultural College (under Addis Ababa University) (now Debub University) to attend a two

years Diploma program in Animal Science and Technology. After graduation he was

employed in Ministry of Agriculture and has worked for more than 15 years. After this much

time service he got an opportunity to join at Alemaya University to attend his degree program

education in Agricultural Extension in Mid-career program from 1999 to 2002. After

completion and graduation his education he turned back and joins at Alemaya University to

attend his M. Sc. degree education in Agricultural Extension Since 2004.

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ACKNOWLEDGEMENT

First and for most, I am greatly indebted to Ranjan S. Karippai (Ph.D) my major advisor and

Senait Regassa (Ph.D) my co-advisor for their unreserved help, advice, directing, insight

guidance, support on the field, critical review of my thesis manuscript, invaluable support and

suggestions as without their professional help it was difficult to be successful in my research

work and Thesis write up; in addition, my acknowledgement should forwarded to Dr. Ranjan

for his professional and critical review and Dr. Senait for her help in SPSS and Limdep

computer soft wares as well as Logit, Probit and Tobit, econometrics models, t-test and x2-test

statistics uses and application. My sincere thanks should also go to Tesfaye Lema (Ph.D) and

Tesfaye Beshah (Ph.D) for their unlimited review of my thesis manuscript help, guide and

continues encouragement to be successful in my study and research.

Above all, I am greatly indebted to Ato Zewdu Teferi and his children (Eyu, David and Dani),

Seid Ahmed (Ph.D), Solomon Asseffa, (PhD) for their greatest financial and material

contribution as well as moral encouragement and all sided help.

My thanks and appreciation should also extend to many individuals, to Belayneh Leggesse

(Ph.D), Prof. Panjabin, Asegdew Gashaw with his wife, Mehadi Egi, Ato Walelign and his

wife Abaye, Bizuhayehu Asfaw and Amare Berhanu and Tewodros Alemayhu from Alemaya

university; to Ato Yishak Berado, Amsalu Bedaso, Admassu Terefe and his wife Belay with

their children Eyuti and Mitisha from Alage technical agricultural college; to Ato Chane

Gebeyhu, Ato Gebyhu, Alemayehu and Tekle from Akaki-Kality sub–city and agricultural

unit; to woreilu wereda agricultural office staff members, and Lulseged Bekele, Mohammod

Yimer , Abebaw gidelew and (Ato Eshetu Woraei and w/o Toyiba -through their every day

pray) from woreilu woreda ; to Ato kasye Afre and his wife Turye Getye with their family,

particularly Mamush, Zelalem and Sisaye Kasye-they always accompanied me to and from

bus station of Addis Ababa in my every travel to or arrival from Alemaya University for

academic and research purposes; to Wondye Kasye, Tesafa Belay, Alemye Argaw, Esubneh

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Checolle, Kasye Mohammed and to my brothers Endale and Eshetu Hamza should deserve

acknowledgement for their moral, financial and material as well as all sided helps, wishes

and encouragements to accomplish my study successfully.

My sincere and special thanks should go to Jifar Tarekegn and Yodit Fekadu for their free

charge and complete computer, office provision and all sided co-operations; particularly, Jifar

Tarekegn for his additional and unlimited helps in computer and statistical manipulation

through out my thesis write up.

My heart felt and deepest thanks should go to Tiruwork Abate and Solomon Dereje my wife

and my son respectively who received and paid all suffers and scarifications but the greatest

contributors and partnership in my research and academic success.

I would like to extend my thanks to my mother-Ayelech Sebsibe, and my wife's mothers

Alganesh Afre, who are always with me in help and wish for my success through their

everyday pray.

Several organizations, Alemaya University, School of Graguate Studies, Department of Rural

Development and Agricultural Extension of Alemaya University, Debre-Zeit Research Center

and Agricultural Unit of Akaki-Kality sub-city, Alage Agricultural College should deserve

acknowledgement for their contributions to my study. At last but not the least, I would like to

extend my acknowledgement to IFAD (International Fund for Agricultural Development) that

offered a budget support for this research through EARO and Debrezeit research center.

viii

LIST OF ABBREVIATIONS

AU Alemaya University

B.B.M. Broad Bed Molder

DA Development Agent

EA Extension Agent

EARO Ethiopian Agricultural Research Organization

EARI Ethiopian Agricultural Research Institute

IR Institution of Research

PA Peasant Association

RKA Rural Kebele Administration

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TABLE OF CONTENTS

APPROVAL SHEET OF THESIS ii

DEDICATION iii

STATEMENT OF AUTHOR iv

BIOGRAPHICAL SKETCH v

ACKNOWLEDGEMENT vi

LIST OF ABBREVIATIONS viii

TABLE OF CONTENTS ix

LIST OF TABLES xi

LIST OF TABLES IN THE APPENDIX xiii

LIST OF TABLES IN THE APPENDIX xiii

ABSTRACT xiv

1. INTRODUCTION 1

1.1. Background 1

1.2. Statement of the Problem 2

1.3. Objectives of the Study 4

1.4. Significance of the Study 4

1.5. The Scope and Limitations of the Study 5

1.6. Organization of the Thesis 6

2. LITERATURE REVIEW 7

2.1. Concept and Theoretical Framework of Adoption 7

2.2. Empirical Studies on Adoption 12

2.3. Farmers Participation in Agricultural Technologies Development and

Evaluations 19

2.4. Conceptual Framework of the Study 23

3. RESEARCH METHODOLOGY 27

3.1. Description of the Study Area (Akaki) 27

3.1.1. Location, relief and climate 27

3.1.2. Agriculture and demographic characteristics of the study area 30

3.1.3. Institutional services of the study area 31

3.2. Description of Data Collection and Data Analysis Methods and Procedures 34

3.2.1. Sources and types of data 34

3.2.2. Sample size and sampling techniques 35

3.2. 3. Data collection methods 36

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TABLE OF CONTENTS (Continued)

3.2.3.1. Quantitative data collection methods 36

3.2.3.2. Qualitative data collection method 37

3.3. Analytical Models 37

3.3.1. Logit model 37

3.3.2. Tobit model 40

3.3.3. Other Quantitative data analysis methods 42

3.3.4. Qualitative data analysis method 43

3.4. Hypotheses Testing and Definitions of Variables 43

3.4.1. The Dependent variables of logit and tobit models 44

3.4.1.1. The Dependent variable of logit model 44

3.4.1.2. The Dependent variable of tobit model 44

3.4.2. The Independent variables and their definitions used in logit and tobit

models 44

4. RESULTS AND DISCUSSION 50

4.1. Analysis through descriptive statistics 50

4.1.1. Sample Households’ Demographic Characteristics 50

4.1.2. Respondents` livestock and land ownership 60

4.1.3. Accessibility of respondents to different institutional services 63

4.1.4. Agricultural information sources of the study area 72

4.1.5. Farmers’ selection and evaluation criteria of improved bread wheat

varieties 76

4.2. Analytical results and discussion 79

4.2.1. Analysis of determinants influencing probability of adoption of improved

bread wheat varieties and their marginal effect 85

4.2.2. Analysis of determinants influencing intensity of adoption of improved

bread wheat varieties and their marginal effects 90

5. SUMMARY AND CONCLUSION 101

5.1. Summary 101

5.2. Conclusion and Recommendations 105

6. REFERENCES 109

7. APPENDICES 114

Appendix.1.Information on sample household demographic and socio-economic

characteristics 115

Appendix.2. Interview Schedule for data collection from. Farmers 126

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LIST OF TABLES

Tables Pages

1.The Livestock and crop types in the study area 29

2.The land use of farmers in the study area 30

3.The summary of oxen ownership 31

4.Improved agricultural input distribution of the study area in different years 32

5. Improved bread wheat seed distribution of the study area in different years 33

6.Credit Distribution of the study area in various years 34

7.Sample household heads distribution by Sex, Kebele and adoption category 51

8.Marital status of respondents 52

9.Association of adoption of improved bread wheat and sex of sample household head 53

10. Respondent farmers’ demographics characteristics 53

11.Adopters and non-adopters’ demographic characteristics 54

12.Reasons given for not using improved bread wheat varieties 56

13.Level of awareness of improved bread wheat varieties 56

14.Sample Farmers perception on benefit of fertilizer 57

15.Beginning time of cultivation of improved bread wheat varieties of sample farmers 58

16.Health status and adoption of improved bread wheat varieties 58

17.Sample household educational status 59

18.Livestock and land ownership of respondents’ farmers 61

19.Respondents land ownership in 1996/97 Ethiopian major cropping season 62

20.Respondents’ opinion on extension service of the study area 63

21.Extension support on improved bread wheat varieties and distance of DA’s office 64

22.Summary of respondents’ opinion on credit 65

23.Association between credit and market service 66

24.Summary of households’ accessibility of off-farm job 67

25.Respondent farmers’ reasons for not involvement of their family in off-farm job 68

26.Rrespondents opinion on decision of off-farm and other household resources 68

27 Pattern of off-farm income utilization of respondent farmers 69

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28.Family labor utilization of respondent farmers 70

29.Types of activities and family labor utilization of respondents 70

30.Respondents’ accessibility to non-family labor and to off-farm income 71

31.Respondent farmers labor sources outside their family members 72

32.Respondents’ participation in training, field day and demonstration 74

33.Respondent farmers’ sources of information 75

34.Farmers’ evaluation and selection criteria of improved bread wheat varieties 77

35.Farmers’ preference (selection and evaluation criteria) of improved bread wheat varieties

disseminated in the study area 78

36.Variable Inflation Factor for the continuous explanatory variables 84

37.Contingency Coefficients for Dummy Variables of Multiple Linear Regressions Model 84

38.Factors affecting Probability of adoption of improved bread wheat varieties and the 88

39.The effects of changes (marginal effect) in the significant explanatory variables on the

intensity of adoption of improved bread wheat varieties 92

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LIST OF TABLES IN THE APPENDIX

Appendix Tables Pages

1.The distribution of sample respondents by age gro 115

2 Educational statuses of sample house hold head farmer 115

3 .The sample household family size 116

4.The sample household family size 116

5. Total Family members of sample households in age group 117

6.Respondents farming experience 117

7.Types of livestock and owners and the number of respondents 117

8 .Sample house hold oxen ownership 118

9. Sample house hold land ownership 118

10.Size of farmland holding of sample household 119

11.Respondents average land area and yield of wheat crops in 1996/97E.C.cropping season119

12 Respondents farm land ownership and crop type grown in 1996/97 E.C.cropping season120

13.Respondents livestock ownership 121

14 Respondents livestock ownership in Tropical Livestock Unit (TLU) 122

15. Conversion factors used to estimate the households’ livestock ownership in tropical

livestock units (TLU) 122

16. Discrete characteristics of respondents 123

17. Respondent farmers’ general information 124

18. Factors affecting Intensity of adoption of improved bread wheat varieties (Maximum

Likelihood Tobit Model Estimation) 125

19. Household characteristics 126

20. Land holding & Farm Characteristics of the sample households 127

21. Livestock ownership 127

22. Types of crop grown in the survey year 128

23. Improved bread wheat varieties characteristics 130

24. Cramer’s V and Pearson’s R values for Discrete and Continuous variables 133

25.Resondents leadership position 133

xiv

ASSESSMENT OF FARMERS’ EVALUATION CRITERIA AND

ADOPTION OF IMPROVED BREAD WHEAT VARIETIES

Major advisor: Ranjan S. Karippai (Ph.D)

Co-advisor: Senait Regassa Bedadda (Ph.D)

ABSTRACT

Wheat is beneficial to man long before the dawn of recorded history. Ethiopia is one of the

largest wheat producers in sub-Saharan African next to South Africa. Wheat is one of the most

important cereal crops grown in the study area, Akaki. It contributes to the major share of daily

consumption and cash source. The objectives of this study were: to identify farmers’

evaluation and selection criteria of improved bread wheat varieties disseminated in the study

area; to assess probability and intensity of adoption of farmers in the study area; and to know

and analyze determinants of probability and intensity of adoption of improved bread wheat

varieties in the study area. In this study, data were collected and analyzed qualitatively and

quantitatively. Quantitative data analysis methods employed in this study were (percentage,

tabulation, t-test and X2, Logit and Tobit models) using SPSS and Limdep computer soft ware

programs and qualitatively through group discussion and observations.. In farmers’ evaluation

and selection criteria of improved bread wheat varieties disseminated in the study area HAR-

1685 ranks first, Paven-76 second and HAR-1709 third. White color, large grain size, market

demand, straw quality were the most important, germination capacity, cooking quality, better

yield performance were the second important, water logging resistance, tillering capacity,

good food quality, short maturity date the third important, disease and pest resistance and frost

resistance the fourth, storage and harvesting quality were the fifth important quality were

identified as a selection and evaluation criteria of improved bread wheat varieties in the study

area. Out of the total 150 samples, adopters were 99(66%) and non-adopters were 51(34%). In

determining factors influencing probability of adoption through logit analysis, distance of DA-

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office from farmers’ home, leadership status of respondent farmer, market accessibility and

sample farmer’s experience in extension were identified and (b) intensity of adoption through

tobit analysis, house hold sex, age, education, health status, off-farm income, distance of DA

office, size of farm land holding and extension service were identified. To enhance probability

and intensity of adoption, closer placement of DAs, encouragement of those farmers having

less education, female farmers, popularization of improved varieties, improving varieties

qualities of characteristics and farmers’ environmental, economic and social situation should

get a serious consideration.

1. INTRODUCTION

1.1. Background

Grain cultivation and the intensive utilization of wild grains in the horn of Africa probably

began by or even before 1300 B.C. However, modern agricultural technologies and crop

improvement activities to increase grain production have been introduced to the region very

recently. Wheat was made beneficial to man long before the dawn of recorded history.

Archeological findings and discoveries have indicated that wheat domestication and use as a

human food has a long history, for at least 6000 years (Pearson, 1967); as early as 7500 B.C.

(Langer and Hill, 1982); that took place between 17,000 and 12,000 B.C. (Tanner and

Raemaekers 2001); and for 8000 years (Curtis, 2002).

Wheat is today, one of the most important of all cultivated plants, more nutritious of cereals

and continue to be most important food grain source to human nutrition (Pearson 1967;

Harlan, 1981; and Curtis, 2002) and its contribution to the human diet puts it clearly in the first

rank of plants that feed the world (Harlan, 1981). World wide, wheat is used as human food,

seed, livestock feed, and as an industrial raw material (Tanner and Raemaekers, 2001).

Ethiopia is the largest wheat producer in sub-Saharan Africa. Wheat is an important food crop

and it is one of the major cereal crops in Ethiopia. Wheat in Ethiopia is ranking fifth in area

and production after teff, maize, barley and sorghum and fourth in productivity. Ethiopia

endowed with a wealth of genetic diversity, particularly for tetraploid-wheats. Nevertheless,

the productivity of wheat has remained very low mainly because, improved production

technologies have not been adopted by the farming community (Adugna et.al, 1991). It is

grown in the highlands at altitudes ranging from 1500 to 3000 masl. However, the most

suitable agro ecological zones for wheat production fall between 1900 and 2700 masl. Major

wheat production areas are located in the Arsi, Bale, Shewa, Illubabor Western Harerghe,

Sidamo,Tigray, Northern Gonder, and Gojam regions (Bekele et. al , 2000).

2

Ethiopia began to use improved varieties of bread wheat on a commercial level in 1968. Most

of the early improved bread wheat varieties released were developed in Kenya. The first

varieties of Mexican origin were released in 1974. The first improved durum wheat was

released in 1976, and the first bread wheat varieties developed in Ethiopia were released in

1980 (Adugna et.al, 1991).

Wheat technology demonstrations have been conducted by MOA (Ministry of Agriculture),

AUA (Alemaya University of Agriculture, now –Alemaya University) and IAR (Institution of

Agricultural Research now renamed - EARI (Ethiopian Agricultural Research Institute), since

1958. Through these demonstrations, many wheat technologies have been transferred to

farmers, particularly improved wheat varieties. (Getachew et al, 2002).

In the past, variety development and recommendation was made based on on-station trial with

testing and selecting of promising genotypes under high external input and optimum crop

management practices with low participation of farmers. In most of the cases, varieties

developed under such conditions were poor and failed to prove their superiority under on-farm

conditions and farmers’ management practices. This could be due to differences in

management levels practiced by researchers and farmers and due to the lack of farmers’

participation and interaction in the variety evaluation and selection processes. To fill the gap

of low adoption of technologies by farmers and increase farmers’ participation in technology

evaluation and recommendation, a participatory research approach through client-oriented

research should be employed widely (Getachew et al, 2002).

1.2. Statement of the Problem

Agriculture is the main economic sector in Ethiopia, providing employment for about 85% of

the population, and accounting for around 50% GDP. Despite the importance of agriculture in

its economy, Ethiopia has been a food deficit country for several decades (Tesfaye, 2004).

Available evidence indicates that peasant agriculture in Ethiopia is characterized by

3

inadequate resource endowment and traditional methods of cultivation and husbandry

practices. The majority of small holders in Ethiopia have limited access to land saving

agricultural innovations such as high yielding varieties, inorganic fertilizers and chemicals

(FAO, 1993).

Wheat is one of the most important cereal crops grown in Akaki, the study area and in the

country. It contributes to the major share of daily consumption demand of rural households. In

addition, it is used as cash source for a household. Wheat is one of the major products

marketed. In the area, farmers grow both the improved and local varieties. Even though there

is a tremendous and continuous effort made by agricultural development workers and

researchers adoption and the yield increment of improved bread wheat varieties have not

reached to the required level. There fore, assessing the level of adoption and the related

problems by involving and participating farmers in the study can help to get reliable

information that can be useful to facilitate and fasten t the production of improved bread wheat

varieties.

Most of the times, in the country as well as in the study area, development, introduction and

promotion of improved bread wheat varieties and other agricultural technologies are

conducted without due consideration of farmers’ circumstances, constraints, local environment

and their participation. As a result, less achievement in adoption of improved bread wheat

varieties as well as in other improved agricultural technologies has been resulted. Therefore,

the information revealed in this study on the probability and intensity of adoption and on

farmers evaluation and selection criteria of improved bread wheat varieties by involving

farmers is believed more reliable to use as an input in promotion of improved bread wheat

varieties in the study area and in other areas having similar socio-economic and geographical

conditions.

4

1.3. Objectives of the Study

In general, the objective of this study was to know the status of adoption of improved bread

wheat varieties in the study area. However, the study was focused on the following specific

objectives:

1.to identify farmers’ evaluation criteria of improved bread wheat varieties distributed in the

study area;

2 .to assess the adoption and intensity of farmers improved bread wheat varieties use in the

study area; and

3 .to identify determinants of adoption and intensity of improved bread wheat varieties use in

the study area.

1.4. Significance of the Study

The findings and the results of this study could help to strengthen the promotion and

production of improved bread wheat in the study area and in other areas having similar

geographical and socio-economic characteristics with the study area. Therefore, based on the

knowledge generated from the study, policy makers, government officials, NGOs, extension

personnel, researchers and other development organizations can use as an input in policy,

decision-making, in their development programs and efforts, in order to accelerate, the

diffusion, dissemination and yield performance of improved bread wheat varieties as well as to

make the quality improvement of the varieties characteristics through their professional

efforts. The findings of this study might be also helpful and serve as a springboard for further

investigation and research activities. More over, research organizations and extension

providers can also use as an input in their activities.

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1.5. The Scope and Limitations of the Study

The most important reasons to select and to conduct this research in Akaki area were the

interest of the funding agencies EARO and Debrezeit research center. The Extension Division

of Debrezeit research center also recommended to be conducted this research in this area and

the recommendation got acceptance by EARO head office crop research department. More

over in this area there is a wide wheat production practices going on; the area is also one of the

sixth on farm research and demonstration sites of Debre Zeit research center; and there is also

one research station to conduct on farm verification and adaptation trial in this area. From

those six research sites of Debre Zeit research center, only Akaki is the area where wheat and

other highland cereal crop research activities are conducted. Since wheat is the first most

important crop in this area, the farming population uses this crop as major food and income

source. These are some of the basic reasons why this research was conducted in Akaki.

The other limitations of this study were the budget or financial scarcities to cover the

payments requested by enumerators for data collection and by respondent farmers for the

information they intended to give and for the time they spent during the interview. But to

maintain the quality of data, efforts were made and some payment arrangements for

respondents per interview and for enumerators per interview schedule were made.

Due to the above-mentioned problems, the study was not conducted in other wheat growing

area of the country and was also constrained to cover wider areas and larger sample size even

in this area. As a result the study was limited to be conducted in Akaki area and cover only two

PAs (Peasant Associations) or RKA (Rural Kebeles Administration) and only 150 respondents

randomly selected from the two-selected sample PAs of this area, Akaki. The other limitation

of this study might arise due to its closer location to the capital city of the country Addis

Ababa since some farmers in this area may not spend their full time on their farm. As a result

there is a doubt that they may not provide the real information.

6

On the other hand, the scope of this study was limited to cover and analyze only those factors

influencing adoption and intensity of adoption behavior of farmers such as farmers’ age,

education, health, gender (sex), leadership, extension service, distance of DA office and credit

providers institutions from the farmers’ village, market and credit accessibility farmers’

farming and extension experience, family size, resource endowment like land, livestock, oxen

and labor source. Other factors like farmers’ perception, knowledge, needs and attitude

towards the various characteristics of improved bread wheat varieties were not covered in this

study; hence, it is required to conduct further investigations.

1.6. Organization of the Thesis

This thesis is organized into seven major parts. Part one constituted the introduction, which

focuses mainly on the background, statement of the problem, objectives, significance, the

scope and limitation of the study as well as the organization of the thesis. Part two deals with

review of different literatures on adoption of improved technologies and factors affecting

adoption and intensity of adoption of improved bread wheat varieties. Part three describes the

materials and methods including a brief description of the study area, data collection

procedures and analytical techniques. Part four contains result and discussion. Part five

constitutes summary and conclusion of the study. The remaining parts of this thesis are

reference and appendices, which are covered under part six and seven respectively.

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2. LITERATURE REVIEW

2.1. Concept and Theoretical Framework of Adoption

Adoption was defined as the degree of use of a new technology in long-run equilibrium when

a farmer has all the information about the new technology and it’s potential. Adoption refers to

the decision to use a new technology, method, practice, etc. by a firm, farmer or consumer.

Adoption of the farm level (individual adoption) reflects the farmer’s decisions to incorporate

a new technology into the production process. On the other hand, aggregate adoption is the

process of spread or diffusion of a new technology within a region or population. Therefore, a

distinction exists between adoption at the individual farm level and aggregate adoption, within

a targeted region or within a given geographical area (Feder et. al., 1985).

If an innovation is modified periodically, the adoption level may not reach equilibrium. This

situation requires the use of economic procedures that can capture both the rate and the

process of adoption. The rate of adoption is defined as the proportion of farmers who have

adopted new technology overtime. The incidence of adoption is the percentage of farmers

using a technology at a specific point in time (e.g. the percentage of farmers using fertilizer).

The intensity of adoption is defined as the aggregate level of adoption of a given technology,

e.g., the number of hectares planted with improved seed. Aggregate adoption is measured by

the aggregate level of use of a given technology with in a geographical area (Feder et. al.,

1985).

Diffusions scholars have long recognized that an individual’s decision about an innovation is

not an instantaneous act. Rather, it is a process that occurs over a period of time and consists

of a series of actions (Rogers and Shoemaker, 1971). Adoption is not a sudden event, but a

process. Farmers do not accept innovations immediately; they need time to think things over

before making a decision. There are several well-known schemes for explaining the adoption

process. A popular one involves awareness, interest, evaluation, trial and adoption; and

8

another; knowledge, persuasion, decision and confirmation (Adams, 1982; and Rogers and

shoemaker, 1971).

They elaborated these four stages of adoption process as follows (1) Knowledge: - when the

individual learns of the existence of the innovation and again some understanding of its

function. (2) Persuasion: - when the individual forms a favorable or unfavorable opinion of the

innovation (3) Decision: - when the individual engages in activities that lead to a choice

between adoption and rejection, (4) Confirmation: - when the individual makes a final

decision to accept or abandon the innovation. According to their expression, it is well known

that some people are more innovative (responsive to new ideas) than others. Therefore,

adopters have been subdivided in to categories on the basis of the relative time they take to

adopt innovations (innovators, early adopters’, early majority, late majority and laggards).

Innovativeness generally can be related to other personal characteristics; background, social

status, affiliations, attitudes, etc. Research has shown that adoption of innovations often

follows a bell shaped or normal curve when plotted against time.

Innovations are new methods, ideas, practices or techniques, which provide the means of

achieving sustained increases in farm productivity and income. It is the extension worker’s

job to encourage farmers to adopt innovations of proven value. It is an idea or object

perceived as a new by an individual. The innovation may not be new to people in general but,

if an individual has not yet accepted it, to that person it is an innovation. Some Innovations

originate from agricultural research stations, others from farmers. Innovations relate to objects

social acts and abstract ideas. Generally, innovations may be classified in to technical and

social innovations (Adams, 1982).

Innovations are also classified into process and product innovation (Adams, 1982). A process

innovation is an idea input to a production process, while product innovation is a material

input to the production process. The term innovation and technology are used interchangeably.

Adoption and diffusion are distinct but inter-related concepts. Adoption refers to the decision

to use a new technology, method, practice, etc. by a firm, farmer or consumer. The concept of

9

diffusion refers to the temporal (of time) and spatial (of area) spread of the new technology

among different economic units (firms, farmers, and consumers). These two concepts defined

by many researchers’ belongings to different academic disciplines (Legesse, 1998).

Among the many definitions that suggested by Rogers (1983) has been used in several

adoption and diffusion studies. He defined aggregate adoption (i.e. diffusion) behavior as the

process by which a technology is communicated through certain channels over time among the

members of a social system. This definition encompasses at least four elements:(1)

Technology, which represents the new idea, practices or objects being diffused (2) Channel of

communication, which represents the way information about the technology flows from

change agents (such as extension workers or technology suppliers to final users or adopters,

(3) Time which represents the period over which a social system adopts a technology and (4)

Social system, which is comprised of individuals, organizations or agencies and their adoption

strategies (Kundson, 1991, in Legesse, 1998). Rogers defined adoption as use or non-use of

new technology by a farmer at a given period of time. This definition can be extended to any

economic units in the social system (Legesse, 1998).

With regard to the measurement of intensity of adoption, a distinction should be made between

technologies that are divisible and technologies that are not divisible. The intensity or extent of

adoption of divisible technologies can be measured at the individual level in a given period of

time by the share of farm area under the new technology or by the per hectare quantity of input

used in relation to the research recommendation (Legesse, 1998).

Feder et al., (1985) suggested that this measure might also be applied at the aggregate level for

a region. In the case of non-divisible agricultural technologies such as tractors and combine

harvesters, the extent of adoption at the farm level at a given period of time is dichotomous

(adoption or non-adoption) and the aggregate measure becomes continuous. Thus, aggregate

adoption of lumpy technology can be measured by calculating the percentage of farmers using

the new technology within a given area.

10

There is also a great difference between the agricultural sectors of developing and developed

countries. Agriculture in developing countries is heavily dependent on natural phenomena,

while the effects of natural factors are, to some extent, mitigated by the application of modern

technology and improved weather forecasting systems in developed countries. Moreover,

farmers in developing and developed countries do not face the same types of constraints and

opportunities. Therefore, conclusions concerning technology adoption cannot be drawn for

agriculture in developing countries based on experience of the agriculture in developed

countries (Legesse, 1998).

All individuals in a social system do not adopt a technology at the same time. Rather they

adopt in ordered time sequence. Based on the time when farmers first begin using a new

technology five possible adopter categories can be identified in any social system: innovators,

early adopters, early majority, late majority, and laggards (Rogers, 1962 and Rogers and

Shoemaker, 1971, in:Legesse, 1998).

In describing the characteristics of these groups, Rogers (1962, cited in: Largesse, 1998))

suggested that the majority of early adopters have expected to be more educated, venturesome,

and willing to take risks. In contrary to this group the late adopters are expected to be less

educated, conservative, and not willing to take risks. A practical aspect of the classification of

adopters into adopter categories has been in the field of deliberate or planned introduction of

innovation. Nevertheless, the usefulness of this categorization is restricted as there is evidence

indicating possible movement from one category to another, depending on the technology

introduced (Runguist, 1984, in: Legesse, 1998).

Attention has also been given to explain the mode (or approach) and sequence of agricultural

technology adoption. Two approaches seem to appear in agricultural technology adoption

literatures. The first approach emphasizes the adoption of the whole package and the second

one stresses the sequential or stepwise adoption of components of a package. The technical

scientists often advocated the former approach while the latter has advocated by the field

practitioners, especially by farming system and participatory research groups. There is a great

11

tendency in agricultural extension programmers to promote technologies in a package form

whereby farmers are expected to adopt the whole package. Experiences of integrated

agricultural development projects such as CADU, in Ethiopia, however, show that this

approach has not brought needed technical change because of resource limitation (Legesse,

1998).

The adoption of agricultural innovations in developing countries attracts considerable attention

because it can provide the basis for increased production and income. That means farmers will

adopt only technologies that suited their needs and circumstances (Nanyeenya et. al., 1997). In

efforts to increase agricultural productivity, researchers and extension staffs in Ethiopia have

typically promoted a technological package consisting of a number of components. However,

because of capital scarcity and risk considerations, farmers are rarely adopting complete

packages (Million and Belay, 2004).

Agricultural development implies the shift from traditional methods of production to new,

science-based methods of production that include new technological components and/or even

new farming systems. For farmers to adopt these new production technologies successfully,

they must first learn about them and how to use them correctly in their farming system

(Swanson and Claar, 1984).

The transfer of technology approach grounded on the diffusion model focuses on technology

generation by scientists then handed over to extension to pass on to farmers. In this model,

farmers considered as passive receivers and extension as technical delivery conveyer belts.

The new roles of farmers, the new participatory approaches and methods and the new learning

environments all imply new roles for agricultural scientists and extension (Kiflu and Berhanu,

2004). They have to learn from farmers and develop technologies that can serve the diverse

and complex farmers’ situations. Farmers show great interest to evaluate the promising

varieties that could be suitable to their local situation (Mergia, 2002).

12

Adoption of a new technology must be preceded by technology diffusion, e.g., the act of

making new technology known to the potential adopters. Diffusion is therefore the link

between research and development and adoption. Effective diffusion is an essential but not

sufficient condition for adoption. The farmers of a given target category must not only be

made aware of an available technology, they must also be convinced that adoption is in their

best interests and above all, they must be able to adopt the proposed technology (Andersen,

2002 and Arnon, 1989). Adoption studies in developing countries started two to three decades

ago following the green revolution in Asian countries. Since then, several studies have been

undertaken to assess the rate, intensity and determinants of adoption. Most of these studies

focused on the Asian countries where the green revolution took place and was successful. In

Africa, new agricultural technologies have only been introduced recently (Roy, 1990 and

Rukuni, 1994, cited in: Legesse, 1998).

The effectiveness of agricultural extension work highly depends on the availability of

extension professionals who are qualified, motivated, committed and responsive to the ever-

changing social, economic and political environment. Adoption of technology by farmers can

be influenced by educating farmers about improved varieties, cropping techniques, optimal

inputs use, price and market conditions more efficient methods of production management

storage, nutrition, etc. (Anderson and Feder, 2002).

2.2. Empirical Studies on Adoption

Feder, et al (1985) estimated the relationships among technologies already adopted by maize

growing farmers in Swaziland by using factors analysis. They found farmers adopted the

technologies investigated in three independent packages (1) improved maize verity, basal

fertilizers and tractor ploughing (2) top dressing fertilizers, and chemical (3) planting date, and

plant population (density). These empirical findings do not support a sequential or stepwise

adoption process. They reported that farmers in Swaziland tend to adopt a package of

technologies and the social system adopts a technology, which is comprised of individuals,

organizations, or agencies with their adopting constraints.

13

Jha et al (1991) made a study in eastern province of Zambia to evaluate how small holders

respond to interventions that promote the use of improved biochemical (seeds and fertilizers)

and mechanical (animal traction) technologies. In eastern province, farmers were adopting

both labor saving and yield increasing techniques. Agro ecological factors play a critical role

even with in a relatively small region. Farm size affects the use of fertilizer in eastern province

of Zambia. The age and gender of head of the household significantly influence the adoption

of hybrid maize. Heads of household that are older and females are not likely to adopt hybrid

maize. Extension makes only a small contribution to the process of adopting and diffusing

technology. It contributes to specialized commodity-oriented programs but not to maize, the

main crop.

A study conducted in Sierra Leone by Adesina and Zinnah (1992) on technology

characteristics, farmers’ perceptions and adoption decision using tobit model analysis. The

result of tobit analysis demonstrated that apart from age, farm size, extension service and

experience were positively related to adoption decisions.

The study done by Legesse (1992) in Aris Negelle area on adoption of new wheat technologies

indicated that experience, credit, expected yield, expected profit, cash availability for down

payment, participation in farm organization as a leader, and close exposure to technology were

the factors which significantly influenced the probability of adoption of improved varieties

and intensity of adoption of fertilizer and herbicide. He found that the probability of adoption

of improved varieties increases with an increase in farming experience. Farmers with higher

experience appear to have often full information and better knowledge and were able to

evaluate the advantage of the technology is considered. The study also revealed that credit is a

crucial factor affecting the probability of adoption of improved varieties. And the quantity of

fertilizer farmers applied was found to be sensitive to access of credit. The coefficient of the

variable expected yield was significant and shows the intensity; of fertilizer application on

wheat and maize is related to its expected profitability. In his study, farm size was not found to

be important factor affecting probability of adoption of improved varieties and intensity of

fertilizer application. However, the variable farm size per person significantly and negatively

14

influenced the intensity of herbicide adoption for weed control in wheat. The role of direct

extension visits (as represented by frequency of visit by extension agent) was not found to be a

significant factor affecting adoption. This can be attributed to the limited frequency of direct

extension agent visits to non-contact farmers. On the other hand, the variable close exposure to

technology was found to significantly affect the probability of adoption of improved varieties.

Chilot (1994) conducted a study using Probit and Tobit analytical models to identify factors

influencing the three dependent variables such as rate of adoption of new wheat varieties,

intensity of fertilizer adoption and intensity of adoption of 2.4.D weed control chemical under

the title of adoption of new wheat technologies, by hypothesizing of eleven independent

variables and this research result showed that several factors were affecting adoption of new

wheat technologies in two extension centers, Wolemera and Addis Alem areas. As the study

revealed, access to timely availability of fertilizer, perceived related profitability of the

improved variety, number of extension contacts and wealth position were positively and

significantly related to new improved wheat variety adoption. None of the household

characteristics were significantly related to variety adoption. With regard to the intensity of

fertilizer use, timely availability of fertilizer, number of livestock owned and perceived

profitability were positively and significantly related to the intensity of fertilizer use. Literacy,

wealth position of the farmers, exposure to improved technology and timely availability of 2, 4

–D were significantly and positively related to the intensity of 2, 4, - D use. The result showed

that only one variable, distance of extension agent office from farmers’ home was the common

influencing independent factor affecting inversely and significantly all the three dependent

variables namely the rate of adoption of new wheat varieties, intensity of fertilizer adoption

and intensity of adoption of 2.4.D weed control chemical.

Another study made by Bisadua and Mwangi (1996) in southern high lands of Tanzania

Mbeya region showed that farmers were at various components of the recommended package

of improved maize production. Besides, farmers have adopted these components gradually.

The four major factors that contributed this gradual adoption were cost of technologies,

environmental factors, timely availability of inputs, and source of information of new

15

technologies. Technologies which require little cash out lay such as row planting are easily

taken up by farmers because it was less costly and had an added advantage of simplifying

weeding. Environmental stress affected the adoption of some of the recommendations

especially where maize is planted during the day season, which utilizes residual moisture in

the soil. Farmers who dry planted their maize did not apply basal fertilizer. This might be

because of the fear of scorching their maize seed due to low soil moisture. Others did not

perform the second weeding, apparently because rigorous weed germination will be

suppressed by the moisture conditions. Lack of timely availability of inputs was widely cited

as constraint to use them. In availability of improved maize seed was considered bottleneck to

its use.

Other study in Sudan highlands also suggested unavailability of inputs as major constraint to

their use (Lyimon and Temu, 1992, In: Bisauda and Mwagi, 1996). Giving the extension

service is charged with the responsibility of extending information on new technologies; their

low rates of contact with farmers may be acting as a constraint to the use of these technologies

(Bisauda and Mwangi, 1996).

A study conducted on factors affecting adoption of maize production technologies in Bako

area, Ethiopia, by Asfaw et al., (1997) using logit analytical model by hypothesizing seven

independent variables to influence three dependent variables namely adoption of fertilizer,

improved variety and row planting. The result of the model analysis showed that only one

variable, namely extension activities was significantly influence adoption of improved

varieties. Among the seven proposed independent variables, only two independent variables

affected fertilizer adoption and two independent variables influenced the adoption of row

planting significantly. In addition, only one independent variable namely extension activity,

was influence all the three dependent variables in common among the seven proposed

independent variables. Though, all of the hypothesized independent variables were expected to

influence significantly the identified three dependent variables. Among hypothesized

variables, one independent variable namely credit was excluded from further model analysis

due to it’s less important to affect adoption of row planting as mentioned as a possible reason

16

in the report of the study but included in model analyses of fertilizer and improved variety

adoption.

An assessment of the adoption of seed and fertilizer packages and the role of credit in small

holder maize production in Kakamega and Vihiga districts, Kenya by Salsya et al (1998)

showed that the age of household head, primary education, cash crop area, farm size, and

credit were not significantly correlated with adoption. Secondary education, cattle ownership,

use of hired labor, and access to extension significantly influenced the adoption of improved

maize varieties. The use of hired labor and manure, cattle ownership and membership in an

organization were significant factors affecting the adoption of fertilizer. Livestock ownership

serves as a source of wealth to purchase inputs that affect significantly and positively in

adoption of fertilizer. Farmers who use manure had lower probabilities of adopting fertilizer.

Membership in an organization increased the likelihood of adopting fertilizer. Farmers who

belong to an organization are likely to benefit from better access to input and to information

on improved farm practices.

Farmers' participation in leadership of farmers' organizations seems to be the best prediction of

adoption behavior of the farmer characteristic variables. The relationship between

technologies may be independent, sequential or simultaneous and the patterns of adoption

follow the domicile Period of time by the share of farm area under the new technology or by

the per hectare quantity of input used in relation to the research recommendation (Rauniyer

and Goode 1996, in:Legesse ,1998).

Another study conducted on adoption of soil conservation technologies in philppines uplands

of two areas namely Cebu and Claveria by Lucila, et al. (1999) using probit model by

hypothesizing nine independent variables to influence adoption of soil conservation practices

in these two areas. The result showed that in Cebu and Claveria in each area only three

independent variables were significant to influence the dependent variable adoption of soil

conservation practices. But the common significant independent variable among those nine

17

hypothesized independent variables to affect the mentioned dependent variable in the two

study areas was only one variable, which was the percent of land slope.

A study conducted on adoption of wheat technologies by Bekele et al (2000) in Adaba and

Dodola woredas of Bale highlands of Ethiopia using tobit analysis model demonstrated that

adopters of improved wheat technologies were younger, more educated, those who had larger

families and farm, hired more labor and owned more livestock.

Another study conducted by Tesfaye and Alemu (2001) on adoption of maize technologies in

northern Ethiopia shows that applying chemical fertilizer, access to credit, access to extension

information, distance from development center, distance from market center and family size

were factors affecting adoption of improved maize positively and significantly. The level of

education was found to have no significant influence on the adoption decision of farmers for

improved maize. Attendance of field day and access to extension information were negatively

and significantly related to the adoption of decision of chemical fertilizer use. In this study,

farm size, though positive, was not found to have a significant influence on the adoption of

chemical fertilizer. Access to credit and use of improved maize are the most important factors

found to positively and significantly influence the adoption decision of chemical fertilizer.

A study conducted by Tesfaye et al, (2001) on adoption of high yielding maize variety in

maize growing regions of Ethiopia indicated that use of chemical fertilizer, access to credit,

attendance of formal training on maize production and other agricultural techniques, access to

extension information, distance to the nearest market center, family size and tropical live stock

unit had significant and positive influence. On the side of adoption decision of chemical

fertilizer, access to credit, level of education, farm experience, total farm size, use of improved

maize, use of community labor were found to have a significant and positive influence.

Another study conducted on adoption of improved wheat technologies by small scale farmers

in Mbeya district, southern highlands of Tanzania by Mussei et al, (2001) clearly indicated

18

that farm size, family size, and the use of hired labor significantly influenced the probability of

land allocation to improved wheat varieties. Farm size, family size, the use of hired labor and

credit significantly influenced the probability of fertilizer use. A unit increased in farm size

among adopters decreased the probability of adopting fertilizer by 2.4% family size by 9.7%,

use of hired labor by 4.7% credit by 5.9% has increased the probability of adopting fertilizer

among adopters. Credit enables farmers to purchase inputs and increased the probability of

adopting fertilizes among adopters by 5.1 %.

Another study conducted by Lelissa and Mullat (2002) on determinants of adoption and

intensity of fertilizer use in Ejera district West Shoa Zone, Ethiopia, using probit and tobit

analytical models and the result of probit model analysis indicated owning of draught power,

credit access, owning of large farm size, access to extension service affect adoption of

fertilizer positively. But age affects negatively and education has no significant influence on

fertilizer adoption. Regarding the result of Tobit model analysis on the determinants that

influence the adoption and intensity of fertilizer use; family size, education, draught power,

access to credit and extension service have influenced positively.

A study conducted by Tesfaye (2004) on adoption of in organic fertilizer on maize in Amhara,

Oromia, and southern regions, shows that on the adoption of chemical fertilizer, farm

experience, access to credit, use of improved crop varieties, use of farm yard manure, family

size, level of education, total farm size were considered significant. The larger the farm size

the greater the probability of adopting of chemical fertilizer. In this study, family size was

found to have a positive and significant impact on the adoption decision of chemical

fertilizers. Access to credit can relax the financial constraints of farmers and allows farmers to

buy inputs. The result of the study revealed that credit availability has significantly and

positively impacted up on chemical fertilizer adoption. Educational level has increased the

probably of adoption of chemical fertilizer. Use of improved variety of crops also influenced

the decision of farmers to use chemical fertilizer positively and significantly.

19

A study conducted in Gumuno area of southern Ethiopian by Million and Belay (2004) to

identify determinants of fertilizer use (adoption decision) shows that age of household head,

access to credit, frequency of development agent visit, livestock holding and off- farm in-come

influenced the adoption of fertilizer positively and significantly.

A study conducted by Adam and Bedru (2005) on adoption of improved haricot bean varieties

in the central Rift Valley of Ethiopia, using logistic analytical model found that sex, total

livestock unit, credit, and participation in extension service affect adoption of haricot bean

varieties but dependent family members and land size affect negatively and significantly.

Another study was conducted in the central highlands of Ethiopia, on adoption of chickpea

varieties by Legesse, et al, (2005) using logistic analytical model. The result of analysis

demonstrated that the level of education of household head, farm size, access to extension

service proportion of chick pea area and access to seed affect positively and significantly the

adoption of chick pea varieties.

2.3. Farmers Participation in Agricultural Technologies Development and Evaluations

According to Hanson (1982), farmers, millers, bakers, and consumers differ in their concepts

of desirable qualities in wheat. To farmers, a variety of wheat has quality if it resists diseases,

matures at the proper time, doesn’t topple over before harvest, and gives a good yield of

plump grains without shattering (grain falling, to the ground before harvest). The miller is

concerned with the grain. The Kernels should be uniform, the grain should be free of foreign

matter, the moisture content should be low and the protein content high and the yield of flour

per 100 kg of wheat should be high. The baker who produces leavened bread looks for flour

that produces dough with desirable characteristics, the dough should be able to hold gas

bubbles and yield a large loaf with good internal texture and color. The consumer does not see

what grain before it is milled, but he or she has strong preferences regarding the appearance,

texture, aroma and flavor of the breads, biscuit, cakes and other products that trace their

20

character partly to the wheat Kernels. These different viewpoints of farmers, millers, bakers,

and consumers must all be considered to raise the wheat production.

It is useful to examine several features of small holders farming system in Ethiopia, and in the

third world in general, and their implications for agricultural researchers. First, farmers are

economically rational, that is, they adopt new practices that are in their interests and reject

those that are not. When farmers resist a new technology, it is probably because it is not

compatible with their objectives, resources or environment and not because of their backward

ness, irrationality or management mistakes. Moreover, the small holder’s farming system is

complex; small holders allocate their limited resources of land, labor and capital among many

enterprises in a manner determined by their agro-ecological and socio- economic environment.

Farmers need to compromise enterprises to increase productivity. Farmers consider both

technical and socio- economic aspects when deciding whether to use a new technology.

Researchers are need to obtain an accurate and balanced assessment of the performance of the

varieties, using both scientific and farmers’ own criteria. Farmers rarely adopt complete

packages all at once, that is, complete set of recommended technological components

concerning how to mange an enterprise. Instead, farmers usually use a step-by –step approach,

testing components individually and incorporate the successful ones into their system.

Therefore, researchers need to evaluate new technologies individually or in simple

combinations under farmers’ own management conditions. The greater the farmers’

participation in the designing and testing of a technology, the greater is the chance that they

adopt it (Franzel, 1992).

A study conducted in West Shoa Zone, Ambo Woreda Birbisa and Cherech service-

cooperative by Ethiopian Rural Self Help Association (ERSHA) in (2000) to evaluate bread

wheat technologies on the farmers’ farm condition using farmers’ criteria. According to the

study, farmers have formulated the criteria to evaluate the bread wheat varieties at different

growing stages. The criteria formulated by farmers were crop stand (uniform germination,

strong and healthy, deep green and many tillers), flowering (uniform flowering), heading

21

(panicle size, number of spike lets per head, resistance to lodging, frost and disease), yield

(superior in yield, easy to thresh, stored for long time), Grain quality, (size, color, full body),

baking (dough quality, baking quality and taste). Farmers’ evaluation criteria judging varieties

during vegetative growth stage in order of importance were: tillering capacity (many tillers per

plant), head size (panicle size, head length), frost and disease tolerant (healthy leaf and shoot,

uniform germination and crop stand, resistance to lodging and shattering. Farmers’ evaluation

criteria for judging grain quality characteristics were: yield per unit area, grain size (fill body,

no shrink seeds and deformed seeds), baking quality (dough quality and being good bread),

and color (important for market) and easy to thresh.

These days, it is well known that farmers’ participation in agricultural research and

development processes are increasingly improved by realizing that the socio- economic and

agricultural conditions of small-scale farmers are too complex, diverse and risk prone.

Conventional approaches, which are well known by station-based researches followed by top-

down technology transfer system, are not often adopted in a sustainable manner. Hence,

building a partnership and management with farmers is needed throughout the cycles of

diagnosis, experimentation and technology dissemination. This increases the understanding of

the opportunities and constraints faced by farmers on top of their technical knowledge. This in

turn enhances the prospects of technological development and its adoption rates (Mergia,

2002).

It was realized that farmers have their own priorities in their production strategy and often

accepts those technologies, which they consider as most advantageous to their production

system. Close engagement with farmers through the cycle of diagnosis, experimentation and

dissemination increases understanding of conditions, of the opportunities and constraints

farmers face, and of their own technical knowledge. The package-testing program also helped

to get the assessments and evaluations of the technologies from the beneficiaries themselves.

The approach has considerably contributed in increasing the understanding of the biological

researches towards the farmers’ complex and linked circumstances and constraints. It has also

22

contributed in improving the linkage between research, extension and farmers as compared to

previous approaches (Mergia, 2002).

A study conducted on the use of B.B.M (Broad Bed Maker) technology on vertisol, Sheriff

(2002) shows that farmers in the study area got an opportunity to identify and select different

crop varieties and grow the crop they preferred that can best meet their needs, interests and the

corresponding agronomic practices of their specific agro-ecological conditions. Farmers do not

operate according to the assumption of policy makers and scientists. It, moreover teaches us

that agricultural knowledge varies and is accorded different social meanings depending on

how it is applied in the running farms. This leads to differential patterns of farm management

style, cropping patterns and levels of production. Farmers are heterogeneous and they are

indeed knowledgeable and capable actors who consciously pursue various objectives.

Technological patterns of development should refer back to various resources and farmers

capacity. The achievement of these objectives is influenced by the images they have of various

aspects involving institutions. These call for the negotiation of values and resulting unintended

consequences that could be referred to as counter development. We therefore, need to learn

that technology transfer has to address different farmers’ needs, perceptions and strategies. We

need to intervene with redesigning their use of technologies (Sherif, 2002).

Technology is not always a product of scientific institutions. Human beings are inherently

capable of modifying their environment in the process of adaptation, where by technology is

created and subsequently utilized. The struggle between the environment and people never

stops, though under some circumstances, a long time may pass before intended changes are

achieved. For various reasons, some societies adhere to certain technologies for centuries

where as others pass comparable level of technology in a relatively shorter period of time. For

instance, the revolution of farming tools for different operations in developed countries and

the stagnation of the same in a developing country such as Ethiopians explain this observation

(Tesfaye, 2003).

23

2.4. Conceptual Framework of the Study

In general, it could be inferred that agricultural technology adoption and diffusion patterns are

often different from area to area. The differences in adoption patterns were attributed to

variations in agro-climatic, information, infrastructures, as well as environmental, institutional

and social factors between areas. Moreover farmers’ adoption behavior, especially and in low-

income countries, is influenced by a complex set of socio- economic, demographic, technical,

institutional and biophysical factors (Feder et al, 1985).

