1

ADOPTION DECISION AND INTENSITY FOR IMPROVED MAIZE

VARIETY BY SMALLHOLDER FARMERS OF JIMMA ARJO

WOREDA OF OROMIA REGION: FACTORS AND CHALLENGES

BY

BIQILA HIRPA BEDASA

ADDIS ABABA UNIVERSITY

SCHOOL OF GRADUATE STUDIES

COLLEGE OF DEVELOPMENT STUDIES

JUNE, 2013

ADDIS ABABA, ETHIOPIA

2

Abbreviations

CSA Central Statistical Agency

CIMMYT International Maize and Wheat Improvement Center

CBSS Community Based Seed System

DOARD District Office of Agricultural and Rural Development

ESE Ethiopian Seed Enterprise

IFPRI International Food Policy Research Institute

MORAD Ministry of Revenue and Development

NGO Non Governmental Organization

OBA Oromia Bureau of Agriculture

OLS Ordinary Least Square

OSE Oromia Seed Enterprise

TLU Tropical Livestock Unit

3

ADOPTION DECISION AND INTENSITY FOR IMPROVED MAIZE VARIETY BY

SMALLHOLDER FARMERS OF JIMMA ARJO WOREDA OF OROMIA REGION:

FACTORS AND CHALLENGES

BIQILA HIRPA

ADDIS ABABA UNIVERSITY, 2013

Abstract

Transforming smallholder agriculture solely rests on increased adoption rate and optimal

utilization of modern agricultural technologies such as improved seed which enhances

yield and hence ensures food security at a household level. Thus, In order to make

modern agricultural technologies widely adopted and intensively used, all the key factors

affecting the adoption of this technology has to be known clearly and comprehensively.

logit and tobit model are respectively employed in this research to model adoption

decision and the proportion of land allocated to improved maize seed by farm households

in Jimma Arjo Woreda of Oromia region using a data collected from 394 sample

households for 2011/12 cropping season. The result of logit estimation shows marital

status, having family members living in a town or abroad, saving, yield perception,

fertilizer application, distance from output market, production technique, total land

holding, and amount of land allocated to maize all found to significantly determine the

decision of whether to use improved maize seed or not. Besides, total land allocated to

maize production, distance from main output market, having a family/relative living in

town or abroad, saving, production of cash crop, cooperative membership, attending

improved maize demonstration, yield perception, fertilizer application ,maize production

technique, and marital Status are those among those factors affecting our second

outcome decision. Finally, this research paper shed light on those factors so that all

actors (policy makers, seed suppliers and microfinance) operating in improved maize

market would give a due emphasis and take corrective action especially on those factors

that have found to commonly affect the two outcome decisions.

Key words: Food security, logit, smallholder, tobit,

4

Acknowledgements

First and foremost, I would like to thank all our instructors, faculties and other staffs, who

helped us, challenged us, prepare us and expose us to new ideas and perspectives about

our world. Most of all, I appreciate your precious efforts.

A special thank goes to my advisor Dr. Workineh Nigatu whose valuable ideas,

constructive criticism and helpful comments have been helpful throughout this thesis.

Progressing much would have been too challenging without his assistance.

I would also like to appreciate Simbo Asrat for her valuable and endless moral and

financial support throughout this study. Besides, I would like to express my deepest

gratitude for people without whom the field work would not have been possible, namely

the enumerators, the facilitators, the key informants, and all case individuals or

households who take part in this research work.

Moreover, I would not pass without mentioning the special thanks to Zeleke G/Yesus,

Getachew Buli, and Arjo Woreda Office of Agriculture for their support and much other

collaboration during the overall data collection, activities.

Above all, many thank goes to almighty God.

5

TABLE OF CONTENT

Page

ABBREVIATIONS ………………………………………………………………...……2

ABSTRACT …………………………………………………………………….……….3

ACKNOWLEDGMENT ……………………………………………………….….….....4

TABEL OF CONTENT………………………………………………………………..…5

LIST OF TABELS………………………………………………..…………….…..........8

LIST OF FIGURES…………..………………….……………………………………...10

CHAPTER ONE: INTRODUCTION

1.1 Background………………………..…………………………………………….…11

1.2 The Statement of the Problem……………………………………………………..14

1.3 Research Question……….………………………………………………………..16

1.4 Objectives of the Study……………………………………………………………16

1.5. Significance of the study………………………………………………………….17

1.6 Scope and Limitation…………………………………………………………..…..18

1.7 Organization of the Study…………………………………………………….....…19

CHAPTER TWO: REVIEW OF RELATED LITERATURE

2.1 Definition of Adoption and Diffusion of Agricultural Technologies……………...20

2.2 Theories of Adoption and Diffusion of Agricultural Technologies…………….....22

2.3 Stages in Adoption Process…………………………….…..……………………..23

2.4 Approaches Used In Modeling Adoption of Modern Agricultural

Technologies…………………………………………………………………………...27

2.4.1 The Choice of Technique Approach…………………………………………...27

2.4.2 The Target-Input Approach …………………………………………………....28

2.4.3 The Risk Adverse Approach…………………………………………………...29

6

2.5 Empirical Correlates of Farm Technology Adoption Decision…………………..…32

2.5.1 Demographic Factors………………………………………………………...33

2.5.2 Socioeconomic Factors…………………………………………………….....35

2.5.3 Institutional Factors…………………………………………………………...36

2.5.4 Psychological Factors…………………………………………………….…...37

2.6 Seed System and Smallholder Agriculture In Ethiopia…………………………....38

2.6.1 The Formal Seed System………………………………………………….…..39

2.6.2 The Informal Seed System…………………………………………………….42

2.6.3 The Supply-Demand Gap In Seed Market……………….…………………...43

2.7 Adoption of Hybrid Maize Seeds and Food Security…………………………..….45

2.8 Analytical Framework……………………………………………………………..46

2.8.1 Conceptual Framework of the Study Area……………………………….……49

CHAPTER THREE: METHODOLOGY

3.1 Sampling Design…………………………………………...……………….….....51

3.2 Method of Data Analysis………………………………………………….……...54

3.2.1 Descriptive Method…………………………………………………………..54

3.2.2 Econometric Method…………………………………………………...….....55

3.3 Variables Definition and Hypothesis…………………………………….………..60

CHAPTER FOUR: BACKGROUND OF THE STUDY AREA AND THE SAMPLE

RESPONDENTS

4.1 Geographical Setting of the Study Area…………………………………….…….69

4.2 Demographic Characteristics of the Sample Respondents………………….….....73

4.2.1 Asset Ownership of Sample Household………………………………….......75

4.2.2 Membership to Local Institutions………………………………………..…...76

CHAPTER FIVE: RESULTS AND DISCUSIONS

5.1 Descriptive Results………………………………………………..………………78

5.1.1 Adoption Decision and Demographic Characteristics of the

Respondent………………………………………………………………………….78

5.1.2. Adoption Decision and Household Endowment Attribute…….…….……….80

7

5.1.3 Adoption Decision and Institution Related Variables…………………………82

5.1.4. Production Technique and Household Perception Related Characteristics…..84

5.2 Proportion of Land Area Allocated to Improved Maize Variety and Household

Characteristics................................................................................................................88

5.2.1 Decision of Intensity and Households Socioeconomic Characterist……….....90

5.2.2 Major Challenges to Intensification of Improved Maize Production ………...94

5.3 Binary Logistic Regression Result………………………………………………...95

5.4 Estimation of Factors Affecting the Intensity of Improved Maize Seed…………101

CHAPTER SIX: CONCLUSION AND RECOMMENDATION

6.1 Conclusion………………………………………………………………………..108

6.2 Recommendations …………………………………………………..…………...110

References……………………………………………………………………………....112

Appendices………………………………………………………………………...........121

8

LIST OF TABELS

Page

Table 2.1: Area planted With Improved seeds and Chemical Fertilizers in Oromia Region

(2010/11…………………………………………………………………..………….…..40

Table 2.2: Seed supply of Major Cereals in Oromia

Region………………………………………………………………………………..….41

Table 2.3: Area Covered (ha) by Informal Seeds Over the Last Five Years (2005/06-

2009/10)…………………………………..……………………………………………...43

Table 4.1 Distribution of Woreda into Kebeles and Agro ecologies………….………….69

Table 4.2 Population Distribution of the District by Sex and Residence

Category…………………………………………………………………………………72

Table 4.3: Total Land Use Pattern of the District…………….……………………….…73

Table 4.4: Demographic Distributions of Sample Households……………………....….74

Table 4.5: distribution of sample respondents According to Asset Ownership……….....76

Table 4.6: Distribution of Sample Respondents With Institutional Membership………..77

Table 5.1: Adoption Decision and Demographic Characteristics………………………..79

Table 5.2: Adoption Decision and Endowment Characteristics…………………….…...81

Table 5.3: Adoption Decision and Institution Related Variable………………………....82

Table 5.4: Trend for Credit Distribution of the Woreda……………….………………...84

Table 5.5: Adoption Decision and Household Perception and Production

Techniques……………………………..………………………………………..…….…85

Table 5.6 The Status of Adoption Rate of the Sample Respondents……………..…..….87

9

Table 5.7: Reason for Discontinuing the Use of Improved Maize by Household……89

Table 5.8 Amount of Land Allocated Under Improved Maize Seed and Household Asset

Or Endowment Variables (Continuous)….………………………………………..…..90

Table 5.9 Intensity of Decision and Household Characteristics (Nominal)…….……..92

Table 5.10 Major Constraint to Increased Utilization……………………..……….….94

Table 5.11 A Multivariate Logitistic Estimation result…………………………….....98

Table 5.12 Tobit Estimation Result for Factors Affecting Intensity of Decision……103

10

LIST OF FIGURES

Page

Figure 2.1: The Nature of Seed Market (demand/supply) of Oromia Region During

2005/06 - 2010/11 Production Season ……………….……………….………….…....44

Figure 2.2: Analytical Framework of the Study Area…………………...…….………50

Figure 4.1 Map of Jimma Arjo Woreda ……………………………….……….....…..70

11

CHAPTER ONE: INTRODUCTION

1.1 Background

Though agricultural production in Africa has virtually been dominated by small-scale

farmers who produce up to 90 percent of the food consumed, such a high percentage of

farmers cannot meet the food demand of the entire region leading to a huge appropriation

of food aid into the continent annually (Odulaja and Fassil, 1996). The central issue in

forefront of government and policy makers therefore, is to work on how to accelerate the

agricultural production growth rate to meet the food needs of ever-growing population

which takes into account the problems of the majority of poor African farmers (Shields et

al., 1993). To this end, various agricultural technologies have been released to farmers

after being proved to be high yielding and potentially capable of solving food shortage

(Robson, 1990).

In Ethiopia, economic growth strategy formulated by the government in 1991 places high

priority on accelerating agricultural growth to achieve food security and poverty

alleviation; the core target of which was to increase cereal yields by focusing on

technological packages that combined credit, fertilizers, improved seeds and other

packages (Howard et al., 2003). However, the impacts of these implemented policies in

changing the livelihood of small holder farmers in particular and the public at large is

questionable with poor productivity growth and in general with no major benefits for

consumers as food prices do not show declining patterns.

12

According to Byerlee (2007), some of the major factors affecting the results of the

intensification program are low technical efficiency in the use of fertilizer, poor

performance of the extension service, shortcomings in seed quality and timeliness of seed

delivery, promotion of regionally inefficient allocation of fertilizer, no emergence of

private-sector retailers negatively affected by the government’s input distribution tied to

credit, and the generation of an unleveled playing field in the rural finance sector by the

guaranteed loan program with below-market interest rates. In Ethiopia, even though

adoption of improved agricultural technologies has been a long term concern, evidence

indicates that adoption rate of modern agricultural technologies in the country is very low

(Kebede et al., 1990). Small scale farmers’ decisions to adopt or reject agricultural

technologies depend on their objectives and constraints as well as cost and benefit

accruing to it (Million and Belay, 2004).

The seed system in Ethiopia consists of the formal seed sector, the informal or farmer

seed system, as well as the occasional emergency seed programs, which are often

operated by some NGOs and relief agencies. In most cases, it is not easy to differentiate

these three sectors in the seed value chain as there exist convergences of meaning with

respect to its operation and thus it is very difficult to set a clear demarcation line between

those systems (OSE, 2011).

The formal seed sector is the place where farmers can get access to a certified improved

seed though the sector could not adequately meet the seed demand of the vast majority

and it is still limited to a few major crop varieties developed by agricultural research

13

(Asrat et al., 2008). Although the government allowed any legal domestic entity to access

breeder seeds developed by the public institutions, the role of private seed companies did

not expand as expected since most farmers in Ethiopia relay heavily on the informal seed

system which accounted for about 80-90 percent (Zewdie et al., 2008).

Despite the release of several technologies, particularly of improved crop varieties, there

has been limited use of improved seeds by the majority of farmers which further

contributes for low agricultural productivity due mainly to, unavailability (outreach) of

quality seeds at the right place and time coupled with poor promotion system (CSA,

2010). In addition, the demand for commercial seed competes with farmer-saved seed,

making demand for the former highly price elastic (Jayne and Mayers, 2007).

In the study area, Jimma Arjo Woreda, the application of improved maize seed has been

a long practice following its introduction. The major types of improved maize varieties

distributed in the Woreda are; BH-660, BH-543, 30G19, and 30D79. Among those, BH-

660 has been the dominant and widely used variety in the area covering 53 percent of the

total improved maize seed distributed during 2011/12. In Jimma Arjo Woreda, though

farmers had been using improved maize seed since its introduction, the rate of adoption

and intensity of its application remained low as the report from the Woreda Office of

Agriculture reveals. The major constraints related to lower rate of adoption and intensity

increased price of the seed, weak credit facilities and problems related with the

distribution of seed and other complementary inputs like fertilizer and pesticides.

14

1.2 The Statement of the Problem

The adoption of new agricultural technology such as improved seed is central to

agricultural growth and poverty reduction through their effect of increasing agricultural

productivity and hence food self sufficiency (Minten and Barret, 2008). The study by

SG2000, (2002) supports the above argument by stating as maize productivity in western

Ethiopia increased to 5.4 tons per hectare from a 1.6 per hectare in 1993 mainly due to a

higher adoption of improved seed and fertilizer. Seemingly, adoption of improved seeds

has the power to improve household welfare as a study from Mexico cited in Becerril and

Abdulahi (2010) reveals. In addition, a report from World Bank, (2008) depicted a very

low adoption of productivity enhancing technologies have hampered efforts to reduce

rural poverty especially in developing countries.

Even though innovation of the new technologies is believed to improve the welfare of the

poor through enhancing agricultural productivity, the rate and level of improved seed

adoption is far below what is expected due mainly to many interrelated problems. The

causes for low adoption rate and fluctuation in seed uptake is expressed by a number of

attributes ranging from farmer and farm related characteristics to attributes related with

the technology itself, perception of its profitability and other socio economic reason.

Ethiopia has the lowest adoption rate in eastern African standards even though there is

dramatic increase since 1992 due mainly to the introduction of a new extension system

(CIMMYT, 1999). Besides, about 90 percent of seed supplied to farmers is from local

informal sectors which covers about 94 percent of the total cultivated land area and the

15

remaining 10 percent is formal seed (improved) sector which is dominated by

government owned enterprises and few private companies (ESE, 2011). So, this confirms

existence of problems related with adoption decision and the proportion of land allocated

to improved seed (improved maize in our case) as a greater proportion of seed source

comes from local exchange which has not passed through any quality certification.

In Jimma Arjo Woreda, the study area, average annual growth rate of improved maize

shows a decreasing trend. For instance, during 2004/05 production season, 48 quintals of

improved maize (BH-660) has been distributed. After 3 years; that is in 2007/08, the

amount decreased to 13 quintals. In addition, during 2011/12 production season, from the

total of 3180 hectare of land allocated to maize cultivation, the share of improved maize

seed amounted only to 22 percent while the remaining 78 percent of land was allocated to

the production of local maize variety showing the lowest adoption rate of improved

maize seed in the study area.

Among other things, factors that adversely or positively affect the adoption to the new

technologies were not given a due concern, the beneficiaries have not actively

participated in the technology development processes, the indigenous knowledge; which

can play a vital role in improving and enhancing technological development process was

overlooked and thus fail to comprehensively incorporate all the possible factors

explaining adoption decision and intensity of seed uptake.

16

Therefore, for a wider adoption and increased seed uptake, it is up to the researcher and

policy makers to search for the major determinants (institutional, socioeconomic and

human) factors affecting adoption decision and intensity of adopting improved maize

seed. It is such a gap that initiated the researcher to undertake a crossectional studies on

factors affecting adoption decision and proportion of land area allocated to improved

maize variety using the data collected from 394 hoouseholds for the 2011/12 croping

season.

1.3 Research Question

These research paper has been intended to answer the following questions;

1.What are those factors affecting the decision of a smallholder households whether to

purchase improved seed ?

2.What are the major factors afecting the proportion of land allocated to improved maize

seed or the intensity of adoption?

3.What are the major observable constraints behind the decision to adopt and amount of

land allocated to improved maize variety ?

1.4 Objectives of the Study

The General Objective of the paper was to understand and isolate important factors

determining smallholder farmer’s decision to adopt improved maize variety and their

level of Adoption.

