Adoption of Improved Maize Varieties: the Case of Kiremu District, Oromia Regional State, Ethiopia

ADOPTION OF IMPROVED MAIZE VARIETIES: THE CASE OF KIREMU DISTRICT, OROMIA REGIONAL STATE, ETHIOPIA

MSc THESIS

ALEMAYEHU KEBA

JUNE, 2019

JIMMA, ETHIOPIA

ADOPTION OF IMPROVED MAIZE VARIETIES: THE CASE OFKIREMU DISTRICT, OROMIA REGIONAL STATE, ETHIOPIA

A Thesis

Submitted to Jimma University College of Agriculture and Veterinary Medicine, Department of Agricultural Economics and Agribusiness management, in partial fulfillment of the Requirements for the Degree of Masters of in Agricultural Economics

Alemayehu Keba Beyene

JUNE, 2019

JIMMA, ETHIOPIA

APPROVAL SHEET

Jimma University College of Agriculture and Veterinary Medicine Thesis Submission Request Form (F-07) Name of Student: ALEMAYEHU KEBA BEYENEID No. RM/1180/10

Program of Study: Degree of Master of Science (M.Sc.) in Agricultural Economics

Title: Adoption of Improved Maize Varieties: The case of Kiremu District.

I have incorporated the suggestion and modification given during the internal thesis defense and got the approval of my advisors. Hence, I hereby kindly request the department to allow me to submit my thesis for external thesis defense.

Alemayehu Keba ______

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We, the thesis advisor has evaluated the contents of the thesis and found it to be satisfactory, executed according to the approved proposal, written according to the standards and formats of the University and is ready to be summated. Hence, we recommended the thesis to be summated for external defense.

Major Advisor: Adeba Gemechu (Associate Professors)

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Co –Advisor: AdmasuTesso (PhD)

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DEDICATION

I dedicated this thesis to my beloved Mother Workitu Akessa and Father Mr. Keba Beyene, all of my sisters, brothers and to my beloved girl friend Lalise Birhanu for their patience and sacrifice during my academic study and all aspects of the research.

STATEMENT OF THE AUTHOR

By my signature below, I declare and corroborate that this Thesis is my own work. I have followed all ethical and technical principles of scholarship in the preparation, data collection, data analysis and compilation of this Thesis. Any scholarly matter that is included in the Thesis has been given recognition through citation.

This Thesis is submitted in partial fulfillment of the requirement for a Master of Science Degree at the Jimma University. The Thesis is deposited in the Jimma University Library and is made available to borrowers under the rule of the Library. I solemnly declare that this thesis has not been submitted to any other institution anywhere for the award of any academic degree, diploma or certificate.

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III

BIOGRAPHICAL SKETCH

The author was born on March 06, 1994 in Gudina Jeregna Kebele, Kiremu District of East Wollega Zone, and Oromia National Regional State, Ethiopia. He attended his elementary school from grade 1-4 at Boka elementary school, 5-8 at Kiremu Elementary school, Secondary School at Kiremu and Preparatory at Gida Ayana at Ayana town. After he successfully passed EGSEC, he joined Wollega University in 2013 and graduated after three years with BSc in Agricultural Resource Economics and management on June 25, 2015.

After graduation, he served in Ethiopian Institute of Agricultural Research at Asosa Agricultural Research center for about two years until he joined Jimma University on October 13, 2017 to pursue his M.Sc. degree in Agricultural Economics program.

ACKNOWLEDGMENTS

At the outset, I would like to praise the everlasting priest and the Prince of love and peace the Almighty God who always let the mass of unfinished work to be completed at a moment.

My particular appreciation and deepest gratefulness goes to Dr.Adeba Gemechu, my teacher and major advisor, without him, the accomplishment of this research would have been difficult. Besides, his gentle advisor ship from the early designs of the work to the final write-up of the thesis by adding valuable, constructive and ever-teaching comments, frequent assistant, subsequent and unreserved technical support are commendable. I want to extend my deepest gratitude and special thanks to co-advisor, Dr.AdmasuTesso for his helpful comments, advice, guidance, material support and cooperation. I would like to express my sincere appreciation and gratitude to Ethiopian Institute of Agricultural Research specially Asosa Agricultural Research Center for their institutional support to give me this opportunity. This thesis research was financially supported by the Ethiopian Institute of Agricultural Research. Their contribution in my study is great and remarkable.