Understanding and considering these factors when analyzing and interpreting farmers’

response to agricultural innovations has, there fore, become important both theoretically and

empirically. Adoption rates were also noted to vary between different groups of farmers due to

differences in access to resources (land, labor, and capital) credit, and information and

differences in farmers’ perceptions of risks and profits associated with new technology. The

direction and degree of impact of adoption determinants are not uniform; the impact varies

depending on type of technology and the conditions of areas where the technology is to be

introduced (Legesse, 1998).

Farmers’ decision to adopt or reject new technologies can also be influenced by factors related

to their objectives and constraints. These factors include farmers’ resource endowments as

measured by (1) size of family labors, farm size and oxen ownership, (2) farmers’ socio –

economic circumstance (age, and formal education, etc) and (3) institutional support system

(available of inputs) (CIMMIYT, 1993).

In many developing countries it has become apparent that the generating new technology

alone has not provided solution to help poor farmers to increase agricultural productivity and

achieve higher standards of living. In spite of the efforts of National and International

development organizations, the problem of technology adoption and hence low agricultural

productivity is still a major concern (CIMMIYT, 1993).

24

The inability of farmers to achieve high yield levels has been blamed on many different

sources on extension services side, for not properly disseminating the research station’s

technologies, on input supply systems side, for failing to make the new technologies available,

on policy decision makers side, for making the new technologies unprofitable to use due to

policy distortion and on farmers themselves, who are alleged (assumed) to be too conservative.

However, many studies point to another cause of low adoption rate – the research center

recommendations that are irrelevant to the small farmers’ priorities, resource constraints, and

the physical, cultural and economic environment (Winkelmann, 1977, in: Mulugetta et al,

1994).

On agricultural technology adoption and diffusion determinant factors in different countries

across the world, Africa including Ethiopia, several and various studies have been conducted

and many researchers have obtained various findings. The researchers’ lack of understanding

of the farmers’ problem and the conditions under which they operate may result in the

development of inappropriate technologies and low rates of technology adoption (Fresco,

1984, in: Mulugeta et al, 1994).

In this study efforts were made to revealed factors affecting adoption and intensity of

adoption, the pattern and direction of adoption of improved bread wheat varieties (part of

agricultural technologies) that varied according to farmers’ resource endowment,

environmental situations, technological development, personal characteristics, accessibilities

to different services such as credit, extension, information market and the importance,

suitability, management and cost of the technologies.

Moreover literatures, practical experiences and field observations have confirmed that

technologies adoption by farmers’ can be fasten, enhanced and make sustainable by

understanding those factors influencing the pattern, degree and direction of adoption and by

designing and establishing technologies diffusion and adoption pattern strategies through

25

farmers empowering, making farmers access to infrastructure, information, technologies,

credit, field support how to utilize new technologies.

Other factors should be also included in agricultural technologies disseminations and adoption.

Farmers’ participation in technologies development, selection and dissemination strategies as

well as result evaluation should be considered, because farmers have a long year of farming

and environmental experience. The need and interest of farmers’ towards agricultural

innovations also varies depending on farmers’ farming environment, their belief, experience,

economic status and their personal background and characteristic. Therefore, disseminating

improved agricultural technologies without consultation of farmers most probably ends with

failure.

Several literatures, practical experiences and observations of the reality have been showed that

one factor may enhance adoption of one technology in one area at one time and may hinder it

in another situation, area and time. Therefore it is difficult to develop a one and unified

adoption model in technology adoption process because of the socio economic and ecological

variations of the different sites, and the various natures of the determinant factors. Hence, the

analytical framework presented in the below figure shows the most importance variables

expected to influence the adoption and intensity of adoption of improved bread wheat varieties

in the study area, Akaki.

26

Note = the above Figure shows the chart of conceptual frame work of the study

Asset endowment and other income source

- Livestock

-Farm land

- Off-farm

Institutional variables

- Extension service

- Credit access

-Market access

- Distance of extension office

-Distance of credit provider Institutions

Household socioeconomic characteristics

- Sex

- Age

-Health

- Education

-Experience in extension

Labor Sources

-Oxen

-Labor source

-Family size

Decision to adopt improved bread

wheat and to increase size of farm

land for improved bread wheat

production

27

3. RESEARCH METHODOLOGY

It is well known, that, there are two research methodologies classified under the broad

headings: the qualitative and quantitative research methodologies.. Methods are the tools of

data generation and analysis techniques practically; methods are the tools of the trade (job) for

social scientists and are chosen on the basis of criteria related to or even dictated by the major

elements of the methodology in which they are embedded, such as perception of reality,

definition of science, perception of human beings, purpose of research, type of research, type

of research units and so on.

As many people described the basic objective of a sample is to draw inferences about the

population from which such sample is drawn. This means that sampling is a technique, which

helps us in understanding the parameters or characteristics of the universe or population by

examining only a small part of it. Therefore, it is necessary that the sampling technique be

reliable. (In general, a study on relatively small number of units, are the sample, should be

representative of the whole target population. Sampling is, thus, the process of choosing the

units that could be included in the study, determine the sample size and the sample selection

procedures. A sample design is a definite plan, completely determined before any data are

collected for obtaining a sample from a given population. In this study under this chapter the

study area description and sample farmers’ demographic, resource ownership and institutional

services has conducted.

3.1. Description of the Study Area (Akaki)

3.1.1. Location, relief and climate

The socio-economic and environmental factors of the area play a great role for better

performance of any activity done in that particular area. There fore it is highly valuable to

28

describe the area where the activity is planed to be under taken. . In addition, its accessibility

and the budget constraint of the research were some of the factors to fix and conduct this

research in this area.

This research activity was decided to undertaken at Akaki area, which was selected by its wide

growing and demonstration of improved wheat crop varieties and wide utilization of other

improved agricultural technologies in this area The reasons to conduct this research, in Akaki

area due to wide wheat production practices and high-improved agricultural inputs utilization

as well as wide demonstration practices on agricultural inputs applications and utilizations in

this area.

Akaki, located at South East of Addis Ababa and it is the rural part of Addis Ababa and Akaki-

kaliti sub-city, one of the sub-cities of Addis Ababa. It is bounded by Oromia region to the

east and southern part. The study area, Akaki, constituted 9 Peasant Associations (PAs) or

Rural Kebele Administrations (RKAs) in the mean time when this research survey was carried

out. But at the end of this research survey, these Rural kebele Administrations /Peasant

Associations were reorganized into four reduced number of Rural Kebele Administrations as a

result of a new restructuring program of Addis Ababa administration.

The agro-ecological zone of the study area, Akaki, is 100% high land and its altitude ranges

from 2100 – 2300 masl. In the study area there are different soil types. The most important and

dominant soil type in area coverage is heavy ver ti sol or black clay soil. Except some few

hilly landscapes of the study area, Akaki, virtually is plain.

Therefore, soil erosion by water is not a problem in the study area. But water logging is a very

serious problem resulted from its flat landscape.

29

Table 1.The Livestock and crop types in the study area

No. Types of crop grown

in the study area

Land

Coverage

in (Ha)

Land

coverage in

percent

Types of

Livestock

reared in the

study area

Number

of

Livestock

1 Cereal crops

Wheat

Teff

Others

3580

1930

1560

90

82.11-

-

-

Cattle

Oxen

(Cow and

Others)

17269

4058

13211

2 Pulse crops

Faba bean

peas

Chick pea

lentils

others

604

80

80

201

44

199

13.85

-

-

-

Sheep

Goat

Pack Animals

(Horse, Mule

And Donkey)

10380

3064

5012

3 Oil crops 30 0.70 Poultry 17807

4 Vegetables 104 2.38 Bee-in-Hive No data

5 Others (Fenu greek) 42 0.96 - -

Total 4360 100 - -

(Source: Akaki Agricultural Unit Office, 2005)

The study area, Akaki, is characterized by the high land climate. It has the main and small

rainy seasons. Farmers in the study area rely on the main rainy season known as kiremt or

Meher rainy season for their agricultural production activities. There is no a practice and

experience of crop production using the rain of small rainy season known as Belg rainy season

and irrigation production. The small rainy season extends some times starting from January or

February and ends some times in May or June. But the main rainy season is similar like

otherparts of Ethiopia that extends from end of or mid of June to most of the time mid of

September, according to Akaki agricultural unit office. Some of the major crops grown in the

study area are Wheat, Teff, Faba bean, Chickpea and Lentils.

30

3.1.2. Agriculture and demographic characteristics of the study area

The total farming population of the study area is 14519. Of which, 7626 were male, and 6893

female. The number of household heads of the farming population is 2490 male and 265

female with a total of 2755 household heads. The average family size of the study area is 5.27

per household. In Akaki, agriculture, which includes crop and livestock production, is the main

stay of the farming community like other parts of Ethiopia. The types of crops, the farm land

coverage by each crop type and the types of livestock and the size of livestock population

reared by farming community in the study area are summarized in Table 1.

Table 2.The land use of farmers in the study area

No Types of land use Coverage in (Ha) Percentage

1 Cultivated land 4360 6 1.33

2 Grazing land 151 2.12 3 Forest land 80 1.13

4 Village and construction 2496 35.11

5 Others 22 0.31 Total 7109 100

(Source: Akaki Agricultural Unit Office)

As indicated in table 2, the larger proportion of land in the study area is used for cultivation,

which is 4360 (61.33 %.). Of which the major proportion goes to cereal crop production

particularly for wheat production followed by Teff production. In the study area, there is a very

serious grazing land scarcity, greatly affecting the livestock production, resulted from high

population pressure and extended farming practice that shrinks grazing land and compete with

livestock production. The farming society in the study area used crop by product for animal

feed though it is poor nutritionally.

31

Table 3.The summary of oxen ownership

No. Oxen Ownership Number of owners Percentage

1 With no oxen 1272 46.17

2 With one oxen 243 8.82

3 With two oxen 555 20 .15

4 With three oxen 139 5.05

5 With four oxen 442 16.04

6 With five % above 104 3.77

Total 2755 100

(Source: kaki Agricultural Unit Office)

In the study area as summarized in table 3, around 46.17% farmers are with out ploughing

oxen, according to the Akaki Agricultural Unit office. In the study area Akaki, there is a fuel

wood scarcity resulted from unwise practice of deforestation for long time. There fore the

farming population has forced to use cow dung as their source of energy for heat and food

preparation. Using of cow dung for fuel can affect the utilization of compost to improve soil

fertility for better crop yield.

3.1.3. Institutional services of the study area

Effective Agricultural Extension services have paramount importance to farmers to get timely

advices and information on the availability, use and application of new, improved and modern

agricultural inputs, technologies and practices. The Akaki Agricultural Unit office is

responsible to offer agricultural extension service in the study area. Under this unit office

different expert with different professions has assigned at the Unit Office level and

Development Agent (DA) center level. The 9 Extension Agents / Development Agents at the

center level were assigned and responsible to give extension service to the farming

community. They were accountable to Akaki Agricultural Unit. But at the end of this research

survey and data collection process due to the new restructuring program of Addis Ababa

Administration some DAs from their center and other professionals from Akaki Agricultural

32

unit office has transferred to other unrelated duties with their professions that can affect

negatively the extension services and rural development efforts of the study area.

Table 4.Improved agricultural input distribution of the study area in different years

Crop Season

DAP

Quit.

Urea

Quit.

Teff

Quit.

Pesticide

K.g.

Pesticide

Lit

Weedi-cide

Lit

1997 5224.5 3000.5 4.5 10 30 50

1998 4695.5 2506.5 5.5 5 30 76

1999 3759 2026.5 - 5 20 48

2000 4172 2235.5 11.85 5 10 33

2001 3866 2034.5 12 4 10 35

2002 3868 2182 13.05 4 4 5

2003 3994 2340 19.5 4 7 50

2004 167.5 167.5 - - - -

Total 29746.50 16493 66.40 37 111 297

(Source: Akaki Agricultural Unit Office)

Availability of improved agricultural inputs to use and credit service to purchase agricultural

inputs is very vital for technology adoption. In the study area different agricultural inputs and

credit were distributed in different time for farmers. Table 4 showed the agricultural input

distribution of the study area in different years.

Table 5 showed the improved bread wheat varieties seed distribution and table 6 showed the

improved credit distributions in different years to the farming community of the study area.

According to the Akaki Agricultural unit office information the major inputs distributed in the

study area were fertilizer (DAP and Urea) and improved bread wheat varieties as well as

improved teff varieties. The major input distribution of the study area from 1995 to 2004 were,

fertilizer in quintal, (DAP=29746.50 and 16493), improved bread wheat varieties 732.75 and

improved Teff 66.40 quintals.

33

Table 5.Improved bread wheat seed distribution of the study area in different years

No Crop Season

(Years G.C.)

Improved

Bread Wheat (Quit.)

Land covered

(Ha.)

No.of Participant

Farmers

1 1997 43.5 29 58

2 1998 74.25 49.5 99

3 1999 48 32 64

4 2000 72 50 100

5 2001 82.5 55 110

6 2002 120 80 160

7 2003 145.5 97 194

8 2004 147 98 196

Total 732.75 490.50 981

(Source: Office of Akaki Agricultural Unit office)

As it is presented in table 4 and table 5, the average annual DAP fertilizer distribution were

3718.3 quintals, urea 2061.63, improved bread wheat seed 8.3 quintals and that of improved

Teff was 4.625 quintals. The fertilizer distribution was almost satisfactory. But the improved

bread wheat and teff seed distribution was very low quantity.

Regarding the credit distribution of the study area as presented in Table 6, the larger credit for

the last seven years a total of 376478.06 Ethiopian birr for fertilizer and improved grain crop

seed purchase was distributed .The larger proportion of the credit were used for fertilizer

purchase. This is because farmers in the study area got credit in the form of fertilizer.

34

Table 6.Credit Distribution of the study area in various years

(Sources: Office of Akaki Agricultural unit office).

In the study area credit were also distributed for livestock production as showed in Table 6.For

this sector of the economy credit was distributed before five years ago. The credit distribution

was covered only for two consecutive years. The credit service limitations in amount, type and

facilities can affect negative the adoption of improved agricultural technologies, agricultural

development and the over all rural community lively hood living situation improvement.

3.2. Description of Data Collection and Data Analysis Methods and Procedures

3.2.1. Sources and types of data

It is very helpful for researchers to anticipate and think over in advance about the sources and

types of data that are relevant to the research and, therefore, need to be gathered. This help to

avoid confusion and unnecessary time, labor, finance and other resources wastages. The types

of data, primary and secondary, were collected to answer and fulfill this research questions and

objectives. All information about determinants of adoption and intensity of adoption and

Credit for No Crop season

(Year G.C.) Fertilizer and Improved seed

(ET.Birr)

Livestock Production

(ET.Birr)

1 1997 27,858.50 -

2 1998 54,076.60 -

3 1999 16,275.75 47,800

4 2000 60,552.70 118,885

5 2001 25,446.61 -

6 2002 94,218.75 -

7 2003 98,049.15 -

Total 376478.06 166685

35

farmers’ evaluation and selection criteria of improved bread wheat varieties, demographic,

socio-economic, environmental situations, wheat production, credit facilities, extension

service and others relevant data to the study were gathered from primary sources quantitatively

through interview schedule and qualitatively through group discussion and observation.

Data also were gathered by examining secondary sources such as documents, reports and

records of DAs (Development Agents), and other related agricultural offices and research

centers. All these data in the process of the study were gathered using different methods and

techniques based on the nature, types and characteristics of the data. Both quantitative and

qualitative data were gathered through different data collection methods from primary and

secondary sources.

3.2.2. Sample size and sampling techniques

This study was determined to conduct in Akaki area, which is the rural part of Addis Ababa

Administration. In this study sample size was determined by taking different factors such as

research cost, time, human resource, accessibility, availability of transport facility, and other

physical resource accessibilities. By taking these factors into account, it was fixed to cover

two Peasant Associations out of 9 PAs and 150 household head respondents from the total

2755 household head population of the study area, Akaki. Through out sample selection

processes simple random selection method has employed. The two stage sampling techniques

were applied in sample selection processes. First, the two Peasant Associations (PAs) or Rural

Kebele Administrations (RKAs) namely Koye and Gelan-Edero involved in improved bread

wheat production were selected out of nine PAs using simple random selection method.

Second 150 sample household head farmers were selected from total wheat growers of the two

samples Pas. About 65 (43%) Sample farmers from Gelan-Edero PA and from Koye sample

PA 85 (57%) sample farmers were selected proportionally. From Gelan-Edero PA 36 were

adopters and 29 were non-adopters and from Koye PA 63 were adopters and 22 were non-

36

adopters. Out of 150 respondents (132) 88% were male and the remaining (12%) was female.

From total respondents (99) 66% were adopters and the rest 51 (34%) were non-adopters.

From total adopters 93% were male adopters and the remaining 7% were female adopters.

Concerning non-adopters 78% were male and 22%were female as presented in Table7. All

sample selection processes were carried out in pursuing of statistical procedures and with

consultation of DAs, Akaki Agricultural Unit Office professionals and PA leaders of the study

area.

3.2. 3. Data collection methods

Data for this study were gathered from sample household head farmer respondents through

interview, group discussion and observations. Both qualitative and quantitative data were

gathered using the mentioned methods. Secondary data that were relevant to this study were

also gathered through examining of published and unpublished data that was gathered and

organized by other bodies for other purposes .In this process care was taking in taking and

selection of the relevant data suitable and relevant for this study.

3.2.3.1. Quantitative data collection methods

In this regard, primary data were collected through personal and face-to-face interview using

structured and pre-tested interview schedule that were filled up by recruited and trained

enumerators under the close supervision of the researcher. Totally, 150 randomly selected

samples household head farmer respondents were covered under the survey. Also, secondary

data were gathered by examining secondary sources such as records, reports, and research

results and other documents and publications from office of agriculture, research centers and

other respective offices.

37

3.2.3.2. Qualitative data collection method

In the study area primary qualitative data on improved bread wheat varieties selection and

evaluation criteria of farmers’ were gathered through group discussion and individual

discussions conducted with farmers and professionals. Researcher’s personal observation and

transect walk were also used in this data gathering processing. Data gathering through these

methods were continued to the point of saturation, to crosscheck, triangulate, elaborate and

enrich the information on both qualitative and quantitative data to increase the reliability and

trustworthiness of the information. The group members and individuals were familiarized to

the discussion points and encouraged to forward their opinion they felt with out any

reservation. In this process, recording, coding, reorganizing and arrangements, refining

expanding of information was conducted.

3.3. Analytical Models

3.3.1. Logit model

Several models are available to analyze factors affecting technology adoption. The choice of

one may depend up on several factors. Some of these alternative models are the discrete

regression models in which the dependent variable assumes discrete values. The simplest of

these models is that in, which the dependent valuable Y is binary (it can assume only two

values denoted by 0 and 1).

The three most commonly used approaches to estimate such models are the linear probability

model (LPM), the logit model and the probit model .The linear probability model has an

obvious defect in that the estimated probability values can lies outside the normal range 0-1

range. The fundamental problem with the LPM is that it is not logically a very attractive model

because it assumes that the marginal or incremental effects of explanatory variables remain

constant, that is Pi=E (Y=1/x) increases linearly with x (Maddala, 1997 and Gujaratti, 1998).

38

The authors suggested that the sigmoid or S-shaped curve were very much resembles the

cumulative distribution function (CDF) of random variable is used to model regressions where

the response variable is dichotomous, taking 0-1values.

The cumulative distribution functions (CDFs), which are commonly chosen to represent the 0-

1response models, are the logit (logistic CDF) model and the probit (normal CDF) model.

Logit and probit models are the convenient functional forms for models with binary

endogenous variables (Tohnston and Dianardo, 1997 cited in Techane, 2002).

These two models are commonly used in studies involving qualitative choices. To explain the

behavior of dichotomous dependent variables we will have to use a suitably chosen cumulative

Distribution Function (CDF). The logit model uses the cumulative logistic function. But this is

not the only CDF that one can use .In some applications the normal CDF has been found

useful. The estimating model that emerges from normal CDF is popularly known as the probit

model (Gujarati, 1999).

The logistic and probit formulations are quite comparable the chief difference being that the

logistic has slightly flatter tails that is the normal curve approaches the axes more quickly than

the logistic curve. There fore, the choice between the two is one of mathematical convenience

and ready availability of computer programs (Gujrati, 1988 cited in Techane, 2002). A

relevant model offers better explanation on the underlying relation ship between adoption

decision and factors influencing it. The most widely used qualitative response models are the

logit and the probit models (Amemiya, 1985).

Both the probit and logit models yield similar parameter estimates and it is difficult to

distinguish them statistically (Aldrich and Nelson, 1984). How ever, because of the fact that

binomial logit model is easier to estimate and simpler to interpret. Therefore a logit model is

used in this study to determine the relation ship between adoption decision and factors

affecting the adoption of improved bread wheat varieties in the study area, Akaki.

39

The specification of the logit model is as follow:

Рі = Рі (Υі = 1) = exp (Zi)

1+exp(Zi)

Where Рі denotes the probability that the ith farmer will fall in the adopters’ class (yi=1) and

exp (Zi) stands for the irrational number “ e” to the power of Zi. The un observable stimulus

index Zi, assumes any value.

However, the Logistics transformation guarantees each corresponding value of Pi to fall inside

the 0-intervals. The stimulus index Zi, also called the Log of the odds ratio, in favor of

improved bread wheat varieties adoption, is actually a linear function of factors influencing

adoption decision as specified hereunder:

=β0+β1χ1і+β2χ2i + .…+ βρχρi+eі

Zі= ln [ pi

]

1-pi

Where: X1, X2, X3,...........Xp=explanatory variables

Β0, β1, β2, β3, β4,…… ,βp = Logit Parameters to be estimated, ei = the error term

= β0+β1χ1i+β2χ2i+β3χ3i +…….βpχpi + χεі

In reality, the significant explanatory variables do not all have the same level of impact on the

adoption decision of the farmers. Therefore, the impact of each significant variable on the

probability of adoption was calculated by keeping the continuous variables at their mean

values and the dummy variables at their most frequent values (zero or one).

The estimated coefficients of the Logit model of improved bread wheat adoption are listed in

the table 20. The likelihood ratio statistics is significant at 10 percent probability level and

implies that the independent factors taken together influenced improved bread wheat varieties

adoption. The model correctly predicted 81.33 percent of the adopters and non-adopters.

40

3.3.2. Tobit model

Adoption studies based up on dichotomous regression model have attempted to explain only

the probability of adoption versus non-adoption rather than the extent and intensity of adoption

Knowledge that a farmer is using high yielding variety may not provide much information

about farmer behavior because he /she may be using some percent or 100 percent of his /her

farm for the new technology. Similarly, with respect to adoption of fertilizer is, a farmer may

be using a small amount or a large amount per hectare area. A strictly dichotomous variable

often is not sufficient for examining the extent and intensity of adoption for some problems

such as fertilizer (Feder et al., 1985).

There is also a broad class of models that have both discrete and continuous parts. One

important model in this category is the Tobit. Tobit is an extension of the probit model and it

is really one approach to deal with the problem of censored data (Johnston and Dinardo, 1997

cited in Techane, 2002).

When examining the empirical studies in the literatures, many researchers have employed the

Tobit model to identify factors influencing the adoption and intensity of technology use. For

example Nykonya et al (1997) ; Lelissa (1998) ; Bezabih (2000); Croppenstedt et al. (1999) as

cited in techane (2002) used the Tobit model to estimate the probability and the intensity of

fertilizer use ( Adesina and zinnah ,1993; in Techane ,2002).

The econometric model applied for analyzing factors influencing the intensity of adoption of

improved bread wheat varieties is the Tobit model shown in equation (1). This model was

chosen because, it has an advantage over other adoption model (LPM, Logistic and Probit

Models) in that, and it can reveal both the probability of adoption of improved bread wheat

varieties and the intensity of use of the varieties.

The Tobit model can be defined as:

(1) * Y if

0Y* if YY

ni U XY

i

ii

iii

00

,...2,1,*

*

≤=

>=

=+= β

41

Where, Yi= the observed dependent variable, in our case the land size in hectare covered with

improved bread wheat variety.

Yi*= the latent variable which is not observable

Xi= vector of factors affecting adoption and intensity of fertilizer use

βi= vector of unknown Parameters

Ui= residuals that are independently and normally distributed with mean zero and a common

variance σ2. Note that the threshold value in the above model is zero. This is not a very

restrictive assumption, because the threshold value can be set to zero or assumed to be only

known or unknown value (Amemiya, 1985). The Tobit model shown above is also called a

censored regression model because it is possible to view the problem as one where

observations of Y* at or below zero are censored

The model parameters are estimated by maximizing the Tobit likelihood function of the

following formula (Maddala, 1997 and Amemiya, 1985).

(2) iX i

iY F

iY

iXi iYf L )0* (

1

0

)(

σ

β

σσ

β −∏ ≤

>

−=C

Where, f and F are respectively the density function and cumulative distribution function of

Yi*. IIYi

*< 0 means the product over those i for which Yi* < 0, and IIYi*<0, and IIYi>0 means the

product over those i for which Yi*>0.

It may not be sensible to interpret the coefficients of Tobit in the same way as one interprets

coefficients in an uncensored linear model. Hence one has to compute the derivatives to

predict the effects of changes in the exogenous variables.

1. The marginal effect of an explanatory variable on the expected value of the dependent

variable is:

(3) ZFX

Yi

i

i β)()(=

∂

Ε∂

42

Where σ

β i i X is denoted by z,

2. The change in the probability of adopting a technology as independent variable Xi change

is:

(4) X

ZF

i σ

β if(Z))(=

∂

∂

3. The change in intensity of adoption with respect to a change in an explanatory variable

among adopters is:

(5) F(Z)

f(Z)(

ZF

ZfZ

X

YYi

i

ii ]))(

)(1[

)0/( 2

*

−−=∂

>Ε∂β

Where, F (z) is the cumulative normal distribution of z, f (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, B is a vector of Tobit maximum likelihood estimates and 0* the standard error

of the error term.

Parameter estimates of the Tobit model for the intensity of adoption of improved bread wheat

varieties (measured in terms of size of land in hectare used for growing of improved bread

wheat varieties over the total wheat land in hectare) as shown in Table 22. And the results are

discussed under section (5.1.2.). The Tobit model was used or applied to analyze the factors

that determine the intensity of adoption of improved bread wheat varieties because the mean

proportion of land allocated to improved bread wheat varieties is a continuous variable but

truncated between zero and one. This model is relevant to predict the intensity of adoption of

improved bread wheat varieties by farmers when the dependent variable is continuous.

3.3.3. Other Quantitative data analysis methods

In this study, in addition to econometrics models, Logit and Tobit models described in the

above, descriptive statistics such as percentage, tabulation, mean, standard deviation, t-test and

χ2 - test data analysis methods were employed in quantitative data analysis of the study.

43

3.3.4. Qualitative data analysis method

The qualitative data analysis has conducted to strengthen the evidences obtained through

quantitative data collection methods or survey method. In this study, the qualitative data

obtained from the group, individual formal and informal discussions and through the

researcher’s personal observation regarding farmers’ selection and evaluation criteria, their

priorities of improved bread wheat varieties disseminated in the study area were summarized

using the criteria established by the group members by analyzing the characteristics of these

varieties to what extent these varieties satisfy and fit their needs, interests and to their

environmental situations. The qualitative data were analyzed through explanation of idea,

opinion, and concept explanation method. Researcher’s personal observations and transect

walk watching were analyzed through, further explanation of the real world under observation.

3.4. Hypotheses Testing and Definitions of Variables

In this study the variables were selected and hypothesized using literatures, by considering

farmers production practices, area situations and objectives of the study. In this study it was

decide to concentrate the research effort and limited resources on socio economic and

environmental conditions and constraints that was expected to influence probability and

intensity of adoption because as the Ethiopian extension history shows that in this area

extension service was provided for long years using different methods such as demonstration

farmers’ field day, on farm field visit and support. More over, farmers in this area have better

access to different information sources .As a result, were expected to have better

understanding, knowledge, and attitude towards improved agricultural technologies. As a

result, more emphasis was given to exogenous socio economic variables than internal

variables to hypothesize, test and analyze using the Logit and Tobit Analytical models.

44

3.4.1. The Dependent variables of logit and tobit models

3.4.1.1. The Dependent variable of logit model

The dependent variable of the binomial logit model’s log-odds ratio is the probability of

adopting or not adopting the improved bread wheat varieties which can used to identify factors

determining adoption of improved bread wheat varieties is the natural logarithm of the ratio of

the probability that a farmer adopts the improved varieties (Pi) to the probability that he /she

will not (1-Pi). The log-odds ratio is a linear function of the explanatory variables.

3.4.1.2. The Dependent variable of tobit model

The dependent variable of Tobit model has continuous value, which should be the intensity,

the use and application of the technology. As observed in different empirical studies this

variable can be expressed in terms of ratio, actual figure and log form depending on the

purpose of the study. For example in their study of factors influencing adoption of fertilizer,

Nkonya et.al, (1997) as cited in Techane (2002) considered fertilizer applied per hectare as the

dependent variable of the tobit model. Likewise Shiyani et al., (2000) as cited in Techane

considered the proportion of area under chickpea varieties in their study of adoption of

improved chickpea varieties. Consequently, in this study the ratio of actual land size under

improved bread wheat varieties to total wheat land size was taken as a dependent variable of

the tobit model.

3.4.2. The Independent variables and their definitions used in logit and tobit models

Adoption literatures provide a long list of factors that may influence the adoption of

agricultural technologies. Generally these factors can be grouped into demographic personal,

socio –economic, physical and institutional factors (Million and Belay, 2004). There fore,

45

farmers’ decision to use improved bread wheat varieties and the intensity of the use in a given

period of time is hypothesized to be influenced by a combined effect of various factors such as

household characteristics, socio-economic and physical environments in which farmers

operate. Based on Feder et.al, (1985) that extensively reviewed factors affecting adoption of

agricultural technologies in low income countries, and on the brief literature review in this

study the variables mentioned below are hypothesized to explain improved bread wheat

varieties adoption and the intensity of the use of these varieties by the sample households.

In the course of identifying factors influencing farmer's decision to use improved bread wheat

varieties, the main task is to analyze which factor influence how and by how much. It is

hypothesized that adoption and intensity of adoption are influenced by the combined effect of

various determinants. There fore, in the following section potential variables that are supposed

to influence adoption and intensity of adoption of improved bread wheat varieties in the study

area will be explained. More specifically, the following potential explanatory variables

hypothesized to influence the adoption and intensity of adoption of improved bread wheat

varieties in the study area, on a priori grounds is indicated below:

1. Farmer’s age (HHHAGE) – As the farmer’s age increases it was expected that farmer

become conservative. Then it is hypothesized that the farmer’s age, adoption, and intensity of

adoption of improved bread wheat varieties are inversely correlated. Therefore, in this study it

was assumed that the lesser age group could adopt improved bread wheat varieties more than

the older age group farmers. Then, in this study farmer’s age and adoption are expected to

relate negatively. As farmers age increase probability of adoption/intensity of adoption is

expected to decrease.

2. Gender/Sex (HHHSEX) - It is hypothesized that male household headed farmers are

expected to adopt improved bread wheat varieties more than female headed ones. Because it is

expected that male-headed farmers have a better opportunity to access to credit and extension

service. In this study gender/sex was coded if the household is male 1 and 0, otherwise.

46

Adoption/Intensity of adoption was expected to increase and correlate positively as the farmer

being male.

3. House hold head Education (EDUHHH) – This represents the level of reading and writing

and formal schooling attended by the household headed. It is expected that educated

farmhouse hold head can make better decision to adopt improved bread wheat varieties than

non-educated ones. Here, education extends from read and write to attending regular school

education. In this study this variable was treated as a dummy variable and has coded if the

house hold head can read and write as well as attending the regular school education as 1 and

0, otherwise. Adoption was expected to correlate positively as education increases.

4. Family size (FAMILYSI) - household heads with large family size are less likely to adopt

improved bread wheat varieties due to risk aversion. In this study it had expected that the

family size, adoption, and intensity of adoption would have related inversely. As family size

increase adoption/intensity of adoption has expected to decrease.

5. Extension-services (GEXSERVE) - the more frequent DA visit, using different extension

teaching methods and training, attending demonstrations and field day can help the farmers to

adopt a new technology and can also increase the intensity of adoption. If the farmers get

better extension services are expected to adopt better-improved bread wheat varieties than

others. In this study this variable had treated as a dummy variable in that if the farmer get

extension service is coded as 1 and 0, otherwise. As extension service increase

adoption/Intensity of adoption was expected to increase and correlate positively.

6. Off- farm income (HHOFFINC) - the household head that have off farm income are

expected to adopt improved wheat varieties better than who have not off farm income. This

variable also was treated as a dummy variable that if the farmer has off-farm income coded 1,

otherwise, 0. As the number of farmers’ number increases to involve in off-farm work it was

expected to increases adoption positively.

47

7. Access to credit (GECRSERV) - It is expected that those who have better access to credit

can adopt improved bread wheat varieties than other who do not have access. Because it is

expected that credit can solve the financial limitation of farmers. The variable in this study

was treated as dummy variable in that, if the farmer gets credit service coded as 1 and 0,

otherwise. As credit service increase adoption/Intensity of adoption was expected to increase

and correlate positively.

8. Livestock ownership (TOTLIVUN) – house holds that have large number of livestock are

likely to adopt improved bread wheat varieties better than others who have less number of

livestock Because those who have better number of livestock can have better opportunity to

get credit. In this study it was assumed that livestock ownership and adoption would be related

positively. As livestock ownership increases adoption/intensity of adoption was expected to

increase and correlate positively.

9. Labor accessibility (OTSOLA1) – those farmers who have access to labor are expected to

adopt improved bread wheat varieties than those who lack labor accessibility since improved

varieties required more labor. The variable has been treated as a dummy variable in that if the

farmer has an access to labor coded 1, otherwise 0. As labor accessibility increases

adoption/Intensity of adoption was expected to increase and correlate positively.

10. Social/leadership status of the respondent (PRTILEDE) - those farmers who have

experience of leadership and better social status previously or currently are likely to adopt

wheat technologies than others. Because, it is expected that they have an opportunity to get

and interpret the information they get about improved bread wheat variety. The variable was

coded as 1 and 0, otherwise. As the number of farmers increase to involve in leadership

position adoption was expected to increase and to correlate positively.

11. Distances from extension agent office (DISDAOF1) – those who are closer to extension

agent are expected to adopt improved bread wheat varieties than others as a result of

48

accessibility. The variable was coded as 1 if the farmer is close to the DA’s office and 0,

otherwise/far. As distance of DA office increase adoption/intensity of adoption was expected

to decrease and correlate negatively. As distance of DA office decrease the correlation will be

vise versa.

12.Distance from input and credit supply institutions (CRINFAR1) – those farmers closer

is likely to adopt improved bread wheat varieties than those who are not close since they can

easily facilitate and follow up the credit process. This variable was treated as dummy variable

and had coded as 1 if the farmer is close, 0 if not close or if far. The far distance might affect

negatively and the close distance affects adoption and Intensity of adoption positively.

13. Oxen ownership (OXTLU) – those who have oxen for ploughing is likely to adopt

improved bread wheat varieties since they could solve ploughing power problem. Then, oxen

ownership and adoption were expected to relate positively. As the number of oxen owned by

farmers’ increased, adoption/Intensity of adoption was expected to increase.

14. Farmers experience in any extension activities (YEXPEXTS)- Farmers who have long

involvement in any agricultural extension activities is expected to use improved bread wheat

varieties than with less experience. Then, this variable was hypothesized to correlate and

influence positively improved bread wheat varieties adoption and the intensity of adoption.

15. Access to market (CRINMFF1) - Access to market was hypothesized to be positively

related to the probability of adoption of improved bread wheat varieties in that if the house

holds near to market tend to buy improved agricultural inputs including improved bread wheat

seed and they can have easy access to dispose of and sell their production in the market.

Therefore, the variable was treated as a dummy variable in that if the house hold has an access

to market has coded as 1and 0, otherwise. As market distance increases adoption and intensity

of adoption was expected to decrease.

49

16. Health status of the household head (HEALSTAT)-this is a dummy variable, which

takes a value 1 if the household head was healthy and 0, otherwise. As farmers’ health statuses

improve adoption and intensity of adoption of improved bread wheat varieties was expected to

increase and correlate positively.

17. Farm land holding (SUMOWRE)-farmer who has large farm size is likely to adopt

improved varieties than those who have lesser farm size. Because farmers with large farm size

can distribute the yield loss risk and better land ownership serve as insurance to get credit

which can use to purchase improved agricultural inputs. As farmers farmland holding

increases adoption/intensity of adoption might increase and correlate positively.

50

4. RESULTS AND DISCUSSION

4.1. Analysis through descriptive statistics

This study was intended to examine the farmers’ evaluation and selection criteria of improved

bread wheat varieties and to identify factors affecting adoption and intensity of adoption of

improved bread wheat varieties in the study area Akaki as well as to know the effect of

hypothesized independent variables on the dependent variables. In this section of analyses

descriptive statistics such as mean standard deviation, percentage, frequency tabulation, t-test

and chi-square test will be employed using SPSS- computer soft ware program.

In this study, adoption of a technology refers to a continued use of the technology on an area

of land, which is large enough to contribute to the economy of the household. Here, the

respondents who have cultivated improved bread wheat varieties and continued growing at

least one of the distributed improved bread wheat varieties in the study area during the survey

year and in any one of the years before the survey year of this study are considered as

adopters. Farmers who never adopted and those who discontinued from growing of improved

bread wheat varieties are categorized as non-adopters.

4.1.1. Sample Households’ Demographic Characteristics

In order to understand the sample households, it is very important to describe their

demographic characteristics. The number of household head respondents was from two

selected Rural Kebele Administrations or Peasant Associations namely Koye and Gelan-

Edero. The sample house hold heads covered in this study from Koye PA/RKA were (76)

89.41% male and (9) 10.59 % female with a total of 85 constituted 56.67% which of the total

sample and (56) 86.15% male and (9) 13.85% female with a total of 65 were from Gelan-

Edero PA/RKA which constituted (43.33%) of the total sample as presented in Table 7.

51

Out of the total 150 respondents in the sample, adopters were 99 (66%) and non-adopters were

51 (34%). From 150 sample respondents, 132 (88%) were male and 18 (12%) were female

respondents. From 132 male respondents 92 (69.70%) were adopters and 40 ((30.30%) were

non-adopters. From total 18 female sample respondents 7 (38.89%) were adopters and 11

(61.11%) were non-adopters as presented in Table 7.

Table 7.Sample household heads distribution by Sex, Kebele and adoption category

Adopters (99) Non-Adopters (51) Total (150)

Samples N Percent N Percent N Percent

Male-sample 92 92.93 40 78.48 132 88

Female-Sample 7 7.07 11 21.57 18 12

Total 99 100.00 51 100.00 150 100.00

Koye-Kebele 63 63.64 22 43.14 85 56.67

Gelan-Edero 36 36.36 29 56.86 65 43.33

Total 99 100.00 51 100.00 150 100.00

Koye Kebele

Male

Female

Total

58

5

63

92.06

7.94

100.00

17

5

22

77.27

22.73

100.00

76

9

85

89.41

10.59

100.00

Gelan-Edero Kebele

Male

Female

Total

32

4

36

88.89

11.11

100.00

24

5

29

82.76

17.24

100.00

56

9

65

86.15

13.85

100.00

(Source: Computed from own survey data, 2005)

Out of the total 99 adopters 92 (92.93%) were male and 7 (7.07%) were female and from the

total 51 non-adopters 40 (78.43%) were male and 11 (21.57%) were female. The numbers of

sample household heads from Koye PA were 63 adopters and 22 non-adopters, where as from

52

Gelan-Edero PA, the number of sample household head adopters was 36 and that of non-

adopters were 29 as indicated in Table 7.

In similar studies in the past, the major reasons for farmers to adopt improved and new

technologies are technical, institutional, social, and economical reasons. Farmers do not adopt

a technology if they are not convinced of its benefits, costs and risk associated with it. By

seeing their fellow farmers, by attracting of high yield performances of improved varieties,

market demand as well as DA’s information and extension support farmers might urged and

motivated to use and adopt improved varieties. On the other hand, the major reasons for those

non-adopters might shortage of improved varieties, credit problem, or cash, land, labor or

other farm resource constraints (Legesse et al., 2005).

Table 8.Marital status of respondents

(Source: Computed from own survey data, 2005)

The marital statuses of respondents are summarized in Table 8 as (121) 80.67% married, (3)

2% unmarried or single, (6) 4% divorced and 20 (13.33%) widow/er. The proportion of

married respondents was much larger than the remaining marriage categories. As indicated in

Table 8, the married adopters were 85.86 percent and that of non-adopters were 70.59 percent.

The remaining categories of respondents constituted fewer proportions of respondents both

in adopters and non- adopters.

Adopters (99) Non-Adopters (51) Total (150) Marital

status N Percent N Percent N Percent

Married 85 85.86 36 70.59 121 80.67

Un-married 1 1.01 2.00 3.92 3 2.00

Divorced 2 2.02 4.00 7.84 6 4.00

Widow/er 11 11.11 9.00 17.65 20 13.33

53

Table 9.Association between adoption of improved bread wheat varieties and sex of sample

household head

House hold

head sex

Adopters Non-adopters Chi-

square

C.Coef df Sig. Total

Female 7 (7.1%) 11 (21.6%) 18 (12%)

Male 92 ((92.9%) 40 (78.4%) 132 (88%)

Total 99 (100%) 51 (100%) 6.700*** 0.207 1 0.01 150 (100%)

(Source: Computed from own survey data, 2005), *** significant at 1%level

Table 10. Respondent farmers’ demographics characteristics

Adoption

Category

Summary of

statistics

House hold

head Age

House hold

family size

House hold head

experience in extension

Farming

experience

Minimum 19 1 2 2

Maximum 80 11 20 55

Adopters

Range 61 10 18 53

Minimum 20 1 1 4

Maximum 80 10 9 60

Non-

adopters Range 60 9 8 56

Minimum 19 1 1 2

Maximum 80 11 20 60

Total

Range 61 10 19 58

(Source: Computed from own survey data)

In adoption of new agricultural technologies, farmers’ age has an influential effect as it was

observed in many adoption studies. The maximum and the minimum ages of total respondents

were 80 and 19 years respectively. The adopters’ maximum age was 80, which was equal to

the non-adopters maximum age. The minimum age of adopters was 19 years and that of non-

adopters was 20 years. The age variation between maximum and minimum age of adopters,

non-adopters and that of total respondents were 61, 60 and 61 respectively as presented in

Table 10.

54

The respondents' maximum years of experience in extension were 20 for adopters 9 for non-

adopters and 20 years for total samples. The minimum years of experience in extension for

adopters was 2 years, for non-adopters 1 year and for total respondents was 1 year. The

variations between maximum and minimum years of experience in extension were 18 years for

adopters, 8 years for non-adopters and 19 years for total respondents as presented in Table 10.

The maximum and minimum farming experience for adopters were 55 and 2 years, for non-

adopters 60 and 4 years and for that of total respondents were 60 and 2 years. The variation

between maximum and minimum total farming experience of adopters was 53, non-adopters

were 56 and that of total respondents was 58 years as indicated in Table 10.

The maximum and minimum family size of adopters respectively were 11 and 1 for adopters,

10 and 1 for non-adopters and 11and 1 for total samples. The variation between maximum and

minimum family size was 10 for adopters, 9 for non- adopters and 10 for that of total

respondents as indicated in Table10.

Table 11.Adopters and non-adopters’ demographic characteristics

Adopters (99) Non-Adopters (51)

Characteristic Mean SD Mean SD

T-test

Significance

Level (2-tailed) Age 46.10 13.256 46.47 14.53 0.157 0.876

Family size 5.85 2.192 5.10 2.385 1.927* 0.056

Experience in

Extension (years)

7.87 4.787 3.765 1.784 -0.907*** 0.000

Farming

Experience (years)

21.90 11.08 20.80 10.98 -0.596 0.552

***and* significant at 0.01and 0.10 p-value respectively.

The average age of adopters, non-adopters and total respondents were, 46.10, 46.47 and 46.23

years respectively. The S.D (Standard Deviation) of adopters, non-adopters and total

respondents ages were 13.256, 14.53 and 13.655 respectively as indicated in Table 11.T-test

55

statistics was run to check whether there is a significant mean difference in age between

adopters and non-adopters. The result of t-test showed that there was no statistically

significant mean age difference.

The respondents’ average/mean and S.D (Standard Deviation) family size of adopters, non-

adopters and total respondents were 5.85, 5.10 and 5.59 respectively as indicated in Table 11.

T-test statistics was run to know whether there is statistically significant variation in average

family size between adopters and non-adopters. The result of t-test analysis showed that there

was statistically significant difference in average family size at 10 percent probability level as

indicated in Table 11.

The respondents’ average (mean) and S.D (Standard Deviation) of experience in extension and

total farming experience in years is presented in Table 11. The average years of experience in

agricultural extension as well as the total farming experience of adopters were 7.87 and 21.9

that of non-adopters were 3.765 and 20.8 and the total respondents experience in extension

were 4.485 and 11.023 years respectively. T-test was conducted to see the variation in average

years of experience in agricultural extension and in total farming experience between adopters

and non-adopters. The result of t-test analysis showed that there is a significant difference in

average years of experience in agricultural extension participation involvement at 1 percent

probability level as indicated in Table 11.

But the total farming experience was not significant in t-test analysis. Because those farmers

who have better experience in extension could got better extension service that help them to

adopt better improved bread wheat varieties.

From the total sample respondents 103 (68.67%) were involved in improved bread wheat

production, while the remaining 47 (31.33%) respondents were not involved in improved

bread wheat production during the survey year. Their reasons why they were not involved had

summarized and presented in Table 12 from the response they gave during the interview.

56

Table 12.Reasons given for not using improved bread wheat varieties

No

Reasons limiting to involvement in

improved bread wheat production

N (47) Percent Rank

1 Farm land shortage 15 32.00 1st

2 Lack of information 9 19.15 2nd

3 Fertilizer shortage 6 12.77 3rd

4 High price of fertilizer 6 12.77 ,,

5 Lack of extension support 4 8.51 4th

6 Labor problem 3 6.38 5th

7 Seed scarcity 2 4.25 6th

8 Lack of ploughing oxen 2 4.25 ,,

(Source: Computed from own survey data, 2005)

Farmland shortage and lack of information were the two most important reasons that limit

farmers in the study area. The remaining reasons were less important for farmers to adopt

improved agricultural practices as indicated in Table 12.

Table 13.Level of awareness of improved bread wheat varieties

Aware Not aware Total Improved bread wheat

varieties N Percent N Percent N Percent

HAR-1685 (Kubsa) 123 82 27 18 150 100

HAR-1709 (Mitike) 54 36 96 64 150 100

Paven-76 115 76.67 35 23.33 150 100

(Source: Computed from own survey data, 2005)

57

Concerning respondents’ awareness of improved bread wheat varieties, interviews were

conducted. About (123) 82% respondents knew HAR-1685, (115) 76.67% knew Paven-76

and 54 (36%) knew HAR-1709 variety. As indicated in Table 13, HAR-1685 was known by

larger proportion of respondents followed by Paven-76 and HAR-1709 was the least known

variety by respondents.

Table 14.Sample Farmers perception on benefit of fertilizer

1 Perception on fertilizer benefit N Percent

1.1 Low profit 98 65.33

1.2 No loss or no profit 26 17.33

1.3 High profit 14 9.33

1.4 Very high profit 9 6.00

1.5 Encountered loss 3 2.00

Total 150 100 2 Perception on fertilizer Problems N Percent

2.1 High price (high interest rate) 75 50

2.2 Un-timely and lately arrival 56 37.33

2.3 Credit scarcity and credit service related problems

to purchase fertilizer

19 12.67

Total 150 100

(Source: computed from own survey data, 2005)

Respondents were interviewed to know their opinion based on their experience about the

benefits obtained from fertilizer use. About 65.33% low profit, for 17.33% reported no loss or

no profit for 9.33% high profit, for 6% very high profit could be obtained and 2% said

encountered loss. In this study the larger proportion of farmers reported the low profit from

fertilizer as indicated in Table 14.

Respondents were also interviewed to get their idea on problems related to fertilizer in their

area. About 50% of respondents have reported high price, 37.33% reported un-timely and late

arrival and about 12.67 % reported credit scarcity and credit service related problems to

58

purchase fertilizer as indicated in Table15. The problems arisen due to weakness of the credit

provider institutions and less attention of the government as observed during data collection

time.

Table 15.Beginning time of cultivation of improved bread wheat varieties of sample farmers

No Starting Years N Percent

1 Before 2000 9 8.74

2 During 2000 10 9.71

3 ,, 2001 27 26.26

4 ,, 2002 28 27.30

5 ,, 2003 27 26.26

6 ,, 2004 2 2.02

Total 103 100

(Source: computed from own survey data, 2005)

In this study to know their commencement or beginning time of using improved bread wheat

production, respondents were interviewed and their responses were summarized in Table 16.