17

The Specific Objectives of the study were:

To identify different farmer socioeconomic, institutional and other related factors

affecting the decision of farmers to adopt or not to adopt improved maize seed.

To investigate possible factors that affecting the simultaneous and inseparable

demand decision by farm households. That is the proportion of land area covered

under improved maize variety.

To explore the challenges related with both outcome decision (adoption decision

and proportion of land area allocated to improved maize variety).

1.5. Significance of the Study

In order to boost adoption rate of improved maize seed and its intensity of application to

a desirable level, stakeholders participating in the overall seed value chain has to

understand all the possible determinants of adoption decision and the subsequent decision

of intensity so that they can respond to the needy farmers accordingly. The prime

objectives investigated in this research was the estimation all the possible factors

affecting adoption decision and proportion of land allocated to improved maize variety

for a specific cropping season.

The result of this research would be helpful for extension workers, cooperative union’s

development agents, seed enterprises and other NGO’s in order to work on the

18

relationships established and gaps identified so as to take a quick response. It would also

be helpful for policy maker to redirect resources used for intervention (extension, credit,

demonstration, subsidy scheme) in accordance with the gap. The estimation result shown

in this research wills also calls for further research that would holistically incorporate all

the possible factors affecting adoption decision and proportion of land allocated to the

production improved maize variety in the major maize growing belts of Western Oromia.

1.6 Scope and Limitation

This study was conducted in Jimma Arjo Woreda located in Oromia Regional State.

Factors affecting adoption decision and proportion of land allocated to the chosen

technology were usually separate across the technology type or across crop variety. Thus,

the estimation of adoption decision and intensity of its application are limited to

improved maize only. The major limitations encountered during the study were the

following:

1. Some households were not willing to give correct data regarding their total land

holding for some questions for fear of tax or any other which probably affected the

quality of the data and the analysis made.

2. Some respondents were unable to tell us the exact figure especially question related

with household endowment attribute due mainly to memory lapse or any other case

which had again an impact on the quality of our data.

19

3. Shortage of time and resources was another constraints faced during the study. Since

the sample size was large, it needs more recourses and time.

1.7 Organization of the Study

The thesis is generally organized into six chapters. The first chapters demonstrate an

introductory part which incorporates: background, statement of the problem, research

questions, objectives, significance of the study, and limitations of the study. The second

chapter is devoted to literature review and formulation of analytical framework of the

study. It illustrates briefly the review of literatures related with adoption of agricultural

innovation, the nature of seed market system in the country, the theoretical justification

and empirical evidences related with adoption decision and intensity of applying

improved maize seed and the subsequent constraint related with it. Chapter three deals

with the research methodology where data set of the study, methods of data analysis, and

variables to be analyzed are hypothesized and briefly explained. Chapter depicts about

the general background of the study area and the sample households interviewed. Chapter

five discuses the results of the research outcomes from the data collected and finally

chapter six presents summary and concluding remarks and recommendations.

20

CHAPTER TWO: LITERATURE REVIEW

2.1 Definition of Adoption and Diffusion of Agricultural Technologies

Adoption is defined as the integration of an innovation into farmers’ normal farming

activities over an extended period of time. It does not refers to a permanent behavior

since a farmer may decide to discontinue the use of an innovation for a variety of

personal, institutional, and social reasons one of which might be the availability of

another practice that is better in satisfying farmers’ needs (Dasgupta,1989).

According to Feder et al., (1985), adoption is classified as an individual (farm level)

adoption and aggregate adoption. The former refers to the degree of use of new

technology in long run equilibrium when the farmer has full information about the new

technology and its potential whereas the later refers to the spread of new technology

within a region and is measured by the aggregate level of specific new technology with a

given geographical area or within the given population. Similarly, according to Rogers

(1983), adoption is a mental process through which individual passes. That is, the process

that starts from hearing about an innovation or technology to final adoption decision

indicating that adoption is not a sudden event. Rogers (1983) state adoption as a process

since farmers do not accept innovations immediately until they take time and think over

things before reaching a decision.

The rate of adoption is defined as the percentage of farmers who have adopted a given

technology. Lionberger (1968) indicates that ordinarily adoptions are very slow at first,

increase at an increasing rate until approximately half of the potential adopters have

21

accepted the change. After this, acceptance continues, but at a decreasing rate indicating

that that the rate of adoption follows S-shaped curve.

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

number of hectares planted with improved seed (also tested as the percentage of each

farm planted to improved seed). Nkonya (1997) defines intensity of adoption as the

amount of input applied per hectare as compared to the total land.

It takes time for an innovation to diffuse through a social system. It is unrealistic to

expect that all farmers in a community will adopt an innovation immediately after its

introduction as there is always a variation among the members of a social system in the

way they respond to innovative idea or practice (Shoemaker, 1971).

While there are always a few members in a social system who are so innovative that they

adopt an innovation almost immediately after they come to know about it, the majority

take a long time before accepting the new idea or practice (Dasgupta, 1989). Moreover,

people adopt the innovation in an ordered time sequence, and they may be classified into

adopter categories on the basis of when they first begin using the new idea. According to

Dasgupta, (1989), although farmers often reject an innovation instead of adopting it, non

adoption of an innovation does not necessarily mean rejection as farmers are sometimes

unable to adopt an innovation, even though they have mentally accepted it (probably

because of economic and situational constraints).

22

2.2 Theories of Adoption and Diffusion of Agricultural Technologies

In the literature of innovation of agricultural technologies, adoption and diffusion are

used together and sometimes interchangeably used both referring to the processes that

govern innovation (Feder et al., 1985). According to Sunding and Zilberman (2001),

adoption studies analyze factors that affect if and when a farmer will begin using an

innovation (whether or not a farmer uses improved seed or how much of their land they

cultivate with improved seed) while diffusion studies analyzes how an innovation

penetrates its potential market (share of farmers who use improved seed or in the share of

land in total agricultural land that is cultivated with improved seed). Earlier empirical

studies relating adoption of agricultural innovations describes diffusion as an S-shaped

function of time which has grouped the determinant of adoption and diffusion into 4 parts

(Morris et al., 1999).

i)Varietal Characteristics: Refers to the net benefit to be achieved from utilizing the

variety as compared to its local counterparts which includes yield or expected gross

margin, input prices, uncertainty associated with the variety riskiness of the variety.

ii) Farm-level Characteristics: Includes the assessment or investigation of the climatic

and agro-ecological suitability of the location for the variety and quality of the land (if

the variety fits with the soil of that specific land to be applied to).

iii) Farmer Characteristics: This are usually tied to institution and policy framework

since institutions and government can influence those characteristics under consideration.

23

It includes agronomic expertise & skills, knowledge about variety, risk aversion, capital

availability, and access to credit.

iv) Institutional Characteristics: Refers to all the actors that would take part in the seed

supply, distribution and innovation which have then a greater role to play in determining

the market supply, consumer price and market demand for improved varieties. We will

further group and illustrate this determinants in our conceptual framework model of our

specific study area.

2.3 Stages in Adoption Process

The classical 5-stage concept (awareness, interest, evaluation, trial, adoption) formulated

by the North Central Rural Sociologists Committee has been the most widely accepted

concept in explaining the stages of adoptions. It was initially exposed to a wider criticism

by Campbell (1966) and later also by Rogers and Shoemaker (1971) who then designed

the innovation decision process. According to Rogers (2003) and Gross (1943), adoption

and diffusion studies focus on how farmers evaluate the new seeds and act on the

evaluations which happen in several stages described below.

Stage- 1 Knowledge: This is the characterstics of decision making unit or farmer

community in terms of prior exposure and knowledge of the general farm technology. It

also refers to the cases where individuals evaluate the payoffs from old and new

technology which also takes into account farmers socioeconomic characterstics,

personality varriables and comunication behaviours (Marra et al.,(2003).

24

As an alternative to individual learning, there are also social processes that might

override or replace empirical evaluations of the relative utility of improved seed.

According to Munshi (2004), the social dynamics affecting adoption of innovation are

based on teaching and imitation which will happen possibly through two ways.

i) Farmers copy other farmers on the basis of prestige, regardless of that farmer’s actual

success with the innovation (reinforcing word of mouth from adopters loop).

ii) Farmers also adopt an innovation when and because it has been adopted by many

others (reinforcing word of mouth from non adopters loop). However, adoption of

innovation through social learning has been criticized to operate in all circumstances and

in all socio cultural setup. For instance, social learning may spread maladaptive beliefs

and thus rely largely on biases and other factors that are weakly connected to actual

profitability evaluations especially in the case when the uncertainty is very high (Stone et

al., 2007).

Stage -2 Persuation: This refers to the percieved character of innovation and thus the

innovator or institutions responsible for supplying this innovation has a major role to play

in this process. Rogers (1995) have identified five characteristics of agricultural

innovations which are important in adoption studies as the following.

25

i) Relative Advantage: Is the degree to which an innovation is perceived as better than the

idea it supersede. In other words, it is to mean the superiority of the given technology in

terms of perhaps yield, maturity period and pest or weed resistance than the one that

preceded it.

ii) Compatibility: The degree to which the farmer perceives an innovation to be

consistent with his/her cultural values and beliefs, traditional management objectives, the

existing level of technology and stages of development.

iii) Complexity: The degree to which an innovation is perceived to be complex to

understand and use by farmers.

iv) Trial Ability: The degree to which the innovation could easily be tried by farmer on

his/her farm as most farmers have seen to be better convinced only when they have

physically exposed to the innovation that they want to apply.

v) Observability: The degree to which results of innovation are visible to farmers in

terms of the special attributes related with the innovation as compared to the conventional

ones (better yield, resistance to pest and disease and quality).In the study area, we have

also observed such a characteristics of the innovation to determine the both the

probability of adoption of the innovation and the scale of operations (we will discuss it

later in chapter five).

26

Stage-3 Decision: This is the product of the above process that is finally made by the

user of the innovations about either to accept or not. Adoption decision involves both

continued adoption and withdrawals. Seemingly, rejection involves continued rejection

and latter adoption.

Stage-4 Implementation: the next step after adoption decision that a farmer convinced

and decides to accept the new innovation and then applied it on its plots.

Stage-5: Confirmation: After farmers have decided to implement the innovation, then

she/he confirms Viability and the relative advantage of the new technology as compared

to the pre existing ones (in terms of yield and other parameters).

The determination of which process best explain and influence adoption of agricultural

innovation however depends on the stages of adoption; the characteristics of specific crop

under studies and the socioeconomic and geographic nature of the societies under

investigation (Sebastian and Birgit, (2009). For instance, adoption decisions are driven

more by objective evaluations in the early stages when the share of adopters on the total

farmer population (measured in the cultivated agricultural land) is still low whereas when

the number of adopter increases social dynamics tends to override objective evaluations.

The choice of specific policies to stimulate adoption and diffusion of improved seed too

depend on the relative dominance of the individual and social learning feedback loops,

specific characteristics of the corresponding crops as well as Countries or Regions.

27

2.4 Approaches Used In Modeling Adoption of Modern Agricultural

Technologies

Economic modeling of technology adoption particularly those of which is related to

adoption of high yield varieties (HYV) has taken many different forms. The most

pertinent approaches noted in agricultural technology adoption literature are the choice of

technique approach based on profit maximization, a target input model that focuses on

learning and the risk adverse approach (Mariapia, 2007).

2.4.1 The Choice of Technique Approach (profit maximization Models)

According to this approach, when the household is a price taker in all markets, for all

commodities which it both consumes and produces, optimal household production can be

determined independent of leisure and consumption choices. Thus, given the maximum

income level derived from profit-maximizing production, family labor supply and

commodity consumption decisions can be made (McGuirk and Mundlak , 1991).

The choice of technique approach focuses on choosing inputs to maximize profit which

in turn leads to decision rule that emphasizes prices, input constraints and environmental

factors as the key determinants of the scale of adoption. The most dominant hypothesis in

this approach is the one that is developed by Schultz’s (1964), the hypothesis that farm

households in developing countries are “poor but efficient” which largely relay on two

major aspects.

Firstly, efficiency is the manifestation of adoption of profit maximized farm household

strategy and thus poor farmers according to Schultz allocate resources in a manner

28

consistent with neoclassical model of a firm. Secondly, since farmers are assumed to be

efficient, development effort should aimed at providing the best condition for the

efficient application of these models. In addition, conditions should be laid down to

facilitate the emergency of technical innovation, the only way through which efficient

peasant can increase their output. Thus, since the potentials of all the existing

technologies are exhausted by poor farmers who have the capacity to respond rapidly to

the new opportunities, what are needed are only the new technological options supported

by price signals.

The central idea of this approach rested on the assumption that a farm household decision

is influenced by a single monetary variable that is profit and thus defines happiness in

terms of profit (the objective function).The other assumption is the one that states

economic agents as they are certain of the outcomes of their decision as they possess a

perfect knowledge and information. In addition, they are also free to combine resources

in amount needed to maximize profit without any cultural and institutional restraints.

Thus, the choice of technique approach will characterize the farmer’s production and

consumption decision as long as the above assumption is valid.

2.4.2 The Target-Input Approach (Utility maximization Theories)

Households operating in developing countries are likely to face more than one market

imperfection, which prevents first-best transactions and investments from taking place as

empirical analyses based on the above approach have generally produced negative results

when applied in farm household decision making.

29

In a situation when economic agents are uncertain of their decision, the market is

imperfect (both product and input market) and there exist information asymmetry,

modeling farm households based on profit maximization approach becomes very difficult

(Janvry et al., (1991). According to this approach, a household tries to maximize the

objective function that is a discounted future stream of expected utility from a list of

consumption goods (including home produced goods, purchased goods, and leisure), but

subject to what may be a large set of constraints in which a missing market is yet another

constraint on the household. This approach thus provides a framework which allows for

the possibility of learning from the experience of neighboring farmers and own

experience through Bayesian updating about optimal input use.

2.4.3 The Risk Adverse Approach

The above two approaches (choice of technique and target input) have many defects in

describing a farm household decision because they ignore the effect on farm household

behavior of the uncertainty and risk involved in peasant production, and the social

context in which peasant production takes place. In addition, most of those models are

static and assume that prospects are certain or, equivalently, that households are risk-

neutral (Taylor and Adelman, 2003).

Peasants produce under very high levels of uncertainty induced by natural hazards like

weather, pests, diseases, natural disasters, market fluctuations and social uncertainty

which pose risks to Peasant production and make farmers very cautious in their decision

30

making. Therefore, farmers are generally assumed to exhibit risk aversion in their

decision making. For instance, Lipton (1968) criticized the profit approach and shows

how the existence of uncertainty and risk eroded the theoretical basis of the profit-

maximizing model. He argued that small farmers are, of necessity, risk-averse, because

they have to secure their household needs from their current production or face starvation

and thus there is no room for aiming at higher income levels by taking risky decisions

(Lipton and Longhurst, 1989).

There are two ways of conceptualizing farm households’ risk-aversion: The Standard

Expected Utility Theory (Food First) and the Disaster Avoidance Approach (Safety

First). According to the former approach, farm households make choices from available

risky alternatives, based on what appeals most to their given preferences in relation to

outcomes and their beliefs about the probability of their occurrence. In such a normative

approach, both household behavior and its revealed attitude toward risk (risk aversion)

are reflected in its utility function.

Other things being equal, a risk-averse household prefers a smooth consumption stream

to a fluctuating one, which in contexts of incomplete capital markets or underdeveloped

institutional arrangements entails a low risk portfolio choice of productive activities

(Morduch, 1994). On the other hand, the complexity of risks faced by peasant farmers

has led some analysts to develop allocative choice models that do not depend on the

ability to calculate expected returns for large numbers of alternative prospects or

knowledge about complex probability distribution of outcomes. However, it fails to

31

specify the decision process that makes the outcomes possible, and thus ignores any

important role of decision costs in analyzing decision-making behavior under uncertainty

(Adelman, 2003).

The other model is called the safety first approach. Here, the decision maker is assumed

to ensure survival for him or herself and therefore wants to avoid the risk of his or her

income or return falling below a certain minimum (subsistence) level. Such a criterion

can lead to the household favoring either risky income streams or low risk alternatives.

This is to say that there are no reasons to expect individuals would behave in conformity

with the expected utility theory at very low levels of income, which is in stressful

circumstances (Dasgupta, 1993). Thus, the attraction of the safety-first approach is that it

is a positive method to capture some specific behaviors that can be culled from the

expected utility theory (as the normative model of choice under uncertainty) near

threshold income levels.

So far, most studies made in adoption of agricultural innovations particularly on adoption

of improved seed varieties analyses the determinants of adoption decision and the

subsequent intensity decision following one of the approaches discussed above. The

selection of those approaches for estimating the determinants of adoption decision has

been usually made by taking into account the general socioeconomic and institutional set

up of that specific society under study that has supposed to fall within the approach

selected. Each of those approach listed has a specific problem inherent in their

assumption and thus fail to fully explain all the possible factors guiding adoption of

32

agricultural innovations that fit with the specific characteristics of the society and little

works have been done so far in this area that investigate all those determinants

comprehensively. Thus, this research will lay down some contribution to the literature of

adoption of agricultural technology which takes into account all possible factors

holistically by incorporating the special attributes inherent in each approach.