TABLE OF CONTENTS Page

DEDICATION ...... II STATEMENT OF THE AUTHOR ...... III BIOGRAPHICAL SKETCH ...... IV ACKNOWLEDGMENTS ...... V TABLEOF CONTENT ...... VI LIST OF TABLE ...... VIII LIST OFFIGURE ...... IX LIST OF THE TABLES IN APPENDIX ...... X LIST OF ACRONYMS AND ABBREVIATIONS ...... XI ABSTRACT ...... XII 1. INTRODUCTION ...... 1 1.1. Back ground of the Study ...... 1 1.2. Statement of the problem ...... 3 1.3. Research Question ...... 5 1.4. Objectives of the Study...... 5 1.4.1.General objective ...... 5 1.4.2.Specific objectives ...... 6 1.5.Significance of the Study ...... 6 1.6.Scope of the Study ...... 6 1.7.Limitation of the Study ...... 6 1.8. Organization of the Thesis ...... 7 2.LITERATURE REVIEW ...... 8 2.1. Definitions and Concepts ...... 8 2.2. Improved maize varieties adoption and diffusion ...... 12 2.3. Participation of farmers in improved Maize technologies ...... 13 2.4. Maize Production in Ethiopia ...... 13 2.5. Maize production in Oromia Regional state ...... 14 2.6. Maize production in East Wollega Zone ...... 15 2.7. Intensity of maize technology adoption ...... 15 2.8. Farmers Perception on maize technology attributes ...... 17

TABLE OF CONTENTS (Continued)

2.9. Empirical studies on farmers’ adoption of improved maize varieties ...... 19 2.10. Conceptual framework ...... 21 3.RESEARCH METHODOLOGY ...... 23 3.1. Description of the Study Area ...... 23 3.2. Data Types, Sources of Data and methods of Data collection ...... 24 3.3. Sampling procedures and Sample Size ...... 24 3.4. Method of Data Analysis ...... 25 3.4.1. Descriptive statistics...... 25 3.4.2. Econometric analysis ...... 25 3.4.3. Definition of Variables and Working Hypothesis ...... 28 4. RESULT AND DISCUSSION ...... 32 4.1. Descriptive Results ...... 33 4.1.1. Land allocation and production of improved maize varieties ...... 33 4.1.2. Adoption of improved maize varieties...... 34 4.1.3. Descriptive Statistics for Continuous Variables ...... 34 4.1.4. Descriptive Statistics for Dummy Variables ...... 36 4.1.5. Major crops produced ...... 37 4.1.6. Sources of Improved Seed ...... 38 4.1.7. Descriptive Statistics for Perception of Farmers for Improved Maize Varieties on Local Maize ...... 38 4.2. Econometric Analysis ...... 42 4.2.1. Determinants of adoption of improved maize varieties ...... 42 4.2.2. Factors determining the Intensity of use of improved maize adoption...... 44 5. SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ...... 47 5.1. Summary ...... 47 5.2. Conclusion ...... 47 5.3. Recommendations ...... 48 6. REFERENCES ...... 51 7. APPENDICES ...... 56

VII

LIST OF TABLES Page

Table 1: Maize Production in Ethiopia ...... 14 Table 2: Maize production in oromia Regional state ...... 14 Table 3: Maize production in East Wollega Zone ...... 15 Table 4: Sample distributions of HHs in the study area...... 24 Table 5 Summary of dependent and independent variables, their definitions and expected effect ...... 32 Table 6: Yield and area of land allocated to improved maize varieties ...... 33 Table 7: Types of improved maize varieties adopted by smallholder farmers ...... 34 Table 8: Descriptive statistics of continuous independent variables ...... 36 Table 9: Descriptive statistics of Dummy/ discrete Independent Variables ...... 37 Table 10: Major crops produced by sampled households (Qt) ...... 38 Table 11: Sources of seed for improved maize varieties ...... 38 Table 12: Perceptions of sampled house hold about Maize varieties attribute ...... 41 Table 13: Marginal effect estimates of1st Hurdle (Probit) model ...... 44 Table 14: Marginal effect estimates of 2nd Hurdle (Truncated regression) model ...... 46