Table 16.Health status and adoption of improved bread wheat varieties

Health status Non- adopters Adopters χ2-test df Co coef Sig. Total

Un-healthy 7 (13.73%) 7(7.07%) 14(9.33%)

Healthy 44 (86.27%) 92 (92.93%) 136(90.67%)

Total 51(100%) 99(100%) 1.762 1 0.108 0.184 150(100%)

(Source: Computed from own survey data, 2005); Co coef =contingency coefficient

To accomplish the agricultural activities as required, the farmers need to be healthy. In this

study, it was tried to assess the household head respondents’ health situation. The respondents

were grouped into healthy and un- healthy farmers (those who face a health problem) to

59

accomplish their day-to-day agricultural activities. From total adopters the healthy farmers

were 92.93% and that of unhealthy were 7.07%. In the case of non-adopters 86.27% were

healthy and 13.73% were unhealthy. Out of 150 respondents, (136) 90.67% were fully healthy

and the remaining (14) 9.33 % had health problem. To check the relationship of the health

situation of the respondents and adoption of improved bread wheat varieties, a chi-square test

was conducted and the result showed that the relationship between health status and adoption

of improved bread wheat varieties was not statistically supported and insignificant as indicated

in Table 16.

Table 17.Sample household educational status

Education NA Ad X2 df Co. coef Sig. Total

Illiterates

Literate

33(64.70%)

18(35.30%)

64(64.65%)

35(35.35%)

97(64.67%)

53(35.33%)

Total 51(100%) 99(100%) 0.000 1 0.001 0.994 150(100%)

(Source: Computed from own survey data, 2005);

* NA=Non-adopters, Ad=Adopters;

*Co coef =contingency coefficient.

Education is very important for the farmers to understand and interpret the information coming

from any direction to them. Farmers’ education is also pivotal for the effective work of

extension personnel because if the farmer has better education status he/she can has a

capability to understand and interpret easily the information transferred to them from

Extension Agent (EA). From total non-adopters 35.30% were literates and 64.70% were

illiterates. In the case of adopters 35.35% were literates and that of 64.65 % were illiterate.

The proportion (percentage) of illiterate adopters and non-adopters as well as that of literate

adopters and non-adopters was almost equal as indicated in the Table 17.

60

In this study the literacy was extended from read & write to attending regular school

education. To see the relationship and the intensity of relationship, the chi-square- test was

conducted. But the result of chi-square- test was not statistically significant as indicated in

Table 17. This means there is no any significant difference in adoption between adopters and

non-adopters due to education.

4.1.2. Respondents` livestock and land ownership

In the study area mixed farming is practiced with crop and livestock production. Each

household owns at least one or more types of livestock and a piece of land for crop and

livestock production.

Livestock in the study area provides traction and manure and also serves as a source of

income through sale of livestock and livestock products. Livestock also serves as a source of

fuel in the study area. Crop residue and by-products serve as livestock feed source.

As it confirmed in many studies farmers who have better livestock ownership status are likely

to adopt improved agricultural technologies like improved bread wheat varieties; because,

livestock can provide cash through sale of them and their products and draught power for

agricultural operations. In this study, it was revealed that the average livestock ownership of

adopters and non-adopters in TLU were 6.834 and 5.02 respectively.

To know whether there is a variation in average livestock ownership between adopters and

non- adopters and as a result if there is any significant difference due to the resource position,

t-test was conducted. The result of t-test showed that there is a significant variation in average

livestock ownership between adopters and non-adopters at one percent probability level as

indicated in Table 18 and the average oxen ownership of adopters was also significantly larger

(3.03) than non-adopters (2.157) at 5 percent probability level as indicated in Table 18. As t-

test indicated, adopters had larger livestock and oxen ownership as compared to non-adopters.

61

This implied that large ownership of oxen and livestock can help farmers to adopt agricultural

innovations by solving the power force need for improved wheat production practices and cash

constraint by providing income from sale of livestock and their by products to purchase

agricultural inputs.

Table 18.Livestock and land ownership of respondents’ farmers

Ads NAs Characteristics Mean SD Mean SD

T-test Significance

(2-tailed)

Livestock

ownership (TLU)

6.8340 3.2724 5.0200 3.2120 -3.236*** 0.001

Oxen owner ship 3.0300 1.6317 2.1570 1.6294 -3.107** 0.002

Improved Bread

wheat land (ha)

0.9621 0.4579 0.0400 0.1759 -13.859*** 0.000

Total wheat land 1.2400 0.6358 0.8530 0.3846 -3.983*** 0.000

Total farm land (ha) 2.7141 1.08 2.0147 1.121 -3.710*** 0.000

Total land (ha) 3.01 1.25 2.28 1.25 -3.382*** 0.001

*** and ** Significant at 1 and 10 percent probability level

Sample farmers vary in their adoption of improved bread wheat varieties by their livestock

and oxen ownership. Adopters average livestock ownership was significantly larger than non-

adopters. This indicate that livestock ownership help farmers to adopt improved bread wheat

varieties since the income from livestock obtained through selling of the animals or their by

products can help to solve their financial limitation s to purchase inputs.

Land is the main asset of farmers in the study area. Farmers in the study area use both their

own land and rent farm land for crop production and grazing land for livestock production .All

150 sample households have their own land and only (24) 16% and (2) 1.33% respondents

rented cultivated and grazing land respectively. The average land holding of adopters was 3.01

hectares total average land holding, 2.7141 hectares total average farmland, 1.24 hectares total

average wheat land and 0.9621 hectares was average farm land used for improved bread wheat

62

production. In the case of non- adopters the total average land holding was 2.28 hectares,

2.0147 hectares was average farmland, 0.8530 hectares was average wheat land and only

among improved bread wheat growers of non-adopters was 0.0400 hectares in average was

used for improved bread wheat production. To know whether there is the mean land holding

variation, between adopters and non-adopters, t-test analysis was carried out and the result

showed that there were the significance differences in all types of land holding at one percent

probability level as indicated in Table 18.The result showed that farmers who have better land

ownership can adopt improved bread wheat varieties better than non-adopters.

Table 19.Respondents land ownership in 1996/97 Ethiopian major cropping season

Area of land ownership in hectares

Max Min Range Average St.D

Total farm Land 6.5 0.25 6.25 2.44 1.13

Total wheat land 4.5 0.25 4.25 1.125 0.605

Total improved B.W.L. 3.25 0.25 3 .00 1 .00 0.457

(Source: Computed from own survey data, 2005)

* N.B=Improved B.W.L. (Improved Bread Wheat Land)

The total farmland ownership of respondents ranges from 0.25 hectares to 6.5 hectares. The

total wheat land ownership of sample households ranges from 0.25 hectares to 4.5 hectares.

The improved bread wheat land holding of sample household ranges from 0.25 hectares to

3.25hectares.

On the average sample households owned 2.44 hectares total farmland, 1.13 hectares total

wheat lands and 0.96 hectares used for improved bread wheat production as presented in Table

19. In the study area respondents farmers allocated most of their farmland for wheat

production as presented in table one.

63

4.1.3. Accessibility of respondents to different institutional services

In this study respondents were interviewed to get their opinion about the importance of

extension services based on their experiences. About 84.67, 3.33% and 12% respondents have

reported important, not important and do not have any opinion respectively.

The respondents have also been interviewed to give their opinion about the extension support

they obtained. About 46.67% reported extremely weak due to un-availability of development

agent, about 27.33% reported very weak even though the Development Agents are available

around. The remaining 26% responded that the extension service they got in their area

becomes extremely weak due to unknown reasons for them as indicated in Table 20.

Table 20.Respondents’ opinion on extension service of the study area

No Respondents opinion on extension service N Percent

1. About importance of extension service

1.1. Important 127 84.67

1.2. Do not have any idea 18 12

1.3. Not important 5 3.33 Total 150 100

2. Status of extension service of the study area

2.1. Extremely weak due to un-availability of DA 70 46.67

2.2. Very weak even though the DA available 41 27.33

2.3. Extremely weak due to un-known reasons for them 39 26

Total 150 100

(Source: Computed from own survey data, 2005)

Data were collected regarding the type extension service obtained by the respondents as

indicated in Table.20. The whole non-adopters and 94.95% of adopters did not get extension

service during the survey year on improved bread wheat variety. As indicated in Table 21 only

64

five respondent farmers reported that they got extension support. The respondents were also

interviewed to get their opinion on the distance of DA’s office from their home. As indicated

in table 22 about 90% reported far and the remaining 10% reported close to their home.

Table 21.Extension support on improved bread wheat varieties and distance of DA’s office

Service accessibility Responses NAs Ads X2 df Sig. C.coef

.

Total

No 51 94 145

Yes - 5 5

Extension service on

bread wheat

Total 51 99 2.665* 1 0.103 0.138 150

Far 48 87 135

Close 3 12 15

Distance of DA office

Total 51 99 1.456 1 0.228 0.098 150

*Significant at 10 % probability level. ; Ccoef = Contingency coefficient

*NAs =Non-adopters, Ads = Adopters, df =Degree of freedom.

To know the association of extension service and distance of DA office with adoption of

improved bread wheat variety, chi-square analysis was conducted. The result of chi-square

analysis (2.665) showed that there is a significant association between extension service and

adoption of improved bread wheat varieties at 10 percent probability level. But the chi-square

–test result of distance of DA office and adoption of improved bread wheat varieties was not

significant as indicated in Table 21.

As it has indicated in many literatures, credit is considered as one of the favorable factors for

improved agricultural technologies adoption because it can solve financial constraints of

farmers to purchase and use improved agricultural inputs. Respondent farmers have reported

about credit institution services and related problems in their area based on their experience.

Of that, 83 (55.33%) have reported that there is scarcity, 26 (17.33%) reported that there is a

complex and boring procedures and the remaining 41(27.34%) reported that there is a high

interest rate problems as indicated in Table 22.

65

Table 22.Summary of respondents’ opinion on credit

(Source: Computed from own survey data, 2005)

Respondent farmers were also interviewed to provide their opinion about the importance of

credit and their suggestions for better credit service in the future. The respondents ’ responses

on these issues were summarized and presented in Table 22. About 67.33% respondent

farmers reported that credit is important and the remaining 32.67% reported that credit is not

important.

From those 101 respondent farmers who supported the credit service as important also

provided their opinion for better credit service. About (59) 58.42% reported reduced processes

and procedures and about (42) 41.58% suggested to reduce the interest rate as indicated in

Table 22.

No Responses on credit related problems N Percent

1 Scarcity 83 55.33

2 High interest rate 41 27.34

3 Complexity of procedures 26 17.33

Total 150 100.00

Responses on the importance of credit

1 Credit is important 101 67.33

2 Credit is not important 49 32.67

Total 150 100

Suggestions for better credit services

1 Easy and Reduced procedures 59 58.42

2 Low Interest rate 42 41.58

Total 101 100

66

As presented in Table 22 from the total 150 sample respondents, only 26 adopters and 8 non-

adopters with a total of 36, which constituted 24% of the total respondents, were credit users

particularly from individual credit sources in the mean time when this study conducted. In

credit utilization, adopters were larger in proportion than non-adopters. Almost all of the credit

users of sample respondents have reported that the major credit sources for them were

informal and private lenders.

Table 23.Association between credit and market service

Accessibility Response NA Ad X2 df Sig. Co.coe Total

No 43(84.31) 71(71.72) 114

Yes 8(15.69) 28(28.28) 36

Credit Service

Total 51(100) 99(100) 2.928* 1 0.087 0.138 150

Far 43(84.31) 89(89.90) 132

Close 8(15.69) 10(10.10) 18

Market Access

Total 51(100) 99(100) 0.994 1 0.319 0.081 150

*Significant at 10% probability level; Numbers in brackets are in percentage

To see the association between adoption of improved bread wheat varieties and credit service,

a chi-square test was carried out. The result showed that there is a significant relationship

between adoption of improved bread wheat varieties and credit services at 10 percent

probability level as indicated in Table 23. Market accessibility is also another important factor

for farmers to adopt improved agricultural inputs. If farmers are closer and having access to

credit services they can easily purchase improved agricultural inputs and sell their agricultural

outputs without moving long distances. Farmers also motivated to use improved agricultural

inputs if they have access to attractive market for their output to sell in good price. In this

study respondent farmers were interviewed to provide their idea regarding the market

accessibility. About 84.3% non- adopters and about 89.90% have reported far from market and

the remaining 15.69% non-adopters and 10.10% adopters reported close to market access as

67

indicated in Table 23. A chi-square-test analysis was carried out to check the association

between market access and adoption of improved bread wheat varieties. The result showed

that the relationship was not statistically significant as indicated in the Table 23.

Table 24.Summary of households’ accessibility of off-farm job

No Respondent farmers access to off-farm job N Percent

1 Have access to off-farm job 26 17.33

Adopters 17 65.38

Non-adopters 9 34.62

2 Have not access to off-farm job 124 82.67

Adopters 82 66.13

Non-adopters 42 33.87

Total 150 100.00

(Source: computed from own survey data, 2005)

Income from off-farm job can play a great role in adoption of improved agricultural

technologies. Because, it has hypothesized that the income from off-farm can solve farmers’

financial constraints to purchase and use improved agricultural inputs. In this study about

17.33% sample households reported that one of their family members has off-farm job and the

remaining 82.67% do not have family members who have off-farm job. From the total 26

sample households that have off-farm job 65.38% were adopters and the remaining 34.62%

were non-adopters as indicated in Table 24.

68

Table 25.Respondent farmers’ reasons for not involvement of their family in off-farm job

(Source: Computed from own survey data, 2005)

From the total 124 sample respondents whose family members were not involved in off-farm

activities had reported their reasons during the interview why the house hold members did not

involve in off-farm job. As indicated in Table 25, about 33.87% have reported that their family

members couldn’t involve in off-farm job due to the under and over age, 9.68% reported that

their family members are students, 41.94% described the time constraint since the household

members would work on the house hold farm, 12.9% reported that they do not have family

members and 1.61% reported less income from off-farm job.

Table 26.Rrespondents opinion on decision of off-farm and other household resources

No Respondents’ opinion on house holds’

resources decision maker

N Percent

1 Husband 121 80.66 2 Husband and wife 27 18.00

3 Wife 1 0.67

4 House hold members together 1 0.67

Total 150 100.00

:

(Source: Computed from own survey data, 2005)

No Reasons N Percent

1 Under and over aged 42 33.87

2 Students 12 9.68

3 Work on the house hold farm 52 41.94

4 Do not have family members 16 12.90

5 Less income from off-farm job 2 1.61

Total 124 100

69

Concerning the decision on the off-farm and other agricultural income, the household head

respondents were interviewed. All of them were reported that the house hold member who

involved directly in the off-farm job make a decision for what purpose the income from off-

farm job need to be used. But concerning the other agricultural incomes, from the total 150

respondents about 80.66%, 18%, 0.67% and 0.67% reported that the decision were made by

husband, by husband and wife, by wife and by the house hold members together, respectively

as indicated in the Table 26. In this study, almost all decision on the agricultural resources of

the farming household made by the husband.

Table 27 Pattern of off-farm income utilization of respondent farmers

No Use of off-farm income N Percent

1 Household food consumption 7 26.92

2 Cloth purchase 10 38.46

3 Health treatment 5 19.23

4 Input purchase 3 11.54

5 Labor hiring 1 3.85

Total 26 100.00

(Sources Computed from own survey data, 2005)

On the use of off-farm income, the Total 24 household respondent farmers who themselves

and their family members had off-farm job reported about their and their family members off-

farm income utilization. About 26.92 %, 38.46 %, 19.23%, 11.54% and 3.85% reported for

household food consumption, cloth purchase, health treatment, input purchase, and labor

hiring purposes respectively as indicated in Table 27. In this study the order of importance in

off-farm income utilization from higher to the lower were, for cloth purchase, food

consumption, health treatment, labor hiring and input purchase. Allocation of off-farm income

to agricultural input purchase took the least proportion.

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Table 28.Family labor utilization of respondent farmers

No Family labor utilization Ad NA Total Percent

1 Utilized family labor 93(93.94) 44(86.27) 137 91.33

2 Not utilized family labor 6(6.06) 7(13.73) 13 8.67

Total 99(100) 51(100) 150 100

(Source: Computed from own survey data, 2005)

In this study the respondent respondents` labor source and their family labor utilization were

revealed through interview. From the total 150 respondents, about (137) 91.33% have reported

that they used their family labor on their farm activities for weeding, harvesting, threshing,

plowing and sowing as indicated in Table 28.

Table 29.Types of activities and family labor utilization of respondents

(Source: Computed from own survey data, 2005)

No 1.Activities on which family labor used N Percent

1.1 Ploughing 5 3.65

1.2 Sowing 2 1.46

1.3 Weeding 120 87.59

1.4 Harvesting, 7 5.11

1.5 Threshing 3 2.19

Total 137 100 No 2.Critically labor required activities N Percent

2.1 Weeding 109 72.66

2.2 Sowing 28 18.67

2.3 Ploughing 13 8.67

Total 150 100

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Out of these sample households who used their family members’ labor on the household farm,

about 67.9% were adopters and the remaining 32.10% were non-adopters. Respondents were

also interviewed to describe the type of agricultural activities they used their family labor.

From the total 137 respondents who used their family labor on their farm, about 87.59%,

5.11%, 2.19%, 3.65%, and 1.46% reported for weeding, harvesting, threshing, plowing and

sowing respectively. Concerning the critical labor requirements of the respondents labor

requirement were about 8.67% were for plowing and sowing, 18.67% for weeding, and

72.66% were reported for harvesting and threshing as indicated in Table 29.

Table 30.Respondents’ accessibility to non-family labor and to off-farm income

Access to Response NA Ad X2 df Sig. C.coef Total

No 48(94.12) 87(87.88) 135

Yes 3(5.88) 12((12.12) 15

Labor

outside the

house hold

labor Total 51(100) 99(100) 1.456 1 0.228 0.098 150

No 42(82.35) 82(82.83) 124

Yes 9(17.65) 17(17.17) 26

Access to

off-farm

income Total 51(100) 99(100) 0.005 1 0.942 0.006 150

(Source: computed from own survey data, 2005)

* Numbers in brackets are in percentage

To check the association between off-farm income of the sample household and adoption of

improved bread wheat varieties chi-square analysis was carried out and the result showed that

there is no a systematic association statistically supported between adoption of improved bread

wheat varieties and off-farm income as revealed in this study as the result presented in Table

30. To see the labor source and accessibility of respondents to labor outside the house hold

labor and its relation ship with adoption of improved bread wheat varieties, chi-square test was

conducted and the result showed that the two variables, adoption and the utilization of labor

outside the household labor is not statistically significant as indicated in Table 30.

72

Table 31.Respondent farmers labor sources outside their family members

(Source: Computed from own survey data, 2005

Respondents were also interviewed for their labor source other than their family labor. About

53.33%, were reported that they used hire or employed labor, about 27.33% reported as they

used exchange labor, 10% were report that they used support labor from relatives and

colleagues and 9.33% were reported that they do not used labor outside their family labor

source. The respondents’ labor source outside the family members’ labor, employed and

exchange labor was very important. In the study area the agricultural activities required the

higher labor are harvesting, threshing and weeding as indicated in Table 31.

4.1.4. Agricultural information sources of the study area

Access to information or extension messages as well as various extension services was one of

the institutional characteristics hypothesized to influence farmer’s decision to adopt a new

technology. One can gain access to information about new technologies through various

means such as attending field days, visiting demonstration fields, participating training,

listening to agricultural programs on radio, through contact with Extension or Development

Agents, and through various forms of communication with neighbors, relatives, other

colleague farmers and leaders of community, religious and PA (Peasant Associations) and

through other means (Tesfaye et al, 2001).

No Types of labor sources N Percent

1 Employed labor 80 53.333 2 Exchange labor 41 27.333

3 Relatives and colleagues support 15 10

4 Not used labor outside their family labor 14 9.333 Total 150 100

73

As shown in various literature, different extension methods such as training, demonstration,

farmers’ field day, farmers meetings (mass-meeting), group discussions, posters, mass

communication methods and other extension methods have described that can be employed to

transfer extension messages to the farmers. Using and practicing these extension methods

properly to transfer extension messages can facilitate diffusion and adoption of improved

agricultural inputs.

In this study the improved bread wheat grower respondents were interviewed to give their

opinion how they got extension messages regarding the utilization and application of

improved bread wheat varieties production and management. Out of 103 respondent farmers

who grew improved bread wheat during the survey year, only (4) 3.88% were non-adopters

and the remaining (99) 96.12 were adopters. From 99 total adopters only (3) 3.03% reported

that they got training on improved bread wheat varieties production and management as

presented in Table 32.

As shown in the Table 35 about 100 of improved bread wheat growers (adopters and non

adopters) who do not got training were interviewed how they precede the improved bread

wheat varieties production. About 89% of improved bread wheat growers reported by seeing

other grower farmers, (copying mechanism or farmer to farmer extension), about 8% reported

by trial and error method and the remaining growers reported by asking the help of

Development Agent and other educated people living in their area.

To know the field day and demonstration program participation of Improved bread wheat

growers, their interview responses had summarized as indicated in Table 32., only 11.77%

respondents told that they got an opportunity to attend field day and demonstration program.

But the remaining (91) 89.23% had not have it. Out of these 12 respondents only 8.33% were

non-adopters and 91.67% were adopters.

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Table 32.Respondents’ participation in training, field day and demonstration

No Training, field day and

demonstration participation

Adopters (99) Non-adopters (4) Total (103)

1. Training participation Total (103)

1.1. Attend training 3 (3.03%) - 3 (2.91)

1.2. Not attending training 96 (96.97%) 4 (100%) 100 (97.09%)

2. Field day and Demonstration

program participation

Total 103

2.1. Attending training 11 (91.67%) 1 (8.33%) 12 (11.77%)

2.2. Not attending training 88 (96.7) 3 (3.3%) 91 (88.23 %)

3. Attending extension meeting

called by DA

Total 150

3.1. Feel happy to attend - - 17 (11.33%)

3.2. Un-happy - - 67 (44.67%)

3.3. No feeling - - 66 (44 %)

(Source: Computed from own survey data, 2005)

The respondent farmers were also interviewed to know their feeling when called by DA for

extension meeting Regarding their feeling they were show when they receive DAs call for

extension meeting; about 11.33%, 44.67% and 44% were reported that they feel happy, un-

happy and did not have any feeling on this issue respectively as indicated in Table 32.

In the study area respondent farmers were interviewed to provide their idea regarding their

agricultural information sources. As it is presented in Table 33, neighbors and colleague

farmers, DA, community leaders, farmers’ field day, PA leaders, demonstrations, radio,

newspaper/news letters, publications, posters, training programs, TV and religious leaders

have served as sources of general and agricultural information sources for them.

75

Table 33.Respondent farmers’ sources of information

(Source: Computed from own survey data, 2005)

As in Table 33 presented farmers’ neighbor and colleagues are the major and the firs important

farmers’ source of information. This survey result is similar with the result of group discussion

conducted in this study. According to this study DA serve as the second information source.

The survey result showed that the third and fourth sources of information are community

leaders and farmers’ field day respectively. As showed in the Table 33 PA leaders,

demonstration and radio serve as fifth source of information. Newspaper/news letter and other

publications serve as sixth information source. The remaining, poster, training TV and

religious leaders serve as seventh, eighth, ninth and tenth sources of information respectively

as indicated in Table 33.

Information Sources N Percent Rank

Neighbors and colleague farmers 138 92 1st

DA 136 90.67 ,,

Community leaders 133 88.67 2nd

Farmers field day 126 84 3rd

PA leaders 123 82 4th

Demonstration 123 82 ,,

Radio 123 82 ,,

News paper/News letter 122 81.33 5th

Other publication 122 81.33 ,,

Poster 107 71.33 6th

Training 105 70 7th

TV 104 69.33 8th

Religious leaders 101 67.33 9th

76

In this study, it was also tried to summarize the agricultural information sources of the farmers

in the study area through group discussion. During the time of group discussion the group

members were familiarized to the discussion point and were expected to identify and prioritize

the agricultural information sources of farmers in their area .The group members took care in

listing of all alternative sources of information available in their area using brain storming

method and tried to refined, summarized and prioritized the listed alternative information

sources listed through brain storming method.

The result of the group discussion showed that; a neighbor stands first and the most important

and TV stands the last and least important. The result of the group discussion findings showed

that farmers got more information easily from their neighbors than other sources available in

their area. The second most important information sources of farmers in the study area were

religious and community leaders. PA leaders and DAs serve as third and fourth respectively

as sources information. Demonstration and field day training and posters serve as fifth, sixth

and seventh sources of information respectively. The remaining, publications, radio and TV

serve as eighth, ninth and tenth sources of information respectively for the farmers in the study

area.

4.1.5. Farmers’ selection and evaluation criteria of improved bread wheat varieties

Varieties characteristics play a vital role in adoption of improved varieties if their

characteristics satisfied the need, interest and in line with the environmental situations of the

farmers. The information on evaluation and selection criteria of improved bread wheat

varieties of the farmers in the study area was analyzed through personal interviews and group

discussion. The procedure to analyze the information through group discussion was conducted

as; first make familiar farmers to the discussion agenda, and let them to establish and set

evaluation and selection criteria of improved bread wheat varieties disseminated in their area.

In the process of setting and establishing the criteria the group were applied the method of

brain storming and list down all the ideas provided and forwarded by the group members .The

group continued to refined the ideas forwarded by the group members and set or established

77

the evaluation and selection criteria with a common agreement. In this study, the result of the

group discussion showed that the best improved bread wheat variety should constituted the

white grain color, large seed size, and high disease, pest and frost resistance, good food

quality, good straw quality as animal feed and attractive market demand characteristics. This

study is in line with the study of (Ethiopian Rural Self Help Association /ERSHA, 2000).

Table 34.Farmers’ evaluation and selection criteria of improved bread wheat varieties

No Variety Characteristics N Percent Rank

1 White grain color 140 93.33 1st

2 Large grain Size 140 93.33 ,,

3 Straw quality 140 93.33 ,,

4 Market demand 140 93.33 ,,

5 Germination capacity 139 92.67 2nd

6 Cooking quality 139 92.67 ,,

7 Better yield performance 139 92.67 ,,

8 Water lodging resistance 138 92.00 3rd

9 Tillering capacity 138 92.00 ,,

10 Food quality 138 92.00 ,,

11 Short maturity date 137 91.33 4th

12 Disease resistance and pest resistance 135 90.00 5th

13 Frost resistance 133 88.67 6th

14 Harvesting quality 97 64.67 7th

15 Storage quality 97 64.67 ,,

(Source: Computed from own survey data, 2005)

The results in evaluation and selection of improved bread wheat varieties disseminated in the

study area has showed in Table 34 that white grain color, large grain size, market demand and

straw quality were the first and most important criteria. The traits such as better yield

performance, cooking quality and germination capacity got the second rank. Food quality,

tillering capacity and water lodging resistance got the third rank Short maturity date, pest and

78

disease resistance and frost resistance were got the fourth, fifth and sixth ranks respectively.

Harvesting and storage qualities were got the seventh rank by farmers’ judgment.

Table 35.Farmers’ preference (selection and evaluation criteria) of improved bread wheat

varieties disseminated in the study area

HAR-1685 (123) HAR-1709 (54) Paven-76 (115) No Variety Characteristics

N Percent N Percent N Percent

1 Market demand 97 78.8.6 12 22.22 41 35..65

2 Cooking quality 86 69.92 23 42.60 41 35.65

3 Water logging résistance 86 69.92 27 50 37 37.12

4 Straw quality 84 68.29 36 66.67 30 26.10

5 Storage quality 83 67.48 33 61.11 34 29.57

6 Frost resistance 80 65.04 27 50 43 37.40

7 Seed size 79 64.23 26 48.15 45 39.13

8 Yield performance 77 62.60 38 70.37 35 30.43

9 Rain shortage and Drought

resistance

77 62.60 32 59.26 41 35.65

10 Weed resistance 76 61.80 42 77.78 32 27.82

11 Grain color 71 57.73 37 68.52 42 36.52

12 Food quality 70 56.91 32 59.26 45 39.13

13 Disease and Pest resistance

66 53.66 40 74.04 44 38.26

Sum 1032 - 405 - 510 -

Average 79.385 - 31.154 - 39.231 -

Rank 1st - 3

rd - 2

nd -

(Source: Computed from own survey data, 2005)

Respondent farmers were interviewed to get their idea on evaluation and selection criteria of

improved bread wheat varieties and their responses has summarized in Table 35.The

respondent farmers responses showed that the improved bread wheat varieties should

constitute the characteristics mentioned in Table 35. These evaluation and selection criteria are

the most important criteria for the farmers in the study area. The respondent farmers have

given their preference of improved bread wheat varieties distributed in their area. The larger

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proportion of respondent farmers selected HAR-1685 improved bread wheat variety. The

remaining varieties Paven –76 and HAR, 1709 ranked second and third respectively. This

survey result was also supported and similar result was obtained from group discussion

conducted in this study.

4.2. Analytical results and discussion

The purpose of this section is to identify the most important hypothesized independent

variables that influence the dependent variables namely the probability of adoption for

nonadopters using logit model and the intensity of adoption for adopters using tobit model

analysis of improved bread wheat varieties in the study area, Akaki. Before conducting the

model analysis selection, screening and verification of hypothesized variables were conducted

by considering various situation to get best variables, those can fit with the analytical models,

describe the sample groups, environmental and practical situation of the study area. This was

done in consultation of professionals and experienced people, based on literatures, practical

situations, observation and experience of the researcher and the relevance as well as the

importance of the variables. As a result, the variable, distance of credit provider institutions

was dropped because the major credit source for the farmer in the study area were the private

individual credit providers in time when this research was conducted. These individual credit

providers do not have a specific place and including this variable in model analysis was not

relevant.

In the case of significant level of hypothesized independent variables, independent sample test

between the groups using t-statistics or t-test for continuous variables to describe the pattern of

sample data and to test the significance of a given independent variable on adopters and non-

adopters groups as well as to check the mean values differences of continuous variables in the

two groups and the chi-square test also to test the differences between the two groups for

discrete variables in relation to dependent variables (Lind and Mason, 1994 as cited in Adane,

2002). In the analysis some independent variables might show significant and others might

show insignificant relationship with dependent variables. The insignificant association doesn’t

80

guarantee about the strength or direction of relationship between those insignificant

hypothesized independent variables and the dependent variables. The reason for the

insignificant relationship of some of the independent variables is mainly because of the fact

that there is a drawback with any univariate approach in that it ignores, but there could be a

possibility in the collection of variables analysis, each of which is weakly associated with the

univariate outcomes can become an important predictor of out come when taken together.

Therefore, we should consider them as candidates to be indicated in the multivariable models

analyses along with all known important variables (Hosmer and Lemeshow, 1989 as cited in

Adane, 2002).

Moreover, there are several literatures and previous research works (Chilot, 1994; Bekele,

2000; Adane N.F., 2002; Adane N.M., 2002; Techane 2002; Endrias, 2003; Yitayal, 2004;

Adam and Bedru, 2005) conducted in a similar way which can substantiate this study. These

previous research results showed that those hypothesized independent variables were included

in econometrics model analyses regardless of the significant or insignificant results of these

hypothesized independent variables in descriptive analysis. The model analyses results might

show significant or insignificant results differently or similarly to descriptive statistics results.

Regarding multicollinearity or high degree of association problem among selected and

screened hypothesize independent variables were primarily checked before including in the

models as well as before running the models analyses.

Secondly, prior to running the Logit and Tobit models, the presence or absence of correlations

or associations between hypothesized independent and dependent variables were checked. The

presence or absence of correlation or association, that is, whether or not there is a correlation

between the variables in question (Sarantakos, 1998).

Existence, direction and strength of correlation are demonstrated in the coefficient of

correlation. A zero correlation indicates that there is no correlation between the variables. The

sign in front of the coefficient indicates whether the variables change in the same direction

(positive correlation) or in opposite direction (negative correlation), except for nominal

81

measures, where the sign has no meaning, in which case coefficient describe only the strength

of the relationship (a high or a low association) between the variables of the study. The value

of the coefficient shows the strength of the association with values close to zero meaning a

weak correlation and those close to 1 a strong correlation. A correlation of +1is just as strong

as one of -1; it is the direction that is different (Sarantakos, 1998). Therefore, in this study, the

presence or absence of association or correlations of hypothesized independent variables with

the dependent variable, adoption of improved bread wheat varieties were assessed to identify

and drop from model estimation if hypothesized independent variables do not have any

relationship with dependent variable, adoption of improved bread wheat varieties. The

Cramer’s v coefficient for discrete variables and Pearson’s correlation coefficient for

continuous variables was calculated using SPSS computer program.

In addition, the direction, range, intensity or degrees of strength of association of each

hypothesized variables with dependent variables were assessed. The result showed that there

was no total absence of association between hypothesized independent variables and

dependent variables. Rather, association between hypothesized independent and dependent

variables exist with various degrees of association ranging from moderate to weak. As a result,

it was decided to include all selected, verified, screened hypothesized independent variables,

those have various degrees of relationship with dependent variables, in models analyses to see

their combined effect they have on dependent variables namely probability and intensity of

adoption. From these total selected independent variables, only farmland showed moderate

correlation with the dependent variable. But the rest showed weak association as indicated in

Appendix table 24.

Thirdly, before including the hypothesized variables and running the model analyses the

existence of a serious of multicollinearity or high degree of association problem among

independent variables for all continuous and discrete variable were checked. There are two

measures that are often suggested to test the existence of multicollinearity or association

problems among independent variables. These are: Variance Inflation Factor (VIF) for

multicollinearity problem among continuous independent variables and contingency

82

coefficients for existence of high degree of association among independent dummy variables.

The technique of variance inflation factor (VIF) was employed to detect the problem of

multicollinearity for continuous variables. VIF shows how the variance of an estimator is

inflated by the presence of multicollinearity (Gujarati, 2003).

It is obvious that multicollinearity problems might arise when at least one of the independent

variables shows a linear combination of the others; with the rest that we have too few

independent normal equations and, hence, cannot derive estimators for all our coefficients.

More formally, the problem is that a high degree of multicollinearity results in larger variances

for the estimators of the coefficients. A larger variance implies that a given percentage

(eg.95%) confidence interval for the corresponding parameter will be relatively wide; a large

range of values of the parameter, perhaps including the value zero, will be consistent with our

interval. This suggests that, even if the corresponding independent variable problem may make

it quite difficult for us to estimate accurately the effect of that variable. Consequently, we may

have little confidence in any policy prescriptions and biased on these estimates (Kelejian and

Outes, 1981).

Very often the data we use in regression analysis cannot give decisive answers to the questions

we pose. This is because the standard errors are very high or the t-ratios are very low. This

sort of situation occurs when the explanatory variables display little variation and/or high

inter-correlations. The situation where the explanatory variables are highly inter -correlated is

referred to as multicollinearity (Maddala, 1992).

According to Maddala (1992), VIF can be defined as: VIF (xi) = 21

1

iR−

Where 2

iR is the square of multiple correlation coefficients that results when one explanatory

variable (Xi) is regressed against all other explanatory variables .A statistical package known

as SPSS was employed to compute the VIF values. Once VIF values were obtained the R2

values can be computed using the formula. The larger the value of VIF, the more will be

“trouble-some” or the collinear of variable Xi. As a rule of thumb, if the VIF of a variable

83

exceeds 10, there is multicollinearity. If Ri2 exceeds 0.90, that variable is said be highly

collinear (Gujarati, 2003). The VIF values displayed in Table 36 have shown that all the

continuous independent variables have no multicollinearity problem.

Similarly, contingency coefficients were computed from survey data to check the existence of

high degree of association problem among discrete independent variables. Contingency

coefficient is a chi-square based measure of association .A value of 0.75 or more indicates a

stronger relationship (Healy, 1984 as cited in Destaw, 2003).

The contingency coefficients are computed as:

2

2

χ

χ

+=

NC

Where, C= Coefficient of contingency

χ2 = Chi-square random variable and

N = total sample size.

Which assumes a value between 0 and 1 to indicate the degree of association between the

discrete variables as indicated in Table 37.The decision rule for contingency coefficients says

that when its value approaches 1, there is a problem of association between independent

discrete variables. As indicated in Table 37 that there is no a problem of high degree of

association among independent discrete variables.

84

Table 36.Variable Inflation Factor for the continuous explanatory variables

Variables R2i Variance Inflation Factors (VIF)

Age 0.047 1.049

TLU

0.637 2.754

Farm land holding

0.342 1.520

Oxen ownership

0.638 2.759

Experience in extension

0.036 1.036

Family size 0.231 1.301

(Source : Own Computation)

Table 37.Contingency Coefficients for Dummy Variables of Multiple Linear Regressions

Model

Source: Own computation

As it has indicated in many studies and literatures, if there will be serious multicollinearity or

a high degrees of association problems among independent variables, these situations can

HHH

SEX

EDU

HHH

HEAL

STAT

PRTI

LEDE

HHOF

FINC

DIS

DAOF1

CRIN

MFF1

OTS

OLA

GEXS

ERVE

GECR

SERV

HHHSEX 1 0.184 0.023 0.128 0.154 0.055 0.073 0.14 0.068 0.063

EDUHHH 1 0.003 0.044 0.030 0.033 0.058 0.196 0.018 0.024

HEALSTAT 1 0.155 0.035 0.046 0.23 0.046 0.068 0.034

PRTILEDE 1 0.088 0.172 0.130 0.106 0.097 0.080

HHOFFINC 1 0.082 0.048 0.094 0.085 0.406

DISDAOF1 1 0.442 0.110 0.062 0.021

CRINMFF1 1 0.055 0.046 0.174

OTSOLA 1 0.183 0.031

GEXSERVE 1 0.155

GECRSERV 1

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create difficulties to differentiate the separate effects of independent variables on dependent

variables and also seriously affect the parameter estimate because of strong relationship among

them. Hence, should not be included in the model analysis (Maddala, 1983; Kathari, 1990 as

cited in Adane, 2002 and Gujarati, 1995). But since there is no a serious multicollinearity or

high degree of association problem among independent variables in this study all the screened

variables were decided to be included in the models analyses.

After conducting and passing all these steps, all screened and verified independent variables

were included in logit model analysis using SPSS computer software program. But a problem

faced in tobit model analysis to include all these screened and verified independent variables

in tobit model analysis using Limdep computer soft ware program due to the limitation of this

soft ware program to accommodate all variables included in logit analysis. Therefore, there

need to select and choose the variables that can be accommodated by the Limdep soft ware

program and most important independent variables for the analysis than others. As a result,

from those independent variables included in the logit analysis only leadership position and

credit service were dropped from tobit model analysis based on practical and actual situations,

researcher’s observation, relevance of the variables and by employing Limdep computer

program to check the number of significant variables those can affect the dependent variable,

intensity of adoption of improved bread wheat varieties.

In this process the number of significant independent variables were increased when the above

two independent variables were dropped individually or together. At last, the remaining

screened and verified hypothesized independent variables were included in tobit model

analysis.

4.2.1. Analysis of determinants influencing probability of adoption of improved bread

wheat varieties and their marginal effect

To identify factors among hypothesized independent variables that significantly influencing

the probability of adoption of improved bread wheat varieties in the study area, Akaki, SPSS

86

computer soft ware program and the binomial econometric analytical model (the binary Logit

model) was employed. In fitting the logistic estimation model, the higher significance of chi-

square statistics (80.187) was taken as a measurement of goodness-of-fit. This indicates that

the explanatory variables together influence the probability of adoption of improved bread

wheat varieties in the study area. In addition, the model correctly classified the respondents

into adopters and non-adopters at 81.33% of correct prediction percentage. The maximum

likelihood estimate of the parameters and the direction of relationship and the effect of

independent variables on probability of adoption were analyzed and presented in Table 38.

As indicated in the methodology and other previous sections, a number of independent

explanatory factors were postulated to influence the probability of adoption of improved bread

wheat varieties in the study area. Among the selected hypothesized explanatory variables and

considered by the model, only four variables were found to have significantly affect farmers’

adoption decision of improved bread wheat varieties. The variables affecting probability of

adoption were distance of DA-office from the farmers home (DISDAOF1), house hold

social/leadership status (PRTILEDE), market accessibility (CRINMFF1), and house hold

farmers experience in extension (YEXPEXTS) as indicated in the Table 38.

Among those significant variables, only one variable, which was market access, related with

adoption of improved bread wheat varieties negatively and the sign was different from the

expectation but statistically significant at 5 percent probability level. In this study, the negative

relationship of market access and adoption of improved bread wheat varieties showed that

those farmers in the study area who do not have access to market are more likely to adopt

better the improved bread wheat varieties than those farmers who have a better access to

market.

The possible reason for this situation might be, those farmers who have better and closer

access to market area might create other income opportunity from their farm and they may

give more attention and priorities to these other alternatives, production activities and other

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substitutions, which may bring better income to them than using their whole wheat farm to

produce improved bread wheat. But those farmers far away from market since they may not

have any other alternatives they give more attention for improved bread wheat production and

to their farming occupation. The other possible reason to these farmers who far away from

market make them to adopt the improved bread wheat varieties than those who are closer to

market might be the better production performance of the varieties to provide food for their

family through out the year since they might not have other food sources of alternatives and

means .On the other way, environmental situations, the soil fertility frost problem variations

might be the possible reasons.

The remaining three significant explanatory variables namely (leadership position/status,

experience in extension and distance of DA-office from the farmers’ home) related with

adoption of improved bread wheat varieties significantly and positively, as of the expectations,

at 1, 1 and 10 percent probability levels respectively as indicated in the Table 38.

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Table 38.Factors affecting Probability of adoption of improved bread wheat varieties and the

marginal effect of the significant explanatory variables

Variables B S.E Wald df Sign. Exp (B)

HHHSEX-sex 0.657 0.777 0.714 1 0.398 1.928

HHHAGE-age -0.023 0.021 1.194 1 0.274 0.977

EDUHHH-education -0.156 0.536 0.094 1 0.772 0.856

HEALSTAT-health 0.942 0.901 1.092 1 0.296 2.565

PRTILEDE –leadership position 2.217 0.756 8.610 1 0.003*** 9.181

HHOFFING-off-farm income -0.688 0.945 0.531 1 0.466 0.502

DISDAOF1-DA-office 1.490 0.910 2.683 1 0.101* 4.436

TOTLIVUM -livestock 0.076 0.146 0.272 1 0.602 1.079

SUMOWRE- farm land 0.361 0.301 1.435 1 0.231 1.435

CRINMFF1-market -1.636 0.819 0.3.993 1 -0.046** 0.195

OTSOLA1-labor 0.110 1.228 0.008 1 0.928 1.117

OXTLU-oxen 0.103 0.278 0.137 1 0.712 1.108

GEXSERVE -extension 7.811 23.647 0.109 1 0.741 2467.236

YEXPEXTS-extension experience 0.475 0.113 17.690 1 0.000*** 1.608

GECRSERV -credit 0.200 0.788 0.064 1 0.800 1.221

FAMILYSI –family size 0.024 0.135 0.030 1 0.862 1.024

Constant -4.053 1.744 5.399 1 0.020 0.017

Notes: Exp (B) shows the predicted changes in odds for a unit increase in the predictor

*Omnibus Tests of model coefficients: Chi-square=74.97, Sign.0.000; * Percentage of correct

prediction=81.30; and *, **and ***Significant at 10%, 5%, and 1% Significant level.

The variable leadership position affects adoption significantly in the study area as indicated in

Table38. Farmers who have a leadership position in the society might give a better opportunity

to access resources and inputs such as labor, fertilizer, seed, to contact with DA for better

information, better access to credit providers, as a result of their leadership position and,

hence, are likely to adopt improved bread wheat varieties better than those who did not have

leadership position in the society this result is in line with the result of Rauniyer and Goode

(1996) as cited in Legesse (1998). This implies that there need to give attention and identify

what those farmers who do not have leadership position lack due to their lower leadership

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position to design a strategy to provide access, support, encourage them to achieve better

adoption of improved bread wheat varieties by them.

The variable, experience in extension influences adoption of improved bread wheat varieties.

Farmers who have longer years of experience in extension have adopted better-improved bread

wheat varieties than those who have the lower years of experience in extension participation.

This showed that the farmers with longer years of experience in extension may use their

experience to using and taking the advantages obtained from new agricultural innovations or

technologies and also they may develop, the confidence in handling the risk, skills in

technology application, and may developed better economical status and better income from

out put of using of these improved agricultural technologies.

Regarding, the distance of DA’s office, from farmers’ home showed influential effect in

adoption of improved bread wheat varieties in the study area as revealed in this study. The

farmers who are nearby the DA‘s office are likely to adopt the improved bread wheat varieties

than those who are far. This implies that the near by farmers to the DA‘s office would have an

opportunity to get better and up dated information on the availability and benefit of improved

varieties easily and better than those far farmers. As a result, they can use these opportunities

to adopt the improved bread wheat varieties than those farmers far away from DA office.

The remaining hypothesized independent variables were not statistically significant to

influence the probability of adoption of improved bread wheat varieties at less than 10%

significant level as indicated in Table 38. Even though they were not significant below 10%

significant probability level in logit model analysis practical and experience situations,

literatures and many research works as well as the test statistics of this study showed that they

have influential impact on adoption of improved technologies and innovations. The result of

the logit analysis and their change or marginal effect of explanatory variables on dependent

variable, probability of adoption of improved bread wheat varieties showed and presented in

Table 38.

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The marginal effect of the variable of the distance of DA-office closer to farmer’s home by

one unit might improve the probability of adoption of improved bread wheat varieties by a

factor of more than four times fold. In the social system the farmer’s leadership position can

improve farmer’s agricultural technologies as observed in this study. In this study farmer’s

leadership position improves the probability of adoption .As there is a change of farmer’s

leadership position from non-leadership to leadership, there is an improvement of adoption of

improved bread wheat varieties by the factors of nine times fold. The farmers’ experience in

any of extension activities and use of improved technologies and innovations, can also

improve the probability of adoption of improved bread wheat varieties. The changes and

improvement of farmers’ experience in extension participation by one year or by one unit can

increase adoption of improved bread wheat varieties by the factor of 61%.

In this study, it was revealed that the market access has a negative relationship with adoption

of improved bread wheat varieties. When farmers are closer to market access by one unit,

there is a decrease of probability of adoption of improved bread wheat varieties by a factor of

0.2 or by a factor of 20 percent. This implies that as mentioned in the above of this section,

farmers who are closer to the market centers and facilities might be influenced and attracted by

other substitution factors created by the market center facilities and might inclined to involve

in these activities and business tasks with out totally leaving the farming occupation. As a

result, they become reluctant to adopt improved bread wheat since improved wheat demand

intensive management and labor work.

4.2.2. Analysis of determinants influencing intensity of adoption of improved bread

wheat varieties and their marginal effects

Parameter estimates of the Tobit model for the intensity of adoption of improved bread wheat

varieties (measured in terms of size of land in hectare used for growing of improved bread

wheat varieties over the total wheat land in hectare). The Tobit model was used or applied to

analyze the factors that determine the intensity of adoption of improved bread wheat varieties

91

because the mean proportion of land allocated to improved bread wheat varieties is a

continuous variable but truncated between zero and one.

The main purpose of this section is to identify the hypothesized independent variables among

the selected and proposed to include in the tobit model analysis that significantly influence the

dependent variable, intensity of adoption. The result of this study indicated and presented in

Table 39. From the total hypothesized independent variables, only eight explanatory variables

were significantly influencing and affecting the intensity of adoption of improved bread wheat

varieties as presented and indicated in Table 39. These significant variables were, household’s

sex (HHHSEX), age (HHHAGE), education (EDUHHH), health status (HEALSTAT), off-

farm income (HHOFFINC), home distance from DA office (DISDAOF1), farmland holding

(SUMOWRE) and extension service (GEXSERVE) were statistically the most important

explanatory variables affecting intensity of adoption of improved bread wheat varieties in the

study area.

The variable household sex was related with the intensity of adoption of improved bread

wheat varieties positively and significantly at 5 percent probability level. The sign was in line

with that of the expectation. AS it was indicated in the identification of hypotheses, probability

and intensity of adoption was expected to relate positively with male sample and negatively

related with female sample. Hence, the positive sign indicates that the male-headed

households were better in intensity of adoption of improved bread wheat varieties than female

farmers. This result showed that male farmers are more likely to allocate larger farmland to

improved wheat than female farmers in the study area. This result is in conformity with the

finding of (Thechane, 2002).

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Table 39.The effects of changes (marginal effect) in the significant explanatory variables on

the intensity of adoption of improved bread wheat varieties

(Source Computed from own survey data, 2005)

*, ** And***Significant at 10,5 and 1 percent probability level

From these significant explanatory variables only one variable namely size of farmland

holding related with the intensity of adoption of improved bread wheat varieties negatively

and significantly at 10 percent probability level. This variable has the different sign from that

was hypothesized. The remaining of the seven significant explanatory variables namely

household sex, age, education, health status, off-farm income, extension service, distance of

DA office from farmers’ home showed statistically significant and positively related with

intensity of adoption of improved bread wheat varieties in the study area, at 10 percent

probability level.