2.5 Empirical Correlates of Farm Technology Adoption Decision

A cross sectional studies conducted at a micro level have laid down a useful tool in

capturing the varying factors affecting decisions on the adoption of improved

technologies by a farm households, the choice of crops that they are actually growing,

their preferences for a specific technology and hence used as an important tool in policy

decisions especially on the allocation of budget to research, extension and agricultural

development project (CIMMYT, 1993).

Different studies in the past follow different procedures and hence respective models in

analyzing the varying factors that affect the adoption decision and demand for improved

seed varieties by farm households. The selection of each procedures and models thus

depends on the objectives and the realities of the specific localities under investigation.

One of the approaches is the one that model farm household production decision and

consumption decision as different and independent of one another. The choice of such a

model is based on the assumption that; there is different stochastic process that

determines both the discrete outcome and the continuous outcome so that each decision is

33

independent of one another and hence they are treated separately. For instance, the

studies by Gezhaegn, (1999), Genanew and Alemu, (2010) analyses adoption decision

and demand for seed based on a separable household approach and estimate factors

affecting the adoption decision and proportion of land allocated to improved maize using

a double hurdle model (joint use of the probit model and the truncated regression model)

in investigating the adoption decision and demand for improved maize varieties.

The second alternative approach is the one that models adoption decision and demand as

one and interrelated process and thus disproves the former approach up on the notion that

such approach is only consistent with commercially oriented farm households in a

competitive market and thus fail to operate in a small holder farm community

characterized by too much uncertainty, no specialization and imperfections in both

product and input market (Hiebert, 1974) and (Smale, 1994).

Therefore, from the review of past literatures one can learn that, it is the job of the

researcher to choose which approach or procedure to follow to model farm household

adoption decision and the subsequent choice. In this paper, factors affecting both

outcome decisions are grouped and analyzed under demographic, socioeconomic,

psychological, and institutional factors.

2.5.1 Demographic Factors

The descriptive information on age and literacy level of a household age has an important

base in determining both the adoption decision and the subsequent decision of intensity.

34

For example, the study by Tesfaye and Shiferaw, (2001) which has used a logit model in

analyzing adoption decision of 1482 farm households in maize growing regions of

Oromia, Amhara and Southern Regions shows that age of a household head and literacy

level as an important factor in adoption decision. Age of a household head was estimated

to affect the decision as the higher the age of the household head, the better his

experience and hence confidence whereas the latter as increased knowledge and

awareness.

With regards to the educational attainment of the household head, most studies shows

educational status of a household head has also significantly associated with both the

decision to adopt or not and the proportion of area allocated to the variety. For instance,

the study by Zavale et al., (2005) has found a positive and significant correlation between

education and the probability of adoption of improved maize seeds. However, many

studies found to have a negative relationship with regards to both outcome decisions. For

instance, according to studies conducted by CIMMYT (1993) in Tanzania, adoption

decision has no statistically significant relationship with years of formal education. In

Ethiopia also, the effect of educational attainment of the household head has a greater

influence on the timing of decision rather than adoption decision (Weir and Knight,

2000).

A review of empirical literature related with family size adjusted to its adult equivalents

shows many contrasting figure. For instance, Sain.G and J. Martinez, (2004) states that

family size has no significant relationship with decision of whether to adopt improved

35

maize or not. Seemingly, the works by Brush, Taylor, and Bellon (1990) supports the

above argument.

2.5.2 Socioeconomic Factors

Among the proxy for wealth measurement factors that has a positive and significant

correlation is the size of household land holding. The size of farmland owned by the

household is associated with the decision to use improved maize varieties, since land is

the scarcest production resource in this part of the country (Bellon, 1990).

According to Feeder et al., (1985), the coefficient of household land size has a

significantly positive correlation with adoption decision due to the fact that it is a

surrogate for a large number of factors such as size of wealth, access to credit, capacity to

bear risk, access to information and other factors. The study conducted by CMMYT,

(1993) in Tanzania dictates that land size has a positive relationship with adoption of

improved maize. In addition, a study by Sain, G., and J. Martinez, (2004) indicates that

an increase of 10 percent in the total farm area results in an increase of approximately

12.5 percent in the probability of sowing part of the maize acreage with hybrid maize.

This figure is also consistent with those found in other studies on adoption of new

technologies (Brush, Taylor, and Bellon, 1990). On contrary, similar studies by the same

institution CIMMYT (1993) in Ethiopia shows that land size has no significant

correlation with adoption decision rather with the amount of fertilizers purchased.

36

2.5.3 Institutional Factors

Institutions; structured by rules and norms of society play a vital role in protecting,

resource mobilization, resource management, service provision, information exchange,

enhancing popular participation, protecting peasant interest and enhancing their claim

making power, and conflict resolution regulating and in the overall management of the

environment and the general ecosystem (Gidden, 1979).

Institution related variables like membership to primary cooperative, acces to credit,

access to extension service, attending maize seed demonstration have a positive and

significant relationship with both adoption decision and proportion of land area allocated

to improved maize. In most studies, farmers who grew improved varieties were more

likely to have extension contact than farmers who were not. Evidence suggests that

network effects are important for individual decisions and that in the particular context of

agricultural innovations; farmers share information and learn from each other (Foster and

Rosenzweig, 1995) and (Conley and Udry, 2000). The study by Tesfaye and Bedasa,

(2001) reveals that 93% and 32% of adopters attended field demonstrations and use credit

and respectively which implies that, for instance, moving a farmer from a situation of no

access to credit to access would significantly improve adoption decisions. In addition,

access to credit is important factors affecting adoption decision. According to a research

conducted in Ethiopia (Bale highland and lowlands) by CIMMYT (1999), lack of credit

was a constraint for 26% of adopters and 31% of non adopters.

37

Distance from the input and output market has also a positive relationship with adoption

decision because the further away a village or a household is from input and output

markets, the smaller is the likelihood that they will adopt new technology and it is

directly associated with extension contact which helps to improve their knowledge and

builds confidence and hence increase the marginal benefit of adopting the new

technology (Birgit and Sebastian, 2009). Besides, extension contacts also provided

inputs, which increased the correlation between extension contact and use of improved

technologies.

Membership to a primary cooperative also appears to have a positive and significant

influence on adoption decisions via improved information dissemination. For instance, it

is the primary cooperative that improved seed distributions are mad so that members can

easily access seed at reasonable price than the non members (ESE, 2011).

According to Uaiene et al., (2006), Membership to some association is also important for

small holder farmers in that it serves as a potential for overcoming credit market failures).

He argued that inventory credit programs have the potential of creating confidence

between farmers and financial institutions thus allowing farmers to have access to farm

credit from such institutions using their collective grains in a community warehouse as

collateral which would be facilitated if farmers are grouped in associations.

2.5.4 Psychological Factors

Knowledge of technology application acts as an intervening variable between attitudes

38

towards the technology and use of the technology (Rogers, 1961). Farmers’ attitudes;

which are evaluative responses towards the technology are important in determining

adoption of improved technology. Chilonda and Van Huylenbroeck, et al., (2001) suggest

that attitudes towards improved maize varieties place great emphasis on two factors:

Production Characteristics and Income Factors and hence adopters tend to hold positive

attitudes towards the technology than non adopters.

Most studies and literatures of adoption dictates that knowledge of the specific

agricultural technology and the attributes related with its relative yield, disease resistance

and seed quality has a significant and positive relationship with the outcome variables.

This is due to the fact that the more farmers became knowledgeable on a given

technology and its application, the more confident they will be and the lower the

probability of failure.

2.6 Seed System and Smallholder Agriculture in Ethiopia

As agriculture is a core driver of Ethiopia's economy supporting 85 percent of the

population's livelihoods, 46 percent of gross domestic product, and 80 percent of export

value, an efficient and vibrant seed system that provides quality seed to meet the

demands of farmers is an essential enabler to sustain the growing economic and social

development of Ethiopia (IFPRI, 2010).

Majority of farmers in Ethiopia are smallholder farms, producing mostly for own

consumption and generating little marketed surplus. Only 40 percent of the smallholders

cultivate more than 0.90 hectare and these ‘medium-sized farms’ account for three-

39

quarters of total area cultivated. Large farms (averaging 32 hectares per farm) are not

widely spread in Ethiopia and the contribution of these farms to total agricultural output

is insignificant (CSA, 2007).In addition, during 2007/08, smallholder farmers (12.8

million farmers) cultivated 12 million hectares of land or 96.3 percent of the total area

cultivated and they have generated 95 percent of total production for the main crops

(cereals, pulses, oilseeds, vegetables, root crops, fruits, and cash crops). In contrast, large

farms contributed to only 5 percent of total production of which about 2.6 percent

accounts for cereal production in particular.

Seed system in Ethiopia represents the entire complex organizational, institutional, and

individual operations associated with the development, multiplication, processing,

storage, distribution, and marketing of seed in the country (ESE, 2011).Seed systems in

Ethiopia can be divided into two broad types: The Formal System and the Informal

System (sometimes called Local or Farmers Seed System) and Integrated Seed System.

There is also a system referred to as Integrated Seed System. That is forms of seed

systems operating in both systems called community-Based Seed System (CBSS). Thus,

since the first two system systems are operating simultaneously in the country, it is

difficult to demarcate between the two.

2.6.1 The Formal Seed System

According to Dawit et al., (2011), the formal seed system is a system that involves the

production of seed using known sources of planting materials that undergoes certification

40

process for its seed production. It encompasses at least breeding system that supply initial

planting material, licensed seed producers and regulatory system that certify the produced

seed which is governed by strict regulations in order to maintain variety identity and

purity as well as to guarantee physical, physiological and sanitary quality (OSE, 2011).

In Ethiopia, the major actors of the formal system are National Agricultural Research

Systems, Ministry of Agriculture, Ethiopian Seed Enterprise and Private Seed Companies

specializing on specific crops like Pioneer Hi Bred Seed (ESE, 2011).Recently, regional

seed enterprises were also established as public seed enterprises such as Oromiya Seed

Enterprise, Amhara Seed Enterprise, and Southern Nations Nationalities and Peoples

Region Seed Enterprise.

Table 2.1: Area Planted with Improved Seeds and Chemical Fertilizers in Oromia Region

(2010/11

Crops Total Area

Area Covered With Improved Seeds

Area (ha) Percentage

Cereals 4,576,387 337,635 7.38

Pulses 552,162 3,858 0.70

Oil crops 307,313 NA 0.00

Vegetables 50,614 48 0.09

Root crops 83,278 36 0.04

Total 5,569,754 341,577 6.13

Source: OSE, (2011)

41

In addition, private seed producers and farmers (both small scale and commercial) also

play a major role in the overall seed system especially in multiplication of breeder seed.

For instance, in 2008, of all commercially produced seed, 83 percent was produced by

ESE of which 8 percent on its own farms, 35 percent through contracts with large farms

and 39 percent through contracts with small farmers), and the remaining 17 percent by

private producers (ESE, 2011).

Table 2.2: Seed Supply of Major Cereals in Oromia Region from 2008/09 to 2010/11

Crop Seed Production in qt (2008-2010/11)

2008/09 2009/10 2010/11

Wheat 475.6 32,754 17,404

Food Barley - 187 760.5

Malt barley - 1071 1,561.1

Tef

-

181.7

319

Maize hybrid 3,000 31,614.4 20,117.2

Maize OPV 606.1 1,368 115

Sorghum - - 16.6

Hair cot bean - 118 174

Soy bean - 107.8 137.6

Sesame - - 55.3

Onion 1.39 1.00 0.54

Total 4,083

67,403 40,661

Source: Compiled from OSE Annual Reports (2011)

42

The Formal Seed Sector supplies 20,000 to 30,000 tons of seed per year across all crops,

representing only 3 to 6 percent of farmers’ actual seed demand which may also vary

across different crops. Improved seed demand across Cereal itself varies showing the

highest for Maize (50 percent) and lowest for Barely(less than 10 percent).

Though supply shortfalls are the major problems of formal seed sector, farmers have too

little access to affordable, quality, high yielding varieties mainly due to lack of

information, high price in relation to local seeds and problems related with seed

distribution and low access to credit there by leading to low adoption rate (OSE, 2011).

2.6.2 The Informal Seed System

Ethiopian seed enterprise defines Informal Seed System as a seed production and

distribution along with the different actors where there is no legal certification in the

process which includes retained seed by farmers, farmer-to-farmer seed exchange,

community based seed multiplication and distribution. According to Cromwell, Friis-

Hansen, and Turner (1992), five key features distinguish the informal from the formal

system among which are: the informal system is traditional; semi structured, operate at

the individual or community level, uses a wide range of exchange mechanisms, and

usually deal with small quantities of seeds often demanded by farmers.

The Informal Seed System is also known as local system or sometimes as farmers

system, because it operates under non-law regulated and characterized by farmer-to-

43

farmer seed exchange which accounts the highest share of seed production and area

coverage (OSE, 2011).

Table 2.3: Area Covered (ha) by Informal seeds over the Last Five Years (2005/06-

2009/10)

Crops Cropping Season

2005/06 2006/07 2007/08 2008/09 2009/10

Cereals 7,636,935 8,127,710 8,309,899 8,333,097 7,660,560

Pulses 1,283,564 1,373,914 1,509,394 1,568,457 1,358,379

Oil crops 790,471 736,791 702,518 851,626 706,361

Vegetables 116,298 94,636 118,026 159,626 122,832

Root crops 167,189 186,804 180,624 143,761 183,254

Total 10,821,810 11,427,794 11,927,093 12,010,042 10,136,744

Source: CSA, (2010)

For countries like Ethiopia, informal seed sector plays a major role in enhancing seed

security since the bulk of total seeds(about 60-70 percent) are supplied by these system

and much of the cultivated land (about 94% in 2010/11 cropping season) is covered with

seed supplied from the informal seed system ESE (2011).

2.6.3 The Supply-Demand Gap in Seed Market

Though the seed market equilibrium is characterized by huge gap or mismatch between

the supply and demand (demand surpass supply), the supply of these technologies,

44

particularly of improved crop varieties, there has been limited use of improved seeds by

the majority of farmers CSA, (2010). The actual demand of seed (as planned by the

Oromia Bureau of Agriculture) has never met during the 2005/06 to 2010/11 production

years. The trend of seed supply does not increase at a rate the demand for the seed

increased in each production year (See Figure 2.1 below).

Figure2.1: The Nature of Seed Market (demand/supply) for Oromia Region during

2005/06 - 2010/11 Production Season.

Source: OBA, (2011)

In general, the major factor for the creation of this imbalance emanates from both factors

affecting seed demand (low adoption rate and fluctuating demand) and supply which will

further be discussed in the conceptual and empirical literature.

45

2.7 Adoption of Hybrid Maize Seeds and Food Security

The adoption of improved technologies for staple crop production is an important means

to increase the productivity of smallholder agriculture in Africa, thereby fostering

economic growth and improved well being for millions of poor households CMMYT,

(1999). Thus, sustainable flow and use of improved agricultural technology is the

solution to increased growth and agricultural productivity. The cultivation of maize

improves the livelihood of small holder farmers both as a means of income and as source

of food calories. For instance in Tanzania, maize is also an important cash crop,

competing with cotton for land and labor and in Kenya, maize is a major staple food and

the main source of income and employment for most households (Ouma et al., 2002).

Maize is grown in most parts of Ethiopia, with the major production regions located in

the southern, western, southwestern, and eastern highlands. In addition, maize is currently

grown across 13 agro-ecological zones which together cover about 90 percent of the

country. Moreover, it is an increasingly popular crop in Ethiopia as the area covered by

improved maize varieties grew from five percent of total area under maize cultivation in

1997 to 20 percent in 2006 (CSA , 2006).

The recent introduction of several new maize varieties in Ethiopia illustrates the potential

importance of this seed industry and the contribution of improved maize varieties to

Ethiopia’s agricultural sector. However, some studies show that despite the predictable

high yield derivable from hybrid maize, the national average grain yields of maize have

fallen short consistently of the potential yields (Alemu, 2001).

46

2.8 Analytical Framework

The Theory of Agricultural Household by Singh, Squire, and Strauss (1986) states

though there are many factors that is considered in adoption decision and demand for

improved technologies, profitability is the major one in determining the overall process

of adoption of new technologies (Byerlee et al., 1980). Such theory applies to decision

making in this context and includes profit maximization as a special case when markets

are perfect and production and consumption decisions are separable. Otherwise, in a case

where the market is not perfect, high uncertainty and limited information. The decision of

whether to use improved seed and subsequent decision of intensity result from the

choices of consumption amounts and product combinations that maximize the utility of

the farm household, subject to a full income constraint that embodies non-farm and farm

income net of expenditures, credit and repayment, and family labor availability.

The starting point for our analysis in this research is based on the assumption that

adoption decision and demand function derived from a non-separable agricultural

household model. That is farm household production and consumption decisions are

unlikely to be separable given imperfections in labor, credit, and other markets. As a

consequence of non-separability, both production and consumption-side variables may

affect household decision to adopt and the subsequent proportion of farm lands allocated

to improved seeds (improved maize in our case). The conceptual model used to estimate

the intensity of use for improved seed can be illustrated using a scheme in which the

farmer must choose between two alternatives: Either using local seed or using improved

47

seed. On the basis of this theory, a household model is specified to explicitly incorporate

variety attributes and used to derive seed demand equations.