VIII

LIST OFFIGURES Page

Figure 1 Conceptual framework Source: own sketch ...... 22 Figure 2 Map of Kiremu District ...... 23

LIST OF THE TABLES IN APPENDIX Page

Appendix table 1: Conversion factors used to calculate Tropical Livestock Units (TLU) . 57 Appendix table 2: VIF ...... 57 Appendix table 3: Result of 1st hurdle and 2nd hurdle together...... 58 Appendix table 4: Heckman model out put ...... 59

LIST OF ACRONYMS AND ABBREVIATIONS

AES Agricultural Extension System ATA Agricultural Transformation Agency CSA Central Statically Agency CIMMYT Centro International de Mejoramiento de Maize y Trigo CDF cumulative density functions DA Development Agents DIT Diffusion of Innovation Theory EIAR Ethiopian Institute of Agricultural Research FAO Food and Agriculture Organization FBO Faith Based Organization Ha Hectare IAR Institute of Agricultural Research IFC International Finance Corporation IMV Improved Maize Varieties Kg Kilogram MoARD Ministry of Agriculture and Rural Development NARS National Agricultural Research system NGO Non Government Organization PAs Peasant Associations PRA Perception for Recognition and Action PDF Probability density functions Ku Kuntal SSA Sub-Saharan Africa TLU Tropical Livestock Unit USD United State Dollar

ADOPTION OF IMPROVED MAIZE VARIETIES IN KIREMU DISTRICT, OROMIA REGIONAL STATE, ETHIOPIA

ABSTRACT

Improving agricultural productivity and development and thereby improving smallholder farmers’ income requires increased efforts in influencing farmer to use yield enhancing technologies like improved maize varieties. It is from this ground the need to analyze the factors that influence the adoption and intensity of use of improved maize varieties. Two - stage sampling procedure was employed to select the target households. In the first stage, out of 19 kebeles in Kiremu district three kebeles were selected using simple random sampling. Secondly, stratified random sampling method was employed to identify sample households. Finally, sample of adopters and non-adopters were selected by using simple random sampling. Structured instrumental questionnaire was developed, pre-tested and used for collecting data from 189 randomly selected households. Descriptive statistic and double hurdle model were employed to analyze data. Results of descriptive analysis showed that there were statistically significant differences between adopter and non- adopter households with family size, education, and distance to market, number of oxen, farm income, livestock owned and frequency of extension contact. Similarly, Double hurdle model results showed that improved maize varieties adoption decision of farm households has positively and significantly determined by education, family size, farm income, livestock owned, number of oxen and frequency of extension contact and intensity of use of adoption of improved maize varieties also positively and significantly determined by education, farm income, number of oxen, membership of farmers’ cooperative union and livestock owned. It is therefore recommended that government and other development organization should create a favorable environment like strengthening farmers’ knowledge on modern agriculture production throughout strengthening of the extension services, creating awareness on the advantage of being the membership of farmers’ cooperatives union and giving more attention to farmers’ priorities and needs related to agriculture.

Key Words: Adoption, Intensity, Improved Maize Varieties, Double Hurdle Model, Ethiopia

XII

1. INTRODUCTION

1.1. Back ground of the Study

As the world’s population is expected to reach 9.1 billion by 2050, the production of food, mainly staple crops is expected to increase accordingly, especially for the 870 million people who are currently food insecure (IFC, 2013). This suggests that the dominant role of agriculture as the primary source of food and employment creation in the developing economies should be stepped up. A study by Alexandratos and Bruinsma (2012) indicated that agricultural production needs an increase of 60% by 2050 to meet the world’s consumption demand. This expected growth means that smallholder farmers who are the principal agent of agricultural production have a significant role to play. In Sub-Saharan Africa (SSA), a majority of the population is agriculture dependent with about 55% in the rural areas (IFC, 2013).