Variables Coefficient Standard Error b/St.Er P (/Z/>z)

HHHSEX 0.1854436505 0.81995580 2.262** 0.0237

HHHAGE 0.3862778245 0.15594257 2.477** 0.0132

EDUHHH 0.1696807312 0.56390468 3.009*** 0.0026

HEALSTAT 0.3813275935 0.72099682 5.289*** 0.0000

FAMILYSI 0.1544088566 0.11636011 1.327 0.1845

HHOFFINC 0.1409564409 0.68463253 2.059** 0.0395

DISDAOF1 0.1684878586 0.88875721 1.896* 0.0580

TOTLIVUN -0.8907270032 0.10883167 -0.818 0.4131

SUMOWRE -0.3801474933 0.22993869 -1.653* 0.0983

CRINMFF1 0.1719597193 0.96915193 0.018 0.9858

OTSOLA1 -0.2466170095 0.80390554 -0.307 0.7590

OXTLU -0.3023853019 0.22090082 -0.137 0.8911

YEXPEXTS 0.4834808429 0.54835151 0.882 0.3779

GEXSERVE 0.2589730719 0.11095968 2.334** 0.0196

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The variable household age was also related with the intensity of adoption of improved bread

wheat varieties positively and significantly at 10 percent probability level. As the result of this

study showed that older farmers may have already developed better experience, face exposure

opportunities with using of large size of improved agricultural technologies through their life

experience and might develop experiences how to manage risks and taking of the first benefits

from newly released varieties. This might help them to develop confidences to allocate larger

farmland to improved bread wheat varieties production more than those lesser and younker

age group farmers in the study area. The sign was different from that of hypothesized. The

hypothesis formulation and establishment was conducted based on literatures, experiences and

observation of actual, practical and existing situations.

Literatures showed that farmers expected to be reluctant to new innovations as their age

increased. But in the context of this study area, it is different from that of literatures. In history

of Ethiopian extension farmers in the study area have better exposure of opportunities to new

agricultural innovations than other areas of Ethiopia. As a result they developed better

experience through their life experience better than other areas of farmers who do not get the

opportunities like farmers in the study area. This finding agreed with the finding of (Chilot,

1994).

Education was also has a positive and significant relationship with the intensity of adoption of

improved bread wheat varieties at 1 percent probability level. In this regard, the proportion of

farmland used for growing of improved bread wheat varieties by farmers who are literate is

likely to be greater than farmers who were illiterate. This suggests that being literate would

improve access to information, capable to interpret the information, easily understand and

analyze the situation better than illiterate farmers. So, farmer who are literate were likely to

allocate larger size of farmland proportion than those illiterate farmers. The sign was as

expected. This result has supported by other previous studies such as the findings of Lelissa

(1998), Techane (2002), Lelissa and Mulate (2002), Yitayal (2004).

94

The variable health status had a positive and significant influence at 1 percent probability level

relationship with the intensity of adoption of improved bread wheat varieties in the study area..

The result of this study showed that farmers who have better health status are likely to allocate

larger farmland size to improved wheat varieties production. It is obvious from practical and

actual situation of the ground that managing and operating the improved agricultural

innovations demanded intensive labor and management practices. Then, health farmers can do

these practices than unhealthy farmers. There fore healthy farmers are likely to allocate larger

farmland size than unhealthy farmers. The sign was as expected. Low intensity of adoption by

un-healthy farmers may be due to the shortage of labor and the problem to conduct intensive

management that the improved bread wheat demanded.

The explanatory variable, off-farm income influenced the dependent variable, intensity of

adoption of improved bread wheat varieties positively and significantly at 5 percent

probability level as hypothesized under the section of hypotheses description. As it is true that

farmers who have better income can adopt new agricultural innovations because their income

allowed them to purchase the new technological inputs, can with stand risks if appear and can

cover labor costs. Off-farm income is one of the alternatives to improve farmers’ income.

From these grounds of realities farmers who have off-farm income can adopt new technologies

in larger proportion than those who do not have off-farm income.

The size of farmland holding, affected the dependent variable, intensity of adoption of

improved bread wheat varieties in the study area negatively and significantly at 10 percent

probability level. The sign was different from that of postulated. This finding is in conformity

with the finding of (Bekele et al, 2000; and Chilot, 1994).

As it is supported by many literatures, those farmers who have larger land size are expected to

adopt improved and new agricultural technologies in larger proportions than those farmers

who have lesser farmland. Since these farmers have larger farm land they do not have fear of

risks, can get credit because they are believed that they can pay their credit, or some part of

their land may serve as mortgage to take credit, seed loan from other farmers and can adopt

95

new agricultural technologies than those who have lesser land size. But in this study the result

showed the different situations from many literatures even though it is in line with some few

literatures as mentioned in the above. The possible reason for this result can be that the actual

situations in the study area different from other areas in that the situations of the area where

this study has conducted is closer to Addis Ababa, the capital of the country where in the mean

time of this study highest construction investment on land were conducted.

Farmers who are closer to the town lease their part of farm land and might reluctant to increase

their farm land allocation to improved bread wheat land since they got better income from land

contract than they got from improved wheat land production. Another possible reason also for

this situation might be that people at the edge boarder of Addis Ababa and the rural part of the

study area have the experience of producing crop for their consumption and profit purpose by

contracting land from the surrounding farming community. As result of the existence of this

situations in the study area farmers might contract their land for these types of par time

farmers due to many situations like for better income than they used for improved wheat

production or for the reason they may face different problems and cash constraint that could

not give some time in the future. Then, those par time farmers might have their own interest of

crop type production and objectives. As a result those first owners and adopters of improved

bread wheat varieties might unable to increase their farmland allocation for improved bread

wheat varieties.

And also in the study area there is an introduction and promotion of white check pea, which

has high price in market. This also shift the wheat adopters to allocate their wheat farm land to

this new crop variety rather than increasing of their land allocation to the improved bread

wheat varieties since wheat land can use for check pea crop interchangeably. It was also

observed that credit and input providers greatly reduced their service provisions due to the

reluctant effect of farmers’ to return their previous credit loan as a basic reason of the highest

interest rate of the loan. These are some of the possible reasons for the inverse relationship of

the independent and dependent variables in this case.

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Extension service influenced dependent variable, intensity of adoption of improved bread

wheat varieties positively and significantly at 5 percent probability level as hypothesized. The

finding of this study is in agreement with the findings of Adesina and Zinnah (1992), Chilot

(1994), Techane (2002), Lelissa and Mulate (2002) and Yitayal (2004). Theoretical and

practical realities showed that extension services provided to the farmers in different forms

like training demonstration field day DA visit on the field and on spot field support can

motivate, empowers kill and knowledge, increase information access and create interest to

improve farmers’ use of improved agricultural technologies.

The independent variable, distance of DA office from the farmer’s home, influenced the

independent variable, intensity of adoption of improved bread wheat varieties in the study area

positively and significantly at 10 percent probability level. The sign was as postulated. The

intensity of adoption of improved bread wheat varieties is higher to the farmers who are closer

to the DA office than those farmers who found far. This result is in line with the result of

(Chilot, 1994). This is also true from theoretical, practical and experience realities when the

Das assigned closer to the farmers village farmers can easily and from near by distance can get

the required information such as availability of inputs, credit services, market situation

government and other development organization supports on time and sufficiently.

The results of the Tobit model analysis also showed the effects of changes or marginal effects

in the explanatory variables on the dependent variable, intensity of adoption of improved

bread wheat varieties, in the study area as indicated and presented in Table 39.

Literatures showed that adoption and intensity of utilization of improved agricultural

innovations has relations with gender. As it was hypothesized male farmers were likely

expected to show better intensity of farmland allocation for improved bread wheat production

than female farmers. The marginal effect of Tobit model analysis showed that male farmers

were better in allocation of farm land as compared to female farmers. The intensity of

farmland size allocation for improved bread wheat varieties production by male farmers was

larger by a factor of 19 % than female farmers.

97

Age is one factor to influence intensity of adoption. The marginal effect of tobit analysis

showed that as age of adopters of improved bread wheat increase by one unit, intensity of

farmland size allocation for improved bread wheat varieties production can improve by the

factor of 38.63%. As mentioned in the above, since farmers in this area have a long years

exposure to extension services than other areas of farmers, they developed better extension

experience in the process of their lifetime experience that plays a great role in intensity of

adoption of improved bread wheat varieties in the study area.

Education plays a positive and significant role in the intensity of adoption of improved bread

wheat varieties in this study. An improvement in education or a change by a unit i.e. from

illiterate to literate can improve farmers allocation of farm land for improved bread wheat

production from their total wheat land can improved by a factor of 17%. In the other way, as

there is an improvement in educational level of adopters’ of improved bread wheat varieties by

one unit, there will be an increased allocation of farm land for production of improved bread

wheat varieties by 17%.

In this study it was identified that as farmers’ health situations improved from unhealthy to

healthy situation, the intensity of adoption of improved bread wheat varieties can increases by

a factor of 38%, because the health farmers can directly involve in every activities of improved

bread wheat production and can them selves manage their farm. As a result the allocation of

intensity of farmland for improved bread wheat production can increase by a factor of 38%.

The variable household farmers’ off-farm income contributes its own part in the intensity of

adoption of improved bread wheat varieties in the study area positively and significantly. As

the involvement of farmers in off-farm income and consequently their income improved by

one unit, their allocation of farmland for improved bread wheat production can increase by the

factor of 14%.

98

When the farmers’ income improves by one unit from off-farm income source, the allocation

of farmland by them for improved bread wheat production can improve by a factor of 14%.

This is due to the fact that the off-farm income can solve farmers’ financial constraints and

increase their purchasing power of improved bread wheat seed, other agricultural inputs such

as fertilizer and other production means relevant to the production of improved bread wheat.

Consequently, farmers might be encouraged in allocating larger area of their wheat farmland

for the production of improved bread wheat varieties than those who do not have off-farm

income.

The distance of DA office plays its role in intensity of adoption of improved bread wheat

varieties in the study area positively and significantly. As the distance of DA office decreases

or closer to the home of farmers by one unit, intensity of adoption could increase by a factor of

17%. This implies that farmers who are closer to the DA’s office can get easy access to

extension support and agricultural information that can give a chance to analyze situations and

allocate their larger farmland for growing of improved bread wheat varieties than those who

are far from DA’s office.

As discussed in the above, the independent explanatory variable, farmland holding related

with the intensity of adoption of improved bred wheat varieties negatively and significantly as

indicated and presented in Table 39. As revealed in this study, when the size of the farm land

holding of farmers increased by one unit intensity of adoption of improved bread wheat

varieties decreased by a factor of 38%. This may be due to the fact substitution of some part of

their wheat land to other highly market demanded crops like for example white chick pea

production, land contracting by receiving larger amount of money, and the farmers themselves

might involve in other activities and reluctant to allocate increased farm land for improved

bread wheat production as a result the result of tobit model analysis showed the inverse

relationship between farm land and intensity of adoption of improved bread wheat varieties in

the study area.

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As it is generally true, the better the extension service can improve the intensity of adoption

and utilization of improved agricultural technologies. The tobit analysis in this study showed

that the change in improving the extension service by one unit can improve the allocation of

farm land for improved bread wheat production by adopter farmers can improve by the factor

of 26% as indicated in Table 39. This implies that when farmers get support from extension

agent, in various forms such as information provision, practical support on the spot of the field

or in the form of demonstration, field day and skill development, can improve farmers

knowledge, interest, motivation and confidence to allocate larger extent of farm land than

those who do not get or who got less extension support.

To summarize the two analytical model results, that the purpose of data analyses using the

econometrics models as discussed through out this section and in the previous sections is to

know which independent variable most important and powerful to affect the intended

dependent variable to which they hypothesized to influence. In this study two-econometrics

models logit for identification of factors affecting probability of adoption of improved bread

wheat varieties for those non-adopter farmers need to adopt in the future and tobit model for

estimation of factors to influence adopters’ intensity of adoption or allocation of farm land size

intensity for improved bread wheat production.

As mentioned at the beginning of this analytical section due to various reasons such as

theoretical, actual, practical, technical reasons some hypothesized independent variables were

dropped from further analyses like for example leadership position and credit service due to

tobit model limitation to accommodate all independent variables included in Logit model

analysis did not included in Tobit model analysis. The result of logit model analysis for

probability of adoption and result of tobit model analysis for intensity of adoption of improved

bread wheat varieties indicated and presented in Tables 38 and 39 respectively.

The most important and significant independent variables below 10% probability level to

influence probability of adoption of farmers who did not adopt improved bread wheat varieties

in the past but expected to adopt in the future, those identified by logit model analysis were

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four namely distance of DA-office from the farmers’ home, house hold head social/leadership

position, market accessibility, and house hold farmers experience in extension as indicated in

Table 38. Regarding the result of tobit model analysis as indicated in Table 39, eight

independent variables namely sex of household head, age, education, health status, off-farm

income, distance of farmers’ home from DA office, farmland holding and extension service

were statistically significant below 10% probability level and most important to influence

adopters’ intensity of farm land allocation to improved bread wheat varieties production from

their total wheat farm land as indicated in Table 39.

In this study, the two models used for two purposes as mentioned in the above. Though the

purpose of this study is not to identify the significant common independent variables among

those variables used in the two models analyses to identify influencing significant factors for

the two dependent variables as mentioned in the above, it is very important to see whether

there is a common influencing independent variables that affect significantly the two

mentioned dependent variables. As a result, it was identified that there was only one

independent variable, distance of DA-office from farmers’ home that commonly and

significantly affected both probability of adoption and intensity of adoption below 10%

probability of significant level. It doesn’t mean that the remaining independent variables

totally do not have any relationship with the dependent variables rather they are not

statistically significant below 10% significant level.

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5. SUMMARY AND CONCLUSION

5.1. Summary

In this study to identify factors influencing probability and intensity of adoption of improved

bread wheat varieties among smallholder farmers the study area, Akaki was selected based on

its wide practices of improved bread wheat production and its suitability for this research .In

this area, agricultural extension and rural development activities like other rural parts of the

country, conducted by Agricultural and Rural Development Unit which comprises several

agricultural professionals in different disciplines at office level and Development Agents

(DAs) at Center and Peasant Association (PA) level. According to the structural framework of

the Addis Ababa Administration, the unit is accounted to and organized under Akaki-kality

sub-city.

In this study, data were obtained from 150 randomly selected respondents through personal

interview schedule conducted by employed and trained enumerators using pre-tested interview

schedule and from group and individual discussions, as well as the researcher’s personal

observations. The respondents, involved in the interview were selected randomly and

proportionally from two sample Peasant Associations (PAs), constituted 99 (66%) adopters

and 51 (34%) non- adopters.

Data were analyzed, and presented quantitatively using different statistical methods such as

percentage, frequency, tabulation, Chi-square–test (for dummy /discrete variables) and (t-test

for continuous variables), Logit, Tobit models and qualitatively through interpretation,

explaining, summarizing of ideas and concepts. T-test and Chi-square test were employed to

test the variation of the sample group they have towards adoption and also used to describe the

patterns of the sample data. Logit and Tobit econometrics models to estimate the effects of

hypothesized independent variables they have on dependent variables, probability and

102

intensity of adoption. Computer soft ware package programs such as SPSS and Limdep were

employed for statistical analyses.

Among the hypothesized independent variables, sex, health, education, extension service, DA-

office, market access, labor source, off-farm income, leadership, were treated as discrete

variables and tested using chi-square-test. In this test the independent variable health, distance

of DA-office, labor source, access to off-farm income were not significant below 10%

significant level. And family size, years of extension experience, age, livestock ownership,

oxen ownership, and farmland holding were considered as continuous variables and tested

using t-test. The t-test result showed that except others only age was not significant below

10% significant level.

The t-test and chi-square test results showed that there were variations between adopters and

non-adopters sample category in family size, extension experience, livestock ownership, oxen

ownership, farm land holding, extension service, sex (gender) and leadership position in

adoption of improved bread wheat varieties. According to the result of test statistics male are

better in adoption of improved bread wheat varieties. On the other hand adopters have larger

family size, livestock ownership, oxen ownership and farmland and they got better extension

service than non-adopters. Due to their variation in these independent variables sample

farmers vary in their adoption behavior in relation to dependent variables.

Except those hypothesized independent variables dropped due to various cases as mentioned

in previous section all screened and verified independent variables were subjected to Logit

model analysis. In the case of Tobit model analysis, all verified hypothesized independent

variables included in Logit model analysis were not included due to the limitation of the model

to accommodate all these independent variables. As a result, leadership position and credit

services were dropped from further Tobit model analysis due to their less importance in the

study as compared to other independent variables.

103

The Logit model result of this study showed that the significant independent variables

affecting probability of adoption were distance of DA office, leadership position of household

head, market access and years of house hold head’s experience in extension and those

independent variables significantly influencing intensity of adoption of improved bread wheat

varieties were, household head’s sex, age, education, health status, off-farm income, distance

of DA office, size of farmland holding, and extension service resulted from tobit analysis. The

distance of DA’s office from farmers’ home was the only explanatory variable influencing

both adoption and intensity of adoption of improved bread wheat varieties in this study.

The farmers’ selection and evaluation criteria of improved bread wheat varieties and ranking

of the improved bread wheat varieties disseminated in the study area were also conducted

through the summary of the survey data, group and individual discussions as well as

researcher’s observation.

In this respect, white grain color, large grain size, straw quality, market demand were the first

most important characteristics; germination capacity, cooking quality good yield performance

were the second most important; water lodging resistance, tillering capacity, good food quality

were the third most important; short maturity date fourth; disease and pest resistance fifth;

frost resistance sixth; harvesting quality and storage quality ranks seventh most important

characteristics as grouped and ranked based on the result of the survey data group and

individual discussions and researchers observation.

Based on the selection and evaluation criteria, the result of the survey summary and group

discussion the ranking result of improved bread wheat varieties disseminated in the study area

has presented as HAR-1685 variety ranks firs, Paven –76 second and HAR-1709 ranks third.

Farmers in the study area got agricultural information from different sources. The most

important information sources as summarized were, neighbors and colleague farmers got the

1st rank, DA and Community leaders the 2

nd rank, farmers field day 3

rd, PA leaders,

104

demonstration and radio 4th, News paper/News letter and other publications 5

th, poster 6

th,

Training 7th, TV

8th and religious leaders ranks 9

th sources of information.

105

5.2. Conclusion and Recommendations

In this study several issues were observed and revealed in relation to adoption of improved

bread wheat varieties disseminated in the study area, Akaki. The result, description and

interpretation of the data were mainly depended on, the context of the research objectives and

the situation of the study area.

The truthfulness of the information provided by the sample farmers for this study was also

depended on the sample farmer’s voluntaries and credibility. Since the study area closer to

Debrezeit research center, Addis Ababa (the capital of the nation) and subjected to long years

of extension services in the past, the result of the study should be seen from this perspectives.

This study may serve as an initial input for further study in this and other similar areas of the

country.

Like other parts of the country, several agricultural innovations were disseminated in the

previous years and extension services were offered to the farmers that have an influential

impact on adoption and use of the disseminated agricultural innovations. From those

disseminated technologies in this area, improved bread wheat varieties was the one on which

this study was focused to identify factors affecting adoption of improved bread wheat varieties

by non-adopter farmers, and to identifying other factors influencing the adopter farmers to

increase the intensity of farm land size allocation to improved bread wheat production from

their total wheat farm land.

Determinants that limit probability of adoption of improved bread wheat varieties were

identified using descriptive statistics (t-test and Chi-square test) and logit model analysis. They

were gender, extension service; leadership position, market access, farmers’ extension

experience and distance of DA office from farmers home were the influencing factors

affecting non-adopters to adopt improved bread wheat varieties in the study area.

106

According to the findings of this research, it is necessary to establish appropriate extension

strategy to bring those non-adopters to adopt improved bread wheat varieties. In this regard,

attention should be given; to encourage, support and motivate female and less extension

experienced farmers to achieve their adoption decision behavior. Some farmers in this area

who have leadership position are better adopters since their position allowed for better access

to information, resources and innovations. Therefore, there need to give attention to support

those people who do not have resources access opportunities.

As it is confirmed in this study distance of DA office from the farmers’ home has an

influential effect on adoption and intensity of adoption. Therefore, attention should be given to

the close assignment and placement of DAs to the rural villages where the farmers can get

them easily for extension advises and supports for better adoption.

In the study area, when farmers closer to the market showed reluctant behavior to adopt the

improved bread wheat varieties. Some of the possible reasons may be due to weak extension

service provided for them or due to fear of intensive management and labor requirement to

operate practices, or may be due to substitution effect and their involvement in other par time

works etc., created by the market facilities. The market in this area showed a negative effect

rather than motivation farmers to adopt improved bread wheat varieties. Therefore, there must

be efforts to formulate appropriate extension service for this area, improve market situation for

bread wheat and improvements of the varieties qualities for better market demand.

The other aspect of this study was to identify factors influencing those adopters to increase and

extend their improved wheat production by allocating larger area of farm land for improved

bread wheat production from their total wheat farm land. According to the result of descriptive

statistics and tobit model analysis gender, extension service, family size, experience in

extension, livestock and oxen ownership, farm land holding, age, education, health, off-farm

income, distance of DA-office were factors affecting intensity of adoption of improved bread

wheat varieties. Attention should be given to improve farmers’ intensity of adoption by

107

designing of compatible extension strategy by considering the findings of this research as an

input.

Regarding farmers’ variety selection and evaluation criteria, it is advisable to involve farmers

through various techniques like for example using group discussion to evaluate and identify

the best suitable varieties that can fit their interest, farming system and environmental

situations. In-group discussion farmers from different angels such as gender, age, ecological

area and educational levels should be involved to get various ideas and opinions. The idea

reflected during group discussions should get attention and need to be incorporated and used

in agricultural technologies development, extension programs formulation and policy

preparations.

In the study area there is a shift of farmers to involve in improved chickpea production as a

result of high price of improved chickpea. There fore, it is necessary to give attention to

improve the quality of improved bread wheat varieties that can bring high market demand

through breeding and genetics improvement programs. It is also necessary to improve the

market facilities for improved bread wheat varieties.

Agricultural information and extension communication are powerful and crucial to achieve

better adoption and intensity of adoption of improved agricultural innovations like improved

bread wheat varieties in this case. Appropriate and timely information should reach to the

intended farmers group to achieve better adoption and intensity of adoption of improved

agricultural technologies. Appropriate information and communication strategy compatible

with farmers and the study area should be designed and practiced.

Suitable strategies for better extension service are another important issue that should get

proper attention. In the study area as observed and the survey data showed, the extension

service is at lower and weak position due to various reasons such as transfer of DAs to other

lateral offices, low motivation, poor credit service low educational background of extension

108

workers. In these respect it need attentions to solve these problems for better improvements of

agricultural technologies adoption and production growths that can bring better living standard

of the farmers in the rural areas. Attention also should be given to the research and extension

linkages, to the empowerment and training of extension people and farmers, to achieve high

level of improvement in adoption of improved agricultural technologies.

109

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7. APPENDICES

115

Appendix.1.Information on sample household demographic and socio-economic

characteristics

Table 1.The distribution of sample respondents by age group

Adopters

N-adopters

Total

Age

(year)

N % N % N %

19-30 11 11.11 11 21.57 22 14.67

31-45 41 41.45 16 31.37 57 38

46-64 34 34.34 15 29.41 49 32.67

Above or

Equal to 65

13 13.13 9 17.65 22

14.67

Total 99 100 51 100 150 100

(Source: Computed from own survey data, 2005)

Table 2.Educational statuses of sample house hold head farmers

Adopters Non-adopters Total Educational Status

N % N % N %

Illiterate 64 64.65 33 64.71 97 64.67

Read & Write 19 19.20 14 27.45 33 22

Grade1-6 4 4.04 4 7.84 8 5.33

Grade7-8 5 5.05 - - 5 3.33

Grade9-12 3 3.03 - - 3 2

Above grade 12 4 4.04 - - 4 2.67

Total

99

100

51

100

150

100

(Source: computed from own survey data, 2005)

116

Table 3 .The sample household family size

Adopters

N-Adopters

Total

Total Family

Members

Family

Size N

%

N

%

N

%

N %

1 2 2.02 4 7.84 6 4 6 0.72

2 2 2.02 - - 2 1.33 4 0.48

3 8 8.08 11 21.60 19 12.67 57 6.80

4 17 17.17 10 19.61 27 18 108 12.87

5 17 17.17 5 9.80 22 14.66 110 13.11

6 18 18.18 6 11.76 24 16 144 17.16

7 13 13.13 5 9.80 18 12 126 15.02

8 11 11.11 4 7.84 15 10 120 14.30

9 5 5.05 5 9.80 10 6.67 90 10.73

10 2 2.02 1 1.95 3 2 30 3.57

11 4 4.04 - - 4 2.67 44 5.24

Total

99

100

51

100

150

100

839

100

(Source: Computed from own survey data, 2005)

Table 4.The sample household family size

Adopters Non-Adopters Total Family

Size

N N N

Maximum 11 10 11

Minimum 1 1 1

Range 10 9 10

Average 5.85 5.10 5.6

St.d.

2.192

2.385

2.27

(Source: Computed from own survey data, 2005)

117

Table 5.Total Family members of sample households in age group

Age- group N % 0-14 age 361 43.03

15-64 age 452 53.87

Above 64 26 3.10

Total

839

100

(Source: Computed from own survey data, 2005)

Table 6.Respondents farming experience

(Source: Computed from own survey data, 2005)

Table 7.Types of livestock and owners and the number of respondents

Adopters Non-adopters Total Types of

Livestock N % N % N % Oxen 98 - 42 - 137 91.33

Cow 68 68.69 33 64.71 101 67.30

Bull 46 46.46 19 37.25 65 43.30

Heifer 41 41.41 14 27.45 55 36.70

Calves 39 39.40 20 39.22 59 37.70

Sheep 48 48.48 29 56.86 77 51.33

Goat 8 8.08 - - 8 5.33

Horse 21 21.21 4 7.84 20 16.67

Mule 25 25.25 6 11.76 31 20.67

Donkey 91 91.92 38 74.51 129 86

Poultry 80 80.81 29 56.86 109 72.67

Bee-in-Hive

5

5.05

1

1.96

6 4

(Source: Computed from Owen survey data, 2005)

Adopters

Non-adopters

Total

Age group

(Year)

N

%

N

%

N

%

1-10 17 17.172 9 17.65 26 17.33

11-20 44 44.444 25 49.02 69 46

21-30 21 21.212 12 23.53 33 22

Above 30 17 17.172 5 9.80 22 14.67

Total

99

100

51

100

150

100

118

Table 8.Sample house hold oxen ownership

Adopters Non-adopters Total Number

Of oxen

N

%

N

%

N

%

No oxen 1 - 9 - 10 -

One ox 3 3.03 3 5.86 6 4

Two oxen 46 46.47 25 49.02 71 47.33

Three oxen 7 7.07 3 5.88 10 6.67

Four oxen 30 30.30 6 11.77 36 24

Five& above 10 10.10 4 7.84 14 9.33

Total

99

100

51

100

150

100

(Source: computed from own survey data)

Table 9.Sample house hold land ownership

Adopters

Non-adopters

Total

Types of

Land ownership

N

%

N

%

N

%

I. Own Land owners

Cultivated Land

99

100

51

100

150

100

Grazing land 52 52.52 29 56.86 81 54

Home stead land 18 18.18 14 27.45 32 21.33

Forest land 4 4.04 2 3.92 6 4

Un-used land 4 4.04 3 5.90 7 4.67

II .Shared/Rent land owners

Cultivated land

20

20.20

4

7.84

24

16

Grazing land 2 2.02 - - 2 1.33

III Growers of variety

HAR-1685 variety

83

83.84

3

5.88

86

57.33

HAR-1709 variety 5 5.05 - - 5 3.33

Paven-76 variety

75

75.76

1

1.96

76

50.67

(Source: Computed from own survey data, 2005)

119

Appendix Table 10.Size of farmland holding of sample household

(Source: Computed from own survey data, 2005)

Table 11.Respondents average land area and yield of wheat crops in

1996/97 E.C. cropping season.

Item/List Max Min Range Average St.D

Area of land in

Hectares

Total farm Land

6.5

0.25

6.25

2.44

1.13

Total wheat land 4.5 0.25 4.25 1.125 0.605

Total improved B.W.L. 3.25 0.25 3 .00 1 .00 0.457

Area of Paven -76 1.75 0.25 1.50 6.30 0.29

Area of HAR-1685 2.00 0.25 1.75 0.64 0.34

Area of HAR-1709 2.00 0.25 1.75 0.535 0.44

Area of Durum Wheat 1.00 0.25 0.75 0.415 0.285

Area of Local Wheat 2.00 0.25 1.75 0.65 0.41

Yield of wheat in

Quintals

Yield of Paven-76

25

0.50

24.5

9.12

4.67

-Yield of HAR-1685 28 3 .00 25 9.33 4.445

Yield of HAR-1709 8 4.00 4 5.5 1.91

Yield of Durum wheat 15 4.00 11 9.165 7.03

Yield of Local wheat

26

2.00

24.

8.255

5.365

(Source: Computed from own survey data, 2005)

N.B=Improved B.W.L. (Improved Bread Wheat Land)

Farm Size

Adopters

Non-adopters

Total

In Ha. N % N % N %

<1Ha 12 12.12 15 29.41 27 18 1-2Ha 32 32.32 14 27.45 46 30.67 2-3Ha 30 30.30 15 29.41 45 30 3-4Ha 18 18.20 6 11.76 24 16 4-5Ha 5 5.05 1 1.96 6 4 5-6Ha 2 2.01 - - 2 1.33 Total

99

100

51

100

150

100

120

Table 12.Respondents farm land ownership and crop type grown in 1996/97 E.C. cropping

season

Items/ list Adopters N-adopters Total

Ha. N % Ha. N % Ha. N %

T.Farm Land 309.26 99 100.00 90.50 51 100.00 399.76 150 100.00

Grazing Land 20.45 54 54.55 9.95 29 56.86 30.40 83 55.33

Forest Land 101.00 4 4.04 0.35 2 3.92 1.36 6 4.00

Improved Bread

Wheat land (sum)

101.75

99

100 .00

1.25

3

5.90

100.00

99

100.00

- HAR-Paven 44.75 75 75.76 0.50 - - 45.25 - -

- HAR-1685 53.50 83 83.84 0.75 - - 54.25 - -

-HAR-1709 3.5 5 5.05 - - - 3.50 - -

-Durum 5.5 4 4.04 - - - 5.50 4 2.67

-Local variety 53.8 86 86.87 28.67 48 94.12 64.55

104 69.33

T.W.L. 161.05 99 100 29.92 51 100 190.97 150 100

Other crop

-Teff

80.25

92

92.93

35.50

43

84.31

115.75

147

98.00

-Chick pea 42.01 98 87.88 17.96 38 74.51 59.97 136 90.67

Lentils 2.63 9 9.09 1.25 4 7.84 3.88 76 13

-Pea 20.38 59 59.60 4.39 17 33.33 24.77 9 8.67

-Faba bean 2.25 6 6.06 1.10 3 5.88 3.35 8 50.67

-Vegetables

0.69

6

6.06

0.38

2

3.92

1.07

5.33

6.00

(Source: Computed from own survey data, 2005)

121

Table 13.Respondents’ livestock ownership

(Source: Computed from own survey date, 2005)

Types of

Livestock

Livestock

No.

Owners

No

owners

Owners

(% )

max

Min

Range

average

per

Owners

Oxen -Adop.

- Nadop.

301

110

98

42

98.99

82.353

10

6

1

1

9

5

3.07

2.62

Cow -Adop

-N-Adop.

85

42

68

33

68.687

64.706

3

3

1

1

2

2

1.25

1.27

Bull -Adop

- N-Adop

64

22

46

19

46.465

37.255

3

2

1

1

2

1

1.40

0.86

Heifer –Adop

- N-Adop

52

18

41

14

41.414

27.451

3

2

1

1

2

1

1.27

1.286

Calf -Adop

- N-Adop

42

29

39

20

39.394

39.216

3

3

1

1

2

2

1.08

1.45

Sheep –Adop

- N-Adop

223

106

80

29

80.81

56.863

18

13

1

1

17

12

2.79

3.65 Gaot –Adop

- N-Adop

25

-

8

-

15.69

-

5

-

2

-

3

-

3.125

-

Horse –Adop

- N-Adop

21

4

21

4

21.212

7.843

1

1

1

1

-

-

1.00

1.00 Mule –Adop

- N-Adop

26

7

25

6

25.253

11.765

2

2

1

1

1

1

1.04

1.17 Donkey –Adop

- N-Adop

178

61

91

38

91.92

74.51

5

4

1

1

4

3

1 .96

1.60 Poultry –Adop

- N-Adop

510

131

80

29

80.81

56.863

45

12

1

1

44

11

6.37

4.572 Bee-hive –Adop

- N-Adop

18

1

5

1

5.051

1.961

10

1

1

1

9

-

3.60

1.00

122

Table 14.Respondents livestock ownership in Tropical Livestock Unit (TLU)

Number of Livestock owned Types of Livestock

By- Adopters By- N- Adopters Total No. of livestock

Oxen 301 110 411

Cow 85 42 127

Bull 48 16.50 64.50

Heifer 39 13.50 52.50

Calf 10.50 7.25 17.75

Sheep 29 13.78 42.78

Goat 3.25 - 3.25

Horse 23.10 4.40 27.50

Mule 28.60 7.70 36.30

Donkey 124.60 42.70 167.30

Poultry 6.63 1.703 8.333

Total 698.68 259.533 958.213

(Source: Computed from own survey data, 2005)

Table 15.Conversion factors used to estimate the households’ livestock ownership into

tropical livestock units (TLU)

Source: Strock et al., (1991)

Animals

TLU-equivalent

Calf 0.25

Heifer & Bull 0.75

Cows & Oxen 1.00

Horse 1.10

Donkey 0.70

Ship & Goat 0.13 Chicken/poultry

0.013

123

Table 16.Discrete characteristics of respondents

Non – Adopters (51) Adopters (99)

Characteristics

Number Percent Number Percent

X2

df

Signifi

cance (2-

sided)

Conting

ency coeffici

ent

Level of Education 8.138 5 0.149 0.227

Illiterate 33 64.71 64 64.65

Literate 4 7.84 4 4..04

Read & Write 14 27.45 5 ..05

Elementary School - - 3 3..03 Junior Secondary - - 4 4..04

High School - - 19 5..05 House hold sex

-Male

-Female

11

40

21.57

78.43

7

92

7.07

92..93

6.700*** 1 0.010 0.207

Health Status

-Un-healthy

-Healthy

7

44

13.73

86.27

7

92

7.07

92.93

1.762 1 0.184 0.108

Access to credit

-Yes

-No

43

8

84.31

15.69

71

28

71.72

28.28

2.928* 1 0.087 0.138

Leadership /Social status

-Yes

-No

48

3

94.12

5.88

73

26

73..74

26.26

8.965*** 1 0.003 0.237

Off-farm income

-Yes

-No

42

9

82.35

17.65

82

17

82.83

17.17

0.005 1 0.942 0.006

Distance of credit institutions

-Far

-Close

43

8

84.31

15.69

89

10

89..90

10.10

0.994 1 0.319 0.081

Distance of DA office

-far

-close

48

3

94.12

5.88

12

87

12.12

87.89

1.456

1 0.228 0.098

Market Access

-Far

-Close

43

8

84.31

15..69

89

10

89.90

10.10

0.994 1 0.319 0.081

Access to labor

-No access

-Employment and other Sources

48

3

94.12

5.88

87

12

87.88

12.12

1.456 1 0.228 0.098

Access to Extension Service

-Yes

-No

-

51

-

100

94

5

94.95

5.05

2.665* 1 0.103 0.132

Education Level -Illiterate

-Literate

18

33

35.29

64.71

35

64

35.35

64..65

0.000 1 0.994 0.001

***, ** and* Significance at P<0.01, P<0.05 and p<0.10 respectively.

124

Table 17.Respondent farmers’ general information

Adoption

Category

Summary of

statistics

House hold

head Age

House hold

family size

Farmers’ extension

experience (Ys)

Farming

experience

Mean 46.1010 5.85 7.8687 21.8990

St.D 13.2560 2.19 4.7866 11.0809

Minimum 19.0000 1.00 2.0000 2.0000

Maximum 80.0000 11.00 20.0000 55.0000

Adopters

Range 61.0000 10.00 18.0000 53.0000

Mean 46.4706 5.10 3.7647 20.7647

St.D 14.5305 2.39 1.7842 10.9829

Minimum 20.0000 1.00 1.0000 4.0000

Maximum 80.0000 10.00 9.0000 60.0000

Non-adopters

Range 60.0000 9.00 8.0000 56.0000

Mean 46.2300 5.59 6.4730 21.5130

St.D 13.6550 2.28 4.4850 11.0230

Minimum 19.0000 1.00 1.0000 2.0000

Maximum 80.0000 11.00 20.0000 60.0000

Total

Range 61.0000 10.00 19.0000 58.0000

(Source: Computed from own survey data)

125

Table 18.Factors affecting Intensity of adoption of improved bread wheat

varieties (Maximum Likelihood Tobit Model Estimation)

Variables Coefficient Standard Error b/St.Er. P (/Z/>z)

HHHSEX 0.1854701150 0.82006662 2.262 0.0237**

HHHAGE 0.3863329500 0.15596286 2.477 0.0132**

EDUHHH 0.1697049462 0.56398485 3.009 0.0026***

HEALSTAT 0.38138220125 0.72107639 5.289 0.0000***

FAMILYSI 0.1544308922 0.11637647 1.327 0.1845

HHOFFINC 0.1409765567 0.68473026 2.059 0.0395**

DISDAOF1 0.1685119034 0.88888172 1.896 0.0580*

TOTLIVUN -0.8908541184 0.10884712 -0.818 0.4131

SUMOWRE -0.3802017439 0.22997223 -1.653 0.0983*

CRINMFF1 0.1719842596 0.96902492 0.018 0.9858

OTSOLA1 -0.2466522041 0.80402014 -0.307 0.7590

OXTLU -0.3024284552 0.22093233 -0.137 0.8911

YEXPEXTS 0.4835498401 0.54842929 0.882 0.3779

GEXSERVE 0.2590100298 0.11097534 2.334 0.0196**

Sigma 0.2268484851 0.15882571 14.283 0.0000

Log likelihood function= 6.582514

*, **And*** indicate the level of significance at 10%, 5% and 1% respectively.

126

Appendix.2. Interview Schedule for data collection from. Farmers

The objective of this Interview Schedule is to collect information from farmer respondents on

improved bread wheat production in Akaki area, rural part of Akaki Kaliti sub-city of Addis

Abeba administration from December/ 2004 to March/ 2005. The study is conducted for

academic purpose. Hence, we request your honest & fair responses to fill up this interview

schedule.

1. General & personal information of the respondent

1. Respondent’s name…………………………………………………..

2. Sex; 0 = female 1 = Male

3. Age……………………………..years

4. Marital statuses; 1.Married, 2.Single or unmarried, 3.Divorced, 4.Widow/Widower.

5. Rural Kebelie Administration/ Peasant Association ………………………………

Village………………………………………………………………………….

6. Previous or current leadership status; 0 = No, 1 = Yes

7. Educational Status: 0 = Illiterate, 1 = Literate

8. Educational level:

1. Read & Write, 2.Grade 1- 6, 3.Grade 7- 8, 4. Grade 9- 12, 5.above grade 12

9. Household Characteristics Information

Table 19.Household characteristics

No Name of house hold members Sex Age Educational

status

127

10. Land holding and farm characteristics of the sample households

Table 20.Land holding & Farm Characteristics of the sample households

No Types of land use Own

(ha)

Rent

(ha)

Total

(ha)

1

2

3

4

5

6

Cultivated (farm) land

Grazing land

Homestead land

Forest land

Unused land

Total land holding

11. Livestock ownership

Appendix Table 21.Livestock ownership

12. Types of crop grown in the survey year

No Types of Livestock Number

1

2

3

4

5

6

7

8

9

10

11

12

Ox

Cow

Calf

Bull

Heifer

Horse

Mules

Donkey

Goats

Sheep

Chicken

Bee in Hive

128

Table 22.Types of crop grown in the survey year

No Types of crops Land in (ha)

13. Involvement in irrigation production; 0 = No, 1 = Yes

14. Land size for irrigation production---------- ha.

15. Involvement in improved bread wheat production: 1.only this year 2.This year & in the

previous year 3.In the previous year but not this year 4. Never involve.

16. Reasons for involvement: 1.High yield 2.High market demand quality 3.Pest/disease

resistance 4.Frost resistance 5.Short maturity date 6.High food quality 7. Good storage quality

8. Good quality of cook ability 9. Good straw quality 10.Seed availability 11.Seed availability

12. Good information service 13. Fertilizer availability

17: Reasons for un-involvement: 1. Low yield 2.Low market demand 3.Low pest/ disease

resistance 4.Low frost resistance 5.Long maturity date 6.Poor food quality7. Poor storage

quality 8.Poor cooks ability 9.Poor straw quality 10. High seed price 11.Shortage of seed

12.Shortage of fertilizer 13.Lack of information 14.Lack of money and credit 15.Late arrival

of seed 16.Late arrival of fertilizer 17.High interest rate of credit

18. Reasons for discontinuity: 1.Poor yield performance 2.Poor pest/ disease resistance3. Poor

market demand 4. Poor frost resistance 5.Poor storage quality 6.Long maturity date 7. Poor

cook ability 8.Poor straw quality 9.Poor food quality 10. High seed price 11. Seed shortage,

12.Fertilizer shortage 13.Poor extension supports 14.Late arrival seed 15.Late arrival of

fertilizer, 16.Lack of money & credit 17.High interest rate of credit.

19. Total farm land/ cultivated land ----- ha.

129

20. Total wheat land---- ha

21. Land for improved bread wheat………ha.

22. Do you know paven-76? 0=no; 1=yes

23. Do you know HAR- 1685 (Kubsa)? 0=no; 1=yes

24. Do you know HAR- 1709 (Mitike)? 0=no; 1=yes

25: Use of disease and pest control chemical: 0 = No, 1 = Yes

26. Your future plan of involvement in improved bread wheat production

0 = Discontinue, 1 = Continue

27. Presence of problems related to fertilizer: 0 = No, 1 = Yes

28. Problems related to fertilizer: 0 = No, 1 = Yes

29.If yes, 1.High fertilizer price 2.Lack of credit to purchase fertilizer 3.High interest rate of

credit to use credit to purchase fertilizer 4.Far distance of distribution center 5.Poor quality

(mixed with impurities and caked) 6.Shortage 7.Lately arrival 8.Lengthy process &

complicated format 9. Poor distribution processes

30. Presence of problems related to improved bread wheat seed: 0 = No, 1 = Yes

31. Types of problems: 1.Shortage 2.Poor seed quality 3.Late availability 4.Far distance of

distribution center 5 Impurity problems and 6.Poor germination problem.

32. Extension support: 0 = No, 1 = Yes

33. Extent of extension support: 1. Poor 2. Medium 3.Good

34. Improved wheat seed rate application: 1.The recommended rate 2. Below the

recommended rate 3.Above the recommended rate

130

35. Fertilizer rate of application 1: Apply the recommended rate, 2: Below the recommended

rate 3. Above recommended rate

36. Chemical application: 1. Apply the recommended rate 2. Below the recommended rate,

3.Above recommended rate

37. Reasons for Below & Above recommendation use of agricultural inputs 1.Low quantity of

input availability 2.High price of inputs 3.High interest rate of credit 4.Lack of credit & money

38. Frequency of weeding: 1.One 2.Two 3.Thrice 4. Four & above

39. Frequency of plowing: 1. One 2.Two 3.Three 4. Four & above

40. Characteristics of improved bread wheat varieties: (1 = High, 2 = Medium, 3 = Low)

Appendix Table 23.Improved bread wheat varieties characteristics

Varieties No Characteristics

HAR-1685 KAR-1709 Fovon-76

1 Frost resistance

2 Pest/Disease resistance

3 Seed size

4 Cocking time

5 Storage quality

6 Yield performance

7 Market demand

8 Food quality

9 Color quality

41. Seed selection criteria: 1.Pest/Disease resistance 2.Frost resistance 3.High yield

performance 4.high market demand 5.Attractive color 6.Short maturity data 7. Good food

quality 8.Low time taking 9. Good straw out put and good quality 10. Good storage quality 11.

Good Germination and till ring capacity

42. Improved bread wheat seed source: 1.Purchase from market, 2.Exchange from other

owners, 3.Own seed from previous product, and 4. Borrow from owner formers,

5.Cooperative, 6.MOA 7. Seed enterprise, 8.Research organization

131

43. Other Agricultural input sources: 1 Cooperatives, 2.MOA, 3.others

44. Access to credit service: 0= No 1: yes

45. Credit sources: 1.Cooperatives, 2.Ethiopian 3. MOA, 4.Other- Credit Institution, 5

Individual/ private lenders

46. Presence of credit problems: 0=no 1=Yes

47. Types of credit problems: 1.Shortage 2.Long and complex process, 3.high interest rate, and

4. Far distance

48. Support from relatives and other colleagues to solve financial constraints to purchase

inputs: 0=no 1=yes

49. Distance of credit providers Institutions=far 1= close

50. Do you have Access to market? 0=no 1=yes

51. Market distance=far 1=close

52. Do you have Access to extension Service? 0=no 1=yes

53. Distance of Development Agent Office: 0=far 1=close

54. Have you attended training? 0=no 1=yes

55. Have you attended demonstration and field day programs? 0=no 1=yes

56. Can the DA call the farmers for extension meeting with out the permission of government

authorities? 0=no 1=yes

57. What did you feel when called for extension meeting? 0=un-happy 1=happy

58. What are your Sources of Agricultural and input information sources? 1. DA 2. Radio,

3.Television, 4.Written materials, 5.Training, 6.Field, day and demonstration, 7.Posters, 8.PA-

leaders, 9.Community leaders, 10.neighbours and colleague farmers,

132

59. From the following, to which one assign your- self? 1. Mode farmer, 2.follower farmer,

3.Neither of them

60. How many years of Experience do you have in agricultural extension? ……Years

61. Total farming experience in years? ...................... Years

62. Have you got training and sufficient information on improved bread wheat? 0=no 1= yes

63. If you did not get training how did you perform production operations? 1. Using try and

error methods, 2. By copying from other experienced farmers, 3. By asking support from DA

64. Purpose and use of off-farm income: 1.for house hold food consumption and other costs,

2.for input purchase, 3.for labor hiring, 4.for health cost covering, 5.for all

65. Do have access to labor outside the household labor? 0=no 1=yes

66. If yes, your sources of labor out side the household labors: 1. Hired labor

2. Cooperation labor from colleague and relative framers, 3. Exchange labor

67. Who make the decision on off-farm income? 1. Family head, 2. Husband

3. Wife, 4.Husband and Wife, 5.The household members

68. Which type of agricultural operation is critical to you and need higher labor? 1. Plowing,

2.Sowing, 3.Weeding, 4.Harvesting, 5. Threshing

69. Do you have plowing oxen? 0=no 1=yes

70. If no, how do you plow your farmland? 1. Using oxen plowing, 2.Through labor

exchanges 3. By asking cooperation

133

Table 24.Cramer’s V and Pearson’s R values for Discrete and Continuous variables

Hypothesized independent variables and their values

Continuous variables Pearson’s R value for

Continuous variables

Discrete variables

Cramer’s V-value for

discrete variables

Age 0.257 Sex 0.211

Family size 0.167 Education 0.001

Farm land 0.292 Health status 0.108

Livestock ownership

in (TLU)

0.257 Leadership-position 0.244

Oxen ownership 0.247 Off-farm income 0.006

- - Distance of DA-office 0.099

- - Extension service 0.133

- - Other labor source 0.099

- - Market access 0.081

- - Credit service 0.140

(Source: Own computation)

*Notice: 0 value=no association, 0-0.4 value= weak, 0.4-0.7= moderate and >0.7= strong

association ((Sarantakos, 1998).

Table 25.Respondents leadership position

Have leadership position ADs NADs X2-test Total

Yes 48 (94.12%) 73 (73.74%) 121

No 3 (5.88%) 26 (26.26%) 29

Total 51(100) 99 (100) 8.965*** 150

(Source: own computation)

ASSESSMENT OF FARMERS’ EVALUATION CRITERIA AND

ADOPTION OF IMPROVED BREAD WHEAT VARIETIES

M. Sc. Thesis

DEREJE HAMZA MUSSA

December 2005

Alemaya University

ASSESSMENT OF FARMERS’ EVALUATION CRITERIA AND

ADOPTION OF IMPROVED BREAD WHEAT VARIETIES

A Thesis Submitted to the Department of

Rural Development and Agricultural Extension, School of Graduate Studies

ALEMAYA UNIVERSITY

In Partial Fulfillment of the Requirements for the Degree of

MASTER OF SCIENCE IN AGRICULTURE

(AGRICULTURAL EXTENSION)

By

Dereje Hamza Mussa

December 2005

Alemaya University

ii

APPROVAL SHEET OF THESIS

SCHOOL OF GRADUATE STUDIES

ALEMAYA UNIVERSITY

As members of the Examining Board of the Final M. Sc. Open Defense, We certify that we

have read and evaluated the thesis prepared by DEREJE HAMZA MUSSA and recommend

that it be accepted as fulfilling the thesis requirement for the degree of MASTER OF

SCIENCE IN AGRICULTURE (AGRICULTURAL EXTENSION)

…………………………………. ………………… ……………………

Name of Chairman Signature Date

……………………………………. ……………………… ………………

Name of Internal Examiner Signature Date

………………………………. ……………………………… …………………

Name of External Examiner Signature Date

Final approval and acceptance of the thesis is contingent up on the submission of the final

copy of the thesis to the council of the Graduate Studies (CGS) through the Departmental

Graduate Committee (DGC) of the candidate’s Major Department.