Let the household utility function of a household id defined as U .Thus, the prime

objective of the household is to will maximize its expected utility(U) subject to different

set of constraints (production technology, income, time,) which is summarized as follows.

MaxU(chm,clm,cm,cl;zh)

(qhm,qlm,llm,lhm,qxhm,chm,cm,cl) ………………………………………1(Objective Function)

Subject to:

Y[qhm,qxhm, qlm,llm,lhm,ls,qxhm;zq] ……………………2 (Technology Constraint)

(pxhmqxhm+pmcm+ phmchm)-(phmqhm +plmqlm + wls +I )……3 (Full Income Constraint)

Ls+llm+lhm+cl =T……………………….………4 (Labor Time Constraint)

The Lagrangian Utility Maximization Function based on the inseparable household

model is explicitly stated as follows:

L =U(chm,clm,cm,cl;zh) + λ1[Y(qhm,qxhm, qlm,llm,lhm,ls,qxhm;zq)] +λ2[(pxhm qxhm + pmcm +

phmchm) - (phmqhm+plmqlm + wls +I )] +λ3(T- Ls - llm - lhm- cl ) …………………………(5)

Thus, the household can determine the decision of whether to apply the technology or to

stay with the local variety by maximizing the utility derived from consuming the two

alternative products by taking into account the set of constraints listed above. After

deciding the best alternative from the option (modern versus local) or the combination of

the two, the subsequent decision will be the amount of land to be allocated for each

48

variety or the decision of intensity.

The Partial Kuhn-Tucker Necessary Conditions for Optimality for Derived Demand

Relationship, which determines the optimal production scale for each crop variety

potentially grown by the household, is given as:

Yi= Yi[chm,clm, qhm, qlm , phm , plm , I,T| zq ,zh ] …………………(6) for all Yi ≥0

Where;

U - Is the utility function to be maximized, chm,clm,cm, and cl are quantities consumed of

hybrid maize, local maize, manufactured good, and leisure, respectively.

zh

- Is a set of household characteristics that influence consumption.

qhm, qlm - Is the quantities of hybrid and local maize produced respectively

llm, lhm and ls - Refers to quantities of labor used in the production of local maize, hybrid

maize and labor sold out by the household respectively.

qxhm - Is the quantity of extra inputs required for the production of hybrid maize, such as

improved seed, pesticides, etc,

zq -Refers to a set of fixed factors in production and farm household specific

49

characteristics that influence production.

The coefficient λ1, λ2, λ3 in the above function is a multiplier associated with production

technology, income and time constraint respectively. Thus, the above conceptual model

elaborates the decision that a farm household makes to adopt a given agricultural

technology by comparing the expected utility derived from applying a given technology

against the local variety and thus chooses the one with the best utility. This decision is

based on the notion that the household is operating in an environment characterized by

too much uncertainty and imperfection it was discussed in the target input approach.

2.8.1 Conceptual Framework of the Study Area

The analytical framework constructed below have mainly aimed at explaining different

factors affecting farmers’ adoption decision and intensity of the decision particularly

those which contribute to the variations in adoption and intensity of adoption of improved

maize variety among farmers. The conceptual framework of the study area constructed

below displays systematic relationship existing between our outcome decision (both

adoption decision and intensity) and the different set of farmhouse hold characteristics

grouped under; institution related, demographic, endowment and perception and

production technique. The framework was built in a way to explain the relationship

between our outcome decision and set of household characteristics grouped under four

major categories. In addition there was also some minimal and unobservable relationship

between those covariates which was found during multicolinierty diagnosis.

50

Figure 2.2: Analytical Framework of the Study Area

Source: Sketch by Author

Demographic

Off-farm income

Literacy

Gender

Marital status

Age

Have family

member in town

Endowment Related

Variables

Land

Family size

Livestock

Hired Labor

Livestock

owned

Irrigation

Institution Related

Variables

Cooperative

membership

Credit Access

Attended Field

day

Distance from

DA

Distance from

market

Contact

Extension Agent

Psychological and

production

attributes

Attitude

Knowledge

Production

technique

Fertilizer

application

Adoption Decision

Decision of Intensity

51

CHAPTER THREE: METHODOLOGY

3.1 Sampling Design Procedure

3.1.1 Sample Size: A sample is said to be a representative when it should be selected

in such a way that generalizations can be made about adoption levels for a country or

region or some other aggregate level, such as an Administrative District or an Agro-

ecological Zone so that generalizations can be made about groups of farmers (CIMMYT,

1999). Thus, considering the heterogeneity of farm households in the study area and

existing literature, a formal and commonly used sample size determination mainly the

one by Kothari, (1990) is used in this research which is stated as follows;

n=Z2α/2 * p (1-p)

d2

Where n=is sample size

P: Estimated proportion of households with sustainable livelihood outcome. as the

proportion was not known, 0.5 was used as P value to obtain maximum number of

households.

D - Is the acceptable error we expect (usually assumed to be 5%)

Z- Is the two tailed value of standard variants at 95 percent confidence interval (Z=1.96).

Accordingly the determination of sample size used in this research is made as:

n= (1.96)2 * 0.5(1-0.5) + 5% (contingency)

(0.05)2

52

n=384 + 20 = 404

3.1.2 Sampling Procedure: After fixing the sample size, the two stage random

sampling technique has been used to collect the primary data from farmers that are used

to make our sample size more representative. First, all the 20 Kebeles (Farmers

Association) found under the administration of the Woreda was grouped into three agro

ecological zones namely Lowland, Mid Highland and Highland. Then, the total number

of kebeles to be included in the sample selection from each agro ecology was made by

applying equal proportion based on their size (number of kebeles). Accordingly, a total of

four kebeles was selected two kebeles from Mid Highlands and one kebele from each

Highland and Lowlands (see appendix).

The decision about the specific kebeles selected for data collection was the issue of

serious concern since there were overlapping similarities in temperature, soil types and

amount of rainfall between some agro ecologies. Thus, with the help of woreda

Agronomist, selection was made keeping those heterogeneities (altitude, soil and

rainfall).

In the second stage, after getting the total number of households living in each selected

kebeles from the Woreda Office of Agriculture, the number of households selected from

each kebele was determined by allocating the total sample size of 404 proportionally

across each. Lastly, Lottery Method was applied in identifying the specific household to

be interviewed in the sample within each kebeles giving special emphasis to female

53

household heads since their number was found to be lower. Thus, from the total of 404

farm households, only 394 of them (about 97.5 percent) have been contacted and given

responses. The remaining households were not willing to respond for some known and

unknown reason.

3.1.3 Method of Data Collection and Ethical Consideration

The data required for this study was collected from both primary and secondary sources.

The primary data was collected using structured questionnaires (both close ended and

open ended) that has addressed household’s resource endowment, institutional related

variables, demographic variables and others. The questionnaires were fist written in

English and then translated into Afaan Oromo. Then, the questionnaire was administered

to the farmers by trained enumerators.

The total of four enumerators have been selected, one from each agro ecological zones

and was given training on the subject matter and Techniques of data collection. The

selection was made based on the criterion of 10th

-grade completion, ability to

communicate in Afaan Oromo language, good knowledge of the agriculture and farming

system of the area and good motivation to work. In addition, one supervisor has been

selected who frequently control the overall data collection activities, cross-check

validities and any other errors.

Along with primary data, secondary data was also collected from the Woreda

Agricultural Bureau, Seed Enterprises Offices (both regional and federal), Research

54

Institutions, and Central Statistical Agency. Besides, different Web Pages, administrative

documents (both published and unpublished), and journals were used.

With regards to ethical consideration, all the selected interviewers has given a letter of

permission from Woreda administration. Then, household has been informed before the

interview that all their responses would be kept secret and also they were told not to

respond to any question if they do not want to do so. Accordingly only volunteer 394

household head have been participated in the study based on their verbal consent.

3.2 Method of Data Analysis

To address the various objectives described above, the framework has an inherent

character to encompass all variables that have direct and indirect influence on the issue of

adoption decision and level of adoption by small holder farmers in the study area. Thus,

both descriptive and econometric method of data analysis has been employed in order to

address the objective inherent in this research.

3.2.1 Descriptive Method

Descriptive method was employed to explain the situation of demographic and

socioeconomic variables. Descriptive method was used to access adoption decision and

the intensity of use of improved maize variety in the study area. The specific methods of

data analysis employed here include tools like frequency, mean, standard deviation and

percentage was computed for different variables.

55

3.2.2 Econometric Method

Different studies follow different approaches and subsequently the model appropriate

under each approach following the existence of divergent thought in adoption studies.

Thus, different analytical models like OLS, double hurdle, logit and tobit were to be used

alternatively after fitting it to their respective data. For instance, the study by Genanew

and Alemu (2010) employed a double hurdle model in estimating adoption decision and

the subsequent decision of intensity based on the assumption that the adoption decision

and intensity of use of improved seed are determined by two separate stochastic

processes. Thus, it is up to the researcher to select the best model that fit with the

observed data taking into account the specific attributes of that society. Accordingly,

binary logistic and tobit model was employed in this research in order to estimate factors

affecting adoption decision and the proportion of land allocated to improved maize

respectively. The detail descriptions of these models are presented below.

i) Estimating Adoption Decision of Farm Households

Usually adoption studies uses either probit or logit models alternatively in estimating

adoption decision. The substantive results in both models are generally indistinguishable

and produce very similar results. But, in many studies, logit model was usually preferred

best since the parameter estimates in a logistic regression tend to be 1.6 to 1.8 times

higher than they are in a corresponding probit model (Long, et al., 1997). Accordingly,

logit model has been applied in this research to estimate factors affecting farm household

decision to adopt improved maize seed for 2011/12 production season.

56

A multivariate binary logistic regression was employed using SPSS software to estimate

the first discrete outcome decision (adoption decision). Thus, in order to estimate binary

outcome of adoption decision of ith

farm households logistic regression function at a time

t, the logistic regression function are stated below:

P(Y) = ln[p/(1-p)] = 0 + 1X

Where

P(Y) – is the probability that ith

household will adopt an improved maize seed at a time t

xis a function of n number of explanatory variables expressed as:

p/(1-p) is the "odds ratio which ranges from 0 to ∞ with values greater than 1 associated

with an event being more likely to occur than to not occur and values less than 1

associated with an event that is less likely to occur than not occur.

ln[p/(1-p)]: log odds ratio, or "logit“ ranging from -∞ to +∞

ix =X1 X2 ...nXn e

i’s referring to the coefficient of the logit parameters while Xi’s refers to the covariates.

The coefficients shows by how much the log-odds is in favor adopting improved maize

seed change as the value of the explanatory variable changes by a unit. Accordingly, the

odds ratio and log odds are calculated to estimate the change in dependent variables due

to a unit change in explanatory variables.

57

e- Refers to the stochastic error term. Thus, the logit is defined as the log of the odds:

=0 + 1X1 + 2X2……+nXn

The result of this estimation result was interpreted depending on the magnitude of the

coefficient of log odds that is the value of log odd greater than one, less than one and a

unity was estimated to increase, decrease and have no effect on the probability of the

odds (adoption decision) to happen respectively.

ii) Estimating Intensity of Decision

The second dependent variable of our interest refers to the continuous outcome decision

of the proportion of land allocated to improved maize seed once a household has decided

to purchase the seed.

The review of past literature used either OLS (ordinary least square method) or tobit

model to estimate the factors affecting the amount of land allocated for improved maize

once a households have made a purchase decision. In this study, both tobit and OLS were

estimated alternatively and thus tobit was finally found best fit the households decision of

intensity as OLS have usually shown to display inconsistent estimate of the

parameter(since the coefficient of parameter in OLS is much lower than that of tobit).

ln ln ln ln 11

podds p p

p

58

Following Hechman, (1979), tobit model has been applied to estimate the proportion of

land allocated under improved maize and is described as follows:

Yi = Xiâ + åi with åi ∼ N(0, ó2).

Di = 1{Ziã + Þi > 0}.

Accordingly, the variance for Þi is normalized to one since we only observe the sign of

Xiã + Þi . For a random sample from the population we observe Di, Zi, and Xi. Only for

observations with Di = 1 do we observe Yi.The two latent variables cannot be observed by

the researcher. We only observes an indicator di when the latent variable d I is positive.

The value of the variable yi = y∗i is only observed if the indicator is 1; that is in the case

of the adopter category.

In other words, the first equation (the decision equation d∗ i ) explains whether an

observation is in the sample or not. The second equation (the regression equation yi*)

determines the value of yi. The expected value of the variable yi is the conditional

expectation of y∗ i conditioned on it being observed (di = 1).

59

The partial effects are used in estimating the impact of explanatory variable on our

continuous outcome which is described as follows. Now, let me compute P(y>0|x)

However, we need to know the overall effect of the parameter estimate rather than the

effect for specific which is stated as:

Thus, our second outcome decision (Tobit Estimation Model) stating the proportion of

land allocated to improved maize (from the total maize production) has the following

form:

Di = βZik + ei ………………………………………………………9

Where;

Di : Is the amount of hectare of land allocated to production of improved maize variety

T : Is the non observed threshold level.

8...............................................................).........()|(

10

1

x

x

xyE

7...............................................................).........(

)(1

),(

)),((

)|0()|0(

10

10

10

10

10

x

x

xxu

P

xxuP

xuxPxyP

60

Zik : A matrix of designed household socioeconomic, institutional and psychological

factors influencing seed demand.

β : Is the parameters to be estimated while.

ei : Is a stochastic error term.

Therefore, tobit model was applied using the STATA software to estimate the

relationship between the household decisions of how much land would be allocated to

improved maize and a set of household characteristics discussed above. Before fitting the

model against the data, several tests like tests for goodness of fit, tests of multicollinierity

and others (the techniques are discussed in detail on analysis section) were made so as to

adjust the model to better estimate our data. The result of tobit model is interpreted and

explained using marginal effects of the explanatory variables on the outcome using the

coefficient of the parameters.

3.3 Variables Definition and Hypothesis

Dependent Variables

The first dependent variable used in this research is the one referring to the binary

outcome variable or the choice decision (adoption decision). It was coded as 1 if the

household has purchased an improved maize seed of any type during the 2011/12

production season and 0 otherwise. In addition that household that has been adopting but

not adopted during 2011/12 was categorized in non adopter category.

61

The second outcome decision refers to the continuous decision preferably named as

intensity of adoption. It refers to the proportion of land area allocated to improved maize

seeds from the total number of land allocated to total maize production (including the

local maize).The value of the second outcome is numeric with values between 0 and 1

excluding the two extremes (1 and 0 are the two border points).Note that only those

households who vote 1 during the binary adoption decision are included in the second

outcome decision.

Explanatory Variables

Regarding the criteria for choosing explanatory variables, as there is no firm economic

theory that clearly dictates the choice of independent variables used in adoption studies as

in Adesina and Zinnah, (1993) and Langyintuo et al., (2003). In this specific model, the

selection of the variables which tested the factors affecting the incidence and intensity of

adoption are based on prior literature which are categorized as the following.

i) Farmers Socio-economic Variables: (age of a household head, income from off-

farm activities, experience in modern farming method, sex of a household head, and

literacy level of a household head).

ii) Institution Related Variables: member of cooperative union ,access to

formal/informal credit, access to extension service, Attend field day or visit

demonstration plot, Formal training on improved maize production, access to

fertilizer and improved maize on time ,formal training on maize, have a Radio.

62

iii) Farmers Endowment Variables: (total farm size, family size (adjusted to adult

equivalent), livestock ownership size (TLU), hired labor (a dummy), use

community labor for farm operation.

iv) Farmers Psychological Variables: (Prior Knowledge, Perception/Attitude): The

details of explanatory variables determining factors affecting adoption decision and

allocation of land under improved maize are hypothesized as follows.

1. Farm Size: This variable is a proxy to measure a household wealth and it is

expected to be positively associated with both the decision to adopt amount to be

applied improved maize technology since a farmer with a large landholding have

a greater access to resources and better to assume risk. It can also encourage

farmers to intensify their production, in which case a larger farm size is expected

to be negatively related to the adoption of improved maize technology.

2. Literacy Level: The more educated the household head is, the more he allocates

his limited resources by analyzing the net benefit to be achieved from applying

the technology. Education of a farm household head can have a positive influence

on the outcome variables thereby increasing the likelihood of adoption decision.

63

3. Livestock Unit: Possession of livestock is one of farm household’s productive

assets. Thus, it is expected to be positively related with both adoption decision

and intensity of adoption (amount of land covered under improved maize).

4. Extension Service: It is through the extension service which disseminates

information about the improved variety in particular and the overall agricultural

technology knowhow in general to the farm community. So, it is expected to be

positively related with the probability of adoption and the intensity of application.

5. Household Size: As household size is grouped under household endowment

category, large household size is expected to positively affect adoption decision.

Each individual family member is converted into their adult equivalents as per the

conversion factor.

6. Field day Attended (attending maize seed demonstration): Attending a field day

or demonstration of improved maize seed is also expected to positively related to

both outcome decisions through its impact of increasing farmers awareness or

exposure to the technology.