Among the countries from this region, Ethiopia remains to be one of the poorest countries in the world and nearly 30% of households in the country are in extreme poverty (IFC, 2013). More than 30% of the population is undernourished and prevalence of food inadequacy is 41.3% (FAO, 2015). Thirty-six percent of Ethiopian farming households are engaged in subsistence farming, living on less than two USD per day (MoA& ATA, 2014).

Therefore the ultimate goal of any rural or farming development strategy or program is to improve the welfare of rural households. This goal is achieved among other things by increasing productivity at farm level and by raising farmer’s income and by improving their welfare. This is possible if and only if improved agriculturaltechnologies are properly transferred and disseminated to farmers so as to deepen and intensify their production. Institutions that are involved in generating agricultural technology need to have the capacity to carry out studies that document the process of adoption and help in explaining the rationale for framer’s decisions (Assefa and Gezahegn, 2009). Also, improving the agricultural production and productivity in the country is not a matter of choice. Enhancing rural households‟ income and food security through improving access to improved agricultural technologies is a key development strategy in Ethiopia. Consequently, successive governments of Ethiopia have taken a keen interest in establishing, supporting and nurturing a dynamic national agricultural research system (NARS) capable of adapting and developing improved agricultural technologies suited to the diverse agro-ecologies and

socio-economic conditions of the country. Over the years, in response to the political and socio-economic dynamics of the country, the NARS and the Agricultural Extension System (AES) have evolved in several respects including organizational structure, agro- ecological coverage, mandate as well as research and extension approaches followed. Currently, agricultural research in Ethiopia is based on a decentralized system of a network of institutions involving the Ethiopian Institute of Agricultural Research (EIAR), Regional Agricultural Research Institutes and Higher Learning Institutions. While the primary focus of NARS remained on agricultural technology adaptation and generation, it has also been involved in technology dissemination efforts, although with limited scale, with the intent of creating technology demand. Agricultural extension efforts pioneered by the research system include the package testing program of the Institute of Agricultural Research (IAR) in the 1980’s, Pre-extension Demonstration and Popularization activities in the 1990’s and early 2000’s and the current agricultural technology pre-scaling up efforts run by the federal and regional agricultural research institutes (Kibebew et al., 2011).

The most important cereal crops cultivated in the country are teff (3,017,914.36ha), maize (2,135,571.85ha), sorghum (1,881,970.73ha), wheat (1,696,082.59ha), and barley (959,273.36ha) (CSA, 2017). Although agriculture is the foundation of the country’s economy, crop productivity has remained low. For instance, the average national yield of important food crops such as teff, maize, sorghum ,wheat and barley were 16.64 , 36.75 , 25.25,26.75 and 21.11 Quantity per hectare respectively (CSA, 2017) while the potential of those crops is ten to eleven times higher than (MoARD, 2008). Food insecurity has been an importunate issue in the country where the recurrent drought considerably affects crop production of its numerous villages (Dercon et al., 2005).

According to Abate et al., (2015) furthermore, the Ethiopian seed market has been dominated by BH660 and BH540; the average of 80 % of the currently grown varieties is more than 20 years. There are also hybrids that came into production between 2005 and 2008, but their amounts remain limited, with the exception of the Pioneer hybrids Shone and Agar.