I here by certify that I have read this thesis prepared under my direction and recommend that it

be accepted as fulfilling the thesis requirement.

Ranjan S. Karippai (Ph.D ) ………………….. ……………….

Name of Thesis Advisor Signature Date

iii

DEDICATION

I dedicate this thesis manuscript to my wife, TIRUWORK ABATE and my son, SOLOMON

DEREJE, for their love and untold-enormous partnership effort in my academic success.

iv

STATEMENT OF AUTHOR

First, I declare that this thesis is my bona fide work and that all sources of materials used for

this thesis have been duly acknowledged. This thesis has been submitted in partial fulfillment

of the requirements for an advanced M. Sc. degree at Alemaya University and is deposited at

the University Library to be made available to borrowers under rules of the Library. I

solemnly declare that this thesis is not submitted to any other institution anywhere for the

award of any academic degree, diploma, or certificate.

Brief quotations from this thesis are allowable without special permission, provided that

accurate acknowledgement of source is made. Requests for permission for extended quotation

from or reproduction of this manuscript in whole or in part may be granted by the head of the

major 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 interests of scholarship. In all other instances,

however, permission must be obtained from the author.

Name: DEREJE HAMZA MUSSA Signature: ……………………

Place: Alemaya University, Alemaya

Date of Submission: December 2005.

v

BIOGRAPHICAL SKETCH

Dereje Hamza Mussa was born in Jamma District (Sora-Micha village), South Wollo Zone,

and Amhara region on August 6, 1964. He attended his elementary and junior education at

Boren -Teklehaimanot and Jamma-Degollo elementary and junior schools (both found in my

district) respectively. He also attended his High-school education at Woreilu secondary Senior

–high school. After completion of his high school education, he joined Awassa Junior

Agricultural College (under Addis Ababa University) (now Debub University) to attend a two

years Diploma program in Animal Science and Technology. After graduation he was

employed in Ministry of Agriculture and has worked for more than 15 years. After this much

time service he got an opportunity to join at Alemaya University to attend his degree program

education in Agricultural Extension in Mid-career program from 1999 to 2002. After

completion and graduation his education he turned back and joins at Alemaya University to

attend his M. Sc. degree education in Agricultural Extension Since 2004.

vi

ACKNOWLEDGEMENT

First and for most, I am greatly indebted to Ranjan S. Karippai (Ph.D) my major advisor and

Senait Regassa (Ph.D) my co-advisor for their unreserved help, advice, directing, insight

guidance, support on the field, critical review of my thesis manuscript, invaluable support and

suggestions as without their professional help it was difficult to be successful in my research

work and Thesis write up; in addition, my acknowledgement should forwarded to Dr. Ranjan

for his professional and critical review and Dr. Senait for her help in SPSS and Limdep

computer soft wares as well as Logit, Probit and Tobit, econometrics models, t-test and x2-test

statistics uses and application. My sincere thanks should also go to Tesfaye Lema (Ph.D) and

Tesfaye Beshah (Ph.D) for their unlimited review of my thesis manuscript help, guide and

continues encouragement to be successful in my study and research.

Above all, I am greatly indebted to Ato Zewdu Teferi and his children (Eyu, David and Dani),

Seid Ahmed (Ph.D), Solomon Asseffa, (PhD) for their greatest financial and material

contribution as well as moral encouragement and all sided help.

My thanks and appreciation should also extend to many individuals, to Belayneh Leggesse

(Ph.D), Prof. Panjabin, Asegdew Gashaw with his wife, Mehadi Egi, Ato Walelign and his

wife Abaye, Bizuhayehu Asfaw and Amare Berhanu and Tewodros Alemayhu from Alemaya

university; to Ato Yishak Berado, Amsalu Bedaso, Admassu Terefe and his wife Belay with

their children Eyuti and Mitisha from Alage technical agricultural college; to Ato Chane

Gebeyhu, Ato Gebyhu, Alemayehu and Tekle from Akaki-Kality sub–city and agricultural

unit; to woreilu wereda agricultural office staff members, and Lulseged Bekele, Mohammod

Yimer , Abebaw gidelew and (Ato Eshetu Woraei and w/o Toyiba -through their every day

pray) from woreilu woreda ; to Ato kasye Afre and his wife Turye Getye with their family,

particularly Mamush, Zelalem and Sisaye Kasye-they always accompanied me to and from

bus station of Addis Ababa in my every travel to or arrival from Alemaya University for

academic and research purposes; to Wondye Kasye, Tesafa Belay, Alemye Argaw, Esubneh

vii

Checolle, Kasye Mohammed and to my brothers Endale and Eshetu Hamza should deserve

acknowledgement for their moral, financial and material as well as all sided helps, wishes

and encouragements to accomplish my study successfully.

My sincere and special thanks should go to Jifar Tarekegn and Yodit Fekadu for their free

charge and complete computer, office provision and all sided co-operations; particularly, Jifar

Tarekegn for his additional and unlimited helps in computer and statistical manipulation

through out my thesis write up.

My heart felt and deepest thanks should go to Tiruwork Abate and Solomon Dereje my wife

and my son respectively who received and paid all suffers and scarifications but the greatest

contributors and partnership in my research and academic success.

I would like to extend my thanks to my mother-Ayelech Sebsibe, and my wife's mothers

Alganesh Afre, who are always with me in help and wish for my success through their

everyday pray.

Several organizations, Alemaya University, School of Graguate Studies, Department of Rural

Development and Agricultural Extension of Alemaya University, Debre-Zeit Research Center

and Agricultural Unit of Akaki-Kality sub-city, Alage Agricultural College should deserve

acknowledgement for their contributions to my study. At last but not the least, I would like to

extend my acknowledgement to IFAD (International Fund for Agricultural Development) that

offered a budget support for this research through EARO and Debrezeit research center.

viii

LIST OF ABBREVIATIONS

AU Alemaya University

B.B.M. Broad Bed Molder

DA Development Agent

EA Extension Agent

EARO Ethiopian Agricultural Research Organization

EARI Ethiopian Agricultural Research Institute

IR Institution of Research

PA Peasant Association

RKA Rural Kebele Administration

ix

TABLE OF CONTENTS

APPROVAL SHEET OF THESIS ii

DEDICATION iii

STATEMENT OF AUTHOR iv

BIOGRAPHICAL SKETCH v

ACKNOWLEDGEMENT vi

LIST OF ABBREVIATIONS viii

TABLE OF CONTENTS ix

LIST OF TABLES xi

LIST OF TABLES IN THE APPENDIX xiii

LIST OF TABLES IN THE APPENDIX xiii

ABSTRACT xiv

1. INTRODUCTION 1

1.1. Background 1

1.2. Statement of the Problem 2

1.3. Objectives of the Study 4

1.4. Significance of the Study 4

1.5. The Scope and Limitations of the Study 5

1.6. Organization of the Thesis 6

2. LITERATURE REVIEW 7

2.1. Concept and Theoretical Framework of Adoption 7

2.2. Empirical Studies on Adoption 12

2.3. Farmers Participation in Agricultural Technologies Development and

Evaluations 19

2.4. Conceptual Framework of the Study 23

3. RESEARCH METHODOLOGY 27

3.1. Description of the Study Area (Akaki) 27

3.1.1. Location, relief and climate 27

3.1.2. Agriculture and demographic characteristics of the study area 30

3.1.3. Institutional services of the study area 31

3.2. Description of Data Collection and Data Analysis Methods and Procedures 34

3.2.1. Sources and types of data 34

3.2.2. Sample size and sampling techniques 35

3.2. 3. Data collection methods 36

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TABLE OF CONTENTS (Continued)

3.2.3.1. Quantitative data collection methods 36

3.2.3.2. Qualitative data collection method 37

3.3. Analytical Models 37

3.3.1. Logit model 37

3.3.2. Tobit model 40

3.3.3. Other Quantitative data analysis methods 42

3.3.4. Qualitative data analysis method 43

3.4. Hypotheses Testing and Definitions of Variables 43

3.4.1. The Dependent variables of logit and tobit models 44

3.4.1.1. The Dependent variable of logit model 44

3.4.1.2. The Dependent variable of tobit model 44

3.4.2. The Independent variables and their definitions used in logit and tobit

models 44

4. RESULTS AND DISCUSSION 50

4.1. Analysis through descriptive statistics 50

4.1.1. Sample Households’ Demographic Characteristics 50

4.1.2. Respondents` livestock and land ownership 60

4.1.3. Accessibility of respondents to different institutional services 63

4.1.4. Agricultural information sources of the study area 72

4.1.5. Farmers’ selection and evaluation criteria of improved bread wheat

varieties 76

4.2. Analytical results and discussion 79

4.2.1. Analysis of determinants influencing probability of adoption of improved

bread wheat varieties and their marginal effect 85

4.2.2. Analysis of determinants influencing intensity of adoption of improved

bread wheat varieties and their marginal effects 90

5. SUMMARY AND CONCLUSION 101

5.1. Summary 101

5.2. Conclusion and Recommendations 105

6. REFERENCES 109

7. APPENDICES 114

Appendix.1.Information on sample household demographic and socio-economic

characteristics 115

Appendix.2. Interview Schedule for data collection from. Farmers 126

xi

LIST OF TABLES

Tables Pages

1.The Livestock and crop types in the study area 29

2.The land use of farmers in the study area 30

3.The summary of oxen ownership 31

4.Improved agricultural input distribution of the study area in different years 32

5. Improved bread wheat seed distribution of the study area in different years 33

6.Credit Distribution of the study area in various years 34

7.Sample household heads distribution by Sex, Kebele and adoption category 51

8.Marital status of respondents 52

9.Association of adoption of improved bread wheat and sex of sample household head 53

10. Respondent farmers’ demographics characteristics 53

11.Adopters and non-adopters’ demographic characteristics 54

12.Reasons given for not using improved bread wheat varieties 56

13.Level of awareness of improved bread wheat varieties 56

14.Sample Farmers perception on benefit of fertilizer 57

15.Beginning time of cultivation of improved bread wheat varieties of sample farmers 58

16.Health status and adoption of improved bread wheat varieties 58

17.Sample household educational status 59

18.Livestock and land ownership of respondents’ farmers 61

19.Respondents land ownership in 1996/97 Ethiopian major cropping season 62

20.Respondents’ opinion on extension service of the study area 63

21.Extension support on improved bread wheat varieties and distance of DA’s office 64

22.Summary of respondents’ opinion on credit 65

23.Association between credit and market service 66

24.Summary of households’ accessibility of off-farm job 67

25.Respondent farmers’ reasons for not involvement of their family in off-farm job 68

26.Rrespondents opinion on decision of off-farm and other household resources 68

27 Pattern of off-farm income utilization of respondent farmers 69

xii

28.Family labor utilization of respondent farmers 70

29.Types of activities and family labor utilization of respondents 70

30.Respondents’ accessibility to non-family labor and to off-farm income 71

31.Respondent farmers labor sources outside their family members 72

32.Respondents’ participation in training, field day and demonstration 74

33.Respondent farmers’ sources of information 75

34.Farmers’ evaluation and selection criteria of improved bread wheat varieties 77

35.Farmers’ preference (selection and evaluation criteria) of improved bread wheat varieties

disseminated in the study area 78

36.Variable Inflation Factor for the continuous explanatory variables 84

37.Contingency Coefficients for Dummy Variables of Multiple Linear Regressions Model 84

38.Factors affecting Probability of adoption of improved bread wheat varieties and the 88

39.The effects of changes (marginal effect) in the significant explanatory variables on the

intensity of adoption of improved bread wheat varieties 92

xiii

LIST OF TABLES IN THE APPENDIX

Appendix Tables Pages

1.The distribution of sample respondents by age gro 115

2 Educational statuses of sample house hold head farmer 115

3 .The sample household family size 116

4.The sample household family size 116

5. Total Family members of sample households in age group 117

6.Respondents farming experience 117

7.Types of livestock and owners and the number of respondents 117

8 .Sample house hold oxen ownership 118

9. Sample house hold land ownership 118

10.Size of farmland holding of sample household 119

11.Respondents average land area and yield of wheat crops in 1996/97E.C.cropping season119

12 Respondents farm land ownership and crop type grown in 1996/97 E.C.cropping season120

13.Respondents livestock ownership 121

14 Respondents livestock ownership in Tropical Livestock Unit (TLU) 122

15. Conversion factors used to estimate the households’ livestock ownership in tropical

livestock units (TLU) 122

16. Discrete characteristics of respondents 123

17. Respondent farmers’ general information 124

18. Factors affecting Intensity of adoption of improved bread wheat varieties (Maximum

Likelihood Tobit Model Estimation) 125

19. Household characteristics 126

20. Land holding & Farm Characteristics of the sample households 127

21. Livestock ownership 127

22. Types of crop grown in the survey year 128

23. Improved bread wheat varieties characteristics 130

24. Cramer’s V and Pearson’s R values for Discrete and Continuous variables 133

25.Resondents leadership position 133

xiv

ASSESSMENT OF FARMERS’ EVALUATION CRITERIA AND

ADOPTION OF IMPROVED BREAD WHEAT VARIETIES

Major advisor: Ranjan S. Karippai (Ph.D)

Co-advisor: Senait Regassa Bedadda (Ph.D)

ABSTRACT

Wheat is beneficial to man long before the dawn of recorded history. Ethiopia is one of the

largest wheat producers in sub-Saharan African next to South Africa. Wheat is one of the most

important cereal crops grown in the study area, Akaki. It contributes to the major share of daily

consumption and cash source. The objectives of this study were: to identify farmers’

evaluation and selection criteria of improved bread wheat varieties disseminated in the study

area; to assess probability and intensity of adoption of farmers in the study area; and to know

and analyze determinants of probability and intensity of adoption of improved bread wheat

varieties in the study area. In this study, data were collected and analyzed qualitatively and

quantitatively. Quantitative data analysis methods employed in this study were (percentage,

tabulation, t-test and X2, Logit and Tobit models) using SPSS and Limdep computer soft ware

programs and qualitatively through group discussion and observations.. In farmers’ evaluation

and selection criteria of improved bread wheat varieties disseminated in the study area HAR-

1685 ranks first, Paven-76 second and HAR-1709 third. White color, large grain size, market

demand, straw quality were the most important, germination capacity, cooking quality, better

yield performance were the second important, water logging resistance, tillering capacity,

good food quality, short maturity date the third important, disease and pest resistance and frost

resistance the fourth, storage and harvesting quality were the fifth important quality were

identified as a selection and evaluation criteria of improved bread wheat varieties in the study

area. Out of the total 150 samples, adopters were 99(66%) and non-adopters were 51(34%). In

determining factors influencing probability of adoption through logit analysis, distance of DA-

xv

office from farmers’ home, leadership status of respondent farmer, market accessibility and

sample farmer’s experience in extension were identified and (b) intensity of adoption through

tobit analysis, house hold sex, age, education, health status, off-farm income, distance of DA

office, size of farm land holding and extension service were identified. To enhance probability

and intensity of adoption, closer placement of DAs, encouragement of those farmers having

less education, female farmers, popularization of improved varieties, improving varieties

qualities of characteristics and farmers’ environmental, economic and social situation should

get a serious consideration.

1. INTRODUCTION

1.1. Background

Grain cultivation and the intensive utilization of wild grains in the horn of Africa probably

began by or even before 1300 B.C. However, modern agricultural technologies and crop

improvement activities to increase grain production have been introduced to the region very

recently. Wheat was made beneficial to man long before the dawn of recorded history.

Archeological findings and discoveries have indicated that wheat domestication and use as a

human food has a long history, for at least 6000 years (Pearson, 1967); as early as 7500 B.C.

(Langer and Hill, 1982); that took place between 17,000 and 12,000 B.C. (Tanner and

Raemaekers 2001); and for 8000 years (Curtis, 2002).

Wheat is today, one of the most important of all cultivated plants, more nutritious of cereals

and continue to be most important food grain source to human nutrition (Pearson 1967;

Harlan, 1981; and Curtis, 2002) and its contribution to the human diet puts it clearly in the first

rank of plants that feed the world (Harlan, 1981). World wide, wheat is used as human food,

seed, livestock feed, and as an industrial raw material (Tanner and Raemaekers, 2001).

Ethiopia is the largest wheat producer in sub-Saharan Africa. Wheat is an important food crop

and it is one of the major cereal crops in Ethiopia. Wheat in Ethiopia is ranking fifth in area

and production after teff, maize, barley and sorghum and fourth in productivity. Ethiopia

endowed with a wealth of genetic diversity, particularly for tetraploid-wheats. Nevertheless,

the productivity of wheat has remained very low mainly because, improved production

technologies have not been adopted by the farming community (Adugna et.al, 1991). It is

grown in the highlands at altitudes ranging from 1500 to 3000 masl. However, the most

suitable agro ecological zones for wheat production fall between 1900 and 2700 masl. Major

wheat production areas are located in the Arsi, Bale, Shewa, Illubabor Western Harerghe,

Sidamo,Tigray, Northern Gonder, and Gojam regions (Bekele et. al , 2000).

2

Ethiopia began to use improved varieties of bread wheat on a commercial level in 1968. Most

of the early improved bread wheat varieties released were developed in Kenya. The first

varieties of Mexican origin were released in 1974. The first improved durum wheat was

released in 1976, and the first bread wheat varieties developed in Ethiopia were released in

1980 (Adugna et.al, 1991).

Wheat technology demonstrations have been conducted by MOA (Ministry of Agriculture),

AUA (Alemaya University of Agriculture, now –Alemaya University) and IAR (Institution of

Agricultural Research now renamed - EARI (Ethiopian Agricultural Research Institute), since

1958. Through these demonstrations, many wheat technologies have been transferred to

farmers, particularly improved wheat varieties. (Getachew et al, 2002).

In the past, variety development and recommendation was made based on on-station trial with

testing and selecting of promising genotypes under high external input and optimum crop

management practices with low participation of farmers. In most of the cases, varieties

developed under such conditions were poor and failed to prove their superiority under on-farm

conditions and farmers’ management practices. This could be due to differences in

management levels practiced by researchers and farmers and due to the lack of farmers’

participation and interaction in the variety evaluation and selection processes. To fill the gap

of low adoption of technologies by farmers and increase farmers’ participation in technology

evaluation and recommendation, a participatory research approach through client-oriented

research should be employed widely (Getachew et al, 2002).

1.2. Statement of the Problem

Agriculture is the main economic sector in Ethiopia, providing employment for about 85% of

the population, and accounting for around 50% GDP. Despite the importance of agriculture in

its economy, Ethiopia has been a food deficit country for several decades (Tesfaye, 2004).

Available evidence indicates that peasant agriculture in Ethiopia is characterized by

3

inadequate resource endowment and traditional methods of cultivation and husbandry

practices. The majority of small holders in Ethiopia have limited access to land saving

agricultural innovations such as high yielding varieties, inorganic fertilizers and chemicals

(FAO, 1993).

Wheat is one of the most important cereal crops grown in Akaki, the study area and in the

country. It contributes to the major share of daily consumption demand of rural households. In

addition, it is used as cash source for a household. Wheat is one of the major products

marketed. In the area, farmers grow both the improved and local varieties. Even though there

is a tremendous and continuous effort made by agricultural development workers and

researchers adoption and the yield increment of improved bread wheat varieties have not

reached to the required level. There fore, assessing the level of adoption and the related

problems by involving and participating farmers in the study can help to get reliable

information that can be useful to facilitate and fasten t the production of improved bread wheat

varieties.

Most of the times, in the country as well as in the study area, development, introduction and

promotion of improved bread wheat varieties and other agricultural technologies are

conducted without due consideration of farmers’ circumstances, constraints, local environment

and their participation. As a result, less achievement in adoption of improved bread wheat

varieties as well as in other improved agricultural technologies has been resulted. Therefore,

the information revealed in this study on the probability and intensity of adoption and on

farmers evaluation and selection criteria of improved bread wheat varieties by involving

farmers is believed more reliable to use as an input in promotion of improved bread wheat

varieties in the study area and in other areas having similar socio-economic and geographical

conditions.

4

1.3. Objectives of the Study

In general, the objective of this study was to know the status of adoption of improved bread

wheat varieties in the study area. However, the study was focused on the following specific

objectives:

1.to identify farmers’ evaluation criteria of improved bread wheat varieties distributed in the

study area;

2 .to assess the adoption and intensity of farmers improved bread wheat varieties use in the

study area; and

3 .to identify determinants of adoption and intensity of improved bread wheat varieties use in

the study area.

1.4. Significance of the Study

The findings and the results of this study could help to strengthen the promotion and

production of improved bread wheat in the study area and in other areas having similar

geographical and socio-economic characteristics with the study area. Therefore, based on the

knowledge generated from the study, policy makers, government officials, NGOs, extension

personnel, researchers and other development organizations can use as an input in policy,

decision-making, in their development programs and efforts, in order to accelerate, the

diffusion, dissemination and yield performance of improved bread wheat varieties as well as to

make the quality improvement of the varieties characteristics through their professional

efforts. The findings of this study might be also helpful and serve as a springboard for further

investigation and research activities. More over, research organizations and extension

providers can also use as an input in their activities.

5

1.5. The Scope and Limitations of the Study

The most important reasons to select and to conduct this research in Akaki area were the

interest of the funding agencies EARO and Debrezeit research center. The Extension Division

of Debrezeit research center also recommended to be conducted this research in this area and

the recommendation got acceptance by EARO head office crop research department. More

over in this area there is a wide wheat production practices going on; the area is also one of the

sixth on farm research and demonstration sites of Debre Zeit research center; and there is also

one research station to conduct on farm verification and adaptation trial in this area. From

those six research sites of Debre Zeit research center, only Akaki is the area where wheat and

other highland cereal crop research activities are conducted. Since wheat is the first most

important crop in this area, the farming population uses this crop as major food and income

source. These are some of the basic reasons why this research was conducted in Akaki.

The other limitations of this study were the budget or financial scarcities to cover the

payments requested by enumerators for data collection and by respondent farmers for the

information they intended to give and for the time they spent during the interview. But to

maintain the quality of data, efforts were made and some payment arrangements for

respondents per interview and for enumerators per interview schedule were made.

Due to the above-mentioned problems, the study was not conducted in other wheat growing

area of the country and was also constrained to cover wider areas and larger sample size even

in this area. As a result the study was limited to be conducted in Akaki area and cover only two

PAs (Peasant Associations) or RKA (Rural Kebeles Administration) and only 150 respondents

randomly selected from the two-selected sample PAs of this area, Akaki. The other limitation

of this study might arise due to its closer location to the capital city of the country Addis

Ababa since some farmers in this area may not spend their full time on their farm. As a result

there is a doubt that they may not provide the real information.

6

On the other hand, the scope of this study was limited to cover and analyze only those factors

influencing adoption and intensity of adoption behavior of farmers such as farmers’ age,

education, health, gender (sex), leadership, extension service, distance of DA office and credit

providers institutions from the farmers’ village, market and credit accessibility farmers’

farming and extension experience, family size, resource endowment like land, livestock, oxen

and labor source. Other factors like farmers’ perception, knowledge, needs and attitude

towards the various characteristics of improved bread wheat varieties were not covered in this

study; hence, it is required to conduct further investigations.

1.6. Organization of the Thesis

This thesis is organized into seven major parts. Part one constituted the introduction, which

focuses mainly on the background, statement of the problem, objectives, significance, the

scope and limitation of the study as well as the organization of the thesis. Part two deals with

review of different literatures on adoption of improved technologies and factors affecting

adoption and intensity of adoption of improved bread wheat varieties. Part three describes the

materials and methods including a brief description of the study area, data collection

procedures and analytical techniques. Part four contains result and discussion. Part five

constitutes summary and conclusion of the study. The remaining parts of this thesis are

reference and appendices, which are covered under part six and seven respectively.

7

2. LITERATURE REVIEW

2.1. Concept and Theoretical Framework of Adoption

Adoption was defined as the degree of use of a new technology in long-run equilibrium when

a farmer has all the information about the new technology and it’s potential. Adoption refers to

the decision to use a new technology, method, practice, etc. by a firm, farmer or consumer.

Adoption of the farm level (individual adoption) reflects the farmer’s decisions to incorporate

a new technology into the production process. On the other hand, aggregate adoption is the

process of spread or diffusion of a new technology within a region or population. Therefore, a

distinction exists between adoption at the individual farm level and aggregate adoption, within

a targeted region or within a given geographical area (Feder et. al., 1985).

If an innovation is modified periodically, the adoption level may not reach equilibrium. This

situation requires the use of economic procedures that can capture both the rate and the

process of adoption. The rate of adoption is defined as the proportion of farmers who have

adopted new technology overtime. The incidence of adoption is the percentage of farmers

using a technology at a specific point in time (e.g. the percentage of farmers using fertilizer).

The intensity of adoption is defined as the aggregate level of adoption of a given technology,

e.g., the number of hectares planted with improved seed. Aggregate adoption is measured by

the aggregate level of use of a given technology with in a geographical area (Feder et. al.,

1985).

Diffusions scholars have long recognized that an individual’s decision about an innovation is

not an instantaneous act. Rather, it is a process that occurs over a period of time and consists

of a series of actions (Rogers and Shoemaker, 1971). Adoption is not a sudden event, but a

process. Farmers do not accept innovations immediately; they need time to think things over

before making a decision. There are several well-known schemes for explaining the adoption

process. A popular one involves awareness, interest, evaluation, trial and adoption; and

8

another; knowledge, persuasion, decision and confirmation (Adams, 1982; and Rogers and

shoemaker, 1971).

They elaborated these four stages of adoption process as follows (1) Knowledge: - when the

individual learns of the existence of the innovation and again some understanding of its

function. (2) Persuasion: - when the individual forms a favorable or unfavorable opinion of the

innovation (3) Decision: - when the individual engages in activities that lead to a choice

between adoption and rejection, (4) Confirmation: - when the individual makes a final

decision to accept or abandon the innovation. According to their expression, it is well known

that some people are more innovative (responsive to new ideas) than others. Therefore,

adopters have been subdivided in to categories on the basis of the relative time they take to

adopt innovations (innovators, early adopters’, early majority, late majority and laggards).

Innovativeness generally can be related to other personal characteristics; background, social

status, affiliations, attitudes, etc. Research has shown that adoption of innovations often

follows a bell shaped or normal curve when plotted against time.

Innovations are new methods, ideas, practices or techniques, which provide the means of

achieving sustained increases in farm productivity and income. It is the extension worker’s

job to encourage farmers to adopt innovations of proven value. It is an idea or object

perceived as a new by an individual. The innovation may not be new to people in general but,

if an individual has not yet accepted it, to that person it is an innovation. Some Innovations

originate from agricultural research stations, others from farmers. Innovations relate to objects

social acts and abstract ideas. Generally, innovations may be classified in to technical and

social innovations (Adams, 1982).

Innovations are also classified into process and product innovation (Adams, 1982). A process

innovation is an idea input to a production process, while product innovation is a material

input to the production process. The term innovation and technology are used interchangeably.

Adoption and diffusion are distinct but inter-related concepts. Adoption refers to the decision

to use a new technology, method, practice, etc. by a firm, farmer or consumer. The concept of

9

diffusion refers to the temporal (of time) and spatial (of area) spread of the new technology

among different economic units (firms, farmers, and consumers). These two concepts defined

by many researchers’ belongings to different academic disciplines (Legesse, 1998).

Among the many definitions that suggested by Rogers (1983) has been used in several

adoption and diffusion studies. He defined aggregate adoption (i.e. diffusion) behavior as the

process by which a technology is communicated through certain channels over time among the

members of a social system. This definition encompasses at least four elements:(1)

Technology, which represents the new idea, practices or objects being diffused (2) Channel of

communication, which represents the way information about the technology flows from

change agents (such as extension workers or technology suppliers to final users or adopters,

(3) Time which represents the period over which a social system adopts a technology and (4)

Social system, which is comprised of individuals, organizations or agencies and their adoption

strategies (Kundson, 1991, in Legesse, 1998). Rogers defined adoption as use or non-use of

new technology by a farmer at a given period of time. This definition can be extended to any

economic units in the social system (Legesse, 1998).

With regard to the measurement of intensity of adoption, a distinction should be made between

technologies that are divisible and technologies that are not divisible. The intensity or extent of

adoption of divisible technologies can be measured at the individual level in a given period of

time by the share of farm area under the new technology or by the per hectare quantity of input

used in relation to the research recommendation (Legesse, 1998).

Feder et al., (1985) suggested that this measure might also be applied at the aggregate level for

a region. In the case of non-divisible agricultural technologies such as tractors and combine

harvesters, the extent of adoption at the farm level at a given period of time is dichotomous

(adoption or non-adoption) and the aggregate measure becomes continuous. Thus, aggregate

adoption of lumpy technology can be measured by calculating the percentage of farmers using

the new technology within a given area.

10

There is also a great difference between the agricultural sectors of developing and developed

countries. Agriculture in developing countries is heavily dependent on natural phenomena,

while the effects of natural factors are, to some extent, mitigated by the application of modern

technology and improved weather forecasting systems in developed countries. Moreover,

farmers in developing and developed countries do not face the same types of constraints and

opportunities. Therefore, conclusions concerning technology adoption cannot be drawn for

agriculture in developing countries based on experience of the agriculture in developed

countries (Legesse, 1998).

All individuals in a social system do not adopt a technology at the same time. Rather they

adopt in ordered time sequence. Based on the time when farmers first begin using a new

technology five possible adopter categories can be identified in any social system: innovators,

early adopters, early majority, late majority, and laggards (Rogers, 1962 and Rogers and

Shoemaker, 1971, in:Legesse, 1998).

In describing the characteristics of these groups, Rogers (1962, cited in: Largesse, 1998))

suggested that the majority of early adopters have expected to be more educated, venturesome,

and willing to take risks. In contrary to this group the late adopters are expected to be less

educated, conservative, and not willing to take risks. A practical aspect of the classification of

adopters into adopter categories has been in the field of deliberate or planned introduction of

innovation. Nevertheless, the usefulness of this categorization is restricted as there is evidence

indicating possible movement from one category to another, depending on the technology

introduced (Runguist, 1984, in: Legesse, 1998).

Attention has also been given to explain the mode (or approach) and sequence of agricultural

technology adoption. Two approaches seem to appear in agricultural technology adoption

literatures. The first approach emphasizes the adoption of the whole package and the second

one stresses the sequential or stepwise adoption of components of a package. The technical

scientists often advocated the former approach while the latter has advocated by the field

practitioners, especially by farming system and participatory research groups. There is a great

11

tendency in agricultural extension programmers to promote technologies in a package form

whereby farmers are expected to adopt the whole package. Experiences of integrated

agricultural development projects such as CADU, in Ethiopia, however, show that this

approach has not brought needed technical change because of resource limitation (Legesse,

1998).

The adoption of agricultural innovations in developing countries attracts considerable attention

because it can provide the basis for increased production and income. That means farmers will

adopt only technologies that suited their needs and circumstances (Nanyeenya et. al., 1997). In

efforts to increase agricultural productivity, researchers and extension staffs in Ethiopia have

typically promoted a technological package consisting of a number of components. However,

because of capital scarcity and risk considerations, farmers are rarely adopting complete

packages (Million and Belay, 2004).

Agricultural development implies the shift from traditional methods of production to new,

science-based methods of production that include new technological components and/or even

new farming systems. For farmers to adopt these new production technologies successfully,

they must first learn about them and how to use them correctly in their farming system

(Swanson and Claar, 1984).

The transfer of technology approach grounded on the diffusion model focuses on technology

generation by scientists then handed over to extension to pass on to farmers. In this model,

farmers considered as passive receivers and extension as technical delivery conveyer belts.

The new roles of farmers, the new participatory approaches and methods and the new learning

environments all imply new roles for agricultural scientists and extension (Kiflu and Berhanu,

2004). They have to learn from farmers and develop technologies that can serve the diverse

and complex farmers’ situations. Farmers show great interest to evaluate the promising

varieties that could be suitable to their local situation (Mergia, 2002).

12

Adoption of a new technology must be preceded by technology diffusion, e.g., the act of

making new technology known to the potential adopters. Diffusion is therefore the link

between research and development and adoption. Effective diffusion is an essential but not

sufficient condition for adoption. The farmers of a given target category must not only be

made aware of an available technology, they must also be convinced that adoption is in their

best interests and above all, they must be able to adopt the proposed technology (Andersen,

2002 and Arnon, 1989). Adoption studies in developing countries started two to three decades

ago following the green revolution in Asian countries. Since then, several studies have been

undertaken to assess the rate, intensity and determinants of adoption. Most of these studies

focused on the Asian countries where the green revolution took place and was successful. In

Africa, new agricultural technologies have only been introduced recently (Roy, 1990 and

Rukuni, 1994, cited in: Legesse, 1998).

The effectiveness of agricultural extension work highly depends on the availability of

extension professionals who are qualified, motivated, committed and responsive to the ever-

changing social, economic and political environment. Adoption of technology by farmers can

be influenced by educating farmers about improved varieties, cropping techniques, optimal

inputs use, price and market conditions more efficient methods of production management

storage, nutrition, etc. (Anderson and Feder, 2002).

2.2. Empirical Studies on Adoption

Feder, et al (1985) estimated the relationships among technologies already adopted by maize

growing farmers in Swaziland by using factors analysis. They found farmers adopted the

technologies investigated in three independent packages (1) improved maize verity, basal

fertilizers and tractor ploughing (2) top dressing fertilizers, and chemical (3) planting date, and

plant population (density). These empirical findings do not support a sequential or stepwise

adoption process. They reported that farmers in Swaziland tend to adopt a package of

technologies and the social system adopts a technology, which is comprised of individuals,

organizations, or agencies with their adopting constraints.

13

Jha et al (1991) made a study in eastern province of Zambia to evaluate how small holders

respond to interventions that promote the use of improved biochemical (seeds and fertilizers)

and mechanical (animal traction) technologies. In eastern province, farmers were adopting

both labor saving and yield increasing techniques. Agro ecological factors play a critical role

even with in a relatively small region. Farm size affects the use of fertilizer in eastern province

of Zambia. The age and gender of head of the household significantly influence the adoption

of hybrid maize. Heads of household that are older and females are not likely to adopt hybrid

maize. Extension makes only a small contribution to the process of adopting and diffusing

technology. It contributes to specialized commodity-oriented programs but not to maize, the

main crop.

A study conducted in Sierra Leone by Adesina and Zinnah (1992) on technology

characteristics, farmers’ perceptions and adoption decision using tobit model analysis. The

result of tobit analysis demonstrated that apart from age, farm size, extension service and

experience were positively related to adoption decisions.

The study done by Legesse (1992) in Aris Negelle area on adoption of new wheat technologies

indicated that experience, credit, expected yield, expected profit, cash availability for down

payment, participation in farm organization as a leader, and close exposure to technology were

the factors which significantly influenced the probability of adoption of improved varieties

and intensity of adoption of fertilizer and herbicide. He found that the probability of adoption

of improved varieties increases with an increase in farming experience. Farmers with higher

experience appear to have often full information and better knowledge and were able to

evaluate the advantage of the technology is considered. The study also revealed that credit is a

crucial factor affecting the probability of adoption of improved varieties. And the quantity of

fertilizer farmers applied was found to be sensitive to access of credit. The coefficient of the

variable expected yield was significant and shows the intensity; of fertilizer application on

wheat and maize is related to its expected profitability. In his study, farm size was not found to

be important factor affecting probability of adoption of improved varieties and intensity of

fertilizer application. However, the variable farm size per person significantly and negatively

14

influenced the intensity of herbicide adoption for weed control in wheat. The role of direct

extension visits (as represented by frequency of visit by extension agent) was not found to be a

significant factor affecting adoption. This can be attributed to the limited frequency of direct

extension agent visits to non-contact farmers. On the other hand, the variable close exposure to

technology was found to significantly affect the probability of adoption of improved varieties.

Chilot (1994) conducted a study using Probit and Tobit analytical models to identify factors

influencing the three dependent variables such as rate of adoption of new wheat varieties,

intensity of fertilizer adoption and intensity of adoption of 2.4.D weed control chemical under

the title of adoption of new wheat technologies, by hypothesizing of eleven independent

variables and this research result showed that several factors were affecting adoption of new

wheat technologies in two extension centers, Wolemera and Addis Alem areas. As the study

revealed, access to timely availability of fertilizer, perceived related profitability of the

improved variety, number of extension contacts and wealth position were positively and

significantly related to new improved wheat variety adoption. None of the household

characteristics were significantly related to variety adoption. With regard to the intensity of

fertilizer use, timely availability of fertilizer, number of livestock owned and perceived

profitability were positively and significantly related to the intensity of fertilizer use. Literacy,

wealth position of the farmers, exposure to improved technology and timely availability of 2, 4

–D were significantly and positively related to the intensity of 2, 4, - D use. The result showed

that only one variable, distance of extension agent office from farmers’ home was the common

influencing independent factor affecting inversely and significantly all the three dependent

variables namely the rate of adoption of new wheat varieties, intensity of fertilizer adoption

and intensity of adoption of 2.4.D weed control chemical.

Another study made by Bisadua and Mwangi (1996) in southern high lands of Tanzania

Mbeya region showed that farmers were at various components of the recommended package

of improved maize production. Besides, farmers have adopted these components gradually.

The four major factors that contributed this gradual adoption were cost of technologies,

environmental factors, timely availability of inputs, and source of information of new

15

technologies. Technologies which require little cash out lay such as row planting are easily

taken up by farmers because it was less costly and had an added advantage of simplifying

weeding. Environmental stress affected the adoption of some of the recommendations

especially where maize is planted during the day season, which utilizes residual moisture in

the soil. Farmers who dry planted their maize did not apply basal fertilizer. This might be

because of the fear of scorching their maize seed due to low soil moisture. Others did not

perform the second weeding, apparently because rigorous weed germination will be

suppressed by the moisture conditions. Lack of timely availability of inputs was widely cited

as constraint to use them. In availability of improved maize seed was considered bottleneck to

its use.

Other study in Sudan highlands also suggested unavailability of inputs as major constraint to

their use (Lyimon and Temu, 1992, In: Bisauda and Mwagi, 1996). Giving the extension

service is charged with the responsibility of extending information on new technologies; their

low rates of contact with farmers may be acting as a constraint to the use of these technologies

(Bisauda and Mwangi, 1996).

A study conducted on factors affecting adoption of maize production technologies in Bako

area, Ethiopia, by Asfaw et al., (1997) using logit analytical model by hypothesizing seven

independent variables to influence three dependent variables namely adoption of fertilizer,

improved variety and row planting. The result of the model analysis showed that only one

variable, namely extension activities was significantly influence adoption of improved

varieties. Among the seven proposed independent variables, only two independent variables

affected fertilizer adoption and two independent variables influenced the adoption of row

planting significantly. In addition, only one independent variable namely extension activity,

was influence all the three dependent variables in common among the seven proposed

independent variables. Though, all of the hypothesized independent variables were expected to

influence significantly the identified three dependent variables. Among hypothesized

variables, one independent variable namely credit was excluded from further model analysis

due to it’s less important to affect adoption of row planting as mentioned as a possible reason

16

in the report of the study but included in model analyses of fertilizer and improved variety

adoption.

An assessment of the adoption of seed and fertilizer packages and the role of credit in small

holder maize production in Kakamega and Vihiga districts, Kenya by Salsya et al (1998)

showed that the age of household head, primary education, cash crop area, farm size, and

credit were not significantly correlated with adoption. Secondary education, cattle ownership,

use of hired labor, and access to extension significantly influenced the adoption of improved

maize varieties. The use of hired labor and manure, cattle ownership and membership in an

organization were significant factors affecting the adoption of fertilizer. Livestock ownership

serves as a source of wealth to purchase inputs that affect significantly and positively in

adoption of fertilizer. Farmers who use manure had lower probabilities of adopting fertilizer.

Membership in an organization increased the likelihood of adopting fertilizer. Farmers who

belong to an organization are likely to benefit from better access to input and to information

on improved farm practices.

Farmers' participation in leadership of farmers' organizations seems to be the best prediction of

adoption behavior of the farmer characteristic variables. The relationship between

technologies may be independent, sequential or simultaneous and the patterns of adoption

follow the domicile Period of time by the share of farm area under the new technology or by

the per hectare quantity of input used in relation to the research recommendation (Rauniyer

and Goode 1996, in:Legesse ,1998).

Another study conducted on adoption of soil conservation technologies in philppines uplands

of two areas namely Cebu and Claveria by Lucila, et al. (1999) using probit model by

hypothesizing nine independent variables to influence adoption of soil conservation practices

in these two areas. The result showed that in Cebu and Claveria in each area only three

independent variables were significant to influence the dependent variable adoption of soil

conservation practices. But the common significant independent variable among those nine

17

hypothesized independent variables to affect the mentioned dependent variable in the two

study areas was only one variable, which was the percent of land slope.

A study conducted on adoption of wheat technologies by Bekele et al (2000) in Adaba and

Dodola woredas of Bale highlands of Ethiopia using tobit analysis model demonstrated that

adopters of improved wheat technologies were younger, more educated, those who had larger

families and farm, hired more labor and owned more livestock.

Another study conducted by Tesfaye and Alemu (2001) on adoption of maize technologies in

northern Ethiopia shows that applying chemical fertilizer, access to credit, access to extension

information, distance from development center, distance from market center and family size

were factors affecting adoption of improved maize positively and significantly. The level of

education was found to have no significant influence on the adoption decision of farmers for

improved maize. Attendance of field day and access to extension information were negatively

and significantly related to the adoption of decision of chemical fertilizer use. In this study,

farm size, though positive, was not found to have a significant influence on the adoption of

chemical fertilizer. Access to credit and use of improved maize are the most important factors

found to positively and significantly influence the adoption decision of chemical fertilizer.

A study conducted by Tesfaye et al, (2001) on adoption of high yielding maize variety in

maize growing regions of Ethiopia indicated that use of chemical fertilizer, access to credit,

attendance of formal training on maize production and other agricultural techniques, access to

extension information, distance to the nearest market center, family size and tropical live stock

unit had significant and positive influence. On the side of adoption decision of chemical

fertilizer, access to credit, level of education, farm experience, total farm size, use of improved

maize, use of community labor were found to have a significant and positive influence.

Another study conducted on adoption of improved wheat technologies by small scale farmers

in Mbeya district, southern highlands of Tanzania by Mussei et al, (2001) clearly indicated

18

that farm size, family size, and the use of hired labor significantly influenced the probability of

land allocation to improved wheat varieties. Farm size, family size, the use of hired labor and

credit significantly influenced the probability of fertilizer use. A unit increased in farm size

among adopters decreased the probability of adopting fertilizer by 2.4% family size by 9.7%,

use of hired labor by 4.7% credit by 5.9% has increased the probability of adopting fertilizer

among adopters. Credit enables farmers to purchase inputs and increased the probability of

adopting fertilizes among adopters by 5.1 %.

Another study conducted by Lelissa and Mullat (2002) on determinants of adoption and

intensity of fertilizer use in Ejera district West Shoa Zone, Ethiopia, using probit and tobit

analytical models and the result of probit model analysis indicated owning of draught power,

credit access, owning of large farm size, access to extension service affect adoption of

fertilizer positively. But age affects negatively and education has no significant influence on

fertilizer adoption. Regarding the result of Tobit model analysis on the determinants that

influence the adoption and intensity of fertilizer use; family size, education, draught power,

access to credit and extension service have influenced positively.

A study conducted by Tesfaye (2004) on adoption of in organic fertilizer on maize in Amhara,

Oromia, and southern regions, shows that on the adoption of chemical fertilizer, farm

experience, access to credit, use of improved crop varieties, use of farm yard manure, family

size, level of education, total farm size were considered significant. The larger the farm size

the greater the probability of adopting of chemical fertilizer. In this study, family size was

found to have a positive and significant impact on the adoption decision of chemical

fertilizers. Access to credit can relax the financial constraints of farmers and allows farmers to

buy inputs. The result of the study revealed that credit availability has significantly and

positively impacted up on chemical fertilizer adoption. Educational level has increased the

probably of adoption of chemical fertilizer. Use of improved variety of crops also influenced

the decision of farmers to use chemical fertilizer positively and significantly.

19

A study conducted in Gumuno area of southern Ethiopian by Million and Belay (2004) to

identify determinants of fertilizer use (adoption decision) shows that age of household head,

access to credit, frequency of development agent visit, livestock holding and off- farm in-come

influenced the adoption of fertilizer positively and significantly.

A study conducted by Adam and Bedru (2005) on adoption of improved haricot bean varieties

in the central Rift Valley of Ethiopia, using logistic analytical model found that sex, total

livestock unit, credit, and participation in extension service affect adoption of haricot bean

varieties but dependent family members and land size affect negatively and significantly.

Another study was conducted in the central highlands of Ethiopia, on adoption of chickpea

varieties by Legesse, et al, (2005) using logistic analytical model. The result of analysis

demonstrated that the level of education of household head, farm size, access to extension

service proportion of chick pea area and access to seed affect positively and significantly the

adoption of chick pea varieties.

2.3. Farmers Participation in Agricultural Technologies Development and Evaluations

According to Hanson (1982), farmers, millers, bakers, and consumers differ in their concepts

of desirable qualities in wheat. To farmers, a variety of wheat has quality if it resists diseases,

matures at the proper time, doesn’t topple over before harvest, and gives a good yield of

plump grains without shattering (grain falling, to the ground before harvest). The miller is

concerned with the grain. The Kernels should be uniform, the grain should be free of foreign

matter, the moisture content should be low and the protein content high and the yield of flour

per 100 kg of wheat should be high. The baker who produces leavened bread looks for flour

that produces dough with desirable characteristics, the dough should be able to hold gas

bubbles and yield a large loaf with good internal texture and color. The consumer does not see

what grain before it is milled, but he or she has strong preferences regarding the appearance,

texture, aroma and flavor of the breads, biscuit, cakes and other products that trace their

20

character partly to the wheat Kernels. These different viewpoints of farmers, millers, bakers,

and consumers must all be considered to raise the wheat production.

It is useful to examine several features of small holders farming system in Ethiopia, and in the

third world in general, and their implications for agricultural researchers. First, farmers are

economically rational, that is, they adopt new practices that are in their interests and reject

those that are not. When farmers resist a new technology, it is probably because it is not

compatible with their objectives, resources or environment and not because of their backward

ness, irrationality or management mistakes. Moreover, the small holder’s farming system is

complex; small holders allocate their limited resources of land, labor and capital among many

enterprises in a manner determined by their agro-ecological and socio- economic environment.

Farmers need to compromise enterprises to increase productivity. Farmers consider both

technical and socio- economic aspects when deciding whether to use a new technology.

Researchers are need to obtain an accurate and balanced assessment of the performance of the

varieties, using both scientific and farmers’ own criteria. Farmers rarely adopt complete

packages all at once, that is, complete set of recommended technological components

concerning how to mange an enterprise. Instead, farmers usually use a step-by –step approach,

testing components individually and incorporate the successful ones into their system.

Therefore, researchers need to evaluate new technologies individually or in simple

combinations under farmers’ own management conditions. The greater the farmers’

participation in the designing and testing of a technology, the greater is the chance that they

adopt it (Franzel, 1992).

A study conducted in West Shoa Zone, Ambo Woreda Birbisa and Cherech service-

cooperative by Ethiopian Rural Self Help Association (ERSHA) in (2000) to evaluate bread

wheat technologies on the farmers’ farm condition using farmers’ criteria. According to the

study, farmers have formulated the criteria to evaluate the bread wheat varieties at different

growing stages. The criteria formulated by farmers were crop stand (uniform germination,

strong and healthy, deep green and many tillers), flowering (uniform flowering), heading

21

(panicle size, number of spike lets per head, resistance to lodging, frost and disease), yield

(superior in yield, easy to thresh, stored for long time), Grain quality, (size, color, full body),

baking (dough quality, baking quality and taste). Farmers’ evaluation criteria judging varieties

during vegetative growth stage in order of importance were: tillering capacity (many tillers per

plant), head size (panicle size, head length), frost and disease tolerant (healthy leaf and shoot,

uniform germination and crop stand, resistance to lodging and shattering. Farmers’ evaluation

criteria for judging grain quality characteristics were: yield per unit area, grain size (fill body,

no shrink seeds and deformed seeds), baking quality (dough quality and being good bread),

and color (important for market) and easy to thresh.

These days, it is well known that farmers’ participation in agricultural research and

development processes are increasingly improved by realizing that the socio- economic and

agricultural conditions of small-scale farmers are too complex, diverse and risk prone.

Conventional approaches, which are well known by station-based researches followed by top-

down technology transfer system, are not often adopted in a sustainable manner. Hence,

building a partnership and management with farmers is needed throughout the cycles of

diagnosis, experimentation and technology dissemination. This increases the understanding of

the opportunities and constraints faced by farmers on top of their technical knowledge. This in

turn enhances the prospects of technological development and its adoption rates (Mergia,

2002).

It was realized that farmers have their own priorities in their production strategy and often

accepts those technologies, which they consider as most advantageous to their production

system. Close engagement with farmers through the cycle of diagnosis, experimentation and

dissemination increases understanding of conditions, of the opportunities and constraints

farmers face, and of their own technical knowledge. The package-testing program also helped

to get the assessments and evaluations of the technologies from the beneficiaries themselves.

The approach has considerably contributed in increasing the understanding of the biological

researches towards the farmers’ complex and linked circumstances and constraints. It has also

22

contributed in improving the linkage between research, extension and farmers as compared to

previous approaches (Mergia, 2002).