7. Off-Farm Income: Access to other income generating activities out of agriculture

is expected to increase the likelihood of adoption decision through diversifying

their income source so that cost of complementary inputs is easily financed.

64

8. Hired Labor: Hired labor helps farmers to overcome labor constraints, especially

with respect to the number of hand weeding required for improved maize. Thus,

access to hired labor is expected to have a positive and significant influence on

the adoption of maize technologies.

9. Cooperative Membership: Being a member of cooperative union could help the

farmer in easily accessing farm inputs and other related information and hence it

is expected to have positively related with both outcome variables.

10. Access to Credit: Not only the physical availability but also easy access to these

resources can affects the decision of farm community positively. Moreover,

access to credit also affects both decisions through affecting the farm household’s

cash constraints.

11. Farmers Perception of Yield: This variable states the perception or the attitude of

farm household towards yield advantage of the improved maize variety (as

compared to the alternative option i.e local maize, or towards productivity or any

other reasons. Perception is expected to be positively affecting the adoption

decision.

12. Household Perception of Better Resistance Attributes of Improved Maize:

Household perception about the better resistance of improved maize to field and

65

storage pests have expected to be positively related with both our outcome

decisions.

13. Age of a Household Head: Is a proxy to measure number of years of farming

experience of head of a household and is expected to affect both our outcome

positively because the higher the age of household head, the higher they get

information and experience in farm management and practice. The age of

household head is incorporated as one of the explanatory variable because it is

believed that with age, farmers accumulate more personal capital and, thus, show

a greater likelihood of investing in innovations (Nkamleu et al., 1998).

14. Fertilizer Application: Knowledge of Fertilizer application according to the

recommended mixing would have been expected to be positively affected the

adoption decision and intensity of adoption.

15. Maize Planting Practice: This is related to whether they obey during maize

production the recommended spacing between rows and across each plant. And

this is expected to be positively affecting our adoption decision.

16. Distance from Market Center: Refers to the location of household existence both

from input (maize seed market) and output market (cereal market) which is to be

measured by the travel hours it takes. It is expected to negatively correlate with

both outcome variables.

66

17. Gender of Household Age: This refers to whether the head of household to be

investigated is male or female and the correlation is expected to be negative with

both our outcome decision.

18. Amount of Total Maize Produced: This is the amount of hectare of land allocated

to both improved and local seeds. Since increased application of maize seed is

associated with the maize popularity and good perception in a household it is

expected to be significantly associated with both our outcome variables.

19. Distance from DA (Development Agents) Office: The longer the house of a farm

household (in hours of travel) from the development agents, the more difficult

would it be in easily accessing information about agricultural technology. Thus

positive relationship is hypothesized for both outcome variables.

20. Marital Status: Marital status of the household is expected to be positively related

with adoption decision and amount of improved seed applied because the more

the household is married the more they share information and allocate resources

and thus enhancing adoption.

21. Have a Family Member living in a Town or Abroad: This is expected to be

positively related with both decision since having a member living in a town may

67

be interpreted with some form of help (financial, technical) or assistance that

helps a farm household in using the technology.

22. Saving in Cash or in Kind: Farm household who saves the annual produce either

in cash or in kind is financially strong enough to survive any kind of production

shock as compared to those who save not. Thus a positive relationship will be

expected with both outcome decisions.

23. Cash Crop Production: Cash crops have a premium price as compared to the

cereal crops and in some cases, they will be produced more than two times a

year(chat for example).so those households who are engaged in cash crop

production along with cereal ones have expected to positively related with both

outcome decision.

24. Access to Irrigation: Access to irrigation is also expected to positively related with

both outcome decision since access to irrigation is related with increased

production and decreased reliance on rain (and hence reduced expectation of risk

of failure).In addition, access to irrigation facilities would be expected to have a

positive relationship with our continuous outcome; our second outcome decision

through its indirect effect on boosting the farmers financial status through the

production of other food and cash crops which may be harvested more than twice

in a year.

68

25. Perception of Cost: This refers to how the household perceive the cost of

improved maize vis. a Vis. Its benefit (perhaps yield advantage) in relative to the

local variety. The more farmers perceive positively about the cost of improved

maize seeds (along with other complementary packages), the higher probability

that they would be adopted and intensively used. So, positive association is

expected with both of our outcome variables.

69

CHAPTER FOUR: BACKGROUND OF THE STUDY AREA

AND THE SAMPLE RESPONDENTS

4.1 Geographical Setting of the Study Area

4.1.1 Location

Jimma Arjo Woreda is located in East Wollega Zone, Oromia National Regional State.

Located at 379 km from Addis Ababa, the Woreda lies between 8045N and 360 29’E. It

occupies nearly 3.05 percent of the zone’s total area & is contiguous with Nunu Kumba

in the east and Leka Dulecha in the North, and Ilu Abba Bora Zone in the South and

North West of the District. Covering the total land area of about 75, 812 hectare, the

district is situated at an altitude greater than 1200 meters above sea level which is

characterized as tropical and sub tropical types of climate with the mean annual

temperature between 15 0c and 20

0c and means annual rainfall ranging from 1400 to

2000mm.

4.1.2 Topography

Table 4.1 Distribution of Woreda into Kebeles and Agro- ecologies

Agro-ecology Area covered(in

square km)

Percentage Number of Kebeles located

under each Agro-ecology

Lowland 221.50 29 5

Midland 447.95 59 10

Highland 89.02 12 5

Total 758.47 100 20

Source: Jimma Arjo Woreda Office of Agriculture

4.1.3 Relief, Climate and Soil Type

The region is characterized by ups and downs like the other districts bordering it. With

70

the exception of areas along the Dhidhessa River valley, most of the land has higher

altitude; especially areas surrounding Jimma Arjo town has an elevation greater than

2000 meters above sea level.

Figure 4.1 Map of Jimma Arjo Woreda

Source: From Dula et.al, (2007)

71

Regarding climate, most areas of Jimma Arjo are situated at an altitude greater than 1200

meters above sea level; the district is characterized as tropical and sub tropical types of

climate. The area is found in the western highlands of the country where rainfall is

expected throughout the year. However, due to altitudinal variation different parts of the

district receive different amount of rainfall. Accordingly, June, July, and August are the

months when the area receives maximum rainfall. Mean monthly rainfall is minimum

during the months of November, December, January, and February.

The mean annual temperature ranges between 15 o

c and 20 o

c and there is no significant

variation in mean monthly temperature of the district. May is the hottest month with

mean temperature of 190c while July and August are the months when the mean monthly

temperature is registered to be 15oc.In addition, mean annual rainfall is between 1400 and

2000mm. June, July, and August are the months when the area receives maximum

rainfall. Mean monthly rainfall is minimum during the months of November, December,

and January.

The soil type of the district among others include; Dystric Nitosols and Orthic Acrisols.

These are the major soil types in the area which have good agricultural potential. On the

other hand, Orthic Acrisols, which occur mostly on sloping terrain and have less

agricultural potential, occupy the areas along the Dhidhessa river valley.

72

4.1.4 Population by Residence Group

As in every places in Ethiopia, the highest segment of the population lives in rural area,

around 90 percent which depend on agricultural activity as their main means of existence.

The remaining 10 percent lives in the urban areas of the District. In addition, as the table

4.2 shows sex ratio of the area is 0.92 revealing the greater number of female population

as compared to male.

Table 4.2 Population Distribution of the District by Sex and Residence Category

Age

Category

Total

Urban

Rural

Sex

Ratio Male Female Sum Total Percentage Total Percentage

0-19 19395 19853 39248 3812 19.5 19940 33.5 0.96

19-50 9085 10476 19561 2054 29.8 17507 29.4 0.81

>50 3332 3903 7235 671 50.7 22058 37.07 0.85

Total 31812 34232 66044 6539 9.9 59505 90.1 0.92

Source: Jimma Arjo Woreda Office of Agriculture

4.1.4 Land Use Pattern

About 64 % of the landscape of the district is potentially cultivable and 16 % is covered

with forests and wood land. Grazing land accounts for 12.9 % of the area while 5.5 % is

Mountainous area.

73

Table 4.3: Total Land Use Pattern of the District

Land use type Size (km 2) Percentage

1 Potential arable land 519.78 76

2 Pasture land / grazing land 88.84 12.99

3 Forest Land 18.19 2.66

4 Swampy & marshy land 3.49 0.51

5 Others 53.62 7.84

Total 683.92 100

Source: Jimma Arjo Woreda Office of Agriculture

4.1.5 Means of Livelihood

The major livelihood incomes in the area includes crop production (Maize, Sorghum, Teff

Nuog, Millet), livestock production (Cattle, Goat, Sheep, Cow’s Milk and Butter sale,

Chicken sale and Egg production) and other food and cash income(Tree sale, sale of

agricultural labor ,Firewood sale, and Honey production).

4.2 Demographic Characteristics of the Sample Respondents

The demographic and socioeconomic descriptors of the community are one of the major

influential factors in dealing with adoption and diffusion of agricultural innovation

particularly of seed and fertilizer (See Table 4.4 below)

74

Table 4.4: Demographic Distributions of Sample Households

Household

demographic

characteristics

Response category Frequency Percentage

Age of

household head

<= 18 1 0.3

19 – 49 294 74.6

50+ 99 25.1

Family size <= 3.8 213 54.1

>3.8 181 45.9

educational level

of household

head

Illiterate 178 45.2

1-6 126 32

> 7 90 22.8

Ecology

Highland 99 25.1

Mid highland 219 55.6

Lowland 76 19.3

Gender Female 102 25.9

Male 292 74.1

Not married 48 12.2

Married 346 87.8

Source: Field survey, 2013

With regards to educational attainment, about 45 percent of the households interviewed

have not attended any formal education while only 54.8 percent of them attended more

than elementary education.

So, more than half of the sample respondents have attended at least formal education. In

addition, majority of the sample household age is between 19 and 49 (74 %) which is

characterized by productive age group who are physically active enough to engage in

production activities. Moreover, about 88 % of sample respondents have in a marital

union at a time when interview made and more than half of them live in midland while

nearly about 25 % and 19 % live in highland and low land respectively.

75

4.2.1 Asset Ownership of Sample Household

The table 4.5 shows that about 61 % have got credit facilities during the 2011/12

production season which help them in financing their purchase of seed as most of farmers

in developing countries are cash trapped and hence, they need financial assistance to

purchase the technologies and their complementary inputs (Ouma et al., 2006).

Access to hired labor is also available for nearly 63 percent of the household in the study

are. Thus, shortage of labor during production or harvest season could be available for

more than half of the population in the study area.

About 90 % of a respondent operate at a small scale which have less than 2 hectare of

land which will also affect the intensity of use of improved technology (Nkonya et al.,

1997). In addition, about 90 % of sample household have no access to small scale

irrigation which have an implication on the number of times a household could produce

maize in a year. In addition, 35 percent of them hold land less than one hectare.

However, many household head interviewed told that land shortage could not be a big

problem as they could usually have the possibility of harvesting on more extra land size

through using other households land on rent basis or under contract agreement of sharing

the outputs equally (See Table 4.5 below)

76

Table 4.5: Distribution of Sample Respondents According to Asset Ownership

Household Asset

Frequency Percentage

Eligible for Credit No

153 38.8

Yes

241

61.2

Access to hired labor No

146

37.1

Yes

248

62.9

Access to Irrigation No

350

88.8

Yes

44

11.2

total farm size <= 8

138

35.0

9 – 16

219

55.6

17+

37

9.4

livestock unit <= 3.8

178 45.2

>3.8

216 54.8

Source: Field survey, 2013

NB: Total land owned are stated in timmad (1 Hectare = 8 timmad)

4.2.2 Membership to Local Institutions

According to the figure in table 4.5, more than half of the sample household has attended

improved maize demonstration during the period of 2011/12 and almost all (about 98

percent) have a regular contact with extension agent (See Table 4.6 below).

As to the institutional variables, The survey results shows that 61.2 percent of the

respondents have been eligible to take credit from microfinance’s during the 2011/12

production season while the remaining households have not taken credit either due to

77

eligibility criteria or unwillingness. There are two micro financing institutions providing

microloan for both urban and rural population of the Woreda one belonging to

government and other private microfinance.

Table 4.6: Distribution of Sample Respondents with Institutional Membership

Institution related variables

Frequency Percentage

Access to Credit No

153 38.8

Yes

241 61.2

Cooperative

membership No

147 37.3

yes

247 62.7

Attend maize field

demonstration No

183 46.4

yes

211 53.6

Extension visit No

9 2.3

Yes 385 97.7

Source: Field survey, 2013

In addition, about 62.7 percent of the respondents belong to at least one of the primary

cooperative and the remaining households have not been a member of any primary

cooperative. Membership to primary cooperative has significant implication to both

adoption and intensity of application since the distribution of improved maize variety has

been channeled through cooperative unions. During the survey, 53.6 percent of sample

households have attended improved maize demonstration and about 97.7 percent have

been visited by extension agents.

78

CHAPTER FIVE: RESULTS AND DISCUSIONS

5.1 Bi-variate Analysis

In order to examine the relationship between adoption decision and different set of

household characteristics, chi square and t-test were employed for nominal and

continuous independent variables respectively. Similarly, the second outcome decision,

the proportion of land allocated to improved maize, has been utilized using Pearson

correlation and t-test for continuous and nominal independent variables respectively.

5.1.1 Adoption Decision and Demographic Characteristics of the

Respondent

According to the data from the table 5.1, there exist significant variation in adoption

decision within a household category having a family member living in a town or abroad

and those who have not. The data reveals that about 62.2 percent of adopters are those

households with a family member living in a town or abroad and 68.6 percent of the non

adopters are those who have no any member of family/close relative living in

town/abroad. This could perhaps be due some flow of resources or some kind of support

(it could be financial, technical, moral and other) or information from their relative to the

household. The chi square test at (p<0.01) also shows this systematic association.

With regards to the marital status of the household head, chi square analysis shows no

significant variation; meaning there is not a significant difference in the probability of

adopting improved maize seed depending on the nature of marital ties ( being married

79

and not). Households who have in some form of marital relation accounts for 85.7 and

90.3 percent in adopter and non adopter category respectively meaning that households

who have married has over represented in both category insignificantly.

Table 5.1: Adoption Decision and Demographic Characteristics

Endowment Variables Non-Adopter

Adopter

t-value

N Mean SD N Mean SD

Age of household

head

185 42.52 9.444

209 43.32 10.293

0.634

Educational

attainment of

household head

185 2.71 3.119

209 2.67 2.891

0.016

Endowment Variables

Household

Response

Non Adopter Adopter

χ2 N

Percentage N

Percentage

Do you have a family

member/relative

living in nearby town

or abroad

No 127 61.6 (68.6) 79 39.4 (37.8)

37.436** Yes

58 30.8 (31.4) 130 69.2 (62.2)

Marital status of

household head

Not

married 18 37.5 (9.7) 30 62.5 (14.3)

1.962 married

167 48.2 (90.3)

179 51.8 (85.7)

Gender of household

head

Female 37

54.4 (20)

31 45.6 (14.8)

0.065 Male

148 45.4 (80)

178 54.6 (84.2)

Source: Computed from the survey data

** Significant at (p<0.01) * Significant at ( p<0.05)

NB: The figure in parenthesis shows the percentage within adopter/non adopter category

The result regarding the gender of household head confirm as there is no significant

difference between adoption decisions across the gender group which implies being male

or female is not varied with respect to adoption decision. For instance, being a male

household head or female poses no difference on adoption decision as 55 percent of

80

female headed household did not adopted (20 percent among non-adoption category)

while about 45 percent of male headed households did not adopted. This is against most

of the empirical literatures which states that women are generally discriminated in terms

of access to external inputs and information which in turn affect adoption decision (Dey,

1981).

5.1.2. Adoption Decision and Household Endowment Attributes

Most of previous literatures regarding adoption of agricultural inputs reveals a significant

association between adoption decision and household endowment attributes which was

usually expressed through its effect on building a strong cash reserves. Similarly, the data

from table 5.2 shows the existence of significant mean difference between adopters and

non adopters based on the difference in livestock unit, distance from output market,

family size adjusted to its adult equivalent, total farm land size and existence of a family

member living in a town or abroad.

With regards to total land owned (unit of measurement is timmad that is 8 timmad = 1

hectare), there is a significant (t=9.271 at p<0.01) mean difference between adopters

(12.36) and non adopters (10.95) showing that difference in adoption decision is observed

due to variation in the mean of total land owned.

As with family size and total land allocated to maize production (including the land

allocated to local maize production), significant mean difference is observed among the

adopters and non adopters (See Table 5.2)

81

Table 5.2: Adoption Decision and Endowment Characteristics

Endowment Variables

Non-Adopter

Adopter

t-value

N Mean SD N Mean SD

Total Land Owned

185 10.95 4.48

209

12.36 4.646

9.271**

Family Size(Adult

Equivalent)

185

3.432 1.362

209

4.12 1.634

20.731**

Distance from Nearby

DA Office

185

31.4137 17.119

209

38.222 123.53

0.552

Distance from Nearby

Market Center

185

143.28 44.8

209

152.77 47.996

4.091*

Total Land Allocated to

Maize(in timmad)

185 1.75 0.917

209 3.99 1.19

429.170**

Endowment Variables

Household

response

Non Adopter

Adopter

χ2

N Percentage

N Percentage

Access to Irrigation

No

176 50.2 (95) 174 49.8 (83.2)

13.965**

Yes 9

20.4 (5)

35 79.6 (16.8)

Source: Computed from the survey data

** Significant at (p<0.01) * Significant at (p<0.05)

NB: The figure in parenthesis shows the percentage within adopter/non adopter category.