Defining rate of adoption as the proportion of households using freshly purchased (un- recycled) improved maize varieties, the DIVA study indicated about 31% of the farmers planted improved varieties (De Groot et.al, 2014).The same study indicated, of the improved maize varieties promoted, BH660 was grown by 27% of households on about

21% of the maize area while BH540 was grown by about 6% of the farmers on about 9% of the maize area during the same season. Other less popular maize varieties among sample farmers include BH543, BHQP542, Morka, Melkassa-1, Melkassa-4, and AMH800. A study by Chilot et al (2016b) designed at tracking maize varietal adoption comparing deoxyribonucleic acid finger printing techniques with household surveys revealed interesting results. While farmer responses suggest that 55.9% of the respondent used improved maize varieties during 2013 production season, the Deoxyribonucleic acid fingerprinting indicated 61.4% of the respondents to have actually used improved maize varieties with a difference of 5.5 percentage points suggesting household survey based adoption estimates under estimate adoption levels. The similar study further revealed that only 30% of the farmers know the variety they cultivated by name. When considering only adopters, the proportion of famers who identified the variety they grew by name increased to about 49%. Farmer knowledge of cultivars, however, are restricted to only four hybrid maize varieties, namely, BH-660, BH-540, BH-140 and Shone.

Generally, From Oromia region Kiremu district is potential producers of Maize and no study was conducted on adoption and intensity of use of improved maize varieties and farmers’ perception regarding of improved maize varieties characteristics on local variety previously in this areas. This study therefore conducted to examine the determinants of adoption and intensity of use of improved maize varieties with a purpose of generating information that help understand and evaluate the key challenges to the adoption of improved maize in the study areas which will enhance informed decision making to improve adoption of maize, their production and productivity by increasing land allocated for improved maize varieties in the study areas.

1.2.Statement of the problem

Improved highland Maize is a new and promising crop gradually becoming important in the highlands of Ethiopia. Its production is rapidly increasing in the highland parts of the country where it has been a minor crop in the past (Milkias and Abdulahi, 2018).In spite of the widespread technology generation and dissemination efforts, yields of major crops such as wheat, maize and teff are still low averaging 2.45 ton/ha, 3.25 ton/ha, and 1.47 ton/ha, respectively, suggesting the country has not fully taped the benefits of the investments made on agricultural technology generation and dissemination efforts (CSA,2014). According to Dawit et al. (2010), one of the main reasons for seed waste in either public or private seed stocks during high demand has been associated with the limited efficiency of targeting seed production and distribution in Ethiopia. It is also believed that some superior cultivars that have been released might not have been adopted because of lack of sufficient considerations of farmers’ preferences in their development process (Derera et al., 2006). According to Alene et al., (2000) Ethiopia also faced severe food shortages within the past two decades and is on constant threat of famine. One major reason for the low agricultural productivity in Ethiopia is the low rates of adoption of improved agricultural production technologies. According to Twumasi-Afriyie et al., (2002) high land maize is one of the major food crops where research brought tangible improvement in production and productivity. However, in sub-humid agro-ecology, smallholder farmers’ knowledge and use of agriculturaltechnologies in general and improved highland maize varieties in particular, are limited.

Smallholder farmers’ knowledge and use of agricultural technologies in general and improved maize varieties in particular, are restricted due to various factors that are either internal or external to the farmers’ circumstances. Most commonly studied internal factors that affect adoption and use of agricultural technologies are farmers’ attitude towards risk, household characteristics that affects the level of production and consumption, resource endowments, etc. External factors could be access to technologies, in particular through a well-developed seed system (Croppenstedt et al., 2003; Alemu et al., 2008; Asfaw et al., 2011), infrastructure, institutions (Beke, 2011), markets, and enabling policy environments (Smale et al., 2011).

The low crop productivity in one hand and availability of proven improved agricultural technologies that would increase productivity by a significant margin as well as the extensive extension efforts to get farmers adopt improved agricultural technologies on the other hand has trigger interest in crop technology adoption and analysis of factors that influence the adoption decision behavior of smallholder farmers in the country (Chilot and Dawit, 2016).

Regardless of the intervention of improved maize varieties widely undertaken in the district, the factor affecting adoption and intensity of use of improved maize varieties and the perception of smallholder farmers about the characteristics of improved maize varieties were not well identified. In the study area, there was no empirical information so far on the adoption of improved maize varieties and the perception smallholder farmers’ about the characteristics of improved maize varieties on local maize variety.