A study conducted on the use of B.B.M (Broad Bed Maker) technology on vertisol, Sheriff

(2002) shows that farmers in the study area got an opportunity to identify and select different

crop varieties and grow the crop they preferred that can best meet their needs, interests and the

corresponding agronomic practices of their specific agro-ecological conditions. Farmers do not

operate according to the assumption of policy makers and scientists. It, moreover teaches us

that agricultural knowledge varies and is accorded different social meanings depending on

how it is applied in the running farms. This leads to differential patterns of farm management

style, cropping patterns and levels of production. Farmers are heterogeneous and they are

indeed knowledgeable and capable actors who consciously pursue various objectives.

Technological patterns of development should refer back to various resources and farmers

capacity. The achievement of these objectives is influenced by the images they have of various

aspects involving institutions. These call for the negotiation of values and resulting unintended

consequences that could be referred to as counter development. We therefore, need to learn

that technology transfer has to address different farmers’ needs, perceptions and strategies. We

need to intervene with redesigning their use of technologies (Sherif, 2002).

Technology is not always a product of scientific institutions. Human beings are inherently

capable of modifying their environment in the process of adaptation, where by technology is

created and subsequently utilized. The struggle between the environment and people never

stops, though under some circumstances, a long time may pass before intended changes are

achieved. For various reasons, some societies adhere to certain technologies for centuries

where as others pass comparable level of technology in a relatively shorter period of time. For

instance, the revolution of farming tools for different operations in developed countries and

the stagnation of the same in a developing country such as Ethiopians explain this observation

(Tesfaye, 2003).

23

2.4. Conceptual Framework of the Study

In general, it could be inferred that agricultural technology adoption and diffusion patterns are

often different from area to area. The differences in adoption patterns were attributed to

variations in agro-climatic, information, infrastructures, as well as environmental, institutional

and social factors between areas. Moreover farmers’ adoption behavior, especially and in low-

income countries, is influenced by a complex set of socio- economic, demographic, technical,

institutional and biophysical factors (Feder et al, 1985).

Understanding and considering these factors when analyzing and interpreting farmers’

response to agricultural innovations has, there fore, become important both theoretically and

empirically. Adoption rates were also noted to vary between different groups of farmers due to

differences in access to resources (land, labor, and capital) credit, and information and

differences in farmers’ perceptions of risks and profits associated with new technology. The

direction and degree of impact of adoption determinants are not uniform; the impact varies

depending on type of technology and the conditions of areas where the technology is to be

introduced (Legesse, 1998).

Farmers’ decision to adopt or reject new technologies can also be influenced by factors related

to their objectives and constraints. These factors include farmers’ resource endowments as

measured by (1) size of family labors, farm size and oxen ownership, (2) farmers’ socio –

economic circumstance (age, and formal education, etc) and (3) institutional support system

(available of inputs) (CIMMIYT, 1993).

In many developing countries it has become apparent that the generating new technology

alone has not provided solution to help poor farmers to increase agricultural productivity and

achieve higher standards of living. In spite of the efforts of National and International

development organizations, the problem of technology adoption and hence low agricultural

productivity is still a major concern (CIMMIYT, 1993).

24

The inability of farmers to achieve high yield levels has been blamed on many different

sources on extension services side, for not properly disseminating the research station’s

technologies, on input supply systems side, for failing to make the new technologies available,

on policy decision makers side, for making the new technologies unprofitable to use due to

policy distortion and on farmers themselves, who are alleged (assumed) to be too conservative.

However, many studies point to another cause of low adoption rate – the research center

recommendations that are irrelevant to the small farmers’ priorities, resource constraints, and

the physical, cultural and economic environment (Winkelmann, 1977, in: Mulugetta et al,

1994).

On agricultural technology adoption and diffusion determinant factors in different countries

across the world, Africa including Ethiopia, several and various studies have been conducted

and many researchers have obtained various findings. The researchers’ lack of understanding

of the farmers’ problem and the conditions under which they operate may result in the

development of inappropriate technologies and low rates of technology adoption (Fresco,

1984, in: Mulugeta et al, 1994).

In this study efforts were made to revealed factors affecting adoption and intensity of

adoption, the pattern and direction of adoption of improved bread wheat varieties (part of

agricultural technologies) that varied according to farmers’ resource endowment,

environmental situations, technological development, personal characteristics, accessibilities

to different services such as credit, extension, information market and the importance,

suitability, management and cost of the technologies.

Moreover literatures, practical experiences and field observations have confirmed that

technologies adoption by farmers’ can be fasten, enhanced and make sustainable by

understanding those factors influencing the pattern, degree and direction of adoption and by

designing and establishing technologies diffusion and adoption pattern strategies through

25

farmers empowering, making farmers access to infrastructure, information, technologies,

credit, field support how to utilize new technologies.

Other factors should be also included in agricultural technologies disseminations and adoption.

Farmers’ participation in technologies development, selection and dissemination strategies as

well as result evaluation should be considered, because farmers have a long year of farming

and environmental experience. The need and interest of farmers’ towards agricultural

innovations also varies depending on farmers’ farming environment, their belief, experience,

economic status and their personal background and characteristic. Therefore, disseminating

improved agricultural technologies without consultation of farmers most probably ends with

failure.

Several literatures, practical experiences and observations of the reality have been showed that

one factor may enhance adoption of one technology in one area at one time and may hinder it

in another situation, area and time. Therefore it is difficult to develop a one and unified

adoption model in technology adoption process because of the socio economic and ecological

variations of the different sites, and the various natures of the determinant factors. Hence, the

analytical framework presented in the below figure shows the most importance variables

expected to influence the adoption and intensity of adoption of improved bread wheat varieties

in the study area, Akaki.

26

Note = the above Figure shows the chart of conceptual frame work of the study

Asset endowment and other income source

- Livestock

-Farm land

- Off-farm

Institutional variables

- Extension service

- Credit access

-Market access

- Distance of extension office

-Distance of credit provider Institutions

Household socioeconomic characteristics

- Sex

- Age

-Health

- Education

-Experience in extension

Labor Sources

-Oxen

-Labor source

-Family size

Decision to adopt improved bread

wheat and to increase size of farm

land for improved bread wheat

production

27

3. RESEARCH METHODOLOGY

It is well known, that, there are two research methodologies classified under the broad

headings: the qualitative and quantitative research methodologies.. Methods are the tools of

data generation and analysis techniques practically; methods are the tools of the trade (job) for

social scientists and are chosen on the basis of criteria related to or even dictated by the major

elements of the methodology in which they are embedded, such as perception of reality,

definition of science, perception of human beings, purpose of research, type of research, type

of research units and so on.

As many people described the basic objective of a sample is to draw inferences about the

population from which such sample is drawn. This means that sampling is a technique, which

helps us in understanding the parameters or characteristics of the universe or population by

examining only a small part of it. Therefore, it is necessary that the sampling technique be

reliable. (In general, a study on relatively small number of units, are the sample, should be

representative of the whole target population. Sampling is, thus, the process of choosing the

units that could be included in the study, determine the sample size and the sample selection

procedures. A sample design is a definite plan, completely determined before any data are

collected for obtaining a sample from a given population. In this study under this chapter the

study area description and sample farmers’ demographic, resource ownership and institutional

services has conducted.

3.1. Description of the Study Area (Akaki)

3.1.1. Location, relief and climate

The socio-economic and environmental factors of the area play a great role for better

performance of any activity done in that particular area. There fore it is highly valuable to

28

describe the area where the activity is planed to be under taken. . In addition, its accessibility

and the budget constraint of the research were some of the factors to fix and conduct this

research in this area.

This research activity was decided to undertaken at Akaki area, which was selected by its wide

growing and demonstration of improved wheat crop varieties and wide utilization of other

improved agricultural technologies in this area The reasons to conduct this research, in Akaki

area due to wide wheat production practices and high-improved agricultural inputs utilization

as well as wide demonstration practices on agricultural inputs applications and utilizations in

this area.

Akaki, located at South East of Addis Ababa and it is the rural part of Addis Ababa and Akaki-

kaliti sub-city, one of the sub-cities of Addis Ababa. It is bounded by Oromia region to the

east and southern part. The study area, Akaki, constituted 9 Peasant Associations (PAs) or

Rural Kebele Administrations (RKAs) in the mean time when this research survey was carried

out. But at the end of this research survey, these Rural kebele Administrations /Peasant

Associations were reorganized into four reduced number of Rural Kebele Administrations as a

result of a new restructuring program of Addis Ababa administration.

The agro-ecological zone of the study area, Akaki, is 100% high land and its altitude ranges

from 2100 – 2300 masl. In the study area there are different soil types. The most important and

dominant soil type in area coverage is heavy ver ti sol or black clay soil. Except some few

hilly landscapes of the study area, Akaki, virtually is plain.

Therefore, soil erosion by water is not a problem in the study area. But water logging is a very

serious problem resulted from its flat landscape.

29

Table 1.The Livestock and crop types in the study area

No. Types of crop grown

in the study area

Land

Coverage

in (Ha)

Land

coverage in

percent

Types of

Livestock

reared in the

study area

Number

of

Livestock

1 Cereal crops

Wheat

Teff

Others

3580

1930

1560

90

82.11-

-

-

Cattle

Oxen

(Cow and

Others)

17269

4058

13211

2 Pulse crops

Faba bean

peas

Chick pea

lentils

others

604

80

80

201

44

199

13.85

-

-

-

Sheep

Goat

Pack Animals

(Horse, Mule

And Donkey)

10380

3064

5012

3 Oil crops 30 0.70 Poultry 17807

4 Vegetables 104 2.38 Bee-in-Hive No data

5 Others (Fenu greek) 42 0.96 - -

Total 4360 100 - -

(Source: Akaki Agricultural Unit Office, 2005)

The study area, Akaki, is characterized by the high land climate. It has the main and small

rainy seasons. Farmers in the study area rely on the main rainy season known as kiremt or

Meher rainy season for their agricultural production activities. There is no a practice and

experience of crop production using the rain of small rainy season known as Belg rainy season

and irrigation production. The small rainy season extends some times starting from January or

February and ends some times in May or June. But the main rainy season is similar like

otherparts of Ethiopia that extends from end of or mid of June to most of the time mid of

September, according to Akaki agricultural unit office. Some of the major crops grown in the

study area are Wheat, Teff, Faba bean, Chickpea and Lentils.

30

3.1.2. Agriculture and demographic characteristics of the study area

The total farming population of the study area is 14519. Of which, 7626 were male, and 6893

female. The number of household heads of the farming population is 2490 male and 265

female with a total of 2755 household heads. The average family size of the study area is 5.27

per household. In Akaki, agriculture, which includes crop and livestock production, is the main

stay of the farming community like other parts of Ethiopia. The types of crops, the farm land

coverage by each crop type and the types of livestock and the size of livestock population

reared by farming community in the study area are summarized in Table 1.

Table 2.The land use of farmers in the study area

No Types of land use Coverage in (Ha) Percentage

1 Cultivated land 4360 6 1.33

2 Grazing land 151 2.12 3 Forest land 80 1.13

4 Village and construction 2496 35.11

5 Others 22 0.31 Total 7109 100

(Source: Akaki Agricultural Unit Office)

As indicated in table 2, the larger proportion of land in the study area is used for cultivation,

which is 4360 (61.33 %.). Of which the major proportion goes to cereal crop production

particularly for wheat production followed by Teff production. In the study area, there is a very

serious grazing land scarcity, greatly affecting the livestock production, resulted from high

population pressure and extended farming practice that shrinks grazing land and compete with

livestock production. The farming society in the study area used crop by product for animal

feed though it is poor nutritionally.

31

Table 3.The summary of oxen ownership

No. Oxen Ownership Number of owners Percentage

1 With no oxen 1272 46.17

2 With one oxen 243 8.82

3 With two oxen 555 20 .15

4 With three oxen 139 5.05

5 With four oxen 442 16.04

6 With five % above 104 3.77

Total 2755 100

(Source: kaki Agricultural Unit Office)

In the study area as summarized in table 3, around 46.17% farmers are with out ploughing

oxen, according to the Akaki Agricultural Unit office. In the study area Akaki, there is a fuel

wood scarcity resulted from unwise practice of deforestation for long time. There fore the

farming population has forced to use cow dung as their source of energy for heat and food

preparation. Using of cow dung for fuel can affect the utilization of compost to improve soil

fertility for better crop yield.

3.1.3. Institutional services of the study area

Effective Agricultural Extension services have paramount importance to farmers to get timely

advices and information on the availability, use and application of new, improved and modern

agricultural inputs, technologies and practices. The Akaki Agricultural Unit office is

responsible to offer agricultural extension service in the study area. Under this unit office

different expert with different professions has assigned at the Unit Office level and

Development Agent (DA) center level. The 9 Extension Agents / Development Agents at the

center level were assigned and responsible to give extension service to the farming

community. They were accountable to Akaki Agricultural Unit. But at the end of this research

survey and data collection process due to the new restructuring program of Addis Ababa

Administration some DAs from their center and other professionals from Akaki Agricultural

32

unit office has transferred to other unrelated duties with their professions that can affect

negatively the extension services and rural development efforts of the study area.

Table 4.Improved agricultural input distribution of the study area in different years

Crop Season

DAP

Quit.

Urea

Quit.

Teff

Quit.

Pesticide

K.g.

Pesticide

Lit

Weedi-cide

Lit

1997 5224.5 3000.5 4.5 10 30 50

1998 4695.5 2506.5 5.5 5 30 76

1999 3759 2026.5 - 5 20 48

2000 4172 2235.5 11.85 5 10 33

2001 3866 2034.5 12 4 10 35

2002 3868 2182 13.05 4 4 5

2003 3994 2340 19.5 4 7 50

2004 167.5 167.5 - - - -

Total 29746.50 16493 66.40 37 111 297

(Source: Akaki Agricultural Unit Office)

Availability of improved agricultural inputs to use and credit service to purchase agricultural

inputs is very vital for technology adoption. In the study area different agricultural inputs and

credit were distributed in different time for farmers. Table 4 showed the agricultural input

distribution of the study area in different years.

Table 5 showed the improved bread wheat varieties seed distribution and table 6 showed the

improved credit distributions in different years to the farming community of the study area.

According to the Akaki Agricultural unit office information the major inputs distributed in the

study area were fertilizer (DAP and Urea) and improved bread wheat varieties as well as

improved teff varieties. The major input distribution of the study area from 1995 to 2004 were,

fertilizer in quintal, (DAP=29746.50 and 16493), improved bread wheat varieties 732.75 and

improved Teff 66.40 quintals.

33

Table 5.Improved bread wheat seed distribution of the study area in different years

No Crop Season

(Years G.C.)

Improved

Bread Wheat (Quit.)

Land covered

(Ha.)

No.of Participant

Farmers

1 1997 43.5 29 58

2 1998 74.25 49.5 99

3 1999 48 32 64

4 2000 72 50 100

5 2001 82.5 55 110

6 2002 120 80 160

7 2003 145.5 97 194

8 2004 147 98 196

Total 732.75 490.50 981

(Source: Office of Akaki Agricultural Unit office)

As it is presented in table 4 and table 5, the average annual DAP fertilizer distribution were

3718.3 quintals, urea 2061.63, improved bread wheat seed 8.3 quintals and that of improved

Teff was 4.625 quintals. The fertilizer distribution was almost satisfactory. But the improved

bread wheat and teff seed distribution was very low quantity.

Regarding the credit distribution of the study area as presented in Table 6, the larger credit for

the last seven years a total of 376478.06 Ethiopian birr for fertilizer and improved grain crop

seed purchase was distributed .The larger proportion of the credit were used for fertilizer

purchase. This is because farmers in the study area got credit in the form of fertilizer.

34

Table 6.Credit Distribution of the study area in various years

(Sources: Office of Akaki Agricultural unit office).

In the study area credit were also distributed for livestock production as showed in Table 6.For

this sector of the economy credit was distributed before five years ago. The credit distribution

was covered only for two consecutive years. The credit service limitations in amount, type and

facilities can affect negative the adoption of improved agricultural technologies, agricultural

development and the over all rural community lively hood living situation improvement.

3.2. Description of Data Collection and Data Analysis Methods and Procedures

3.2.1. Sources and types of data

It is very helpful for researchers to anticipate and think over in advance about the sources and

types of data that are relevant to the research and, therefore, need to be gathered. This help to

avoid confusion and unnecessary time, labor, finance and other resources wastages. The types

of data, primary and secondary, were collected to answer and fulfill this research questions and

objectives. All information about determinants of adoption and intensity of adoption and

Credit for No Crop season

(Year G.C.) Fertilizer and Improved seed

(ET.Birr)

Livestock Production

(ET.Birr)

1 1997 27,858.50 -

2 1998 54,076.60 -

3 1999 16,275.75 47,800

4 2000 60,552.70 118,885

5 2001 25,446.61 -

6 2002 94,218.75 -

7 2003 98,049.15 -

Total 376478.06 166685

35

farmers’ evaluation and selection criteria of improved bread wheat varieties, demographic,

socio-economic, environmental situations, wheat production, credit facilities, extension

service and others relevant data to the study were gathered from primary sources quantitatively

through interview schedule and qualitatively through group discussion and observation.

Data also were gathered by examining secondary sources such as documents, reports and

records of DAs (Development Agents), and other related agricultural offices and research

centers. All these data in the process of the study were gathered using different methods and

techniques based on the nature, types and characteristics of the data. Both quantitative and

qualitative data were gathered through different data collection methods from primary and

secondary sources.

3.2.2. Sample size and sampling techniques

This study was determined to conduct in Akaki area, which is the rural part of Addis Ababa

Administration. In this study sample size was determined by taking different factors such as

research cost, time, human resource, accessibility, availability of transport facility, and other

physical resource accessibilities. By taking these factors into account, it was fixed to cover

two Peasant Associations out of 9 PAs and 150 household head respondents from the total

2755 household head population of the study area, Akaki. Through out sample selection

processes simple random selection method has employed. The two stage sampling techniques

were applied in sample selection processes. First, the two Peasant Associations (PAs) or Rural

Kebele Administrations (RKAs) namely Koye and Gelan-Edero involved in improved bread

wheat production were selected out of nine PAs using simple random selection method.

Second 150 sample household head farmers were selected from total wheat growers of the two

samples Pas. About 65 (43%) Sample farmers from Gelan-Edero PA and from Koye sample

PA 85 (57%) sample farmers were selected proportionally. From Gelan-Edero PA 36 were

adopters and 29 were non-adopters and from Koye PA 63 were adopters and 22 were non-

36

adopters. Out of 150 respondents (132) 88% were male and the remaining (12%) was female.

From total respondents (99) 66% were adopters and the rest 51 (34%) were non-adopters.

From total adopters 93% were male adopters and the remaining 7% were female adopters.

Concerning non-adopters 78% were male and 22%were female as presented in Table7. All

sample selection processes were carried out in pursuing of statistical procedures and with

consultation of DAs, Akaki Agricultural Unit Office professionals and PA leaders of the study

area.

3.2. 3. Data collection methods

Data for this study were gathered from sample household head farmer respondents through

interview, group discussion and observations. Both qualitative and quantitative data were

gathered using the mentioned methods. Secondary data that were relevant to this study were

also gathered through examining of published and unpublished data that was gathered and

organized by other bodies for other purposes .In this process care was taking in taking and

selection of the relevant data suitable and relevant for this study.

3.2.3.1. Quantitative data collection methods

In this regard, primary data were collected through personal and face-to-face interview using

structured and pre-tested interview schedule that were filled up by recruited and trained

enumerators under the close supervision of the researcher. Totally, 150 randomly selected

samples household head farmer respondents were covered under the survey. Also, secondary

data were gathered by examining secondary sources such as records, reports, and research

results and other documents and publications from office of agriculture, research centers and

other respective offices.

37

3.2.3.2. Qualitative data collection method

In the study area primary qualitative data on improved bread wheat varieties selection and

evaluation criteria of farmers’ were gathered through group discussion and individual

discussions conducted with farmers and professionals. Researcher’s personal observation and

transect walk were also used in this data gathering processing. Data gathering through these

methods were continued to the point of saturation, to crosscheck, triangulate, elaborate and

enrich the information on both qualitative and quantitative data to increase the reliability and

trustworthiness of the information. The group members and individuals were familiarized to

the discussion points and encouraged to forward their opinion they felt with out any

reservation. In this process, recording, coding, reorganizing and arrangements, refining

expanding of information was conducted.

3.3. Analytical Models

3.3.1. Logit model

Several models are available to analyze factors affecting technology adoption. The choice of

one may depend up on several factors. Some of these alternative models are the discrete

regression models in which the dependent variable assumes discrete values. The simplest of

these models is that in, which the dependent valuable Y is binary (it can assume only two

values denoted by 0 and 1).

The three most commonly used approaches to estimate such models are the linear probability

model (LPM), the logit model and the probit model .The linear probability model has an

obvious defect in that the estimated probability values can lies outside the normal range 0-1

range. The fundamental problem with the LPM is that it is not logically a very attractive model

because it assumes that the marginal or incremental effects of explanatory variables remain

constant, that is Pi=E (Y=1/x) increases linearly with x (Maddala, 1997 and Gujaratti, 1998).

38

The authors suggested that the sigmoid or S-shaped curve were very much resembles the

cumulative distribution function (CDF) of random variable is used to model regressions where

the response variable is dichotomous, taking 0-1values.

The cumulative distribution functions (CDFs), which are commonly chosen to represent the 0-

1response models, are the logit (logistic CDF) model and the probit (normal CDF) model.

Logit and probit models are the convenient functional forms for models with binary

endogenous variables (Tohnston and Dianardo, 1997 cited in Techane, 2002).

These two models are commonly used in studies involving qualitative choices. To explain the

behavior of dichotomous dependent variables we will have to use a suitably chosen cumulative

Distribution Function (CDF). The logit model uses the cumulative logistic function. But this is

not the only CDF that one can use .In some applications the normal CDF has been found

useful. The estimating model that emerges from normal CDF is popularly known as the probit

model (Gujarati, 1999).

The logistic and probit formulations are quite comparable the chief difference being that the

logistic has slightly flatter tails that is the normal curve approaches the axes more quickly than

the logistic curve. There fore, the choice between the two is one of mathematical convenience

and ready availability of computer programs (Gujrati, 1988 cited in Techane, 2002). A

relevant model offers better explanation on the underlying relation ship between adoption

decision and factors influencing it. The most widely used qualitative response models are the

logit and the probit models (Amemiya, 1985).

Both the probit and logit models yield similar parameter estimates and it is difficult to

distinguish them statistically (Aldrich and Nelson, 1984). How ever, because of the fact that

binomial logit model is easier to estimate and simpler to interpret. Therefore a logit model is

used in this study to determine the relation ship between adoption decision and factors

affecting the adoption of improved bread wheat varieties in the study area, Akaki.

39

The specification of the logit model is as follow:

Рі = Рі (Υі = 1) = exp (Zi)

1+exp(Zi)

Where Рі denotes the probability that the ith farmer will fall in the adopters’ class (yi=1) and

exp (Zi) stands for the irrational number “ e” to the power of Zi. The un observable stimulus

index Zi, assumes any value.

However, the Logistics transformation guarantees each corresponding value of Pi to fall inside

the 0-intervals. The stimulus index Zi, also called the Log of the odds ratio, in favor of

improved bread wheat varieties adoption, is actually a linear function of factors influencing

adoption decision as specified hereunder:

=β0+β1χ1і+β2χ2i + .…+ βρχρi+eі

Zі= ln [ pi

]

1-pi

Where: X1, X2, X3,...........Xp=explanatory variables

Β0, β1, β2, β3, β4,…… ,βp = Logit Parameters to be estimated, ei = the error term

= β0+β1χ1i+β2χ2i+β3χ3i +…….βpχpi + χεі

In reality, the significant explanatory variables do not all have the same level of impact on the

adoption decision of the farmers. Therefore, the impact of each significant variable on the

probability of adoption was calculated by keeping the continuous variables at their mean

values and the dummy variables at their most frequent values (zero or one).

The estimated coefficients of the Logit model of improved bread wheat adoption are listed in

the table 20. The likelihood ratio statistics is significant at 10 percent probability level and

implies that the independent factors taken together influenced improved bread wheat varieties

adoption. The model correctly predicted 81.33 percent of the adopters and non-adopters.

40

3.3.2. Tobit model

Adoption studies based up on dichotomous regression model have attempted to explain only

the probability of adoption versus non-adoption rather than the extent and intensity of adoption

Knowledge that a farmer is using high yielding variety may not provide much information

about farmer behavior because he /she may be using some percent or 100 percent of his /her

farm for the new technology. Similarly, with respect to adoption of fertilizer is, a farmer may

be using a small amount or a large amount per hectare area. A strictly dichotomous variable

often is not sufficient for examining the extent and intensity of adoption for some problems

such as fertilizer (Feder et al., 1985).

There is also a broad class of models that have both discrete and continuous parts. One

important model in this category is the Tobit. Tobit is an extension of the probit model and it

is really one approach to deal with the problem of censored data (Johnston and Dinardo, 1997

cited in Techane, 2002).

When examining the empirical studies in the literatures, many researchers have employed the

Tobit model to identify factors influencing the adoption and intensity of technology use. For

example Nykonya et al (1997) ; Lelissa (1998) ; Bezabih (2000); Croppenstedt et al. (1999) as

cited in techane (2002) used the Tobit model to estimate the probability and the intensity of

fertilizer use ( Adesina and zinnah ,1993; in Techane ,2002).

The econometric model applied for analyzing factors influencing the intensity of adoption of

improved bread wheat varieties is the Tobit model shown in equation (1). This model was

chosen because, it has an advantage over other adoption model (LPM, Logistic and Probit

Models) in that, and it can reveal both the probability of adoption of improved bread wheat

varieties and the intensity of use of the varieties.

The Tobit model can be defined as:

(1) * Y if

0Y* if YY

ni U XY

i

ii

iii

00

,...2,1,*

*

≤=

>=

=+= β

41

Where, Yi= the observed dependent variable, in our case the land size in hectare covered with

improved bread wheat variety.

Yi*= the latent variable which is not observable

Xi= vector of factors affecting adoption and intensity of fertilizer use

βi= vector of unknown Parameters

Ui= residuals that are independently and normally distributed with mean zero and a common

variance σ2. Note that the threshold value in the above model is zero. This is not a very

restrictive assumption, because the threshold value can be set to zero or assumed to be only

known or unknown value (Amemiya, 1985). The Tobit model shown above is also called a

censored regression model because it is possible to view the problem as one where

observations of Y* at or below zero are censored

The model parameters are estimated by maximizing the Tobit likelihood function of the

following formula (Maddala, 1997 and Amemiya, 1985).

(2) iX i

iY F

iY

iXi iYf L )0* (

1

0

)(

σ

β

σσ

β −∏ ≤

>

−=C

Where, f and F are respectively the density function and cumulative distribution function of

Yi*. IIYi

*< 0 means the product over those i for which Yi* < 0, and IIYi*<0, and IIYi>0 means the

product over those i for which Yi*>0.

It may not be sensible to interpret the coefficients of Tobit in the same way as one interprets

coefficients in an uncensored linear model. Hence one has to compute the derivatives to

predict the effects of changes in the exogenous variables.

1. The marginal effect of an explanatory variable on the expected value of the dependent

variable is:

(3) ZFX

Yi

i

i β)()(=

∂

Ε∂

42

Where σ

β i i X is denoted by z,

2. The change in the probability of adopting a technology as independent variable Xi change

is:

(4) X

ZF

i σ

β if(Z))(=

∂

∂

3. The change in intensity of adoption with respect to a change in an explanatory variable

among adopters is:

(5) F(Z)

f(Z)(

ZF

ZfZ

X

YYi

i

ii ]))(

)(1[

)0/( 2

*

−−=∂

>Ε∂β

Where, F (z) is the cumulative normal distribution of z, f (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, B is a vector of Tobit maximum likelihood estimates and 0* the standard error

of the error term.

Parameter estimates of the Tobit model for the intensity of adoption of improved bread wheat

varieties (measured in terms of size of land in hectare used for growing of improved bread

wheat varieties over the total wheat land in hectare) as shown in Table 22. And the results are

discussed under section (5.1.2.). The Tobit model was used or applied to analyze the factors

that determine the intensity of adoption of improved bread wheat varieties because the mean

proportion of land allocated to improved bread wheat varieties is a continuous variable but

truncated between zero and one. This model is relevant to predict the intensity of adoption of

improved bread wheat varieties by farmers when the dependent variable is continuous.

3.3.3. Other Quantitative data analysis methods

In this study, in addition to econometrics models, Logit and Tobit models described in the

above, descriptive statistics such as percentage, tabulation, mean, standard deviation, t-test and

χ2 - test data analysis methods were employed in quantitative data analysis of the study.

43

3.3.4. Qualitative data analysis method

The qualitative data analysis has conducted to strengthen the evidences obtained through

quantitative data collection methods or survey method. In this study, the qualitative data

obtained from the group, individual formal and informal discussions and through the

researcher’s personal observation regarding farmers’ selection and evaluation criteria, their

priorities of improved bread wheat varieties disseminated in the study area were summarized

using the criteria established by the group members by analyzing the characteristics of these

varieties to what extent these varieties satisfy and fit their needs, interests and to their

environmental situations. The qualitative data were analyzed through explanation of idea,

opinion, and concept explanation method. Researcher’s personal observations and transect

walk watching were analyzed through, further explanation of the real world under observation.

3.4. Hypotheses Testing and Definitions of Variables

In this study the variables were selected and hypothesized using literatures, by considering

farmers production practices, area situations and objectives of the study. In this study it was

decide to concentrate the research effort and limited resources on socio economic and

environmental conditions and constraints that was expected to influence probability and

intensity of adoption because as the Ethiopian extension history shows that in this area

extension service was provided for long years using different methods such as demonstration

farmers’ field day, on farm field visit and support. More over, farmers in this area have better

access to different information sources .As a result, were expected to have better

understanding, knowledge, and attitude towards improved agricultural technologies. As a

result, more emphasis was given to exogenous socio economic variables than internal

variables to hypothesize, test and analyze using the Logit and Tobit Analytical models.

44

3.4.1. The Dependent variables of logit and tobit models

3.4.1.1. The Dependent variable of logit model

The dependent variable of the binomial logit model’s log-odds ratio is the probability of

adopting or not adopting the improved bread wheat varieties which can used to identify factors

determining adoption of improved bread wheat varieties is the natural logarithm of the ratio of

the probability that a farmer adopts the improved varieties (Pi) to the probability that he /she

will not (1-Pi). The log-odds ratio is a linear function of the explanatory variables.

3.4.1.2. The Dependent variable of tobit model

The dependent variable of Tobit model has continuous value, which should be the intensity,

the use and application of the technology. As observed in different empirical studies this

variable can be expressed in terms of ratio, actual figure and log form depending on the

purpose of the study. For example in their study of factors influencing adoption of fertilizer,

Nkonya et.al, (1997) as cited in Techane (2002) considered fertilizer applied per hectare as the

dependent variable of the tobit model. Likewise Shiyani et al., (2000) as cited in Techane

considered the proportion of area under chickpea varieties in their study of adoption of

improved chickpea varieties. Consequently, in this study the ratio of actual land size under

improved bread wheat varieties to total wheat land size was taken as a dependent variable of

the tobit model.

3.4.2. The Independent variables and their definitions used in logit and tobit models

Adoption literatures provide a long list of factors that may influence the adoption of

agricultural technologies. Generally these factors can be grouped into demographic personal,

socio –economic, physical and institutional factors (Million and Belay, 2004). There fore,

45

farmers’ decision to use improved bread wheat varieties and the intensity of the use in a given

period of time is hypothesized to be influenced by a combined effect of various factors such as

household characteristics, socio-economic and physical environments in which farmers

operate. Based on Feder et.al, (1985) that extensively reviewed factors affecting adoption of

agricultural technologies in low income countries, and on the brief literature review in this

study the variables mentioned below are hypothesized to explain improved bread wheat

varieties adoption and the intensity of the use of these varieties by the sample households.

In the course of identifying factors influencing farmer's decision to use improved bread wheat

varieties, the main task is to analyze which factor influence how and by how much. It is

hypothesized that adoption and intensity of adoption are influenced by the combined effect of

various determinants. There fore, in the following section potential variables that are supposed

to influence adoption and intensity of adoption of improved bread wheat varieties in the study

area will be explained. More specifically, the following potential explanatory variables

hypothesized to influence the adoption and intensity of adoption of improved bread wheat

varieties in the study area, on a priori grounds is indicated below:

1. Farmer’s age (HHHAGE) – As the farmer’s age increases it was expected that farmer

become conservative. Then it is hypothesized that the farmer’s age, adoption, and intensity of

adoption of improved bread wheat varieties are inversely correlated. Therefore, in this study it

was assumed that the lesser age group could adopt improved bread wheat varieties more than

the older age group farmers. Then, in this study farmer’s age and adoption are expected to

relate negatively. As farmers age increase probability of adoption/intensity of adoption is

expected to decrease.

2. Gender/Sex (HHHSEX) - It is hypothesized that male household headed farmers are

expected to adopt improved bread wheat varieties more than female headed ones. Because it is

expected that male-headed farmers have a better opportunity to access to credit and extension

service. In this study gender/sex was coded if the household is male 1 and 0, otherwise.

46

Adoption/Intensity of adoption was expected to increase and correlate positively as the farmer

being male.

3. House hold head Education (EDUHHH) – This represents the level of reading and writing

and formal schooling attended by the household headed. It is expected that educated

farmhouse hold head can make better decision to adopt improved bread wheat varieties than

non-educated ones. Here, education extends from read and write to attending regular school

education. In this study this variable was treated as a dummy variable and has coded if the

house hold head can read and write as well as attending the regular school education as 1 and

0, otherwise. Adoption was expected to correlate positively as education increases.

4. Family size (FAMILYSI) - household heads with large family size are less likely to adopt

improved bread wheat varieties due to risk aversion. In this study it had expected that the

family size, adoption, and intensity of adoption would have related inversely. As family size

increase adoption/intensity of adoption has expected to decrease.

5. Extension-services (GEXSERVE) - the more frequent DA visit, using different extension

teaching methods and training, attending demonstrations and field day can help the farmers to

adopt a new technology and can also increase the intensity of adoption. If the farmers get

better extension services are expected to adopt better-improved bread wheat varieties than

others. In this study this variable had treated as a dummy variable in that if the farmer get

extension service is coded as 1 and 0, otherwise. As extension service increase

adoption/Intensity of adoption was expected to increase and correlate positively.

6. Off- farm income (HHOFFINC) - the household head that have off farm income are

expected to adopt improved wheat varieties better than who have not off farm income. This

variable also was treated as a dummy variable that if the farmer has off-farm income coded 1,

otherwise, 0. As the number of farmers’ number increases to involve in off-farm work it was

expected to increases adoption positively.

47

7. Access to credit (GECRSERV) - It is expected that those who have better access to credit

can adopt improved bread wheat varieties than other who do not have access. Because it is

expected that credit can solve the financial limitation of farmers. The variable in this study

was treated as dummy variable in that, if the farmer gets credit service coded as 1 and 0,

otherwise. As credit service increase adoption/Intensity of adoption was expected to increase

and correlate positively.

8. Livestock ownership (TOTLIVUN) – house holds that have large number of livestock are

likely to adopt improved bread wheat varieties better than others who have less number of

livestock Because those who have better number of livestock can have better opportunity to

get credit. In this study it was assumed that livestock ownership and adoption would be related

positively. As livestock ownership increases adoption/intensity of adoption was expected to

increase and correlate positively.

9. Labor accessibility (OTSOLA1) – those farmers who have access to labor are expected to

adopt improved bread wheat varieties than those who lack labor accessibility since improved

varieties required more labor. The variable has been treated as a dummy variable in that if the

farmer has an access to labor coded 1, otherwise 0. As labor accessibility increases

adoption/Intensity of adoption was expected to increase and correlate positively.

10. Social/leadership status of the respondent (PRTILEDE) - those farmers who have

experience of leadership and better social status previously or currently are likely to adopt

wheat technologies than others. Because, it is expected that they have an opportunity to get

and interpret the information they get about improved bread wheat variety. The variable was

coded as 1 and 0, otherwise. As the number of farmers increase to involve in leadership

position adoption was expected to increase and to correlate positively.

11. Distances from extension agent office (DISDAOF1) – those who are closer to extension

agent are expected to adopt improved bread wheat varieties than others as a result of

48

accessibility. The variable was coded as 1 if the farmer is close to the DA’s office and 0,

otherwise/far. As distance of DA office increase adoption/intensity of adoption was expected

to decrease and correlate negatively. As distance of DA office decrease the correlation will be

vise versa.

12.Distance from input and credit supply institutions (CRINFAR1) – those farmers closer

is likely to adopt improved bread wheat varieties than those who are not close since they can

easily facilitate and follow up the credit process. This variable was treated as dummy variable

and had coded as 1 if the farmer is close, 0 if not close or if far. The far distance might affect

negatively and the close distance affects adoption and Intensity of adoption positively.

13. Oxen ownership (OXTLU) – those who have oxen for ploughing is likely to adopt

improved bread wheat varieties since they could solve ploughing power problem. Then, oxen

ownership and adoption were expected to relate positively. As the number of oxen owned by

farmers’ increased, adoption/Intensity of adoption was expected to increase.

14. Farmers experience in any extension activities (YEXPEXTS)- Farmers who have long

involvement in any agricultural extension activities is expected to use improved bread wheat

varieties than with less experience. Then, this variable was hypothesized to correlate and

influence positively improved bread wheat varieties adoption and the intensity of adoption.

15. Access to market (CRINMFF1) - Access to market was hypothesized to be positively

related to the probability of adoption of improved bread wheat varieties in that if the house

holds near to market tend to buy improved agricultural inputs including improved bread wheat

seed and they can have easy access to dispose of and sell their production in the market.

Therefore, the variable was treated as a dummy variable in that if the house hold has an access

to market has coded as 1and 0, otherwise. As market distance increases adoption and intensity

of adoption was expected to decrease.

49

16. Health status of the household head (HEALSTAT)-this is a dummy variable, which

takes a value 1 if the household head was healthy and 0, otherwise. As farmers’ health statuses

improve adoption and intensity of adoption of improved bread wheat varieties was expected to

increase and correlate positively.

17. Farm land holding (SUMOWRE)-farmer who has large farm size is likely to adopt

improved varieties than those who have lesser farm size. Because farmers with large farm size

can distribute the yield loss risk and better land ownership serve as insurance to get credit

which can use to purchase improved agricultural inputs. As farmers farmland holding

increases adoption/intensity of adoption might increase and correlate positively.

50

4. RESULTS AND DISCUSSION

4.1. Analysis through descriptive statistics

This study was intended to examine the farmers’ evaluation and selection criteria of improved

bread wheat varieties and to identify factors affecting adoption and intensity of adoption of

improved bread wheat varieties in the study area Akaki as well as to know the effect of

hypothesized independent variables on the dependent variables. In this section of analyses

descriptive statistics such as mean standard deviation, percentage, frequency tabulation, t-test

and chi-square test will be employed using SPSS- computer soft ware program.

In this study, adoption of a technology refers to a continued use of the technology on an area

of land, which is large enough to contribute to the economy of the household. Here, the

respondents who have cultivated improved bread wheat varieties and continued growing at

least one of the distributed improved bread wheat varieties in the study area during the survey

year and in any one of the years before the survey year of this study are considered as

adopters. Farmers who never adopted and those who discontinued from growing of improved

bread wheat varieties are categorized as non-adopters.

4.1.1. Sample Households’ Demographic Characteristics

In order to understand the sample households, it is very important to describe their

demographic characteristics. The number of household head respondents was from two

selected Rural Kebele Administrations or Peasant Associations namely Koye and Gelan-

Edero. The sample house hold heads covered in this study from Koye PA/RKA were (76)

89.41% male and (9) 10.59 % female with a total of 85 constituted 56.67% which of the total

sample and (56) 86.15% male and (9) 13.85% female with a total of 65 were from Gelan-

Edero PA/RKA which constituted (43.33%) of the total sample as presented in Table 7.

51

Out of the total 150 respondents in the sample, adopters were 99 (66%) and non-adopters were

51 (34%). From 150 sample respondents, 132 (88%) were male and 18 (12%) were female

respondents. From 132 male respondents 92 (69.70%) were adopters and 40 ((30.30%) were

non-adopters. From total 18 female sample respondents 7 (38.89%) were adopters and 11

(61.11%) were non-adopters as presented in Table 7.

Table 7.Sample household heads distribution by Sex, Kebele and adoption category

Adopters (99) Non-Adopters (51) Total (150)

Samples N Percent N Percent N Percent

Male-sample 92 92.93 40 78.48 132 88

Female-Sample 7 7.07 11 21.57 18 12

Total 99 100.00 51 100.00 150 100.00

Koye-Kebele 63 63.64 22 43.14 85 56.67

Gelan-Edero 36 36.36 29 56.86 65 43.33

Total 99 100.00 51 100.00 150 100.00

Koye Kebele

Male

Female

Total

58

5

63

92.06

7.94

100.00

17

5

22

77.27

22.73

100.00

76

9

85

89.41

10.59

100.00

Gelan-Edero Kebele

Male

Female

Total

32

4

36

88.89

11.11

100.00

24

5

29

82.76

17.24

100.00

56

9

65

86.15

13.85

100.00

(Source: Computed from own survey data, 2005)

Out of the total 99 adopters 92 (92.93%) were male and 7 (7.07%) were female and from the

total 51 non-adopters 40 (78.43%) were male and 11 (21.57%) were female. The numbers of

sample household heads from Koye PA were 63 adopters and 22 non-adopters, where as from

52

Gelan-Edero PA, the number of sample household head adopters was 36 and that of non-

adopters were 29 as indicated in Table 7.

In similar studies in the past, the major reasons for farmers to adopt improved and new

technologies are technical, institutional, social, and economical reasons. Farmers do not adopt

a technology if they are not convinced of its benefits, costs and risk associated with it. By

seeing their fellow farmers, by attracting of high yield performances of improved varieties,

market demand as well as DA’s information and extension support farmers might urged and

motivated to use and adopt improved varieties. On the other hand, the major reasons for those

non-adopters might shortage of improved varieties, credit problem, or cash, land, labor or

other farm resource constraints (Legesse et al., 2005).

Table 8.Marital status of respondents

(Source: Computed from own survey data, 2005)

The marital statuses of respondents are summarized in Table 8 as (121) 80.67% married, (3)

2% unmarried or single, (6) 4% divorced and 20 (13.33%) widow/er. The proportion of

married respondents was much larger than the remaining marriage categories. As indicated in

Table 8, the married adopters were 85.86 percent and that of non-adopters were 70.59 percent.

The remaining categories of respondents constituted fewer proportions of respondents both

in adopters and non- adopters.

Adopters (99) Non-Adopters (51) Total (150) Marital

status N Percent N Percent N Percent

Married 85 85.86 36 70.59 121 80.67

Un-married 1 1.01 2.00 3.92 3 2.00

Divorced 2 2.02 4.00 7.84 6 4.00

Widow/er 11 11.11 9.00 17.65 20 13.33

53

Table 9.Association between adoption of improved bread wheat varieties and sex of sample

household head

House hold

head sex

Adopters Non-adopters Chi-

square

C.Coef df Sig. Total

Female 7 (7.1%) 11 (21.6%) 18 (12%)

Male 92 ((92.9%) 40 (78.4%) 132 (88%)

Total 99 (100%) 51 (100%) 6.700*** 0.207 1 0.01 150 (100%)

(Source: Computed from own survey data, 2005), *** significant at 1%level

Table 10. Respondent farmers’ demographics characteristics

Adoption

Category

Summary of

statistics

House hold

head Age

House hold

family size

House hold head

experience in extension

Farming

experience

Minimum 19 1 2 2

Maximum 80 11 20 55

Adopters

Range 61 10 18 53

Minimum 20 1 1 4

Maximum 80 10 9 60

Non-

adopters Range 60 9 8 56

Minimum 19 1 1 2

Maximum 80 11 20 60

Total

Range 61 10 19 58

(Source: Computed from own survey data)

In adoption of new agricultural technologies, farmers’ age has an influential effect as it was

observed in many adoption studies. The maximum and the minimum ages of total respondents

were 80 and 19 years respectively. The adopters’ maximum age was 80, which was equal to

the non-adopters maximum age. The minimum age of adopters was 19 years and that of non-

adopters was 20 years. The age variation between maximum and minimum age of adopters,

non-adopters and that of total respondents were 61, 60 and 61 respectively as presented in

Table 10.

54

The respondents' maximum years of experience in extension were 20 for adopters 9 for non-

adopters and 20 years for total samples. The minimum years of experience in extension for

adopters was 2 years, for non-adopters 1 year and for total respondents was 1 year. The

variations between maximum and minimum years of experience in extension were 18 years for

adopters, 8 years for non-adopters and 19 years for total respondents as presented in Table 10.

The maximum and minimum farming experience for adopters were 55 and 2 years, for non-

adopters 60 and 4 years and for that of total respondents were 60 and 2 years. The variation

between maximum and minimum total farming experience of adopters was 53, non-adopters

were 56 and that of total respondents was 58 years as indicated in Table 10.

The maximum and minimum family size of adopters respectively were 11 and 1 for adopters,

10 and 1 for non-adopters and 11and 1 for total samples. The variation between maximum and

minimum family size was 10 for adopters, 9 for non- adopters and 10 for that of total

respondents as indicated in Table10.

Table 11.Adopters and non-adopters’ demographic characteristics

Adopters (99) Non-Adopters (51)

Characteristic Mean SD Mean SD

T-test

Significance

Level (2-tailed) Age 46.10 13.256 46.47 14.53 0.157 0.876

Family size 5.85 2.192 5.10 2.385 1.927* 0.056

Experience in

Extension (years)

7.87 4.787 3.765 1.784 -0.907*** 0.000

Farming

Experience (years)

21.90 11.08 20.80 10.98 -0.596 0.552

***and* significant at 0.01and 0.10 p-value respectively.

The average age of adopters, non-adopters and total respondents were, 46.10, 46.47 and 46.23

years respectively. The S.D (Standard Deviation) of adopters, non-adopters and total

respondents ages were 13.256, 14.53 and 13.655 respectively as indicated in Table 11.T-test

55

statistics was run to check whether there is a significant mean difference in age between

adopters and non-adopters. The result of t-test showed that there was no statistically

significant mean age difference.

The respondents’ average/mean and S.D (Standard Deviation) family size of adopters, non-

adopters and total respondents were 5.85, 5.10 and 5.59 respectively as indicated in Table 11.

T-test statistics was run to know whether there is statistically significant variation in average

family size between adopters and non-adopters. The result of t-test analysis showed that there

was statistically significant difference in average family size at 10 percent probability level as

indicated in Table 11.

The respondents’ average (mean) and S.D (Standard Deviation) of experience in extension and

total farming experience in years is presented in Table 11. The average years of experience in

agricultural extension as well as the total farming experience of adopters were 7.87 and 21.9

that of non-adopters were 3.765 and 20.8 and the total respondents experience in extension

were 4.485 and 11.023 years respectively. T-test was conducted to see the variation in average

years of experience in agricultural extension and in total farming experience between adopters

and non-adopters. The result of t-test analysis showed that there is a significant difference in

average years of experience in agricultural extension participation involvement at 1 percent

probability level as indicated in Table 11.

But the total farming experience was not significant in t-test analysis. Because those farmers

who have better experience in extension could got better extension service that help them to

adopt better improved bread wheat varieties.

From the total sample respondents 103 (68.67%) were involved in improved bread wheat

production, while the remaining 47 (31.33%) respondents were not involved in improved

bread wheat production during the survey year. Their reasons why they were not involved had

summarized and presented in Table 12 from the response they gave during the interview.

56

Table 12.Reasons given for not using improved bread wheat varieties

No

Reasons limiting to involvement in

improved bread wheat production

N (47) Percent Rank

1 Farm land shortage 15 32.00 1st

2 Lack of information 9 19.15 2nd

3 Fertilizer shortage 6 12.77 3rd

4 High price of fertilizer 6 12.77 ,,

5 Lack of extension support 4 8.51 4th

6 Labor problem 3 6.38 5th

7 Seed scarcity 2 4.25 6th

8 Lack of ploughing oxen 2 4.25 ,,

(Source: Computed from own survey data, 2005)

Farmland shortage and lack of information were the two most important reasons that limit

farmers in the study area. The remaining reasons were less important for farmers to adopt

improved agricultural practices as indicated in Table 12.

Table 13.Level of awareness of improved bread wheat varieties

Aware Not aware Total Improved bread wheat

varieties N Percent N Percent N Percent

HAR-1685 (Kubsa) 123 82 27 18 150 100

HAR-1709 (Mitike) 54 36 96 64 150 100

Paven-76 115 76.67 35 23.33 150 100

(Source: Computed from own survey data, 2005)

57

Concerning respondents’ awareness of improved bread wheat varieties, interviews were

conducted. About (123) 82% respondents knew HAR-1685, (115) 76.67% knew Paven-76

and 54 (36%) knew HAR-1709 variety. As indicated in Table 13, HAR-1685 was known by

larger proportion of respondents followed by Paven-76 and HAR-1709 was the least known

variety by respondents.