Significant variation is observed with adopter and non adopters with regards to

households having access to small irrigation and not. The result in table 5.2 shows that

households who have no access to small scale irrigation accounts for 95 percent in non

adopter group and 16.8 percent of households who have purchased improved maize seed

during the 2011/12 production season have access to irrigation. Moreover, the chi square

82

test also confirms such variation significantly (p < 0.01).

5.1.3 Adoption Decision and Institution Related Variables

Institutions have usually found to pose significant effect on adoption of agricultural

inputs through disseminating useful information to and thus easing the utilization of the

technology. In addition, its within institutional membership that an alternative means of

financing perhaps credit is possible.

Table 5.3: Adoption Decision and Institution Related Variable.

Institution related

variable

Househol

d

response

Non Adopter

Adopter χ

2

N Percentage

N Percentage

Take Credit for

2011/12

No 80

52.3 (43)

73 47.7 (35)

2.856

Yes 105

43.6 (57)

136 56.4 (65)

Membership to any

primary Cooperative

No 112

76.2 (60.5)

35 23.8 (17)

78.71**

Yes

73 29.6 (39.5) 174 70.4 (83)

Attend field

day(demonstration)

No 140 76.5 (75.6) 43 23.5 (20.5) 117.53**

Yes 45

21.3 (24.4)

166 78.7 (79.5)

Extension contact No 8

88.9 (4)

1 11.1 (0.5)

6.503*

Yes

177 46.0 (96) 208 54.0 (99.5)

Source: Computed from the survey data

** Significant at ( p<0.01) * Significant at (p<0.05)

NB: the figure in parenthesis shows the percentage within adopter/non adopter category.

In our study area, with the exception of credit, all institution related variables (attending

maize seed demonstration, being a member to a primary cooperative and contact with the

extension workers) have shown to have variation between adopters and non adopters (see

83

table 5.3). For instance, about 76 percent of those who are not a member of primary

cooperative have not adopted which accounts for 60 percent within non adopter category

and 70 percent of those who are member has adopted for the year 2011/12 (which

accounts 83 percent among adopter category). This could perhaps be due to the fact that

membership to a primary cooperative is related with efficient delivery of information and

sometimes price discount (from the non members) about the technology.

Likewise, attending demonstration of improved maize seed is in some ways associated

with the probability of adoption decision. About 79.5 percent of those households who

have attended the trial have adopted. Seemingly, from those non adopters, 75.6 percent

have not attended improved maize seed demonstration adopters. The chi square test (at

p<0.01) also confirms this significant association.

On contrary, the Data related with credit shows the existence of some variation in

adoption decision across those who take credit and who do not though not significant.

The review of past literature shows household access to financial capital like credit helps

the farmer in financing their purchase of input as there are huge gap between production

and harvest season (Feder, and Zilberman, 1985). I have tried to take additional data

concerning the overall nature of credit delivery (the timing, the eligibility criteria, the rate

of interest and repayment period) from the manager of Wasasa micro financing manager

of Jimma Arjo district). I have asked him if there has been any problem in loan

repayment, eligibility criteria, and any other problem related with credit delivery that may

contribute for the problem of effective utilization of loan for the purchase of input. He

told me that there is no problem with respect to the problem listed above as for instance

84

number of household beneficiaries and amount of credit given has been increasing from

time to time (See the Table 5.4 below).

Table 5.4: Trend for Credit Distribution of the Woreda

Year

Amount of Credit Given(in birr)

Number of Households that

Take Loan

2000 1,435700 559

2001 2,7507200 975

2002 4,709100 1830

2003 8,986400 4767

2004 11,031900 4289

2005 14,679750 5106

Total

Source: Wasasa Microfinance Arjo District (2012)

He rather states the problem as: “There is no coordination with between lending

institutions and other stakeholders; for instance, input supplying institution like farmer

union which affects the main objective for which credit is meant for. What is expected

from us is only facilitating the timely delivery of credit at an affordable rate (12 percent)

and broadening the eligibility criteria so that every farmer can use the scheme”. One

major problem is, he adds; there is a huge time gap between the time when loan is given

and the time when input distributed (at least a 3 months gap) thereby making effective

utilization of loan for seed purchase under question.

5.1.4. Production Technique and Household Perception Related

Characteristics

There is also a significant difference between adopters and non adopters based on

perception (or knowledge of improved maize seed) and choice of production techniques.

85

For example, the data in yield perception reveals that about 91 percent of non adopters

are those who do not perceive the better yield advantage of improved maize seed than the

local one.

Table 5.5: Adoption Decision and Household Perception and Production Technique

Attributes.

Perception and

technology

related variable

Household

response Non-Adopters Adopters Pearson chi

square

N Percentage N Percentage χ2

Yield

Perception

No 96 51.9 (91.5)

9 4.3 (8.5)

11.792**

Yes 89 48.1 (30.8)

200 95.7 (69.2)

Resistance to

pest and weed

No 135 73.0 (75.8)

43 20.6 (24.2)

108.789**

Yes

50 27.0 (23) 166 79.4 (77)

Cost of

improved

maize seed

No 29 15.7 (30)

68 32.5 (70)

16.124**

Yes 156 84.3 (52.5) 141 67.5 (47.5)

Fertilizer

Recommendati

on

No 129 69.7 (87.7)

18 8.6 (12.3) 154.391**

Yes 56 30.3 (22.6) 191 91.4 (77.4)

Row planting

of maize

No 87 47.0 (95.6) 4 1 (4.4) 110.620**

Yes 98

53.0 (32)

205 98.1 ( 68)

Source: Computed from the survey data ** Significant at (p<0.01)

NB: the figure in the parenthesis shows the percentage within response category

Likewise, 95.7 percent of those who believes in the better yield advantage of improved

seed has adopted during the period (which accounts for 69.2 percent in the adopter

category). That means, by telling a farmhouse hold about the better yield advantage of

improved maize seed, we can increase the probability of adoption significantly. This

86

finding agrees with Ojiako et al., (2007) stating as; the better the yield the particular

improved soya bean seed have, the more probability would it be adopted by the farmer in

northern Nigeria. In addition the work by Adesina and Zinna (1993) also confirms the

same argument stating that yield has significantly influenced farmers’ decision to adopt

improved mangrove swamp varieties of rice in Sierra Leone. Besides, perception about

the better resistance capability to pests and weed, and knowledge of cost effectiveness in

using the seed by the household has also effect on the probability that the seed would be

adopted.

There is also significant association between adoption decision and intensity of fertilizer

application as the result shows that 77.4 percent of adopters have applied fertilizer as per

the recommendation while those who do not obey appropriate fertilizer recommendation

accounts for 87.7 percent of non adopters. Moreover, there are also variation in adoption

decision between those who plant maize in rows (with the recommended spacing between

rows and plants) and those who do not. This manifests some variation across adopter

categories based on differences in production techniques particularly of maize production

technique which in turn give us some clue that differences in production techniques

would induce the decision of adopting improved maize seed.

5.1.5 Major Challenges to Increased Adoption Rate and its Sustainable Use

According to the data from the sample households shown in table 5.6 below, 19 percent

of the household interviewed have never adopted improved varieties of any type during

the period and about 27.9 percent have discontinued using the seed. On the other hand,

11.1 percent of the sample households were new adopters and have never been tested the

87

technology before 2011/12 production season. In addition, 42 percent have continued

using the technology. Accordingly adoption rate in the study area remains about 79

percent.

Table 5.6 The Status of Adoption Rate of the Sample Respondents.

Household

Response

Category

Non Adopter

Adopter

Total

Count

Percentage

Count

Percentage

Have you ever

adopted improved

maize of any type

No

75

19

44

11.1

119

Yes

110

27.9

165

42

275

Total

185

46.9

209

53.1

394

Source: Computed from the survey data

Regarding those non adopters who have discontinued using the seed, I have included an

open ended question asking the reason why they have decided not to purchase the seed

during the 2011/12 production year. About 39 percent respondent stated the reason as “

because of the increasing price of the seed from time to time” while about 35 percent (see

table 5.7) blamed the quality of seed (seed enterprise could not provide a quality

seed).The remaining 19 percent of them responded its due to fertilizer price and other

complementary inputs. The table 5.7 provides the detailed summary for the reason to

discontinuing.

88

Table 5.7: Reason for Discontinuing the Use of Improved Maize by Households

Reason for Discontinuing Number of

Household

Voted

Percentage

Increased price of the seed 43 39

Seed quality becomes low(deteriorated) 38 35

Cannot afford the price of fertilizer with seed 21 19

Others 8 7

Total 110 100

Source: Computed from the survey data

5.2 Proportion of Land Area Allocated to Improved Maize Variety and

Household Characteristics

The second decision following the adoption decision is the decision of intensity of

application (on how much hectare of land to apply the seed) which depends on the same

set of household characteristics as in the case of adoption decision. With intensity, we

mean the proportion of land allocated to improved maize seed from the total land maize

land .Thus, since the adopter households also produce local maize, the proportion of land

allocated to improved maize would lie between 0 and 1 meaning that the amount of land

allocated to improved maize seed would lie between this number.

Households has been classified as less intensive user and more intensive in this research

based on the proportion of the land they have allocated allocate to improved maize from

the total maize produced. That is, as the farmer reduces the amount of land allocated to

local maize, the proportion of land allocated to improved maize reduces relatively as they

share common resources.

89

Table 5.8 Amount of Land Allocated Under Improved Maize Seed and Household Asset

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

According to the data from the table 5.8, the proportion of land allocated to improved

maize seed have shown to display a strong relationship with the age of household head. A

negative relationship reveals the difficulty and complexity associated with the

management of improved maize cultivation. Besides, the degree of conservativeness and

ambitiousness of household head decreases as their age increases which ultimately make

Households Endowment and Other

Related Characteristics

Proportion of Land Allocated to Improved Maize Seed

Age

Pearson Correlation

-0.23**

Sig. (2-tailed) 0.00

Years of Schooling for Household Head

Pearson Correlation

0.15*

Sig. (2-tailed) 0.03

Total Land Holding

Pearson Correlation

-0.57**

Sig. (2-tailed) 0.00

Total land Allocated to Maize

Pearson Correlation

0.47**

Sig. (2-tailed) 0.00

Livestock Unit

Pearson Correlation

0.02

Sig. (2-tailed) 0.76

Family Size

Pearson Correlation

-0.12

Sig. (2-tailed) 0.08

Distance from Input Market

Pearson Correlation

0.01

Sig. (2-tailed) 0.87

Distance from Output Market

Pearson Correlation

0.00

Sig. (2-tailed) 0.96

90

the intensification process very difficult.

The literacy level of a household head measured in years of schooling have shown to

have a positive a significant relationship with the proportion of land allocated to

improved maize variety. The possible explanation behind such relationship is the fact that

household with more years of schooling would easily realize the benefit versus the risk

associated with the utilization of modern agricultural technology and thus easily votes in

favor the intensification process. Besides, this could probably be justified through the fact

that a literate household head can be better in terms of proper management of the

technology and the subsequent yield or gain in productivity which ultimately encourages

them to intensify the utilization.

The proportion of land allocated to improved maize seed has a strong but negative

relationship with the total land holding .That is, as the land holding of a household

increases, the decision to intensify improved maize seed decreases. Besides, such

relationship would be observed due to the credit and other financial and market related

complexity associated with applying modern agricultural technology as the farmer has a

relatively more land. Moreover, Fear of risk related with expected uncertainty in output

price would also work against the intensification process.

Contrarily, household endowment factors like family size and access to or experience of

using hired labor has also revealed insignificant variation across our continuous outcome

category showing the relatively lower importance of these assets in affecting the

proportion of land allocated to improved maize seed. I have tried to include an open

91

ended question asking the respondent where they had labor during the production and

harvest time. More than 90% told me that they have a good mutual help culture within

the community commonly called “Debo” meaning self help association during production

and harvest season so that they interchangeably make a planned use of the family labor

and even sometimes Oxen turn by turn. This would strengthen the above arguments that

place the number of family labor and access to hired labor would pose little or no threat

to the amount of hectare of land allocated under improved maize.

According to the result from table 5.9, there has been an observable significant variation

in proportion of land allocated to improved maize seed between the two household

groups. That is between those household who engaged in other off farm activity and those

who were not. Such variation would be observed due to the variation in means and source

of income earning which helps in financing the utilization of improved maize especially

when the probability of getting credit becomes questionable. Besides, such a variation

might be due to the variation in risk taking ability between the two groups which makes

the intensification process easier for those farmers engaged in other off farm activity as

compared to those who were not.

As with cash crop production, there has also shown an observable mean variation as

those household producing cash crop and those who were not. The proportion of land

allocated to improved maize significantly varies between those who produce only food

crops and those who produce both cash crops and food crops mainly due to the existing

trade off or opportunity cost related with the allocation of land allocated to Maize

92

Table 5.9 Intensity of Decision and Household Characteristics

Variables Household

Category

N Mean T Value Sig. (2-tailed)

Gender of a household

head

Female 31 .2074 -1.560 .120

Male 178 .2389

Marital status Not married 30 .2311 -.178 .859

Married 179 .2388

Engage in Of farm activity No 168 .2216 -3.650 .000

Yes 41 .2859

Access to hired labor No 69 .2362 .187 .852

Yes 140 .2333

Saving habit No 19 .2720 1.667 .097

Yes 190 .2305

Produce cash crop No 130 .2206 -2.460 .015

Yes 79 .2567

Access to small scale

irrigation

No 174 .2216 -4.058 .000

Yes 35 .2971

Take credit No 73 .2270 -.730 .466

Yes 136 .2381

Member of primary

cooperative

No 35 .2149 -1.206 .229

Yes 174 .2381

Attend maize seed

demonstration

No 43 .2285 -.403 .687

Yes 166 .2357

Extension visit No 1 .5000 2.594 .010

Yes 208 .2330

Yield perception No 9 .2398 .164 .870

Yes 200 .2340

Perception about resistance

to pests

No 43 .2859 3.766 .000

Yes 166 .2208

Cost perception No 68 .2204 -1.331 .185

Yes 141 .2409

Fertilizer

application(recommended)

No 18 .2454 .475 .635

Yes 191 .2332

Row planting(using the

recommended spacing)

No 4 .1932 -.795 .427

Yes 205 .2350

Have a relative from other

town or abroad

No 79 .2191 -1.641 .102

Yes 130 .2434

* Significant at (p<0.05)

93

The analysis for irrigation access has also shown significant variation as the proportion of

land allocated to improved maize between those households that uses small scale

irrigation and those who were not. This might be associated with the fact that those

household with access to irrigation facilities have sufficient access to production of cash

crops with a premium earning thereby posing a significant variation.

The proportion of land allocated to improved maize also varies significantly between

those farmers who frequently visit extension services and those who were not visiting.

Such variation might be due to the fact that extension service would deliver important

information and farm techniques related with the technology and enhance the

intensification. Besides, the extension service will equip farmers with sufficient skills and

knowledge about the technology utilization and thus convince farmers.

The proportion of land allocated to improved maize also varies significantly between

those farmers who perceive in the better resistance of improved maize seeds to pests and

disease and who were not. Such a variation would be observed due to the difference in

the risk of crop failure observed between the two groups under investigation.

The rest of household attributes stated in the above has found to have no mean variation

with the main outcome category since t statistics shown no significant mean differences.

With the exception of extension contact, most Institution related variables and household

perception and technique of production have shown to have no significant variation. That

means, once the household have already adopted these improved maize seed, there shall

94

not be any observable difference in the intensity of its application due to the differential

access to the institutional and other perception attributes (see table 5.9).

5.2.1 Major Challenges to Intensification of Improved Maize Production

The data collected from respondent’s shows that, households (adopter households) differ

in terms of the proportion of land area allocated to improved maize as they rarely apply

the seed in higher proportion as the maximum result shows to be 0.89. I have included an

open ended question (only for adopter households) to state the major reason for lower

allocation of land to the production of improved maize seed. More than 40 percent of

adopter households respond it as “fear of risk” stating that they are not certain about the

profitability of the technology since there are many uncertainties (for instance, lagging of

rainfall, pest outbreak and others) in the whole production process (see table 5.10).

Likewise, 12 percent attribute the reason to the increased cost of seed and other

complementary inputs. Moreover, about 18 percent of adopters state the reason saying

that: “the local seed variety is better in maturing early as compared to the improved ones

and hence better for early consumption by our family”. The study by Ogola and Ayieko,

(2012) also supports these arguments.

Table 5.10 Major Constraint to Increased Utilization of Improved Maize Seed.