Therefore, improving agricultural productivity and development and thereby improving smallholder farmers’ income requires increased efforts in influencing farmer to use yield enhancing technologies like improved maize varieties. It is from this ground the need to determine the factors that influence the adoption and intensity of use of improved maize varieties in kiremu district study area seen as a thoughtful gap that must be bridged if the problem of limited improved maize varieties adoption among farmers is to be addressed to be improved.

1.3. Research Question  What are the factors affecting the adoption of improved maize varieties to the study area?  What are the factors affecting the intensity of use of improved maize varieties to the study area?  What are the farmers’ perceptions regarding to improved maize varieties on local seed to the study area?

1.4. Objectives of the Study

1.4.1. General objective

The main objective of this study is to analyze the Adoption of Improved Maize Varieties in Kiremu District.

1.4.2. Specific objectives

 To identify the factors affecting adoption of improved maize varieties in the study area.  To identify the factors affecting the intensity of use of improved maize varieties in the study area.  To identify Perception of farmers towards improved maize varieties attributes on local maize seed in the study area.

1.5. Significance of the Study

There are several reasons to invest in studying the adoption of agricultural technologies. These include improving the efficiency of technology generation, assessing the effectiveness of technology transfer, understanding the role of policy in the adoption of new technology, and demonstrating the impact of investing in technology generation. All development partners like technology generators, technical assistants, extension agents, policy makers, NGOs and development agents involved in agricultural development must be aware and understand the factors affecting the adoption and intensity of improved maize varieties. Policy makers will benefit from the research output since they require micro level information to formulate and revise policies and strategies. This could make easy allocation of major resources for research, extension and development programs.

1.6. Scope of the Study

The study covers only Kiremu district Oromia region. The data used for this study is based on a farm-household survey. Besides, the study paying attention on the application of double hurdle model to assess the adoption and intensity of use of improved maize varieties.

1.7. Limitation of the Study A range of studies are aimed at establishing factors underlying adoption and intensity of use improved maize varieties. As such, there is an extensive body of literature on the economic theory of technology adoption. Several factors have been found to affect technological adoption. These include government policies, technological change, market forces, environmental concerns, demographic factors, institutional factors and delivery mechanism. However, the study is concerned only with socioeconomic factors, demographic factors and institutional factors to assess factors that affect farmer’s decisions 6

to adopt improved maize varieties and to assess factors that affect intensity of use of improved maize varieties and also the perception of farmers towards improved maize varieties on local maize variety. This is mainly because of the limited resource available to the study on a wider scale.

1.8. Organization of the Thesis

This study is organized into five chapters. The first chapter outlined introduction, statement of the problem, research questions, objectives, significance and, scope and limitations of the study. Concepts and definition used in the present study along with a review of the past works are discussed in chapter two. Chapter three describes the study area and research methodology applied. Chapter four deals with descriptive results and discussions, econometric analysis results and discussions, Chapter five, deal with summary, conclusion and recommendations.

2. LITERATURE REVIEW

The literature review encompasses the conceptual definitions /theoretical descriptions and empirical evidences related to adoption of agricultural technologies, farmers’ decision making behavior in adoption of improved crop varieties, overview of maize varieties and production in Ethiopia and tracking diffusion of improved agricultural technologies has been reviewed and also encompasses the conceptual frame work of the study.

2.1. Definitions and Concepts The adoption of a production technology is not a unit and instant act; it consists of several stages and involves sequence of thoughts and decisions. According to Youngseek and Crowston (2011) adoption is a process consists of three stages namely pre- adoption, adoption and post- adoption. At the pre-adoption stage, people may examine a new technology and consider adopting it. At the adoption stage, they form an intention to adopt the technology, and they eventually purchase and use it. At the post-adoption stage, people can either continue or discontinue using the technology. It is well recognized that improvement in agricultural productivity among farmers is achieved through improved agricultural technologies (Moshi, 1997).