Table 14.Sample Farmers perception on benefit of fertilizer

1 Perception on fertilizer benefit N Percent

1.1 Low profit 98 65.33

1.2 No loss or no profit 26 17.33

1.3 High profit 14 9.33

1.4 Very high profit 9 6.00

1.5 Encountered loss 3 2.00

Total 150 100 2 Perception on fertilizer Problems N Percent

2.1 High price (high interest rate) 75 50

2.2 Un-timely and lately arrival 56 37.33

2.3 Credit scarcity and credit service related problems

to purchase fertilizer

19 12.67

Total 150 100

(Source: computed from own survey data, 2005)

Respondents were interviewed to know their opinion based on their experience about the

benefits obtained from fertilizer use. About 65.33% low profit, for 17.33% reported no loss or

no profit for 9.33% high profit, for 6% very high profit could be obtained and 2% said

encountered loss. In this study the larger proportion of farmers reported the low profit from

fertilizer as indicated in Table 14.

Respondents were also interviewed to get their idea on problems related to fertilizer in their

area. About 50% of respondents have reported high price, 37.33% reported un-timely and late

arrival and about 12.67 % reported credit scarcity and credit service related problems to

58

purchase fertilizer as indicated in Table15. The problems arisen due to weakness of the credit

provider institutions and less attention of the government as observed during data collection

time.

Table 15.Beginning time of cultivation of improved bread wheat varieties of sample farmers

No Starting Years N Percent

1 Before 2000 9 8.74

2 During 2000 10 9.71

3 ,, 2001 27 26.26

4 ,, 2002 28 27.30

5 ,, 2003 27 26.26

6 ,, 2004 2 2.02

Total 103 100

(Source: computed from own survey data, 2005)

In this study to know their commencement or beginning time of using improved bread wheat

production, respondents were interviewed and their responses were summarized in Table 16.

Table 16.Health status and adoption of improved bread wheat varieties

Health status Non- adopters Adopters χ2-test df Co coef Sig. Total

Un-healthy 7 (13.73%) 7(7.07%) 14(9.33%)

Healthy 44 (86.27%) 92 (92.93%) 136(90.67%)

Total 51(100%) 99(100%) 1.762 1 0.108 0.184 150(100%)

(Source: Computed from own survey data, 2005); Co coef =contingency coefficient

To accomplish the agricultural activities as required, the farmers need to be healthy. In this

study, it was tried to assess the household head respondents’ health situation. The respondents

were grouped into healthy and un- healthy farmers (those who face a health problem) to

59

accomplish their day-to-day agricultural activities. From total adopters the healthy farmers

were 92.93% and that of unhealthy were 7.07%. In the case of non-adopters 86.27% were

healthy and 13.73% were unhealthy. Out of 150 respondents, (136) 90.67% were fully healthy

and the remaining (14) 9.33 % had health problem. To check the relationship of the health

situation of the respondents and adoption of improved bread wheat varieties, a chi-square test

was conducted and the result showed that the relationship between health status and adoption

of improved bread wheat varieties was not statistically supported and insignificant as indicated

in Table 16.

Table 17.Sample household educational status

Education NA Ad X2 df Co. coef Sig. Total

Illiterates

Literate

33(64.70%)

18(35.30%)

64(64.65%)

35(35.35%)

97(64.67%)

53(35.33%)

Total 51(100%) 99(100%) 0.000 1 0.001 0.994 150(100%)

(Source: Computed from own survey data, 2005);

* NA=Non-adopters, Ad=Adopters;

*Co coef =contingency coefficient.

Education is very important for the farmers to understand and interpret the information coming

from any direction to them. Farmers’ education is also pivotal for the effective work of

extension personnel because if the farmer has better education status he/she can has a

capability to understand and interpret easily the information transferred to them from

Extension Agent (EA). From total non-adopters 35.30% were literates and 64.70% were

illiterates. In the case of adopters 35.35% were literates and that of 64.65 % were illiterate.

The proportion (percentage) of illiterate adopters and non-adopters as well as that of literate

adopters and non-adopters was almost equal as indicated in the Table 17.

60

In this study the literacy was extended from read & write to attending regular school

education. To see the relationship and the intensity of relationship, the chi-square- test was

conducted. But the result of chi-square- test was not statistically significant as indicated in

Table 17. This means there is no any significant difference in adoption between adopters and

non-adopters due to education.

4.1.2. Respondents` livestock and land ownership

In the study area mixed farming is practiced with crop and livestock production. Each

household owns at least one or more types of livestock and a piece of land for crop and

livestock production.

Livestock in the study area provides traction and manure and also serves as a source of

income through sale of livestock and livestock products. Livestock also serves as a source of

fuel in the study area. Crop residue and by-products serve as livestock feed source.

As it confirmed in many studies farmers who have better livestock ownership status are likely

to adopt improved agricultural technologies like improved bread wheat varieties; because,

livestock can provide cash through sale of them and their products and draught power for

agricultural operations. In this study, it was revealed that the average livestock ownership of

adopters and non-adopters in TLU were 6.834 and 5.02 respectively.

To know whether there is a variation in average livestock ownership between adopters and

non- adopters and as a result if there is any significant difference due to the resource position,

t-test was conducted. The result of t-test showed that there is a significant variation in average

livestock ownership between adopters and non-adopters at one percent probability level as

indicated in Table 18 and the average oxen ownership of adopters was also significantly larger

(3.03) than non-adopters (2.157) at 5 percent probability level as indicated in Table 18. As t-

test indicated, adopters had larger livestock and oxen ownership as compared to non-adopters.

61

This implied that large ownership of oxen and livestock can help farmers to adopt agricultural

innovations by solving the power force need for improved wheat production practices and cash

constraint by providing income from sale of livestock and their by products to purchase

agricultural inputs.

Table 18.Livestock and land ownership of respondents’ farmers

Ads NAs Characteristics Mean SD Mean SD

T-test Significance

(2-tailed)

Livestock

ownership (TLU)

6.8340 3.2724 5.0200 3.2120 -3.236*** 0.001

Oxen owner ship 3.0300 1.6317 2.1570 1.6294 -3.107** 0.002

Improved Bread

wheat land (ha)

0.9621 0.4579 0.0400 0.1759 -13.859*** 0.000

Total wheat land 1.2400 0.6358 0.8530 0.3846 -3.983*** 0.000

Total farm land (ha) 2.7141 1.08 2.0147 1.121 -3.710*** 0.000

Total land (ha) 3.01 1.25 2.28 1.25 -3.382*** 0.001

*** and ** Significant at 1 and 10 percent probability level

Sample farmers vary in their adoption of improved bread wheat varieties by their livestock

and oxen ownership. Adopters average livestock ownership was significantly larger than non-

adopters. This indicate that livestock ownership help farmers to adopt improved bread wheat

varieties since the income from livestock obtained through selling of the animals or their by

products can help to solve their financial limitation s to purchase inputs.

Land is the main asset of farmers in the study area. Farmers in the study area use both their

own land and rent farm land for crop production and grazing land for livestock production .All

150 sample households have their own land and only (24) 16% and (2) 1.33% respondents

rented cultivated and grazing land respectively. The average land holding of adopters was 3.01

hectares total average land holding, 2.7141 hectares total average farmland, 1.24 hectares total

average wheat land and 0.9621 hectares was average farm land used for improved bread wheat

62

production. In the case of non- adopters the total average land holding was 2.28 hectares,

2.0147 hectares was average farmland, 0.8530 hectares was average wheat land and only

among improved bread wheat growers of non-adopters was 0.0400 hectares in average was

used for improved bread wheat production. To know whether there is the mean land holding

variation, between adopters and non-adopters, t-test analysis was carried out and the result

showed that there were the significance differences in all types of land holding at one percent

probability level as indicated in Table 18.The result showed that farmers who have better land

ownership can adopt improved bread wheat varieties better than non-adopters.

Table 19.Respondents land ownership in 1996/97 Ethiopian major cropping season

Area of land ownership in hectares

Max Min Range Average St.D

Total farm Land 6.5 0.25 6.25 2.44 1.13

Total wheat land 4.5 0.25 4.25 1.125 0.605

Total improved B.W.L. 3.25 0.25 3 .00 1 .00 0.457

(Source: Computed from own survey data, 2005)

* N.B=Improved B.W.L. (Improved Bread Wheat Land)

The total farmland ownership of respondents ranges from 0.25 hectares to 6.5 hectares. The

total wheat land ownership of sample households ranges from 0.25 hectares to 4.5 hectares.

The improved bread wheat land holding of sample household ranges from 0.25 hectares to

3.25hectares.

On the average sample households owned 2.44 hectares total farmland, 1.13 hectares total

wheat lands and 0.96 hectares used for improved bread wheat production as presented in Table

19. In the study area respondents farmers allocated most of their farmland for wheat

production as presented in table one.

63

4.1.3. Accessibility of respondents to different institutional services

In this study respondents were interviewed to get their opinion about the importance of

extension services based on their experiences. About 84.67, 3.33% and 12% respondents have

reported important, not important and do not have any opinion respectively.

The respondents have also been interviewed to give their opinion about the extension support

they obtained. About 46.67% reported extremely weak due to un-availability of development

agent, about 27.33% reported very weak even though the Development Agents are available

around. The remaining 26% responded that the extension service they got in their area

becomes extremely weak due to unknown reasons for them as indicated in Table 20.

Table 20.Respondents’ opinion on extension service of the study area

No Respondents opinion on extension service N Percent

1. About importance of extension service

1.1. Important 127 84.67

1.2. Do not have any idea 18 12

1.3. Not important 5 3.33 Total 150 100

2. Status of extension service of the study area

2.1. Extremely weak due to un-availability of DA 70 46.67

2.2. Very weak even though the DA available 41 27.33

2.3. Extremely weak due to un-known reasons for them 39 26

Total 150 100

(Source: Computed from own survey data, 2005)

Data were collected regarding the type extension service obtained by the respondents as

indicated in Table.20. The whole non-adopters and 94.95% of adopters did not get extension

service during the survey year on improved bread wheat variety. As indicated in Table 21 only

64

five respondent farmers reported that they got extension support. The respondents were also

interviewed to get their opinion on the distance of DA’s office from their home. As indicated

in table 22 about 90% reported far and the remaining 10% reported close to their home.

Table 21.Extension support on improved bread wheat varieties and distance of DA’s office

Service accessibility Responses NAs Ads X2 df Sig. C.coef

.

Total

No 51 94 145

Yes - 5 5

Extension service on

bread wheat

Total 51 99 2.665* 1 0.103 0.138 150

Far 48 87 135

Close 3 12 15

Distance of DA office

Total 51 99 1.456 1 0.228 0.098 150

*Significant at 10 % probability level. ; Ccoef = Contingency coefficient

*NAs =Non-adopters, Ads = Adopters, df =Degree of freedom.

To know the association of extension service and distance of DA office with adoption of

improved bread wheat variety, chi-square analysis was conducted. The result of chi-square

analysis (2.665) showed that there is a significant association between extension service and

adoption of improved bread wheat varieties at 10 percent probability level. But the chi-square

–test result of distance of DA office and adoption of improved bread wheat varieties was not

significant as indicated in Table 21.

As it has indicated in many literatures, credit is considered as one of the favorable factors for

improved agricultural technologies adoption because it can solve financial constraints of

farmers to purchase and use improved agricultural inputs. Respondent farmers have reported

about credit institution services and related problems in their area based on their experience.

Of that, 83 (55.33%) have reported that there is scarcity, 26 (17.33%) reported that there is a

complex and boring procedures and the remaining 41(27.34%) reported that there is a high

interest rate problems as indicated in Table 22.

65

Table 22.Summary of respondents’ opinion on credit

(Source: Computed from own survey data, 2005)

Respondent farmers were also interviewed to provide their opinion about the importance of

credit and their suggestions for better credit service in the future. The respondents ’ responses

on these issues were summarized and presented in Table 22. About 67.33% respondent

farmers reported that credit is important and the remaining 32.67% reported that credit is not

important.

From those 101 respondent farmers who supported the credit service as important also

provided their opinion for better credit service. About (59) 58.42% reported reduced processes

and procedures and about (42) 41.58% suggested to reduce the interest rate as indicated in

Table 22.

No Responses on credit related problems N Percent

1 Scarcity 83 55.33

2 High interest rate 41 27.34

3 Complexity of procedures 26 17.33

Total 150 100.00

Responses on the importance of credit

1 Credit is important 101 67.33

2 Credit is not important 49 32.67

Total 150 100

Suggestions for better credit services

1 Easy and Reduced procedures 59 58.42

2 Low Interest rate 42 41.58

Total 101 100

66

As presented in Table 22 from the total 150 sample respondents, only 26 adopters and 8 non-

adopters with a total of 36, which constituted 24% of the total respondents, were credit users

particularly from individual credit sources in the mean time when this study conducted. In

credit utilization, adopters were larger in proportion than non-adopters. Almost all of the credit

users of sample respondents have reported that the major credit sources for them were

informal and private lenders.

Table 23.Association between credit and market service

Accessibility Response NA Ad X2 df Sig. Co.coe Total

No 43(84.31) 71(71.72) 114

Yes 8(15.69) 28(28.28) 36

Credit Service

Total 51(100) 99(100) 2.928* 1 0.087 0.138 150

Far 43(84.31) 89(89.90) 132

Close 8(15.69) 10(10.10) 18

Market Access

Total 51(100) 99(100) 0.994 1 0.319 0.081 150

*Significant at 10% probability level; Numbers in brackets are in percentage

To see the association between adoption of improved bread wheat varieties and credit service,

a chi-square test was carried out. The result showed that there is a significant relationship

between adoption of improved bread wheat varieties and credit services at 10 percent

probability level as indicated in Table 23. Market accessibility is also another important factor

for farmers to adopt improved agricultural inputs. If farmers are closer and having access to

credit services they can easily purchase improved agricultural inputs and sell their agricultural

outputs without moving long distances. Farmers also motivated to use improved agricultural

inputs if they have access to attractive market for their output to sell in good price. In this

study respondent farmers were interviewed to provide their idea regarding the market

accessibility. About 84.3% non- adopters and about 89.90% have reported far from market and

the remaining 15.69% non-adopters and 10.10% adopters reported close to market access as

67

indicated in Table 23. A chi-square-test analysis was carried out to check the association

between market access and adoption of improved bread wheat varieties. The result showed

that the relationship was not statistically significant as indicated in the Table 23.

Table 24.Summary of households’ accessibility of off-farm job

No Respondent farmers access to off-farm job N Percent

1 Have access to off-farm job 26 17.33

Adopters 17 65.38

Non-adopters 9 34.62

2 Have not access to off-farm job 124 82.67

Adopters 82 66.13

Non-adopters 42 33.87

Total 150 100.00

(Source: computed from own survey data, 2005)

Income from off-farm job can play a great role in adoption of improved agricultural

technologies. Because, it has hypothesized that the income from off-farm can solve farmers’

financial constraints to purchase and use improved agricultural inputs. In this study about

17.33% sample households reported that one of their family members has off-farm job and the

remaining 82.67% do not have family members who have off-farm job. From the total 26

sample households that have off-farm job 65.38% were adopters and the remaining 34.62%

were non-adopters as indicated in Table 24.

68

Table 25.Respondent farmers’ reasons for not involvement of their family in off-farm job

(Source: Computed from own survey data, 2005)

From the total 124 sample respondents whose family members were not involved in off-farm

activities had reported their reasons during the interview why the house hold members did not

involve in off-farm job. As indicated in Table 25, about 33.87% have reported that their family

members couldn’t involve in off-farm job due to the under and over age, 9.68% reported that

their family members are students, 41.94% described the time constraint since the household

members would work on the house hold farm, 12.9% reported that they do not have family

members and 1.61% reported less income from off-farm job.

Table 26.Rrespondents opinion on decision of off-farm and other household resources

No Respondents’ opinion on house holds’

resources decision maker

N Percent

1 Husband 121 80.66 2 Husband and wife 27 18.00

3 Wife 1 0.67

4 House hold members together 1 0.67

Total 150 100.00

:

(Source: Computed from own survey data, 2005)

No Reasons N Percent

1 Under and over aged 42 33.87

2 Students 12 9.68

3 Work on the house hold farm 52 41.94

4 Do not have family members 16 12.90

5 Less income from off-farm job 2 1.61

Total 124 100

69

Concerning the decision on the off-farm and other agricultural income, the household head

respondents were interviewed. All of them were reported that the house hold member who

involved directly in the off-farm job make a decision for what purpose the income from off-

farm job need to be used. But concerning the other agricultural incomes, from the total 150

respondents about 80.66%, 18%, 0.67% and 0.67% reported that the decision were made by

husband, by husband and wife, by wife and by the house hold members together, respectively

as indicated in the Table 26. In this study, almost all decision on the agricultural resources of

the farming household made by the husband.

Table 27 Pattern of off-farm income utilization of respondent farmers

No Use of off-farm income N Percent

1 Household food consumption 7 26.92

2 Cloth purchase 10 38.46

3 Health treatment 5 19.23

4 Input purchase 3 11.54

5 Labor hiring 1 3.85

Total 26 100.00

(Sources Computed from own survey data, 2005)

On the use of off-farm income, the Total 24 household respondent farmers who themselves

and their family members had off-farm job reported about their and their family members off-

farm income utilization. About 26.92 %, 38.46 %, 19.23%, 11.54% and 3.85% reported for

household food consumption, cloth purchase, health treatment, input purchase, and labor

hiring purposes respectively as indicated in Table 27. In this study the order of importance in

off-farm income utilization from higher to the lower were, for cloth purchase, food

consumption, health treatment, labor hiring and input purchase. Allocation of off-farm income

to agricultural input purchase took the least proportion.

70

Table 28.Family labor utilization of respondent farmers

No Family labor utilization Ad NA Total Percent

1 Utilized family labor 93(93.94) 44(86.27) 137 91.33

2 Not utilized family labor 6(6.06) 7(13.73) 13 8.67

Total 99(100) 51(100) 150 100

(Source: Computed from own survey data, 2005)

In this study the respondent respondents` labor source and their family labor utilization were

revealed through interview. From the total 150 respondents, about (137) 91.33% have reported

that they used their family labor on their farm activities for weeding, harvesting, threshing,

plowing and sowing as indicated in Table 28.

Table 29.Types of activities and family labor utilization of respondents

(Source: Computed from own survey data, 2005)

No 1.Activities on which family labor used N Percent

1.1 Ploughing 5 3.65

1.2 Sowing 2 1.46

1.3 Weeding 120 87.59

1.4 Harvesting, 7 5.11

1.5 Threshing 3 2.19

Total 137 100 No 2.Critically labor required activities N Percent

2.1 Weeding 109 72.66

2.2 Sowing 28 18.67

2.3 Ploughing 13 8.67

Total 150 100

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Out of these sample households who used their family members’ labor on the household farm,

about 67.9% were adopters and the remaining 32.10% were non-adopters. Respondents were

also interviewed to describe the type of agricultural activities they used their family labor.

From the total 137 respondents who used their family labor on their farm, about 87.59%,

5.11%, 2.19%, 3.65%, and 1.46% reported for weeding, harvesting, threshing, plowing and

sowing respectively. Concerning the critical labor requirements of the respondents labor

requirement were about 8.67% were for plowing and sowing, 18.67% for weeding, and

72.66% were reported for harvesting and threshing as indicated in Table 29.

Table 30.Respondents’ accessibility to non-family labor and to off-farm income

Access to Response NA Ad X2 df Sig. C.coef Total

No 48(94.12) 87(87.88) 135

Yes 3(5.88) 12((12.12) 15

Labor

outside the

house hold

labor Total 51(100) 99(100) 1.456 1 0.228 0.098 150

No 42(82.35) 82(82.83) 124

Yes 9(17.65) 17(17.17) 26

Access to

off-farm

income Total 51(100) 99(100) 0.005 1 0.942 0.006 150

(Source: computed from own survey data, 2005)

* Numbers in brackets are in percentage

To check the association between off-farm income of the sample household and adoption of

improved bread wheat varieties chi-square analysis was carried out and the result showed that

there is no a systematic association statistically supported between adoption of improved bread

wheat varieties and off-farm income as revealed in this study as the result presented in Table

30. To see the labor source and accessibility of respondents to labor outside the house hold

labor and its relation ship with adoption of improved bread wheat varieties, chi-square test was

conducted and the result showed that the two variables, adoption and the utilization of labor

outside the household labor is not statistically significant as indicated in Table 30.

72

Table 31.Respondent farmers labor sources outside their family members

(Source: Computed from own survey data, 2005

Respondents were also interviewed for their labor source other than their family labor. About

53.33%, were reported that they used hire or employed labor, about 27.33% reported as they

used exchange labor, 10% were report that they used support labor from relatives and

colleagues and 9.33% were reported that they do not used labor outside their family labor

source. The respondents’ labor source outside the family members’ labor, employed and

exchange labor was very important. In the study area the agricultural activities required the

higher labor are harvesting, threshing and weeding as indicated in Table 31.

4.1.4. Agricultural information sources of the study area

Access to information or extension messages as well as various extension services was one of

the institutional characteristics hypothesized to influence farmer’s decision to adopt a new

technology. One can gain access to information about new technologies through various

means such as attending field days, visiting demonstration fields, participating training,

listening to agricultural programs on radio, through contact with Extension or Development

Agents, and through various forms of communication with neighbors, relatives, other

colleague farmers and leaders of community, religious and PA (Peasant Associations) and

through other means (Tesfaye et al, 2001).

No Types of labor sources N Percent

1 Employed labor 80 53.333 2 Exchange labor 41 27.333

3 Relatives and colleagues support 15 10

4 Not used labor outside their family labor 14 9.333 Total 150 100

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As shown in various literature, different extension methods such as training, demonstration,

farmers’ field day, farmers meetings (mass-meeting), group discussions, posters, mass

communication methods and other extension methods have described that can be employed to

transfer extension messages to the farmers. Using and practicing these extension methods

properly to transfer extension messages can facilitate diffusion and adoption of improved

agricultural inputs.

In this study the improved bread wheat grower respondents were interviewed to give their

opinion how they got extension messages regarding the utilization and application of

improved bread wheat varieties production and management. Out of 103 respondent farmers

who grew improved bread wheat during the survey year, only (4) 3.88% were non-adopters

and the remaining (99) 96.12 were adopters. From 99 total adopters only (3) 3.03% reported

that they got training on improved bread wheat varieties production and management as

presented in Table 32.

As shown in the Table 35 about 100 of improved bread wheat growers (adopters and non

adopters) who do not got training were interviewed how they precede the improved bread

wheat varieties production. About 89% of improved bread wheat growers reported by seeing

other grower farmers, (copying mechanism or farmer to farmer extension), about 8% reported

by trial and error method and the remaining growers reported by asking the help of

Development Agent and other educated people living in their area.

To know the field day and demonstration program participation of Improved bread wheat

growers, their interview responses had summarized as indicated in Table 32., only 11.77%

respondents told that they got an opportunity to attend field day and demonstration program.

But the remaining (91) 89.23% had not have it. Out of these 12 respondents only 8.33% were

non-adopters and 91.67% were adopters.

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Table 32.Respondents’ participation in training, field day and demonstration

No Training, field day and

demonstration participation

Adopters (99) Non-adopters (4) Total (103)

1. Training participation Total (103)

1.1. Attend training 3 (3.03%) - 3 (2.91)

1.2. Not attending training 96 (96.97%) 4 (100%) 100 (97.09%)

2. Field day and Demonstration

program participation

Total 103

2.1. Attending training 11 (91.67%) 1 (8.33%) 12 (11.77%)

2.2. Not attending training 88 (96.7) 3 (3.3%) 91 (88.23 %)

3. Attending extension meeting

called by DA

Total 150

3.1. Feel happy to attend - - 17 (11.33%)

3.2. Un-happy - - 67 (44.67%)

3.3. No feeling - - 66 (44 %)

(Source: Computed from own survey data, 2005)

The respondent farmers were also interviewed to know their feeling when called by DA for

extension meeting Regarding their feeling they were show when they receive DAs call for

extension meeting; about 11.33%, 44.67% and 44% were reported that they feel happy, un-

happy and did not have any feeling on this issue respectively as indicated in Table 32.

In the study area respondent farmers were interviewed to provide their idea regarding their

agricultural information sources. As it is presented in Table 33, neighbors and colleague

farmers, DA, community leaders, farmers’ field day, PA leaders, demonstrations, radio,

newspaper/news letters, publications, posters, training programs, TV and religious leaders

have served as sources of general and agricultural information sources for them.

75

Table 33.Respondent farmers’ sources of information

(Source: Computed from own survey data, 2005)

As in Table 33 presented farmers’ neighbor and colleagues are the major and the firs important

farmers’ source of information. This survey result is similar with the result of group discussion

conducted in this study. According to this study DA serve as the second information source.

The survey result showed that the third and fourth sources of information are community

leaders and farmers’ field day respectively. As showed in the Table 33 PA leaders,

demonstration and radio serve as fifth source of information. Newspaper/news letter and other

publications serve as sixth information source. The remaining, poster, training TV and

religious leaders serve as seventh, eighth, ninth and tenth sources of information respectively

as indicated in Table 33.

Information Sources N Percent Rank

Neighbors and colleague farmers 138 92 1st

DA 136 90.67 ,,

Community leaders 133 88.67 2nd

Farmers field day 126 84 3rd

PA leaders 123 82 4th

Demonstration 123 82 ,,

Radio 123 82 ,,

News paper/News letter 122 81.33 5th

Other publication 122 81.33 ,,

Poster 107 71.33 6th

Training 105 70 7th

TV 104 69.33 8th

Religious leaders 101 67.33 9th

76

In this study, it was also tried to summarize the agricultural information sources of the farmers

in the study area through group discussion. During the time of group discussion the group

members were familiarized to the discussion point and were expected to identify and prioritize

the agricultural information sources of farmers in their area .The group members took care in

listing of all alternative sources of information available in their area using brain storming

method and tried to refined, summarized and prioritized the listed alternative information

sources listed through brain storming method.

The result of the group discussion showed that; a neighbor stands first and the most important

and TV stands the last and least important. The result of the group discussion findings showed

that farmers got more information easily from their neighbors than other sources available in

their area. The second most important information sources of farmers in the study area were

religious and community leaders. PA leaders and DAs serve as third and fourth respectively

as sources information. Demonstration and field day training and posters serve as fifth, sixth

and seventh sources of information respectively. The remaining, publications, radio and TV

serve as eighth, ninth and tenth sources of information respectively for the farmers in the study

area.

4.1.5. Farmers’ selection and evaluation criteria of improved bread wheat varieties

Varieties characteristics play a vital role in adoption of improved varieties if their

characteristics satisfied the need, interest and in line with the environmental situations of the

farmers. The information on evaluation and selection criteria of improved bread wheat

varieties of the farmers in the study area was analyzed through personal interviews and group

discussion. The procedure to analyze the information through group discussion was conducted

as; first make familiar farmers to the discussion agenda, and let them to establish and set

evaluation and selection criteria of improved bread wheat varieties disseminated in their area.

In the process of setting and establishing the criteria the group were applied the method of

brain storming and list down all the ideas provided and forwarded by the group members .The

group continued to refined the ideas forwarded by the group members and set or established

77

the evaluation and selection criteria with a common agreement. In this study, the result of the

group discussion showed that the best improved bread wheat variety should constituted the

white grain color, large seed size, and high disease, pest and frost resistance, good food

quality, good straw quality as animal feed and attractive market demand characteristics. This

study is in line with the study of (Ethiopian Rural Self Help Association /ERSHA, 2000).

Table 34.Farmers’ evaluation and selection criteria of improved bread wheat varieties

No Variety Characteristics N Percent Rank

1 White grain color 140 93.33 1st

2 Large grain Size 140 93.33 ,,

3 Straw quality 140 93.33 ,,

4 Market demand 140 93.33 ,,

5 Germination capacity 139 92.67 2nd

6 Cooking quality 139 92.67 ,,

7 Better yield performance 139 92.67 ,,

8 Water lodging resistance 138 92.00 3rd

9 Tillering capacity 138 92.00 ,,

10 Food quality 138 92.00 ,,

11 Short maturity date 137 91.33 4th

12 Disease resistance and pest resistance 135 90.00 5th

13 Frost resistance 133 88.67 6th

14 Harvesting quality 97 64.67 7th

15 Storage quality 97 64.67 ,,

(Source: Computed from own survey data, 2005)

The results in evaluation and selection of improved bread wheat varieties disseminated in the

study area has showed in Table 34 that white grain color, large grain size, market demand and

straw quality were the first and most important criteria. The traits such as better yield

performance, cooking quality and germination capacity got the second rank. Food quality,

tillering capacity and water lodging resistance got the third rank Short maturity date, pest and

78

disease resistance and frost resistance were got the fourth, fifth and sixth ranks respectively.

Harvesting and storage qualities were got the seventh rank by farmers’ judgment.

Table 35.Farmers’ preference (selection and evaluation criteria) of improved bread wheat

varieties disseminated in the study area

HAR-1685 (123) HAR-1709 (54) Paven-76 (115) No Variety Characteristics

N Percent N Percent N Percent

1 Market demand 97 78.8.6 12 22.22 41 35..65

2 Cooking quality 86 69.92 23 42.60 41 35.65

3 Water logging résistance 86 69.92 27 50 37 37.12

4 Straw quality 84 68.29 36 66.67 30 26.10

5 Storage quality 83 67.48 33 61.11 34 29.57

6 Frost resistance 80 65.04 27 50 43 37.40

7 Seed size 79 64.23 26 48.15 45 39.13

8 Yield performance 77 62.60 38 70.37 35 30.43

9 Rain shortage and Drought

resistance

77 62.60 32 59.26 41 35.65

10 Weed resistance 76 61.80 42 77.78 32 27.82

11 Grain color 71 57.73 37 68.52 42 36.52

12 Food quality 70 56.91 32 59.26 45 39.13

13 Disease and Pest resistance

66 53.66 40 74.04 44 38.26

Sum 1032 - 405 - 510 -

Average 79.385 - 31.154 - 39.231 -

Rank 1st - 3

rd - 2

nd -

(Source: Computed from own survey data, 2005)

Respondent farmers were interviewed to get their idea on evaluation and selection criteria of

improved bread wheat varieties and their responses has summarized in Table 35.The

respondent farmers responses showed that the improved bread wheat varieties should

constitute the characteristics mentioned in Table 35. These evaluation and selection criteria are

the most important criteria for the farmers in the study area. The respondent farmers have

given their preference of improved bread wheat varieties distributed in their area. The larger

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proportion of respondent farmers selected HAR-1685 improved bread wheat variety. The

remaining varieties Paven –76 and HAR, 1709 ranked second and third respectively. This

survey result was also supported and similar result was obtained from group discussion

conducted in this study.

4.2. Analytical results and discussion

The purpose of this section is to identify the most important hypothesized independent

variables that influence the dependent variables namely the probability of adoption for

nonadopters using logit model and the intensity of adoption for adopters using tobit model

analysis of improved bread wheat varieties in the study area, Akaki. Before conducting the

model analysis selection, screening and verification of hypothesized variables were conducted

by considering various situation to get best variables, those can fit with the analytical models,

describe the sample groups, environmental and practical situation of the study area. This was

done in consultation of professionals and experienced people, based on literatures, practical

situations, observation and experience of the researcher and the relevance as well as the

importance of the variables. As a result, the variable, distance of credit provider institutions

was dropped because the major credit source for the farmer in the study area were the private

individual credit providers in time when this research was conducted. These individual credit

providers do not have a specific place and including this variable in model analysis was not

relevant.

In the case of significant level of hypothesized independent variables, independent sample test

between the groups using t-statistics or t-test for continuous variables to describe the pattern of

sample data and to test the significance of a given independent variable on adopters and non-

adopters groups as well as to check the mean values differences of continuous variables in the

two groups and the chi-square test also to test the differences between the two groups for

discrete variables in relation to dependent variables (Lind and Mason, 1994 as cited in Adane,

2002). In the analysis some independent variables might show significant and others might

show insignificant relationship with dependent variables. The insignificant association doesn’t

80

guarantee about the strength or direction of relationship between those insignificant

hypothesized independent variables and the dependent variables. The reason for the

insignificant relationship of some of the independent variables is mainly because of the fact

that there is a drawback with any univariate approach in that it ignores, but there could be a

possibility in the collection of variables analysis, each of which is weakly associated with the

univariate outcomes can become an important predictor of out come when taken together.

Therefore, we should consider them as candidates to be indicated in the multivariable models

analyses along with all known important variables (Hosmer and Lemeshow, 1989 as cited in

Adane, 2002).

Moreover, there are several literatures and previous research works (Chilot, 1994; Bekele,

2000; Adane N.F., 2002; Adane N.M., 2002; Techane 2002; Endrias, 2003; Yitayal, 2004;

Adam and Bedru, 2005) conducted in a similar way which can substantiate this study. These

previous research results showed that those hypothesized independent variables were included

in econometrics model analyses regardless of the significant or insignificant results of these

hypothesized independent variables in descriptive analysis. The model analyses results might

show significant or insignificant results differently or similarly to descriptive statistics results.

Regarding multicollinearity or high degree of association problem among selected and

screened hypothesize independent variables were primarily checked before including in the

models as well as before running the models analyses.

Secondly, prior to running the Logit and Tobit models, the presence or absence of correlations

or associations between hypothesized independent and dependent variables were checked. The

presence or absence of correlation or association, that is, whether or not there is a correlation

between the variables in question (Sarantakos, 1998).

Existence, direction and strength of correlation are demonstrated in the coefficient of

correlation. A zero correlation indicates that there is no correlation between the variables. The

sign in front of the coefficient indicates whether the variables change in the same direction

(positive correlation) or in opposite direction (negative correlation), except for nominal

81

measures, where the sign has no meaning, in which case coefficient describe only the strength

of the relationship (a high or a low association) between the variables of the study. The value

of the coefficient shows the strength of the association with values close to zero meaning a

weak correlation and those close to 1 a strong correlation. A correlation of +1is just as strong

as one of -1; it is the direction that is different (Sarantakos, 1998). Therefore, in this study, the

presence or absence of association or correlations of hypothesized independent variables with

the dependent variable, adoption of improved bread wheat varieties were assessed to identify

and drop from model estimation if hypothesized independent variables do not have any

relationship with dependent variable, adoption of improved bread wheat varieties. The

Cramer’s v coefficient for discrete variables and Pearson’s correlation coefficient for

continuous variables was calculated using SPSS computer program.

In addition, the direction, range, intensity or degrees of strength of association of each

hypothesized variables with dependent variables were assessed. The result showed that there

was no total absence of association between hypothesized independent variables and

dependent variables. Rather, association between hypothesized independent and dependent

variables exist with various degrees of association ranging from moderate to weak. As a result,

it was decided to include all selected, verified, screened hypothesized independent variables,

those have various degrees of relationship with dependent variables, in models analyses to see

their combined effect they have on dependent variables namely probability and intensity of

adoption. From these total selected independent variables, only farmland showed moderate

correlation with the dependent variable. But the rest showed weak association as indicated in

Appendix table 24.

Thirdly, before including the hypothesized variables and running the model analyses the

existence of a serious of multicollinearity or high degree of association problem among

independent variables for all continuous and discrete variable were checked. There are two

measures that are often suggested to test the existence of multicollinearity or association

problems among independent variables. These are: Variance Inflation Factor (VIF) for

multicollinearity problem among continuous independent variables and contingency

82

coefficients for existence of high degree of association among independent dummy variables.

The technique of variance inflation factor (VIF) was employed to detect the problem of

multicollinearity for continuous variables. VIF shows how the variance of an estimator is

inflated by the presence of multicollinearity (Gujarati, 2003).

It is obvious that multicollinearity problems might arise when at least one of the independent

variables shows a linear combination of the others; with the rest that we have too few

independent normal equations and, hence, cannot derive estimators for all our coefficients.

More formally, the problem is that a high degree of multicollinearity results in larger variances

for the estimators of the coefficients. A larger variance implies that a given percentage

(eg.95%) confidence interval for the corresponding parameter will be relatively wide; a large

range of values of the parameter, perhaps including the value zero, will be consistent with our

interval. This suggests that, even if the corresponding independent variable problem may make

it quite difficult for us to estimate accurately the effect of that variable. Consequently, we may

have little confidence in any policy prescriptions and biased on these estimates (Kelejian and

Outes, 1981).

Very often the data we use in regression analysis cannot give decisive answers to the questions

we pose. This is because the standard errors are very high or the t-ratios are very low. This

sort of situation occurs when the explanatory variables display little variation and/or high

inter-correlations. The situation where the explanatory variables are highly inter -correlated is

referred to as multicollinearity (Maddala, 1992).

According to Maddala (1992), VIF can be defined as: VIF (xi) = 21

1

iR−

Where 2

iR is the square of multiple correlation coefficients that results when one explanatory

variable (Xi) is regressed against all other explanatory variables .A statistical package known

as SPSS was employed to compute the VIF values. Once VIF values were obtained the R2

values can be computed using the formula. The larger the value of VIF, the more will be

“trouble-some” or the collinear of variable Xi. As a rule of thumb, if the VIF of a variable

83

exceeds 10, there is multicollinearity. If Ri2 exceeds 0.90, that variable is said be highly

collinear (Gujarati, 2003). The VIF values displayed in Table 36 have shown that all the

continuous independent variables have no multicollinearity problem.

Similarly, contingency coefficients were computed from survey data to check the existence of

high degree of association problem among discrete independent variables. Contingency

coefficient is a chi-square based measure of association .A value of 0.75 or more indicates a

stronger relationship (Healy, 1984 as cited in Destaw, 2003).

The contingency coefficients are computed as:

2

2

χ

χ

+=

NC

Where, C= Coefficient of contingency

χ2 = Chi-square random variable and

N = total sample size.

Which assumes a value between 0 and 1 to indicate the degree of association between the

discrete variables as indicated in Table 37.The decision rule for contingency coefficients says

that when its value approaches 1, there is a problem of association between independent

discrete variables. As indicated in Table 37 that there is no a problem of high degree of

association among independent discrete variables.

84

Table 36.Variable Inflation Factor for the continuous explanatory variables

Variables R2i Variance Inflation Factors (VIF)

Age 0.047 1.049

TLU

0.637 2.754

Farm land holding

0.342 1.520

Oxen ownership

0.638 2.759

Experience in extension

0.036 1.036

Family size 0.231 1.301

(Source : Own Computation)

Table 37.Contingency Coefficients for Dummy Variables of Multiple Linear Regressions

Model

Source: Own computation

As it has indicated in many studies and literatures, if there will be serious multicollinearity or

a high degrees of association problems among independent variables, these situations can

HHH

SEX

EDU

HHH

HEAL

STAT

PRTI

LEDE

HHOF

FINC

DIS

DAOF1

CRIN

MFF1

OTS

OLA

GEXS

ERVE

GECR

SERV

HHHSEX 1 0.184 0.023 0.128 0.154 0.055 0.073 0.14 0.068 0.063

EDUHHH 1 0.003 0.044 0.030 0.033 0.058 0.196 0.018 0.024

HEALSTAT 1 0.155 0.035 0.046 0.23 0.046 0.068 0.034

PRTILEDE 1 0.088 0.172 0.130 0.106 0.097 0.080

HHOFFINC 1 0.082 0.048 0.094 0.085 0.406

DISDAOF1 1 0.442 0.110 0.062 0.021

CRINMFF1 1 0.055 0.046 0.174

OTSOLA 1 0.183 0.031

GEXSERVE 1 0.155

GECRSERV 1

85

create difficulties to differentiate the separate effects of independent variables on dependent

variables and also seriously affect the parameter estimate because of strong relationship among

them. Hence, should not be included in the model analysis (Maddala, 1983; Kathari, 1990 as

cited in Adane, 2002 and Gujarati, 1995). But since there is no a serious multicollinearity or

high degree of association problem among independent variables in this study all the screened

variables were decided to be included in the models analyses.

After conducting and passing all these steps, all screened and verified independent variables

were included in logit model analysis using SPSS computer software program. But a problem

faced in tobit model analysis to include all these screened and verified independent variables

in tobit model analysis using Limdep computer soft ware program due to the limitation of this

soft ware program to accommodate all variables included in logit analysis. Therefore, there

need to select and choose the variables that can be accommodated by the Limdep soft ware

program and most important independent variables for the analysis than others. As a result,

from those independent variables included in the logit analysis only leadership position and

credit service were dropped from tobit model analysis based on practical and actual situations,

researcher’s observation, relevance of the variables and by employing Limdep computer

program to check the number of significant variables those can affect the dependent variable,

intensity of adoption of improved bread wheat varieties.

In this process the number of significant independent variables were increased when the above

two independent variables were dropped individually or together. At last, the remaining

screened and verified hypothesized independent variables were included in tobit model

analysis.

4.2.1. Analysis of determinants influencing probability of adoption of improved bread

wheat varieties and their marginal effect

To identify factors among hypothesized independent variables that significantly influencing

the probability of adoption of improved bread wheat varieties in the study area, Akaki, SPSS

86

computer soft ware program and the binomial econometric analytical model (the binary Logit

model) was employed. In fitting the logistic estimation model, the higher significance of chi-

square statistics (80.187) was taken as a measurement of goodness-of-fit. This indicates that

the explanatory variables together influence the probability of adoption of improved bread

wheat varieties in the study area. In addition, the model correctly classified the respondents

into adopters and non-adopters at 81.33% of correct prediction percentage. The maximum

likelihood estimate of the parameters and the direction of relationship and the effect of

independent variables on probability of adoption were analyzed and presented in Table 38.

As indicated in the methodology and other previous sections, a number of independent

explanatory factors were postulated to influence the probability of adoption of improved bread

wheat varieties in the study area. Among the selected hypothesized explanatory variables and

considered by the model, only four variables were found to have significantly affect farmers’

adoption decision of improved bread wheat varieties. The variables affecting probability of

adoption were distance of DA-office from the farmers home (DISDAOF1), house hold

social/leadership status (PRTILEDE), market accessibility (CRINMFF1), and house hold

farmers experience in extension (YEXPEXTS) as indicated in the Table 38.

Among those significant variables, only one variable, which was market access, related with

adoption of improved bread wheat varieties negatively and the sign was different from the

expectation but statistically significant at 5 percent probability level. In this study, the negative

relationship of market access and adoption of improved bread wheat varieties showed that

those farmers in the study area who do not have access to market are more likely to adopt

better the improved bread wheat varieties than those farmers who have a better access to

market.

The possible reason for this situation might be, those farmers who have better and closer

access to market area might create other income opportunity from their farm and they may

give more attention and priorities to these other alternatives, production activities and other

87

substitutions, which may bring better income to them than using their whole wheat farm to

produce improved bread wheat. But those farmers far away from market since they may not

have any other alternatives they give more attention for improved bread wheat production and

to their farming occupation. The other possible reason to these farmers who far away from

market make them to adopt the improved bread wheat varieties than those who are closer to

market might be the better production performance of the varieties to provide food for their

family through out the year since they might not have other food sources of alternatives and

means .On the other way, environmental situations, the soil fertility frost problem variations

might be the possible reasons.

The remaining three significant explanatory variables namely (leadership position/status,

experience in extension and distance of DA-office from the farmers’ home) related with

adoption of improved bread wheat varieties significantly and positively, as of the expectations,

at 1, 1 and 10 percent probability levels respectively as indicated in the Table 38.

88

Table 38.Factors affecting Probability of adoption of improved bread wheat varieties and the

marginal effect of the significant explanatory variables

Variables B S.E Wald df Sign. Exp (B)

HHHSEX-sex 0.657 0.777 0.714 1 0.398 1.928

HHHAGE-age -0.023 0.021 1.194 1 0.274 0.977

EDUHHH-education -0.156 0.536 0.094 1 0.772 0.856

HEALSTAT-health 0.942 0.901 1.092 1 0.296 2.565

PRTILEDE –leadership position 2.217 0.756 8.610 1 0.003*** 9.181

HHOFFING-off-farm income -0.688 0.945 0.531 1 0.466 0.502

DISDAOF1-DA-office 1.490 0.910 2.683 1 0.101* 4.436

TOTLIVUM -livestock 0.076 0.146 0.272 1 0.602 1.079

SUMOWRE- farm land 0.361 0.301 1.435 1 0.231 1.435

CRINMFF1-market -1.636 0.819 0.3.993 1 -0.046** 0.195

OTSOLA1-labor 0.110 1.228 0.008 1 0.928 1.117

OXTLU-oxen 0.103 0.278 0.137 1 0.712 1.108

GEXSERVE -extension 7.811 23.647 0.109 1 0.741 2467.236

YEXPEXTS-extension experience 0.475 0.113 17.690 1 0.000*** 1.608

GECRSERV -credit 0.200 0.788 0.064 1 0.800 1.221

FAMILYSI –family size 0.024 0.135 0.030 1 0.862 1.024

Constant -4.053 1.744 5.399 1 0.020 0.017

Notes: Exp (B) shows the predicted changes in odds for a unit increase in the predictor

*Omnibus Tests of model coefficients: Chi-square=74.97, Sign.0.000; * Percentage of correct

prediction=81.30; and *, **and ***Significant at 10%, 5%, and 1% Significant level.

The variable leadership position affects adoption significantly in the study area as indicated in

Table38. Farmers who have a leadership position in the society might give a better opportunity

to access resources and inputs such as labor, fertilizer, seed, to contact with DA for better

information, better access to credit providers, as a result of their leadership position and,

hence, are likely to adopt improved bread wheat varieties better than those who did not have

leadership position in the society this result is in line with the result of Rauniyer and Goode

(1996) as cited in Legesse (1998). This implies that there need to give attention and identify

what those farmers who do not have leadership position lack due to their lower leadership

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position to design a strategy to provide access, support, encourage them to achieve better

adoption of improved bread wheat varieties by them.

The variable, experience in extension influences adoption of improved bread wheat varieties.

Farmers who have longer years of experience in extension have adopted better-improved bread

wheat varieties than those who have the lower years of experience in extension participation.

This showed that the farmers with longer years of experience in extension may use their

experience to using and taking the advantages obtained from new agricultural innovations or

technologies and also they may develop, the confidence in handling the risk, skills in

technology application, and may developed better economical status and better income from

out put of using of these improved agricultural technologies.

Regarding, the distance of DA’s office, from farmers’ home showed influential effect in

adoption of improved bread wheat varieties in the study area as revealed in this study. The

farmers who are nearby the DA‘s office are likely to adopt the improved bread wheat varieties

than those who are far. This implies that the near by farmers to the DA‘s office would have an

opportunity to get better and up dated information on the availability and benefit of improved

varieties easily and better than those far farmers. As a result, they can use these opportunities

to adopt the improved bread wheat varieties than those farmers far away from DA office.

The remaining hypothesized independent variables were not statistically significant to

influence the probability of adoption of improved bread wheat varieties at less than 10%

significant level as indicated in Table 38. Even though they were not significant below 10%

significant probability level in logit model analysis practical and experience situations,

literatures and many research works as well as the test statistics of this study showed that they

have influential impact on adoption of improved technologies and innovations. The result of

the logit analysis and their change or marginal effect of explanatory variables on dependent

variable, probability of adoption of improved bread wheat varieties showed and presented in

Table 38.

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The marginal effect of the variable of the distance of DA-office closer to farmer’s home by

one unit might improve the probability of adoption of improved bread wheat varieties by a

factor of more than four times fold. In the social system the farmer’s leadership position can

improve farmer’s agricultural technologies as observed in this study. In this study farmer’s

leadership position improves the probability of adoption .As there is a change of farmer’s

leadership position from non-leadership to leadership, there is an improvement of adoption of

improved bread wheat varieties by the factors of nine times fold. The farmers’ experience in

any of extension activities and use of improved technologies and innovations, can also

improve the probability of adoption of improved bread wheat varieties. The changes and

improvement of farmers’ experience in extension participation by one year or by one unit can

increase adoption of improved bread wheat varieties by the factor of 61%.

In this study, it was revealed that the market access has a negative relationship with adoption

of improved bread wheat varieties. When farmers are closer to market access by one unit,

there is a decrease of probability of adoption of improved bread wheat varieties by a factor of

0.2 or by a factor of 20 percent. This implies that as mentioned in the above of this section,

farmers who are closer to the market centers and facilities might be influenced and attracted by

other substitution factors created by the market center facilities and might inclined to involve

in these activities and business tasks with out totally leaving the farming occupation. As a

result, they become reluctant to adopt improved bread wheat since improved wheat demand

intensive management and labor work.

4.2.2. Analysis of determinants influencing intensity of adoption of improved bread

wheat varieties and their marginal effects

Parameter estimates of the Tobit model for the intensity of adoption of improved bread wheat

varieties (measured in terms of size of land in hectare used for growing of improved bread

wheat varieties over the total wheat land in hectare). The Tobit model was used or applied to

analyze the factors that determine the intensity of adoption of improved bread wheat varieties

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because the mean proportion of land allocated to improved bread wheat varieties is a

continuous variable but truncated between zero and one.

The main purpose of this section is to identify the hypothesized independent variables among

the selected and proposed to include in the tobit model analysis that significantly influence the

dependent variable, intensity of adoption. The result of this study indicated and presented in

Table 39. From the total hypothesized independent variables, only eight explanatory variables

were significantly influencing and affecting the intensity of adoption of improved bread wheat

varieties as presented and indicated in Table 39. These significant variables were, household’s

sex (HHHSEX), age (HHHAGE), education (EDUHHH), health status (HEALSTAT), off-

farm income (HHOFFINC), home distance from DA office (DISDAOF1), farmland holding

(SUMOWRE) and extension service (GEXSERVE) were statistically the most important

explanatory variables affecting intensity of adoption of improved bread wheat varieties in the

study area.