Reason for low utilization of improved maize

variety

Count

Percentage

1.High price of seed(cost of seed) 67 12

2.Risk of crop failure(due to pests and weed,

rainfall shortage and others)

89 43

3.Not matured early for consumption by

household

37 18

Others 15 7

Total 208 100

Source: Computed from the data

95

5.3 Binary Logistic Regression Result

Factors Affecting Adoption Decision of Improved Maize Variety

The t-test made in the above section, will only tell us the existence of some association

between the two from the observed difference in mean and do not tell us the nature of

association that exist between the response and explanatory variables. In that case,

multiple regressions are used to estimate the nature of association existing between the

two groups accounting for effects of confounders.

Different researchers used different models to estimate most of the possible factors

affecting the adoption decision and demand for improved maize variety. In these research

a logistic regression was computed so as to estimate the factors affecting improved maize

seed purchase decision. As to this, a binary logistic regression has been applied with the

value 1 if the household has purchased the seed (improved maize) and 0 if not for the

period of 2011/12.

Multi-co linearity Diagnostic and Test of Goodness of Fit

Before estimating the logistic regression two tests are made. One is multicollinierity

diagnostics and this test is made so as to avoid if there exist any systematic correlation

within any of the explanatory variables which inflate the standard error and thus result in

false rejection of the parameter coefficients. so a correlation matrix was computed for all

explanatory variables and thus two variables has shown to have good correlation and thus

one variable is dropped from the model so as to fix the problem.

The second test is made in an attempt of knowing whether the model chosen fits with our

96

data or not. As to this, a classification table and Hosmer and Lemeshow tests are used.

Accordingly, 91.3 percent of the non adopters are correctly predicted as non adopters

while 92.3 percent of adopters are correctly predicted as adopters. In general 91.9 percent

of the households are correctly classified in the model (see annex). Regarding the Hosmer

and Lemeshow tests, the data from the model shows the significance level of 0.790 (see

annex) which leads us to accept the null hypothesis (since it is greater than 0.05) that

states as the model best explains the data.

Multiple Logistic Regression Results

The table below shows all the possible factors affecting (socioeconomic, institutional

technique of production and perception related) the household purchase of improved

maize seed. As to this end, coefficient of parameters, and odds ratio are displayed at

various significance levels. From the total of 23 covariates, only 9 have found to

significantly estimate the binary decision (adoption decision).Out of the 23 variables

entered, only 9 of them have found to significantly describe the outcome decisions. The

other 14 variables failed to significantly estimate the farm household decision of whether

to adopt improved maize seed or not.

To start with, the variable related with household distance from the output market shows

that those who walk greater than 150 minute have 7.104 times more likelihood to

purchase the improved maize than those who walk less than 150 minutes. Such a

relationship may emanate from the expansion of output market at the farm gate that may

benefit those household living at a distant location in real terms than those who reside

closer to the output market. Similar study by Beatrice, Wilfred and Domisiano, (2007)

97

also illustrates such an inverse relationship and attribute the reason to the expansion of

informal seed market which has worked in favor of those households that are located far

from the output market. The result is against our hypothesis and most of the empirical

literatures.

The other parameter is related with the amount of total land allocated to maize production

(both improved and local). The result reveals that those households who allocate more

than 3 timmad of their land to maize seed have a greater likelihood of adopting improved

seed than those household in the reference category. That is, the odds of purchasing

improved maize seed for those who allocate more than 3 timmad of land to maize

cultivation is 26.617 times than the household in the reference category. Possible

justification to such a relationship could illustrate how far the better popularity of maize

(perhaps for consumption) within a family would enhance the probability of adoption

decision.

The data related with the marital status of the household head shows that the likelihood of

deciding to use improved maize decreases by 98.5 percent for those married household

head than the households in the reference category confirming the better likelihood of

adopting for those who were not in a marital ties. Possible reason for such relationship

would lie in the fact that women are cautious and resistant in undertaking a risky business

(Acacia, 2002). Besides, since marital ties are attached to existence of dependents family

members which further made the decision to engage in a risky business more difficult.

98

Table 5.11: Binary Logitistic Regression Result

B

S.E. Wald Exp(B)

Gender 0.655 0.917 0.511 1.926

Marital status (Not married) -4.188 1.379 9.228 0.015**

Family member living in town (No) 1.536 0.504 9.298 4.644**

Of farm (No) 1.133 0.770 2.164 3.104

Hired labor (No) -0.922 0.522 3.114 0.398

Save (No) 2.152 0.602 12.798 8.603**

Cash crop (No) -1.015 0.617 2.702 0.363

Irrigation (No) 0.812 0.909 0.798 2.253

Take Credit (No) 0.674 0.521 1.675 1.963

Cooperative (No) 0.490 0.542 0.819 1.633

Demonstration (No) 0.813 0.517 2.479 2.255

Yield (No) 1.888 0.724 6.798 6.608**

Resistance (No) 0.256 0.577 0.198 1.292

Cost (No) -1.052 0.606 3.015 0.349

Fertilizer application (No) 2.939 0.640 21.073 18.904**

Row planting (No) 3.409 1.142 8.915 30.230**

Distance from market (< 150) 1.961 0.575 11.617 7.104**

Total land (< 8) -1.258 0.549 5.251 0.284**

Total maize land (< 3) 3.282 0.729 20.261 26.617**

Distance from DA (< 30) -0.791 0.556 2.025 0.454

Family size (< 3.8) -0.050 0.522 0.009 0.952

Livestock unit (3.8) 0.570 0.489 1.360 1.768

Education level (not literate) 0.279 0.570 0.240 1.322

Educational level -0.146 0.589 0.062 0.864

Constant -6.161 0.816 0.424 1.702**

-2log likelihood

142.22

Nagelkerke R Square

0.854

Source: Computed from the survey data

** Significant at (p<0.01)

NB: The figure in the parenthesis refers to the reference category

On the other hand, those households whose have a close relative or member of the family

living in the nearby town or abroad have a better chance of deciding to apply improved

maize seed on their land which is about 4.644 higher than the those who have no relative.

99

These could be explained partly through the existence of some flow of financial or

technical resources across those households’ heads and their relatives which thereby

resulted in high probability of adoption as compared to the households in the reference

category. The figure also in line with the descriptive result depicted in the above section.

The estimation result concerning saving shows a better likelihood for those who saves as

compared to the reference category which depicts that those who saves have a more

likelihood of(8.603 times) adopting improved maize variety than who do not save. Other

things remain constant, saving would help a farm household’s liquidity constraint that

exists between the production and harvest season and helps them in easily financing their

input purchase. This is in line with our descriptive result, the empirical literature and our

hypothesis.

Knowledge of the farm household about the yield advantage of the improved maize has

shown to increase the probability of adopting improved maize by 6.6 times than those

households in the reference category. These shows that as a household get a deeper

knowledge of the varieties special attribute his confidence on the comparative advantage

of the seed increased there by resulting increased likelihood in purchase decision. The

finding by Augustine and Mulugeta, (2010) also supports the same findings stating that

convincing farmers that a given improved variety is superior to the local ones in terms of

yield and resistance to field pests would increase adoption rate by 20% and 6%,

respectively.

100

Households who apply fertilizer as per the blanket recommendation during maize

cultivation have a better likelihood of deciding to buy the improved varieties than their

counterparts. This also shows that users of improved maize are applying complementary

inputs (perhaps fertilizers) more intensively than those farmers who use a local maize

seed. A study by Edilegnaw (2003) and Mulugeta, (2006) are also in line with this

finding.

Similarly, those households that use row planting techniques during maize production has

about 30.230 times greater likelihood of adopting improved maize seed than those who

do not use. This could be also explained as the former has a better knowledge about

modern extension service and its feasibility as compared to the reference category which

can in turn help them in deciding the utilization the new variety (improved maize seed).

Last but not least is the total land holding. Result reveals the kind of inverse relationship

between high total land holding and probability of adoption..That is, the odd of adopting

improved maize seed decreases by 71.6 percent for households with the total land greater

than 8 timmadd or on hectare than those in the reference category. These could perhaps

be justified as those who have small land holding would efficiently utilize the benefit of

the improved technology (perhaps by intensifying it) where as those households having

larger size of land fail to do so (may be due to the costly nature of applying the

technology with other complementary inputs which prohibits them from applying the

seed on larger proportion of their land. Similar study by Uaiene and Rafael, (2005)

supports this fact stating that an easy access to additional land discourages fertilizer and

101

pesticide use, which land is saving technologies.

Moreover, variables like literacy level of a household head, membership to primary

cooperative, engaging on of farm activity, attending maize field day, credit access, access

to small scale irrigation and engaging in cash crop production have found to display the

expected result though not significant.

5.4 Estimation of Factors Affecting the Intensity of Improved Maize

Seed

In the above estimation result (the binary logistic regression), we have grouped those

households into 1 and 0 (adopter and non adopter respectively) based on whether they

have bought improved maize seed or not for the 2011/12 production season. It is clearly

observed that those households who have decided to purchase improved maize seeds

during the period under study could not have totally applied improved maize seeds on all

of their plot; rather, they prefer to produce the local varieties too along with the improved

ones (adopted it partially).Within the adopter household itself, there is variation in the

proportion of land allocated to improved maize variety. Thus, in order to investigate such

a variation, factors determining intensity of applying the seed has to be clearly noted. One

of the objective of these research paper is thus to find those socioeconomic, institutional,

psychological and demographic factors accounting for such variation.

As we have discussed in methodology section, since our data is censored, Tobit model is

selected and tested against the conventional OLS model and thus fitted best to estimate

102

factors affecting the proportion of land allocated to improved seed. Multicollinierity

problem was tested before the model is fitted and as usual, a series of correlation matrix

were developed so that explanatory variable can separately contribute to the variation in

the dependent variable.

The Likelihood ratio tests are made to know if model fits our data and thus the test result

shows the determinants selected can significantly estimate the model with no

distributional violation thereby rejecting the null hypothesis as the determinants selected

was tested to be significantly different from zero. Thus, Tobit model was selected to

isolate the possible factors affecting the proportion of land allocated to improved maize

seed in the study area during the year under review. Thus, the coefficient of parameters

stated in the table 5.12, the coefficient of the parameter shows the marginal effect of the

respective explanatory variable on outcome variable (tested at both 1 and 5 percent of

significance).

Saving is also estimated to have a positive and significant relationship with the

continuous outcome decision (p< 0.05) because saving is usually associated with

ownership of resources and hence little or no reliance on financial support that prohibit

most farmers from scaling up the intensity of modern technology application. This result

is also consistent with the empirical literature and the descriptive results.

103

Table 5:12 Tobit Estimation Result for Factors Affecting Intensity of Decision

Explanatory Variables

Coefficients t

Educational attainment of household head -0.0038304 -0.62 Total land owned 0.0005319 0.12 Total land allocated to maize production 0.115998 8.49* Livestock ownership 0.0110374 1.23 Family size -0.0065125 -0.58 Distance from DA office -0.0000923 -0.62 Distance from main output market 0.00096 2.52* Age of household head 0.0001069 0.05 Access to irrigation 0.0871821 1.62 Have family/relative living in town or abroad 0.0700654 2.06* Engage in of farm activity 0.0886299 1.87 Availability of hired labor -0.0700383 -1.88 Save(in cash or in kind) 0.166949 3.36** Produce cash crop -0.0987285 -2.48* Take credit 0.0664849 1.83 Cooperative member 0.0984085 2.20* Attend improved maize demonstration 0.104087 2.60* Extension Contact 0.1750656 0.80 Yield Perception 0.2391719 3.71* Perception about resistance 0.0776015 1.78 Perception about cost -0.0248686 -0.63 Fertilizer application 0.2492921 4.93* Apply Row Planting(recommended) 0.3281166 3.67* Marital Status -0.2406579 -3.57* Gender 0.0055458 0.09 Constant -2.538024 -4.95

*

Prob > chi2 0.0000

Log likelihood -74.479772

Pseudo R2 0.7752

LR chi2(25) 513.64

Source: Computed from the survey data

* Significant at (p<0.01) ** significant at (p< 0.05)

According to the estimation result in Table 5.12, distance from output market is

significantly related with the amount of a kilogram seed purchased from the seed market.

Result shows that as the farm household is located 1 minute far from the output market,

104

the amount of timmad of land allocated to improved maize seed increase by .00096

timmad. As discussed in the above logit estimation, such a relationship could be justified

as; since the household is located far from the output market, it promotes crop saving and

avoids misuse of crop output for unwanted consumption there by encouraging household

to intensify the application of the technology use (improved maize in our case). The study

by Beatrice, Wilfred and Domisiano, (2007) made in Western Kenya using probit model

also supports these finding stating that distance from the nearest output market have

positively and significantly related with the probability of adopting stress tolerant maize.

Moreover, the result is against most of empirical literature and our hypothesis though it’s

in line with the descriptive result.

With regards to the production of cash crops, the estimation result also shows statistically

significant but negative relationship between cash crop production and the number of

timmad of land allocated to improved maize seed. Such a negative relationship shows the

tradeoff between the land allocated to both and may be due to the fact that cash crop

earns a premium price than cereal and thus those household who produce cash crops

usually preferred to increase the amount of land allocated to cash crops at the expense of

food crops (improved maize in our case).The study by Salasya, (2005) and Strasberg et

al., (1999) supports this finding stating that households with a large acreage under cash

crops tend to concentrate on them and pay less attention to the food crops. The

descriptive result also gives similar result (chi square test) but it’s opposite to our

hypothesis.

105

Institution related variables like being a member of a primary cooperative and attending

improved maize demonstration is estimated to have a significant and positive relationship

with the intensity of decision. The result being significant at 1 percent significance level

shows the importance of seed promotion and differential access to information in

enhancing the intensity of adoption.

The association with regards to cooperative membership shows the likelihood of

intensifying improved maize seed was higher for those households who belong to at least

one of the primary cooperative. These could be explained as since it is through the

primary cooperative that seed distribution is made, there is price difference across

members and non members that may probably help the members to utilize more than the

non members.

The likelihood of intensifying the application of improved maize is higher for those

household who attends the demonstration of maize significantly (p< 0.01). Attending

demonstration would help in increasing farmer’s knowledge and confidence on the

special attributes of the variety and hence help in increasing the intensification. The study

conducted in western Oromia also supports this finding stating that attending field day

visit has a significant and positive relationship with the probability of participating in

wheat seed multiplication and the amount of land allocated to it (Abdisa et al., 2001).

The estimation result with respect to yield perception shows a significant and positive

relationship to the amount of land allocated to improved maize variety (both at 1 and 5

106

percent significance level) and the result is congruent with both the descriptive and our

hypothesis.

Apparently, the result in fertilizer recommendation depicts significant and positive and

significant association with our outcome variables .which is also in line with the

empirical and theoretical literature.

Using row planting techniques in maize production (in line with the recommended

spacing between rows and column) has also shown to have a positive relationship with

the continuous outcome decision and is attached to knowledge of appropriate production

technique. This result is similar with the theoretical literature, our hypothesis and the

descriptive result.

The result concerning the marital status of a household head shows a negative association

with the amount of seed purchased. The likelihood of intensifying improved maize

increases for the households who were not in a marital tie significantly (at p< 0.01).

possible reason for such a relationship could be due to the fact that decision of intensity

for married households would be difficult since they (especially women’s) are more

reluctant to engage in a business characterized by many risks and uncertainties (Acacia,

2002). This result is against both the descriptive result and our hypothesis.

The result estimated for those farm households who have family member living in a town

or abroad, have a significantly a positive relationship with the proportion of land

107

allocated to improved seed. It shows that those who have member of families living in a

town or abroad have a better chance of intensifying the improved maize production than

those who have not. This may probably affect our outcome variable through remittance

which makes farm households financially strong to purchase seed and other

complementary inputs. This result is also in line with our hypothesis and other empirical

literature.

108

CHAPTER SIX: CONCLUSION AND RECOMENDATION

6.1 Conclusion

Many efforts has been made since long to realize the increased rate of adoption and

optimum uptake of improved maize seed by smallholder farmer with the objective of

increasing their yield and hence enhancing food security. But, such attempt has not

brought the desired result as the data from the study area shows both adoption decision

and intensive use of this yield enhancing technologies was minimum as farmers has little

confidence on the probability that the technology if applied would brought the stated

result. In addition, the adopter household itself either discontinues using the technology

or adopt partially after he has tested it. In order to investigate and justify possible reason

associated to such problem, one has to isolate factors determining adoption decision and

its intensity of use has to be clearly known.

This research is thus intended to shed light on those possible factors behind lower

adoption rate and minimum uptake of improved maize variety by analyzing the major

determinants of adoption decision and proportion of land allocated to improved seeds

based on data collected from 394 farm households during the 2011/12 cropping season.

Thus conclusions are to be forwarded accordingly as the following:

In some instances, those stakeholders concerned in the overall seed market (seed

suppliers, seed multipliers, distributors and microfinance’s) has made little coordinated

efforts in attaining the major objectives for which they are striving for.

109

The major socioeconomic and demographic factors affecting adoption decision and

intensity is distance from main output market, distance from DA office, marital status,

engaging in cash crop production, saving and having a family member or relative living

in town or abroad.

Both adoption decision and intensity of application has found to be less dependent on

household endowment factors as household asset ownership factors like total land owned,

livestock ownership, and availability of both family and hired labor as the estimation

result shows insignificant relationship in both outcome decisions. Thus we can conclude

that adoption decision and intensity of its application is less dependent on of household

resource ownership status and no more a constraints if other factors have found to be

placed accordingly.