The Adoption process is the change that takes place within individuals with regard to an innovation from the moment that they first become aware of the innovation to the final decision to either use it or not. Also, as it is emphasized by Ray (2001) adoption does not necessarily follow the suggested stages from awareness to adoption; trial may not always be practiced by farmers to adopt new technology, they may adopt the new technology by passing the trial stage. The adoption pattern for a technological change in agriculture is a comprehensive process. A large number of personal, situational and social characteristics of farmers have been found to be related to their adoption rate.

Dissemination of innovation theory: Dissemination of innovation theory by Rogers (2003) is the theory guiding this cram. According to Medlin, (2001) DIT is the most appropriate for investigating the adoption of technology in higher education and educational environments. Actually Rogers (2003) used the word innovation and technology as synonyms. He defined technology as a design for instrumental action that reduces the uncertainty in the cause-effect relationships involved in achieving a desired

outcome. Adoption as the decision of full use of an innovation as the best course of action available where as rejection is a decision not to adopt an innovation and diffusion is the process in which an innovation is communicated through certain channels over time among the members of a social system. As expressed in the definition of diffusion, innovation, communication channels, time, and social system are the four key components of the diffusion of innovations. The most important objective of this theory is to understand the adoption of innovation in terms of four elements, including innovation, communication channels, time and social systems and five stages, including knowledge stage, persuasion stage, decision stage, implementation stage and confirmation stage.

Innovation: Rogers describe innovation as an idea, practice, or project that is perceived as new by an individual or other unit of adoption. It may have been invented a long time ago, but if individuals perceive it as new, then it may still be an innovation for them. The newness characteristic of an innovation is more related to the three steps, namely knowledge, persuasion, and decision of the innovation-decision process. According to Rogers (2003) uncertainty is an important obstacle to the adoption of innovations. An innovation’s consequences may create uncertainty, whereas consequences are the changes that occur in an individual or a social system as a result of the adoption or rejection of an innovation. To reduce the uncertainty of adopting the innovation, individuals should be informed about its advantages and disadvantages to make them aware of all its consequences.

Communication channels: The second element of the diffusion of the innovation process is communication channels. For Rogers (2003) communication is a method in which participants create and share information with one another in order to reach a mutual understanding. This communication occurs through channels between sources. Besides Rogers defines source is an individual or an institution that originates a message and the channel is the means by which a message gets from the source to the receiver. In addition Rogers states that diffusion is a specific kind of communication and includes these communication elements: an innovation, two individuals or other units of adoption, and a communication channel. Mass media and interpersonal communication are two communication channels. While mass media channels include a mass medium such as TV, radio, and newspaper, interpersonal channels consists of a two-way communication between two or more individuals. On the other hand, diffusion is a very social process that

involves interpersonal communication relationships. Thus, interpersonal channels are more powerful to create or change strong attitudes held by an individual. In interpersonal channels, the communication may have a characteristic of homophiles, that is, the level to which two or more individuals who interact are similar in certain attributes, such as beliefs, education, socioeconomic status, and the like, but the diffusion of innovation requires at least some degree of heterophony, which is the degree to which two or more individuals who interact are different in certain attributes. In fact, one of the most distinctive problems in the diffusion of innovations is that the participants are usually quite heterophilous.

Time: According to Rogers (2003) the time aspect is unnoticed in most behavioral research. He argues that including the time dimension in diffusion research illustrates one of its strengths. The innovation-diffusion process, adopter categorization, and rate of adoptions all include a time dimension.

Social System: The social system is the last element in the diffusion process. Rogers (2003) defined the social system as a set of consistent units engaged in joint problem solving to accomplish a common goal. Since diffusion of innovations takes place in the social system, it is influenced by the social structure of the social system. For Rogers (2003) structure is the patterned arrangements of the units in a system. He further claimed that the nature of the social system affects individuals’ innovativeness, which is the main criterion for categorizing adopters. Furthermore, technology adoption-decision process involves information-seeking and information-processing activity, where an individual is motivated to reduce uncertainty about the advantages and disadvantages of that technology. As demonstrated by Rogers (2003) the technology adoption-decision process involves five steps, namely knowledge, persuasion, decision, implementation and confirmation. These stages typically follow each other in a time-ordered manner as described below.