The variable household sex was related with the intensity of adoption of improved bread

wheat varieties positively and significantly at 5 percent probability level. The sign was in line

with that of the expectation. AS it was indicated in the identification of hypotheses, probability

and intensity of adoption was expected to relate positively with male sample and negatively

related with female sample. Hence, the positive sign indicates that the male-headed

households were better in intensity of adoption of improved bread wheat varieties than female

farmers. This result showed that male farmers are more likely to allocate larger farmland to

improved wheat than female farmers in the study area. This result is in conformity with the

finding of (Thechane, 2002).

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Table 39.The effects of changes (marginal effect) in the significant explanatory variables on

the intensity of adoption of improved bread wheat varieties

(Source Computed from own survey data, 2005)

*, ** And***Significant at 10,5 and 1 percent probability level

From these significant explanatory variables only one variable namely size of farmland

holding related with the intensity of adoption of improved bread wheat varieties negatively

and significantly at 10 percent probability level. This variable has the different sign from that

was hypothesized. The remaining of the seven significant explanatory variables namely

household sex, age, education, health status, off-farm income, extension service, distance of

DA office from farmers’ home showed statistically significant and positively related with

intensity of adoption of improved bread wheat varieties in the study area, at 10 percent

probability level.

Variables Coefficient Standard Error b/St.Er P (/Z/>z)

HHHSEX 0.1854436505 0.81995580 2.262** 0.0237

HHHAGE 0.3862778245 0.15594257 2.477** 0.0132

EDUHHH 0.1696807312 0.56390468 3.009*** 0.0026

HEALSTAT 0.3813275935 0.72099682 5.289*** 0.0000

FAMILYSI 0.1544088566 0.11636011 1.327 0.1845

HHOFFINC 0.1409564409 0.68463253 2.059** 0.0395

DISDAOF1 0.1684878586 0.88875721 1.896* 0.0580

TOTLIVUN -0.8907270032 0.10883167 -0.818 0.4131

SUMOWRE -0.3801474933 0.22993869 -1.653* 0.0983

CRINMFF1 0.1719597193 0.96915193 0.018 0.9858

OTSOLA1 -0.2466170095 0.80390554 -0.307 0.7590

OXTLU -0.3023853019 0.22090082 -0.137 0.8911

YEXPEXTS 0.4834808429 0.54835151 0.882 0.3779

GEXSERVE 0.2589730719 0.11095968 2.334** 0.0196

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The variable household age was also related with the intensity of adoption of improved bread

wheat varieties positively and significantly at 10 percent probability level. As the result of this

study showed that older farmers may have already developed better experience, face exposure

opportunities with using of large size of improved agricultural technologies through their life

experience and might develop experiences how to manage risks and taking of the first benefits

from newly released varieties. This might help them to develop confidences to allocate larger

farmland to improved bread wheat varieties production more than those lesser and younker

age group farmers in the study area. The sign was different from that of hypothesized. The

hypothesis formulation and establishment was conducted based on literatures, experiences and

observation of actual, practical and existing situations.

Literatures showed that farmers expected to be reluctant to new innovations as their age

increased. But in the context of this study area, it is different from that of literatures. In history

of Ethiopian extension farmers in the study area have better exposure of opportunities to new

agricultural innovations than other areas of Ethiopia. As a result they developed better

experience through their life experience better than other areas of farmers who do not get the

opportunities like farmers in the study area. This finding agreed with the finding of (Chilot,

1994).

Education was also has a positive and significant relationship with the intensity of adoption of

improved bread wheat varieties at 1 percent probability level. In this regard, the proportion of

farmland used for growing of improved bread wheat varieties by farmers who are literate is

likely to be greater than farmers who were illiterate. This suggests that being literate would

improve access to information, capable to interpret the information, easily understand and

analyze the situation better than illiterate farmers. So, farmer who are literate were likely to

allocate larger size of farmland proportion than those illiterate farmers. The sign was as

expected. This result has supported by other previous studies such as the findings of Lelissa

(1998), Techane (2002), Lelissa and Mulate (2002), Yitayal (2004).

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The variable health status had a positive and significant influence at 1 percent probability level

relationship with the intensity of adoption of improved bread wheat varieties in the study area..

The result of this study showed that farmers who have better health status are likely to allocate

larger farmland size to improved wheat varieties production. It is obvious from practical and

actual situation of the ground that managing and operating the improved agricultural

innovations demanded intensive labor and management practices. Then, health farmers can do

these practices than unhealthy farmers. There fore healthy farmers are likely to allocate larger

farmland size than unhealthy farmers. The sign was as expected. Low intensity of adoption by

un-healthy farmers may be due to the shortage of labor and the problem to conduct intensive

management that the improved bread wheat demanded.

The explanatory variable, off-farm income influenced the dependent variable, intensity of

adoption of improved bread wheat varieties positively and significantly at 5 percent

probability level as hypothesized under the section of hypotheses description. As it is true that

farmers who have better income can adopt new agricultural innovations because their income

allowed them to purchase the new technological inputs, can with stand risks if appear and can

cover labor costs. Off-farm income is one of the alternatives to improve farmers’ income.

From these grounds of realities farmers who have off-farm income can adopt new technologies

in larger proportion than those who do not have off-farm income.

The size of farmland holding, affected the dependent variable, intensity of adoption of

improved bread wheat varieties in the study area negatively and significantly at 10 percent

probability level. The sign was different from that of postulated. This finding is in conformity

with the finding of (Bekele et al, 2000; and Chilot, 1994).

As it is supported by many literatures, those farmers who have larger land size are expected to

adopt improved and new agricultural technologies in larger proportions than those farmers

who have lesser farmland. Since these farmers have larger farm land they do not have fear of

risks, can get credit because they are believed that they can pay their credit, or some part of

their land may serve as mortgage to take credit, seed loan from other farmers and can adopt

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new agricultural technologies than those who have lesser land size. But in this study the result

showed the different situations from many literatures even though it is in line with some few

literatures as mentioned in the above. The possible reason for this result can be that the actual

situations in the study area different from other areas in that the situations of the area where

this study has conducted is closer to Addis Ababa, the capital of the country where in the mean

time of this study highest construction investment on land were conducted.

Farmers who are closer to the town lease their part of farm land and might reluctant to increase

their farm land allocation to improved bread wheat land since they got better income from land

contract than they got from improved wheat land production. Another possible reason also for

this situation might be that people at the edge boarder of Addis Ababa and the rural part of the

study area have the experience of producing crop for their consumption and profit purpose by

contracting land from the surrounding farming community. As result of the existence of this

situations in the study area farmers might contract their land for these types of par time

farmers due to many situations like for better income than they used for improved wheat

production or for the reason they may face different problems and cash constraint that could

not give some time in the future. Then, those par time farmers might have their own interest of

crop type production and objectives. As a result those first owners and adopters of improved

bread wheat varieties might unable to increase their farmland allocation for improved bread

wheat varieties.

And also in the study area there is an introduction and promotion of white check pea, which

has high price in market. This also shift the wheat adopters to allocate their wheat farm land to

this new crop variety rather than increasing of their land allocation to the improved bread

wheat varieties since wheat land can use for check pea crop interchangeably. It was also

observed that credit and input providers greatly reduced their service provisions due to the

reluctant effect of farmers’ to return their previous credit loan as a basic reason of the highest

interest rate of the loan. These are some of the possible reasons for the inverse relationship of

the independent and dependent variables in this case.

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Extension service influenced dependent variable, intensity of adoption of improved bread

wheat varieties positively and significantly at 5 percent probability level as hypothesized. The

finding of this study is in agreement with the findings of Adesina and Zinnah (1992), Chilot

(1994), Techane (2002), Lelissa and Mulate (2002) and Yitayal (2004). Theoretical and

practical realities showed that extension services provided to the farmers in different forms

like training demonstration field day DA visit on the field and on spot field support can

motivate, empowers kill and knowledge, increase information access and create interest to

improve farmers’ use of improved agricultural technologies.

The independent variable, distance of DA office from the farmer’s home, influenced the

independent variable, intensity of adoption of improved bread wheat varieties in the study area

positively and significantly at 10 percent probability level. The sign was as postulated. The

intensity of adoption of improved bread wheat varieties is higher to the farmers who are closer

to the DA office than those farmers who found far. This result is in line with the result of

(Chilot, 1994). This is also true from theoretical, practical and experience realities when the

Das assigned closer to the farmers village farmers can easily and from near by distance can get

the required information such as availability of inputs, credit services, market situation

government and other development organization supports on time and sufficiently.

The results of the Tobit model analysis also showed the effects of changes or marginal effects

in the explanatory variables on the dependent variable, intensity of adoption of improved

bread wheat varieties, in the study area as indicated and presented in Table 39.

Literatures showed that adoption and intensity of utilization of improved agricultural

innovations has relations with gender. As it was hypothesized male farmers were likely

expected to show better intensity of farmland allocation for improved bread wheat production

than female farmers. The marginal effect of Tobit model analysis showed that male farmers

were better in allocation of farm land as compared to female farmers. The intensity of

farmland size allocation for improved bread wheat varieties production by male farmers was

larger by a factor of 19 % than female farmers.

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Age is one factor to influence intensity of adoption. The marginal effect of tobit analysis

showed that as age of adopters of improved bread wheat increase by one unit, intensity of

farmland size allocation for improved bread wheat varieties production can improve by the

factor of 38.63%. As mentioned in the above, since farmers in this area have a long years

exposure to extension services than other areas of farmers, they developed better extension

experience in the process of their lifetime experience that plays a great role in intensity of

adoption of improved bread wheat varieties in the study area.

Education plays a positive and significant role in the intensity of adoption of improved bread

wheat varieties in this study. An improvement in education or a change by a unit i.e. from

illiterate to literate can improve farmers allocation of farm land for improved bread wheat

production from their total wheat land can improved by a factor of 17%. In the other way, as

there is an improvement in educational level of adopters’ of improved bread wheat varieties by

one unit, there will be an increased allocation of farm land for production of improved bread

wheat varieties by 17%.

In this study it was identified that as farmers’ health situations improved from unhealthy to

healthy situation, the intensity of adoption of improved bread wheat varieties can increases by

a factor of 38%, because the health farmers can directly involve in every activities of improved

bread wheat production and can them selves manage their farm. As a result the allocation of

intensity of farmland for improved bread wheat production can increase by a factor of 38%.

The variable household farmers’ off-farm income contributes its own part in the intensity of

adoption of improved bread wheat varieties in the study area positively and significantly. As

the involvement of farmers in off-farm income and consequently their income improved by

one unit, their allocation of farmland for improved bread wheat production can increase by the

factor of 14%.

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When the farmers’ income improves by one unit from off-farm income source, the allocation

of farmland by them for improved bread wheat production can improve by a factor of 14%.

This is due to the fact that the off-farm income can solve farmers’ financial constraints and

increase their purchasing power of improved bread wheat seed, other agricultural inputs such

as fertilizer and other production means relevant to the production of improved bread wheat.

Consequently, farmers might be encouraged in allocating larger area of their wheat farmland

for the production of improved bread wheat varieties than those who do not have off-farm

income.

The distance of DA office plays its role in intensity of adoption of improved bread wheat

varieties in the study area positively and significantly. As the distance of DA office decreases

or closer to the home of farmers by one unit, intensity of adoption could increase by a factor of

17%. This implies that farmers who are closer to the DA’s office can get easy access to

extension support and agricultural information that can give a chance to analyze situations and

allocate their larger farmland for growing of improved bread wheat varieties than those who

are far from DA’s office.

As discussed in the above, the independent explanatory variable, farmland holding related

with the intensity of adoption of improved bred wheat varieties negatively and significantly as

indicated and presented in Table 39. As revealed in this study, when the size of the farm land

holding of farmers increased by one unit intensity of adoption of improved bread wheat

varieties decreased by a factor of 38%. This may be due to the fact substitution of some part of

their wheat land to other highly market demanded crops like for example white chick pea

production, land contracting by receiving larger amount of money, and the farmers themselves

might involve in other activities and reluctant to allocate increased farm land for improved

bread wheat production as a result the result of tobit model analysis showed the inverse

relationship between farm land and intensity of adoption of improved bread wheat varieties in

the study area.

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As it is generally true, the better the extension service can improve the intensity of adoption

and utilization of improved agricultural technologies. The tobit analysis in this study showed

that the change in improving the extension service by one unit can improve the allocation of

farm land for improved bread wheat production by adopter farmers can improve by the factor

of 26% as indicated in Table 39. This implies that when farmers get support from extension

agent, in various forms such as information provision, practical support on the spot of the field

or in the form of demonstration, field day and skill development, can improve farmers

knowledge, interest, motivation and confidence to allocate larger extent of farm land than

those who do not get or who got less extension support.

To summarize the two analytical model results, that the purpose of data analyses using the

econometrics models as discussed through out this section and in the previous sections is to

know which independent variable most important and powerful to affect the intended

dependent variable to which they hypothesized to influence. In this study two-econometrics

models logit for identification of factors affecting probability of adoption of improved bread

wheat varieties for those non-adopter farmers need to adopt in the future and tobit model for

estimation of factors to influence adopters’ intensity of adoption or allocation of farm land size

intensity for improved bread wheat production.

As mentioned at the beginning of this analytical section due to various reasons such as

theoretical, actual, practical, technical reasons some hypothesized independent variables were

dropped from further analyses like for example leadership position and credit service due to

tobit model limitation to accommodate all independent variables included in Logit model

analysis did not included in Tobit model analysis. The result of logit model analysis for

probability of adoption and result of tobit model analysis for intensity of adoption of improved

bread wheat varieties indicated and presented in Tables 38 and 39 respectively.

The most important and significant independent variables below 10% probability level to

influence probability of adoption of farmers who did not adopt improved bread wheat varieties

in the past but expected to adopt in the future, those identified by logit model analysis were

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four namely distance of DA-office from the farmers’ home, house hold head social/leadership

position, market accessibility, and house hold farmers experience in extension as indicated in

Table 38. Regarding the result of tobit model analysis as indicated in Table 39, eight

independent variables namely sex of household head, age, education, health status, off-farm

income, distance of farmers’ home from DA office, farmland holding and extension service

were statistically significant below 10% probability level and most important to influence

adopters’ intensity of farm land allocation to improved bread wheat varieties production from

their total wheat farm land as indicated in Table 39.

In this study, the two models used for two purposes as mentioned in the above. Though the

purpose of this study is not to identify the significant common independent variables among

those variables used in the two models analyses to identify influencing significant factors for

the two dependent variables as mentioned in the above, it is very important to see whether

there is a common influencing independent variables that affect significantly the two

mentioned dependent variables. As a result, it was identified that there was only one

independent variable, distance of DA-office from farmers’ home that commonly and

significantly affected both probability of adoption and intensity of adoption below 10%

probability of significant level. It doesn’t mean that the remaining independent variables

totally do not have any relationship with the dependent variables rather they are not

statistically significant below 10% significant level.

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5. SUMMARY AND CONCLUSION

5.1. Summary

In this study to identify factors influencing probability and intensity of adoption of improved

bread wheat varieties among smallholder farmers the study area, Akaki was selected based on

its wide practices of improved bread wheat production and its suitability for this research .In

this area, agricultural extension and rural development activities like other rural parts of the

country, conducted by Agricultural and Rural Development Unit which comprises several

agricultural professionals in different disciplines at office level and Development Agents

(DAs) at Center and Peasant Association (PA) level. According to the structural framework of

the Addis Ababa Administration, the unit is accounted to and organized under Akaki-kality

sub-city.

In this study, data were obtained from 150 randomly selected respondents through personal

interview schedule conducted by employed and trained enumerators using pre-tested interview

schedule and from group and individual discussions, as well as the researcher’s personal

observations. The respondents, involved in the interview were selected randomly and

proportionally from two sample Peasant Associations (PAs), constituted 99 (66%) adopters

and 51 (34%) non- adopters.

Data were analyzed, and presented quantitatively using different statistical methods such as

percentage, frequency, tabulation, Chi-square–test (for dummy /discrete variables) and (t-test

for continuous variables), Logit, Tobit models and qualitatively through interpretation,

explaining, summarizing of ideas and concepts. T-test and Chi-square test were employed to

test the variation of the sample group they have towards adoption and also used to describe the

patterns of the sample data. Logit and Tobit econometrics models to estimate the effects of

hypothesized independent variables they have on dependent variables, probability and

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intensity of adoption. Computer soft ware package programs such as SPSS and Limdep were

employed for statistical analyses.

Among the hypothesized independent variables, sex, health, education, extension service, DA-

office, market access, labor source, off-farm income, leadership, were treated as discrete

variables and tested using chi-square-test. In this test the independent variable health, distance

of DA-office, labor source, access to off-farm income were not significant below 10%

significant level. And family size, years of extension experience, age, livestock ownership,

oxen ownership, and farmland holding were considered as continuous variables and tested

using t-test. The t-test result showed that except others only age was not significant below

10% significant level.

The t-test and chi-square test results showed that there were variations between adopters and

non-adopters sample category in family size, extension experience, livestock ownership, oxen

ownership, farm land holding, extension service, sex (gender) and leadership position in

adoption of improved bread wheat varieties. According to the result of test statistics male are

better in adoption of improved bread wheat varieties. On the other hand adopters have larger

family size, livestock ownership, oxen ownership and farmland and they got better extension

service than non-adopters. Due to their variation in these independent variables sample

farmers vary in their adoption behavior in relation to dependent variables.

Except those hypothesized independent variables dropped due to various cases as mentioned

in previous section all screened and verified independent variables were subjected to Logit

model analysis. In the case of Tobit model analysis, all verified hypothesized independent

variables included in Logit model analysis were not included due to the limitation of the model

to accommodate all these independent variables. As a result, leadership position and credit

services were dropped from further Tobit model analysis due to their less importance in the

study as compared to other independent variables.

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The Logit model result of this study showed that the significant independent variables

affecting probability of adoption were distance of DA office, leadership position of household

head, market access and years of house hold head’s experience in extension and those

independent variables significantly influencing intensity of adoption of improved bread wheat

varieties were, household head’s sex, age, education, health status, off-farm income, distance

of DA office, size of farmland holding, and extension service resulted from tobit analysis. The

distance of DA’s office from farmers’ home was the only explanatory variable influencing

both adoption and intensity of adoption of improved bread wheat varieties in this study.

The farmers’ selection and evaluation criteria of improved bread wheat varieties and ranking

of the improved bread wheat varieties disseminated in the study area were also conducted

through the summary of the survey data, group and individual discussions as well as

researcher’s observation.

In this respect, white grain color, large grain size, straw quality, market demand were the first

most important characteristics; germination capacity, cooking quality good yield performance

were the second most important; water lodging resistance, tillering capacity, good food quality

were the third most important; short maturity date fourth; disease and pest resistance fifth;

frost resistance sixth; harvesting quality and storage quality ranks seventh most important

characteristics as grouped and ranked based on the result of the survey data group and

individual discussions and researchers observation.

Based on the selection and evaluation criteria, the result of the survey summary and group

discussion the ranking result of improved bread wheat varieties disseminated in the study area

has presented as HAR-1685 variety ranks firs, Paven –76 second and HAR-1709 ranks third.

Farmers in the study area got agricultural information from different sources. The most

important information sources as summarized were, neighbors and colleague farmers got the

1st rank, DA and Community leaders the 2

nd rank, farmers field day 3

rd, PA leaders,

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demonstration and radio 4th, News paper/News letter and other publications 5

th, poster 6

th,

Training 7th, TV

8th and religious leaders ranks 9

th sources of information.

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5.2. Conclusion and Recommendations

In this study several issues were observed and revealed in relation to adoption of improved

bread wheat varieties disseminated in the study area, Akaki. The result, description and

interpretation of the data were mainly depended on, the context of the research objectives and

the situation of the study area.

The truthfulness of the information provided by the sample farmers for this study was also

depended on the sample farmer’s voluntaries and credibility. Since the study area closer to

Debrezeit research center, Addis Ababa (the capital of the nation) and subjected to long years

of extension services in the past, the result of the study should be seen from this perspectives.

This study may serve as an initial input for further study in this and other similar areas of the

country.

Like other parts of the country, several agricultural innovations were disseminated in the

previous years and extension services were offered to the farmers that have an influential

impact on adoption and use of the disseminated agricultural innovations. From those

disseminated technologies in this area, improved bread wheat varieties was the one on which

this study was focused to identify factors affecting adoption of improved bread wheat varieties

by non-adopter farmers, and to identifying other factors influencing the adopter farmers to

increase the intensity of farm land size allocation to improved bread wheat production from

their total wheat farm land.

Determinants that limit probability of adoption of improved bread wheat varieties were

identified using descriptive statistics (t-test and Chi-square test) and logit model analysis. They

were gender, extension service; leadership position, market access, farmers’ extension

experience and distance of DA office from farmers home were the influencing factors

affecting non-adopters to adopt improved bread wheat varieties in the study area.

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According to the findings of this research, it is necessary to establish appropriate extension

strategy to bring those non-adopters to adopt improved bread wheat varieties. In this regard,

attention should be given; to encourage, support and motivate female and less extension

experienced farmers to achieve their adoption decision behavior. Some farmers in this area

who have leadership position are better adopters since their position allowed for better access

to information, resources and innovations. Therefore, there need to give attention to support

those people who do not have resources access opportunities.

As it is confirmed in this study distance of DA office from the farmers’ home has an

influential effect on adoption and intensity of adoption. Therefore, attention should be given to

the close assignment and placement of DAs to the rural villages where the farmers can get

them easily for extension advises and supports for better adoption.

In the study area, when farmers closer to the market showed reluctant behavior to adopt the

improved bread wheat varieties. Some of the possible reasons may be due to weak extension

service provided for them or due to fear of intensive management and labor requirement to

operate practices, or may be due to substitution effect and their involvement in other par time

works etc., created by the market facilities. The market in this area showed a negative effect

rather than motivation farmers to adopt improved bread wheat varieties. Therefore, there must

be efforts to formulate appropriate extension service for this area, improve market situation for

bread wheat and improvements of the varieties qualities for better market demand.

The other aspect of this study was to identify factors influencing those adopters to increase and

extend their improved wheat production by allocating larger area of farm land for improved

bread wheat production from their total wheat farm land. According to the result of descriptive

statistics and tobit model analysis gender, extension service, family size, experience in

extension, livestock and oxen ownership, farm land holding, age, education, health, off-farm

income, distance of DA-office were factors affecting intensity of adoption of improved bread

wheat varieties. Attention should be given to improve farmers’ intensity of adoption by

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designing of compatible extension strategy by considering the findings of this research as an

input.

Regarding farmers’ variety selection and evaluation criteria, it is advisable to involve farmers

through various techniques like for example using group discussion to evaluate and identify

the best suitable varieties that can fit their interest, farming system and environmental

situations. In-group discussion farmers from different angels such as gender, age, ecological

area and educational levels should be involved to get various ideas and opinions. The idea

reflected during group discussions should get attention and need to be incorporated and used

in agricultural technologies development, extension programs formulation and policy

preparations.

In the study area there is a shift of farmers to involve in improved chickpea production as a

result of high price of improved chickpea. There fore, it is necessary to give attention to

improve the quality of improved bread wheat varieties that can bring high market demand

through breeding and genetics improvement programs. It is also necessary to improve the

market facilities for improved bread wheat varieties.

Agricultural information and extension communication are powerful and crucial to achieve

better adoption and intensity of adoption of improved agricultural innovations like improved

bread wheat varieties in this case. Appropriate and timely information should reach to the

intended farmers group to achieve better adoption and intensity of adoption of improved

agricultural technologies. Appropriate information and communication strategy compatible

with farmers and the study area should be designed and practiced.

Suitable strategies for better extension service are another important issue that should get

proper attention. In the study area as observed and the survey data showed, the extension

service is at lower and weak position due to various reasons such as transfer of DAs to other

lateral offices, low motivation, poor credit service low educational background of extension

108

workers. In these respect it need attentions to solve these problems for better improvements of

agricultural technologies adoption and production growths that can bring better living standard

of the farmers in the rural areas. Attention also should be given to the research and extension

linkages, to the empowerment and training of extension people and farmers, to achieve high

level of improvement in adoption of improved agricultural technologies.

109

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7. APPENDICES

115

Appendix.1.Information on sample household demographic and socio-economic

characteristics

Table 1.The distribution of sample respondents by age group

Adopters

N-adopters

Total

Age

(year)

N % N % N %

19-30 11 11.11 11 21.57 22 14.67

31-45 41 41.45 16 31.37 57 38

46-64 34 34.34 15 29.41 49 32.67

Above or

Equal to 65

13 13.13 9 17.65 22

14.67

Total 99 100 51 100 150 100

(Source: Computed from own survey data, 2005)

Table 2.Educational statuses of sample house hold head farmers

Adopters Non-adopters Total Educational Status

N % N % N %

Illiterate 64 64.65 33 64.71 97 64.67

Read & Write 19 19.20 14 27.45 33 22

Grade1-6 4 4.04 4 7.84 8 5.33

Grade7-8 5 5.05 - - 5 3.33

Grade9-12 3 3.03 - - 3 2

Above grade 12 4 4.04 - - 4 2.67

Total

99

100

51

100

150

100

(Source: computed from own survey data, 2005)

116

Table 3 .The sample household family size

Adopters

N-Adopters

Total

Total Family

Members

Family

Size N

%

N

%

N

%

N %

1 2 2.02 4 7.84 6 4 6 0.72

2 2 2.02 - - 2 1.33 4 0.48

3 8 8.08 11 21.60 19 12.67 57 6.80

4 17 17.17 10 19.61 27 18 108 12.87

5 17 17.17 5 9.80 22 14.66 110 13.11

6 18 18.18 6 11.76 24 16 144 17.16

7 13 13.13 5 9.80 18 12 126 15.02

8 11 11.11 4 7.84 15 10 120 14.30

9 5 5.05 5 9.80 10 6.67 90 10.73

10 2 2.02 1 1.95 3 2 30 3.57

11 4 4.04 - - 4 2.67 44 5.24

Total

99

100

51

100

150

100

839

100

(Source: Computed from own survey data, 2005)

Table 4.The sample household family size

Adopters Non-Adopters Total Family

Size

N N N

Maximum 11 10 11

Minimum 1 1 1

Range 10 9 10

Average 5.85 5.10 5.6

St.d.

2.192

2.385

2.27

(Source: Computed from own survey data, 2005)

117

Table 5.Total Family members of sample households in age group

Age- group N % 0-14 age 361 43.03

15-64 age 452 53.87

Above 64 26 3.10

Total

839

100

(Source: Computed from own survey data, 2005)

Table 6.Respondents farming experience

(Source: Computed from own survey data, 2005)

Table 7.Types of livestock and owners and the number of respondents

Adopters Non-adopters Total Types of

Livestock N % N % N % Oxen 98 - 42 - 137 91.33

Cow 68 68.69 33 64.71 101 67.30

Bull 46 46.46 19 37.25 65 43.30

Heifer 41 41.41 14 27.45 55 36.70

Calves 39 39.40 20 39.22 59 37.70

Sheep 48 48.48 29 56.86 77 51.33

Goat 8 8.08 - - 8 5.33

Horse 21 21.21 4 7.84 20 16.67

Mule 25 25.25 6 11.76 31 20.67

Donkey 91 91.92 38 74.51 129 86

Poultry 80 80.81 29 56.86 109 72.67

Bee-in-Hive

5

5.05

1

1.96

6 4

(Source: Computed from Owen survey data, 2005)

Adopters

Non-adopters

Total

Age group

(Year)

N

%

N

%

N

%

1-10 17 17.172 9 17.65 26 17.33

11-20 44 44.444 25 49.02 69 46

21-30 21 21.212 12 23.53 33 22

Above 30 17 17.172 5 9.80 22 14.67

Total

99

100

51

100

150

100

118

Table 8.Sample house hold oxen ownership

Adopters Non-adopters Total Number

Of oxen

N

%

N

%

N

%

No oxen 1 - 9 - 10 -

One ox 3 3.03 3 5.86 6 4

Two oxen 46 46.47 25 49.02 71 47.33

Three oxen 7 7.07 3 5.88 10 6.67

Four oxen 30 30.30 6 11.77 36 24

Five& above 10 10.10 4 7.84 14 9.33

Total

99

100

51

100

150

100

(Source: computed from own survey data)

Table 9.Sample house hold land ownership

Adopters

Non-adopters

Total

Types of

Land ownership

N

%

N

%

N

%

I. Own Land owners

Cultivated Land

99

100

51

100

150

100

Grazing land 52 52.52 29 56.86 81 54

Home stead land 18 18.18 14 27.45 32 21.33

Forest land 4 4.04 2 3.92 6 4

Un-used land 4 4.04 3 5.90 7 4.67

II .Shared/Rent land owners

Cultivated land

20

20.20

4

7.84

24

16

Grazing land 2 2.02 - - 2 1.33

III Growers of variety

HAR-1685 variety

83

83.84

3

5.88

86

57.33

HAR-1709 variety 5 5.05 - - 5 3.33

Paven-76 variety

75

75.76

1

1.96

76

50.67

(Source: Computed from own survey data, 2005)

119

Appendix Table 10.Size of farmland holding of sample household

(Source: Computed from own survey data, 2005)

Table 11.Respondents average land area and yield of wheat crops in

1996/97 E.C. cropping season.

Item/List Max Min Range Average St.D

Area of land in

Hectares

Total farm Land

6.5

0.25

6.25

2.44

1.13

Total wheat land 4.5 0.25 4.25 1.125 0.605

Total improved B.W.L. 3.25 0.25 3 .00 1 .00 0.457

Area of Paven -76 1.75 0.25 1.50 6.30 0.29

Area of HAR-1685 2.00 0.25 1.75 0.64 0.34

Area of HAR-1709 2.00 0.25 1.75 0.535 0.44

Area of Durum Wheat 1.00 0.25 0.75 0.415 0.285

Area of Local Wheat 2.00 0.25 1.75 0.65 0.41

Yield of wheat in

Quintals

Yield of Paven-76

25

0.50

24.5

9.12

4.67

-Yield of HAR-1685 28 3 .00 25 9.33 4.445

Yield of HAR-1709 8 4.00 4 5.5 1.91

Yield of Durum wheat 15 4.00 11 9.165 7.03

Yield of Local wheat

26

2.00

24.

8.255

5.365

(Source: Computed from own survey data, 2005)

N.B=Improved B.W.L. (Improved Bread Wheat Land)

Farm Size

Adopters

Non-adopters

Total

In Ha. N % N % N %

<1Ha 12 12.12 15 29.41 27 18 1-2Ha 32 32.32 14 27.45 46 30.67 2-3Ha 30 30.30 15 29.41 45 30 3-4Ha 18 18.20 6 11.76 24 16 4-5Ha 5 5.05 1 1.96 6 4 5-6Ha 2 2.01 - - 2 1.33 Total

99

100

51

100

150

100

120

Table 12.Respondents farm land ownership and crop type grown in 1996/97 E.C. cropping

season

Items/ list Adopters N-adopters Total

Ha. N % Ha. N % Ha. N %

T.Farm Land 309.26 99 100.00 90.50 51 100.00 399.76 150 100.00

Grazing Land 20.45 54 54.55 9.95 29 56.86 30.40 83 55.33

Forest Land 101.00 4 4.04 0.35 2 3.92 1.36 6 4.00

Improved Bread

Wheat land (sum)

101.75

99

100 .00

1.25

3

5.90

100.00

99

100.00

- HAR-Paven 44.75 75 75.76 0.50 - - 45.25 - -

- HAR-1685 53.50 83 83.84 0.75 - - 54.25 - -

-HAR-1709 3.5 5 5.05 - - - 3.50 - -

-Durum 5.5 4 4.04 - - - 5.50 4 2.67

-Local variety 53.8 86 86.87 28.67 48 94.12 64.55

104 69.33

T.W.L. 161.05 99 100 29.92 51 100 190.97 150 100

Other crop

-Teff

80.25

92

92.93

35.50

43

84.31

115.75

147

98.00

-Chick pea 42.01 98 87.88 17.96 38 74.51 59.97 136 90.67

Lentils 2.63 9 9.09 1.25 4 7.84 3.88 76 13

-Pea 20.38 59 59.60 4.39 17 33.33 24.77 9 8.67

-Faba bean 2.25 6 6.06 1.10 3 5.88 3.35 8 50.67

-Vegetables

0.69

6

6.06

0.38

2

3.92

1.07

5.33

6.00

(Source: Computed from own survey data, 2005)

121

Table 13.Respondents’ livestock ownership

(Source: Computed from own survey date, 2005)

Types of

Livestock

Livestock

No.

Owners

No

owners

Owners

(% )

max

Min

Range

average

per

Owners

Oxen -Adop.

- Nadop.

301

110

98

42

98.99

82.353

10

6

1

1

9

5

3.07

2.62

Cow -Adop

-N-Adop.

85

42

68

33

68.687

64.706

3

3

1

1

2

2

1.25

1.27

Bull -Adop

- N-Adop

64

22

46

19

46.465

37.255

3

2

1

1

2

1

1.40

0.86

Heifer –Adop

- N-Adop

52

18

41

14

41.414

27.451

3

2

1

1

2

1

1.27

1.286

Calf -Adop

- N-Adop

42

29

39

20

39.394

39.216

3

3

1

1

2

2

1.08

1.45

Sheep –Adop

- N-Adop

223

106

80

29

80.81

56.863

18

13

1

1

17

12

2.79

3.65 Gaot –Adop

- N-Adop

25

-

8

-

15.69

-

5

-

2

-

3

-

3.125

-

Horse –Adop

- N-Adop

21

4

21

4

21.212

7.843

1

1

1

1

-

-

1.00

1.00 Mule –Adop

- N-Adop

26

7

25

6

25.253

11.765

2

2

1

1

1

1

1.04

1.17 Donkey –Adop

- N-Adop

178

61

91

38

91.92

74.51

5

4

1

1

4

3

1 .96

1.60 Poultry –Adop

- N-Adop

510

131

80

29

80.81

56.863

45

12

1

1

44

11

6.37

4.572 Bee-hive –Adop

- N-Adop

18

1

5

1

5.051

1.961

10

1

1

1

9

-

3.60

1.00

122

Table 14.Respondents livestock ownership in Tropical Livestock Unit (TLU)

Number of Livestock owned Types of Livestock

By- Adopters By- N- Adopters Total No. of livestock

Oxen 301 110 411

Cow 85 42 127

Bull 48 16.50 64.50

Heifer 39 13.50 52.50

Calf 10.50 7.25 17.75

Sheep 29 13.78 42.78

Goat 3.25 - 3.25

Horse 23.10 4.40 27.50

Mule 28.60 7.70 36.30

Donkey 124.60 42.70 167.30

Poultry 6.63 1.703 8.333

Total 698.68 259.533 958.213

(Source: Computed from own survey data, 2005)

Table 15.Conversion factors used to estimate the households’ livestock ownership into

tropical livestock units (TLU)

Source: Strock et al., (1991)

Animals

TLU-equivalent

Calf 0.25

Heifer & Bull 0.75

Cows & Oxen 1.00

Horse 1.10

Donkey 0.70

Ship & Goat 0.13 Chicken/poultry

0.013

123

Table 16.Discrete characteristics of respondents

Non – Adopters (51) Adopters (99)

Characteristics

Number Percent Number Percent

X2

df

Signifi

cance (2-

sided)

Conting

ency coeffici

ent

Level of Education 8.138 5 0.149 0.227

Illiterate 33 64.71 64 64.65

Literate 4 7.84 4 4..04

Read & Write 14 27.45 5 ..05

Elementary School - - 3 3..03 Junior Secondary - - 4 4..04

High School - - 19 5..05 House hold sex

-Male

-Female

11

40

21.57

78.43

7

92

7.07

92..93

6.700*** 1 0.010 0.207

Health Status

-Un-healthy

-Healthy

7

44

13.73

86.27

7

92

7.07

92.93

1.762 1 0.184 0.108

Access to credit

-Yes

-No

43

8

84.31

15.69

71

28

71.72

28.28

2.928* 1 0.087 0.138

Leadership /Social status

-Yes

-No

48

3

94.12

5.88

73

26

73..74

26.26

8.965*** 1 0.003 0.237

Off-farm income

-Yes

-No

42

9

82.35

17.65

82

17

82.83

17.17

0.005 1 0.942 0.006

Distance of credit institutions

-Far

-Close

43

8

84.31

15.69

89

10

89..90

10.10

0.994 1 0.319 0.081

Distance of DA office

-far

-close

48

3

94.12

5.88

12

87

12.12

87.89

1.456

1 0.228 0.098

Market Access

-Far

-Close

43

8

84.31

15..69

89

10

89.90

10.10

0.994 1 0.319 0.081

Access to labor

-No access

-Employment and other Sources

48

3

94.12

5.88

87

12

87.88

12.12

1.456 1 0.228 0.098

Access to Extension Service

-Yes

-No

-

51

-

100

94

5

94.95

5.05

2.665* 1 0.103 0.132

Education Level -Illiterate

-Literate

18

33

35.29

64.71

35

64

35.35

64..65

0.000 1 0.994 0.001

***, ** and* Significance at P<0.01, P<0.05 and p<0.10 respectively.

124

Table 17.Respondent farmers’ general information

Adoption

Category

Summary of

statistics

House hold

head Age

House hold

family size

Farmers’ extension

experience (Ys)

Farming

experience

Mean 46.1010 5.85 7.8687 21.8990

St.D 13.2560 2.19 4.7866 11.0809

Minimum 19.0000 1.00 2.0000 2.0000

Maximum 80.0000 11.00 20.0000 55.0000

Adopters

Range 61.0000 10.00 18.0000 53.0000

Mean 46.4706 5.10 3.7647 20.7647

St.D 14.5305 2.39 1.7842 10.9829

Minimum 20.0000 1.00 1.0000 4.0000

Maximum 80.0000 10.00 9.0000 60.0000

Non-adopters

Range 60.0000 9.00 8.0000 56.0000

Mean 46.2300 5.59 6.4730 21.5130

St.D 13.6550 2.28 4.4850 11.0230

Minimum 19.0000 1.00 1.0000 2.0000

Maximum 80.0000 11.00 20.0000 60.0000

Total

Range 61.0000 10.00 19.0000 58.0000

(Source: Computed from own survey data)

125

Table 18.Factors affecting Intensity of adoption of improved bread wheat

varieties (Maximum Likelihood Tobit Model Estimation)

Variables Coefficient Standard Error b/St.Er. P (/Z/>z)

HHHSEX 0.1854701150 0.82006662 2.262 0.0237**

HHHAGE 0.3863329500 0.15596286 2.477 0.0132**

EDUHHH 0.1697049462 0.56398485 3.009 0.0026***

HEALSTAT 0.38138220125 0.72107639 5.289 0.0000***

FAMILYSI 0.1544308922 0.11637647 1.327 0.1845

HHOFFINC 0.1409765567 0.68473026 2.059 0.0395**

DISDAOF1 0.1685119034 0.88888172 1.896 0.0580*

TOTLIVUN -0.8908541184 0.10884712 -0.818 0.4131

SUMOWRE -0.3802017439 0.22997223 -1.653 0.0983*

CRINMFF1 0.1719842596 0.96902492 0.018 0.9858

OTSOLA1 -0.2466522041 0.80402014 -0.307 0.7590

OXTLU -0.3024284552 0.22093233 -0.137 0.8911

YEXPEXTS 0.4835498401 0.54842929 0.882 0.3779

GEXSERVE 0.2590100298 0.11097534 2.334 0.0196**

Sigma 0.2268484851 0.15882571 14.283 0.0000

Log likelihood function= 6.582514

*, **And*** indicate the level of significance at 10%, 5% and 1% respectively.

126

Appendix.2. Interview Schedule for data collection from. Farmers

The objective of this Interview Schedule is to collect information from farmer respondents on

improved bread wheat production in Akaki area, rural part of Akaki Kaliti sub-city of Addis

Abeba administration from December/ 2004 to March/ 2005. The study is conducted for

academic purpose. Hence, we request your honest & fair responses to fill up this interview

schedule.

1. General & personal information of the respondent

1. Respondent’s name…………………………………………………..

2. Sex; 0 = female 1 = Male

3. Age……………………………..years

4. Marital statuses; 1.Married, 2.Single or unmarried, 3.Divorced, 4.Widow/Widower.

5. Rural Kebelie Administration/ Peasant Association ………………………………

Village………………………………………………………………………….

6. Previous or current leadership status; 0 = No, 1 = Yes

7. Educational Status: 0 = Illiterate, 1 = Literate

8. Educational level:

1. Read & Write, 2.Grade 1- 6, 3.Grade 7- 8, 4. Grade 9- 12, 5.above grade 12

9. Household Characteristics Information

Table 19.Household characteristics

No Name of house hold members Sex Age Educational

status

127

10. Land holding and farm characteristics of the sample households

Table 20.Land holding & Farm Characteristics of the sample households

No Types of land use Own

(ha)

Rent

(ha)

Total

(ha)

1

2

3

4

5

6

Cultivated (farm) land

Grazing land

Homestead land

Forest land

Unused land

Total land holding

11. Livestock ownership

Appendix Table 21.Livestock ownership

12. Types of crop grown in the survey year

No Types of Livestock Number

1

2

3

4

5

6

7

8

9

10

11

12

Ox

Cow

Calf

Bull

Heifer

Horse

Mules

Donkey

Goats

Sheep

Chicken

Bee in Hive

128

Table 22.Types of crop grown in the survey year

No Types of crops Land in (ha)

13. Involvement in irrigation production; 0 = No, 1 = Yes

14. Land size for irrigation production---------- ha.

15. Involvement in improved bread wheat production: 1.only this year 2.This year & in the

previous year 3.In the previous year but not this year 4. Never involve.

16. Reasons for involvement: 1.High yield 2.High market demand quality 3.Pest/disease

resistance 4.Frost resistance 5.Short maturity date 6.High food quality 7. Good storage quality

8. Good quality of cook ability 9. Good straw quality 10.Seed availability 11.Seed availability

12. Good information service 13. Fertilizer availability

17: Reasons for un-involvement: 1. Low yield 2.Low market demand 3.Low pest/ disease

resistance 4.Low frost resistance 5.Long maturity date 6.Poor food quality7. Poor storage

quality 8.Poor cooks ability 9.Poor straw quality 10. High seed price 11.Shortage of seed

12.Shortage of fertilizer 13.Lack of information 14.Lack of money and credit 15.Late arrival

of seed 16.Late arrival of fertilizer 17.High interest rate of credit

18. Reasons for discontinuity: 1.Poor yield performance 2.Poor pest/ disease resistance3. Poor

market demand 4. Poor frost resistance 5.Poor storage quality 6.Long maturity date 7. Poor

cook ability 8.Poor straw quality 9.Poor food quality 10. High seed price 11. Seed shortage,

12.Fertilizer shortage 13.Poor extension supports 14.Late arrival seed 15.Late arrival of

fertilizer, 16.Lack of money & credit 17.High interest rate of credit.

19. Total farm land/ cultivated land ----- ha.

129

20. Total wheat land---- ha

21. Land for improved bread wheat………ha.

22. Do you know paven-76? 0=no; 1=yes

23. Do you know HAR- 1685 (Kubsa)? 0=no; 1=yes

24. Do you know HAR- 1709 (Mitike)? 0=no; 1=yes

25: Use of disease and pest control chemical: 0 = No, 1 = Yes

26. Your future plan of involvement in improved bread wheat production

0 = Discontinue, 1 = Continue

27. Presence of problems related to fertilizer: 0 = No, 1 = Yes

28. Problems related to fertilizer: 0 = No, 1 = Yes

29.If yes, 1.High fertilizer price 2.Lack of credit to purchase fertilizer 3.High interest rate of

credit to use credit to purchase fertilizer 4.Far distance of distribution center 5.Poor quality

(mixed with impurities and caked) 6.Shortage 7.Lately arrival 8.Lengthy process &

complicated format 9. Poor distribution processes

30. Presence of problems related to improved bread wheat seed: 0 = No, 1 = Yes

31. Types of problems: 1.Shortage 2.Poor seed quality 3.Late availability 4.Far distance of

distribution center 5 Impurity problems and 6.Poor germination problem.

32. Extension support: 0 = No, 1 = Yes

33. Extent of extension support: 1. Poor 2. Medium 3.Good

34. Improved wheat seed rate application: 1.The recommended rate 2. Below the

recommended rate 3.Above the recommended rate

130

35. Fertilizer rate of application 1: Apply the recommended rate, 2: Below the recommended

rate 3. Above recommended rate

36. Chemical application: 1. Apply the recommended rate 2. Below the recommended rate,

3.Above recommended rate

37. Reasons for Below & Above recommendation use of agricultural inputs 1.Low quantity of

input availability 2.High price of inputs 3.High interest rate of credit 4.Lack of credit & money

38. Frequency of weeding: 1.One 2.Two 3.Thrice 4. Four & above

39. Frequency of plowing: 1. One 2.Two 3.Three 4. Four & above

40. Characteristics of improved bread wheat varieties: (1 = High, 2 = Medium, 3 = Low)

Appendix Table 23.Improved bread wheat varieties characteristics

Varieties No Characteristics

HAR-1685 KAR-1709 Fovon-76

1 Frost resistance

2 Pest/Disease resistance

3 Seed size

4 Cocking time

5 Storage quality

6 Yield performance

7 Market demand

8 Food quality

9 Color quality

41. Seed selection criteria: 1.Pest/Disease resistance 2.Frost resistance 3.High yield

performance 4.high market demand 5.Attractive color 6.Short maturity data 7. Good food

quality 8.Low time taking 9. Good straw out put and good quality 10. Good storage quality 11.

Good Germination and till ring capacity

42. Improved bread wheat seed source: 1.Purchase from market, 2.Exchange from other

owners, 3.Own seed from previous product, and 4. Borrow from owner formers,

5.Cooperative, 6.MOA 7. Seed enterprise, 8.Research organization

131

43. Other Agricultural input sources: 1 Cooperatives, 2.MOA, 3.others

44. Access to credit service: 0= No 1: yes

45. Credit sources: 1.Cooperatives, 2.Ethiopian 3. MOA, 4.Other- Credit Institution, 5

Individual/ private lenders

46. Presence of credit problems: 0=no 1=Yes

47. Types of credit problems: 1.Shortage 2.Long and complex process, 3.high interest rate, and

4. Far distance

48. Support from relatives and other colleagues to solve financial constraints to purchase

inputs: 0=no 1=yes

49. Distance of credit providers Institutions=far 1= close

50. Do you have Access to market? 0=no 1=yes

51. Market distance=far 1=close

52. Do you have Access to extension Service? 0=no 1=yes

53. Distance of Development Agent Office: 0=far 1=close

54. Have you attended training? 0=no 1=yes

55. Have you attended demonstration and field day programs? 0=no 1=yes

56. Can the DA call the farmers for extension meeting with out the permission of government

authorities? 0=no 1=yes

57. What did you feel when called for extension meeting? 0=un-happy 1=happy

58. What are your Sources of Agricultural and input information sources? 1. DA 2. Radio,

3.Television, 4.Written materials, 5.Training, 6.Field, day and demonstration, 7.Posters, 8.PA-

leaders, 9.Community leaders, 10.neighbours and colleague farmers,

132

59. From the following, to which one assign your- self? 1. Mode farmer, 2.follower farmer,

3.Neither of them

60. How many years of Experience do you have in agricultural extension? ……Years

61. Total farming experience in years? ...................... Years

62. Have you got training and sufficient information on improved bread wheat? 0=no 1= yes

63. If you did not get training how did you perform production operations? 1. Using try and

error methods, 2. By copying from other experienced farmers, 3. By asking support from DA

64. Purpose and use of off-farm income: 1.for house hold food consumption and other costs,

2.for input purchase, 3.for labor hiring, 4.for health cost covering, 5.for all

65. Do have access to labor outside the household labor? 0=no 1=yes

66. If yes, your sources of labor out side the household labors: 1. Hired labor

2. Cooperation labor from colleague and relative framers, 3. Exchange labor

67. Who make the decision on off-farm income? 1. Family head, 2. Husband

3. Wife, 4.Husband and Wife, 5.The household members

68. Which type of agricultural operation is critical to you and need higher labor? 1. Plowing,

2.Sowing, 3.Weeding, 4.Harvesting, 5. Threshing

69. Do you have plowing oxen? 0=no 1=yes

70. If no, how do you plow your farmland? 1. Using oxen plowing, 2.Through labor

exchanges 3. By asking cooperation

133

Table 24.Cramer’s V and Pearson’s R values for Discrete and Continuous variables

Hypothesized independent variables and their values

Continuous variables Pearson’s R value for

Continuous variables

Discrete variables

Cramer’s V-value for

discrete variables

Age 0.257 Sex 0.211

Family size 0.167 Education 0.001

Farm land 0.292 Health status 0.108

Livestock ownership

in (TLU)

0.257 Leadership-position 0.244

Oxen ownership 0.247 Off-farm income 0.006

- - Distance of DA-office 0.099

- - Extension service 0.133

- - Other labor source 0.099

- - Market access 0.081

- - Credit service 0.140

(Source: Own computation)

*Notice: 0 value=no association, 0-0.4 value= weak, 0.4-0.7= moderate and >0.7= strong

association ((Sarantakos, 1998).

Table 25.Respondents leadership position

Have leadership position ADs NADs X2-test Total

Yes 48 (94.12%) 73 (73.74%) 121

No 3 (5.88%) 26 (26.26%) 29

Total 51(100) 99 (100) 8.965*** 150

(Source: own computation)