Distance from DA office (in minute travelled) is found to be negatively related with

adoption decision showing the better advantage development agents in promoting

improved maize adoption.

Production of cash crops has found to have negatively related with the second outcome

decision (intensity of improved maize application) showing the tradeoff between the

lands allocated to cash crop and food crops.

Institution related variables like conducting improved maize demonstration on farmers

plot and membership to primary cooperatives and saving (in cash or in kind) have found

110

to be vital especially for increasing the intensity of adoption. Besides, institutions also

play a major role not only in promoting the new technology, but also in sustaining it.

Intensive application of other complementary inputs like fertilizer has found to be

significant factors in determining both adoption decision and intensity of its application

as the covariate related with this shows a positive and significant association.

In addition, knowledge about the special attribute of the improved variety in relation to

the conventional ones (like perception about yield advantage, resistance to field and

storage pests) has a pivotal role in enhancing wider adoption. These variables also clearly

manifest the importance of training (both formal and informal) and demonstration in

improving adoption rate and sustain it.

6.2 Recommendations

All the stakeholders (research institutions, cooperative unions, agriculture and rural

development offices and lending institution like microfinance’s) participating in the

overall seed value chain have to jointly operate in a coordinated fashion so as to enhance

adoption rate and boost the intensity of adopting improved maize seed.

Organizations especially those are responsible for seed supply and distribution has to give

a special emphasis and work hard with changing the perception of the variety as the

above analysis shows that farmers have no full confidence in the attributes of improved

maize (like the profitability, yield and resistance to pests and other disease).These

111

findings will also calls for the expansion and further development of farmer based seed

multiplication activities that allows for farmers participation at every stage. It will also

increase farmer’s knowledge about the special attribute of the variety and hence help in

building their confidence. Besides, the results implies that Agricultural research

institutions should continue and strengthen research on production characteristics of

improved maize varieties, including seed quality, drought tolerance and maize meal

quality.

There are no common consensus and sometimes divergences in adoption studies as the

estimated regression results across most studies shows contrasting findings(especially

those made at crop level) showing the existence of some gaps (perhaps variable definition

that stick to specific socioeconomic setup or any other) that needs to be investigated.

This study also recommends the better importance of other complementary inputs to

improved maize seed like fertilizer in enhancing the seed purchase decision and optimum

uptake of improved maize seed by smallholder farmers. Thus facilitating the supply and

efficient distribution of these complementary inputs (not only on timely basis but also at a

remunerative price) has to be given a due emphasis so as to achieve the desired

objectives.

112

References

Abdisa Gemeda, Girma Aboma, H. Verkuijl, and Mwangi, W. (2001.). "Farmers' Maize

Seed Systems in Western Oromia, Ethiopia", Mexico, D.F: International Maize

and Wheat improvement center (CIMMTY) and Ethiopia Agricultural Research

Organization (EARO).

Adesina, A. and M. Zinnah (1993). "Technology characteristics, farmers’ perceptions

and adoption decisions: a Tobit model application in Sierra Leone", A Journal of

Agricultural Economics 9: 297- 311.

Alemu, D. and J. Spillman (2006) "The Ethiopian Seed Systems: Regulation, Institutions

and Stakeholders.", Paper submitted for ESSP polices conference.

Asrat Asfaw, (2008). "Seed relief intervention and resilience of local seed systems under

stress: The case of Humbo Woreda in Southern Ethiopia", Proceedings of

workshop in the improvement of Farmer Based Seed production and revitalization

of the informal Seed supply of local crops and varieties in Ethiopia. 10 April

2008, Wageningen International.

Beatrice,S., W. Mwangi, D. Mwabu and A. Diallo (2007). "Factors influencing adoption

of stress-tolerant maize hybrid (WH 502) in western Kenya", African Journal of

Agricultural Research 2 (10): 544-551,available online at:

<http://www.academicjournals.org/AJAR>.

Becerril, J. and Abdulai,A. (2010). "The impact of improved maize varieties on poverty

in Mexico: A propensity score matching approach", Journal of World Dev. 38(7):

1024-1035.

113

Bekele, G. and Alemu, M. (2010). "Investments in Land Conservation in the Ethiopian

Highlands. A Household Plot-Level Analysis of the Roles of Poverty, Tenure

Security, and Market Incentives." , Environment for Development, Discussion

Paper Series.

Bezabih Emana (2000). "The Role of New Varieties and Chemical Fertilizers under Risk:

The Case of Smallholder in Eastern Oromia, Ethiopia", Ph.D dissertation,

Germany.

Bishaw, Z., S.Yonas, and S.Belay (2008). "The status of the Ethiopian seed industry", in

Marja HT, B Zewdie, B Abdurahman and S Walter(eds) Farmers, Seeds and

Varieties. Wageningen International, the Netherlands.

Brush, S., E. Taylor and M. Bellon (1990). "Biological Diversity and Technology

Adoption in Andean Potato Agriculture", University of California. Mimeo: pp. 9-

20.

Byerlee, D., J. Spielman, D. Alemu, and M.Gautam,(2007). "Policies to Promote Cereal

Intensification in Ethiopia: A Review of Evidence and Experience", IFPRI

Discussion Paper; Washington, D C: International Food Policy Research Institute.

00707.

Campbell, R. (1966.). "A Suggested Paradigm of the Individual Adoption Process".

Rural Sociology 31: 458-466.

CSA, (2006). "Agricultural Sample Survey”, Conducted by Central Statistical Authority

for the year 2006/2007 from September to December, Volume I.

CIMMYT ( 1993). "The adoption of agricultural technology: A guide for survey design",

CIMMYT Economics Program.

114

Conley, T.(2001). "Social Learning through Networks: The Adoption of New Agricultural

Technologies in Ghana", American Journal of Agricultural Economics, 83(3):

668-673.

Cromwell, E., E. Friis-Hansen, and M. Turner (1992). "The Seed Sector in Developing

Countries: A Framework for Performance Analysis “, Working Paper 65, London:

Overseas Development Institute.

Dasgupta, S. (1989). "Diffusion of Agricultural Innovations in Village India", Department

of Sociology and Anthropology, University of Prince Edward Island, Canada.

Dawit Alemu, Mwangi, W., Mandefro Nigussie and J. Spillman (2008). "The Maize Seed

System in Ethiopia: Challenges and Opportunities in drought prone areas",

African Journal of Agricultural Research. 3 (4): 305-314.

Doss, R. (2003). "Understanding farm level technology adoption: Lessons learned from

micro surveys in Eastern Africa." CIMMYT Economics Program Working Paper

No. 03-07.

Edilegnaw Wale ( 2003). "What do farmers financially lose if they fail to use improved

seeds? Some econometric results for wheat and implications for agricultural

extension policy in Ethiopia", Ethio J. Agric. Eco XII (2): 59-79.

Fasil Kebebw and Girma Balcha ( 2004). "Conservation and Utilization of Biodiversity in

Ethiopia, National Strategy and Priorities."

Feder, G. and D. Umali (1993). "The Adoption of Agricultural Innovations” A Review.

Technological Forecasting and Social Change 43: 215-239.

115

Feder, G., Just, R. and Zilberman, D. (1985). "Adoption of agricultural innovations in

developing countries", A survey by Economic Development and Cultural Change

33: 255-298.

Foster, A. and M. Rosenzweig (1995). "Learning by Doing and Learning from Others:

Human Capital and Farm household Change in Agriculture", Journal of Political

Economy 103(6): 1176-1209.

Gemeda, A. and Aboma, G. (2001). "Farmers' Maize Seed Systems in Western Oromia,

Ethiopia", Mexico, D.F: International Maize and Wheat improvement center

(CIMMTY) and Ethiopia Agricultural Research Organization (EARO).

Genanew Bekele and Alemu Mekonnen (2010). "Investments in Land Conservation in the

Ethiopian Highlands", A Household Plot-Level Analysis of the Roles of Poverty,

Tenure Security, and Market Incentives, Discussion Paper Series.

Getahun, D. , W. Mwangi, H. Verkuijl and A. Wondimu (2000). " An assessment of the

adoption of seed and fertilizer packages and the role of credit in smallholder

maize production in Sidamo and North Omo Zone, Ethiopia."

Gezhaegn Ayele (1999). "Economic Analysis of Innovation and adoption of Vertisol

Technology", A Study of Small holders Mixed Farming in the Highlands of

Ethiopia, Stuttgart.

Greene, W. (2000). "Econometric Analysis." 4th Edition, Upper Saddle River, New

Jersey.

Hiebert, D. (1974). "Risk, learning and the adoption of fertilizer responsive varieties."

American Journal of Agricultural Economics 56: 764-68.

116

Howard, J., E. Crawford, V. Kelly, M. Demeke, and J. Jeje (2003). "Promoting High-

Input Maize Technologies in Africa", the Sasakawa-Global 2000 Experience in

Ethiopia and Mozambique. Journal of Food Policy 28: 335-348.

Jayne, T., R. Myers, and J. Nyoro (2007). "The Effects of Government Maize Marketing

Policies on Maize Market Prices in Kenya", Contributed paper, International

Association of Agricultural Economics Tri-Annual Meetings, Gold Coast,

Australia, August 12-18, 2006.

Janvry, M. (1991). "Peasant household behavior with missing markets-some paradoxes

explained." Economic Journal 101: 1400- 1417.

Kebede, Y., Gunjal, K. and Coffin, G. (1990). "Adoption of new technologies in Ethiopian

agriculture: the case of Tegulet-Bulga District, Shoa Province", Agricultural

Economics Journal 4: 27-43.

Kothari (1990). "Research Methodology: Methods and Techniques", 2nd Edition; Wishwa

Prakashan, New Delhi.

Lipton, M. ( 1968). "The Theory of Optimizing Peasant Farming", Journal of

development Studies, 4: 327-351.

Lipton, M., R. Longhurst, W. Mas-Colell, and J. Green (1989). "New Seeds and Poor

People", London.

Marapia (2007). "Farm Household Production Theories:A Review of Institutional and

Behavioral Responses" , Asian Development Review 24(1): 49-68.

Marra, M., Pannell,J and Abadi Ghadim ( 2003). "The economics of risk, uncertainty and

learning in the adoption of new agricultural technologies: where are we on the

117

learning curve?" , A Journal for Agricultural Systems 75: 215-314.

McGuirk, A. and Y. Mundlak (1991). "Incentives and constraints in the transformation

of Punjab agriculture". A Research Report (87) Presented by International Food

Policy Research Institute, Washington D.C.

Million Tadesse, Belay Kasa, Legesse Dadi and Dawit Alemu (2004). "Determinants of

fertilizer use in Gununo area". Proceedings of agricultural technology evaluation

adoption and marketing Ethiopia; Workshop held to discuss results of 1998-2002,

pp: 21-31.

Minten, B. (2008). "Agricultural technology, productivity, and poverty in Madagascar",

World Dev. 36(5): 797-822.

Morduch, J. (1994). "Risk, Production, and Saving: Theory and Evidence from Indian

Households", Harvard University, Cambridge.

Morris, L., Tripp, R. and Dankyi, A. (1999). "Adoption and impact of improved maize

production technologies. A case study of the Ghana Grains Development Project

Economics Program Paper 99-01: pp:38.

Munshi, K. (2004). "Social learning in a heterogeneous population: technology diffusion

in the Indian Green Revolution." Journal of Development Economics 73: 185-

213.

Mwangi, W. and H. Teklewold (2006). " Determinants of adoption of poultry

technology: A double-hurdle approach ", Livestock Research for Rural

Development. vol 3: 1-14.

Nkonya, E., T. Schroeder and D. Norman (1997) "Factors affecting adoption of improved

maize seed and fertilizer in North Tanzania.", Indian journal of Agricultural

118

economics. 48(1): 1-12.

Odulaja, A. and G. Fassil (1996). "Modeling Agricultural Production of Small- Scale in

SSA: A Case Study in Western Kenya" , Agricultural Economics Journal 14 (1-3):

85-91.

Ojiako, A. and V. Manyong (2007). "Determinants of rural farmers’ improved Soybean

adoption decision in northern Nigeria ", Journal of Food Agric. Environ., 5 (2):

215-223.

Ouma, J. and Owuor, G. (2006). "Determinants of improved maize seed and fertilizer use

in Kenya: Policy implication", A Paper presented at the International Association

of Agricultural Economists’ Conference, Gold Cost, Australia, August 12-18.

Robson, E. and M. Smale ( 1991). "Sahellian Africa awaits its ‘green revolution’, Maize

Diversity, Variety Attributes, and Farmers’ Choices in South Eastern Guanajuato"

, journal of World Economic Development and Cultural Change ; 4(6),50(1): 21-

23, Mexico

Rogers, M. (1962). "Diffusion of innovations" , New York: Free Press of Glencoe.

Rogers, M. (1983). "Diffusion of Innovations" , 3rd edition. New York: Macmillan

Publishing Co.Inc..

Rogers, M. (1995). "Diffusion of Innovations" , New York: Free Press.

Rogers, M. and F. Shoemaker (1971). "Communication of Innovation: A Cross-cultural

Approach", Second Edition. The Free Press, New York.

Sain,G.and J.Martínez ( 1999). "Adoption and use of improved maize by small-scale

farmers in Southeast Guatemala", Economics Paper pp.99-104. Mexico D.F.:

119

CIMMYT.

Schultz, W. (1964). "Transforming Traditional Agriculture", New Haven, Yale University

Press.

SG 2000 (2002). "project Activities and outputs", An assessment Conducted from 1993-

2001:Addis Ababa, Ethiopia.

Shields,M. and G.Ranniyan ( 1993). "A Longitudinal Analysis of Factors Influencing

Increased Technology Adoption in Swaziland from 1985 to 1991", Journal of

Development Areas 27: 469-484.

Singh, I., L. Squire and J. Strauss ( 1986). "Agricultural Household Models: Extensions,

Applications and Policy", Baltimore, MD, US: John Hopkins University Press.

Smale, M., E. Just and D. Leathers (1994). "Land allocation in HYV adoption models: An

investigation of alternative explanations ", American Journal of Agricultural

Economics 76: 535-546.

Stone, D. (2007). "Agricultural deskilling and the spread of genetically modified cotton

in Warangal." Current Anthropology 48: 67-87.

Sunding, D. and D. Zilberman (2000). "The agricultural innovation process: Research

and technology adoption in a changing agricultural sector." In: B.L. Gardner and

G.C. Rausser(Eds) Handbook of agricultural economics Vol. 1A.

Taylor, J. and I.Adelman (2003). "Agricultural Household Models: Genesis,Evolution

and Extensions." Review of Economics of the Household 1(1): 33-58.

Teklewold, H. and L. Dadi (2006). "Determinants of adoption of poultry technology: A

double-hurdle approach", Livestock Research for Rural Development 18(3): 1-14.

120

Tesfaye Zegeye, D. Tanner, H. Verkuijl, A.Agidie, and W. Mwangi (2001). " Adoption of

Improved Bread Wheat Varieties and Inorganic Fertilizer by Small-Scale Farmers

in Yelmana Densa and Farta Districts of Northwestern Ethiopia", Mexico, D.F.:

International Maize and Wheat Improvement Center (CIMMYT) and Ethiopian

Agricultural Research Organization (EARO).

Uaiene, N. (2004). "Determinants of Agricultural Technology Adoption in Mozambique",

IFPRI Discussion Paper.

World Bank (2008). "World Development Report on Agriculture for Development”

Washington, DC.

Zavale, H., Mabaya, E. and Christy, R. (2005). "Adoption of Improved Maize Seed by

Smallholder Farmers in Mozambique", SP 2005-03 September 2005. Department

of Applied Economics and Management. Cornell University, Ithaca, New York."

14853-7801.

121

Appendices

Appendix I: Classification table for Binary logistic Regression

Observed

Predicted

Adoption Decision

Percentage

Correct

no yes

Adoption Decision

no

168

16

91.3

yes

16

193

92.3

Overall Percentage

91.9

Source: computed from the data

Appendix II: Hosmer and Lemeshow Test

Chi-square

value df( at 95 CI) Sig.

4.687

8 0.790

Source: computed from the data

Appendix III: Conversion factor used to estimate tropical for livestock Unit(TLU)

Livestock type Weight

oxen and cow 1.0,

Goat 0.08

Sheep 0.08

Poultry 0.02

Calf 0.25

Donkey 0.7

Horse 1.1

Heifer 0.75)

Source: Jahnke et al, (1982)

122

Appendix IV: Conversion Factor Used to Estimate for Adult Equivalent

Age category

Adult Equivalent

Male Female

Below 10 years 0.6

0.6

Between 10 and 13 0.9

0.8

More than 14 years 1

0.75

Source: Stork et al. (1999)

Appendix V: List of Selected Kebeles and Number of Sample Households

Agro

ecology

Names of Selected

Kebeles

Total No of households

Sample Selected

Male

headed

Female

headed

Total Male

headed

Female

headed

Total

Highlan

d

Abayi Asandabo

750

66

816

80

19

99

Mid

highland

Jarso Kamisa Beera

781

123

904

71

39

110

Wayyu kumba

771

124

895

71

38

109

Lowland Badhasa dhidhessa

596

26

619

70

6

76

Total

2884

348

3232

292

102

394

Source: Field survey, 2013