The knowledge stage: The technology adoption-decision process starts with the knowledge stage. Where an individual learns about the existence of new technology and seeks information about it. “What?” “How?” and “why?” are the critical questions in the knowledge phase? In this phase, the individual attempts to determine “what the new technology is and how and why it works”. According to Rogers (2003) the questions from three types of knowledge namely awareness-knowledge, how-to-knowledge and

principles-knowledge. Awareness-knowledge represents the knowledge of the technology’s existence and it can motivate the individual to learn more about the technology and then to adopt it. With the type of how-to-knowledge contains information about how to use the technology at the expected level correctly. According to Rogers (2003) how-to-knowledge is an essential variable in the technology adoption-decision process. To increase the adoption chance of the technology, an individual should have a sufficient level of how-to-knowledge prior to the trial of this technology. On the other side, principles-knowledge is the knowledge that includes functioning principles describing how and why the technology works. The technology can be adopted without this knowledge, but the misuse of the technology may cause its discontinuance. For Seemann (2003) to create new knowledge, technology education and practice should provide not only a how- to experience but also know-why experience. In fact, an individual may have all the necessary knowledge, but this does not mean that the individual will adopt the technology because the individual’s attitudes also shape the adoption or rejection of the technology.

The Persuasion stage: This stage occurs when an individual has a positive or negative attitude toward the new technology, but the formation of a positive or negative attitude toward the technology does not always lead directly or indirectly to an adoption or rejection The individual shapes his or her attitude after he or she knows about the technology, so the persuasion stage follows the knowledge stage in the technology adoption-decision process. Furthermore, Rogers (2003) states that while the knowledge stage is more cognitive- centered, the persuasion stage is more effective-centered. Thus, the individual is involved more sensitively with the innovation at the persuasion stage.

The decision stage: In the technology adoption-decision process, the decision stage is where an individual chooses to adopt or reject the new technology. If the technology has a partial assessment basis, it is usually adopted more quickly, since most individuals first want to try the technology in their own situation and then come to an adoption decision. The clear assessment can speed up the technology adoption-decision process. However, rejection is possible in every stage of the technology adoption-decision process.

The implementation stage: In this stage, the technology is put into practice. However, the technology brings the newness in which some degree of uncertainty is involved in diffusion. Uncertainty about the outcomes of the technology still can be a problem at this

stage. Thus, the implementer may need technical assistance from change agents and others to reduce the degree of uncertainty about the consequences.

The confirmation stage: The technology adoption-decision already has been made, but at the confirmation stage the individual looks for support for his or her decision. According to Rogers (2003), this decision can be reversed if the individual is exposed to conflicting messages about the innovation. However, the individual tends to stay away from these messages and seeks supportive messages that confirm his or her decision. Thus, attitudes become more crucial at the confirmation stage. Depending on the support for adoption of the technology and the attitude of the individual, later adoption or discontinuance happens during confirmation stage.

2.2. Improved maize varieties adoption and diffusion

Is the nearly everyone widely cultivated cereal after teff in terms of area but is produced by more farms than any other crop (close to 8.8 million farming households). It accounts for the largest share of production by volume at 25.8%. Is grown chiefly between elevations of l500 and 2200 masl and requires large amounts of rainfall. Suitable temperature for maize is in the range of 19- 300c. The soil type, clay loam is preferred for maize production. In addition to food grain, maize residues are also used as fodder, fencing materials, and cooking fuel (Tewodros et al., 2016).

The adoption of new technologies such as fertilizer and improved seed is central to agricultural growth and poverty reduction efforts (Tura et al., 2010). Likewise, in sub- Saharan Africa, adoption of improved maize is indicated to have positive outcomes (Alene et al., 2009).

Barret (2001) in Ethiopia observed that, farmers continue to lose in terms of crop yields despite introduction of new agricultural technologies since the cost of fertilizers and improved seeds continue to be high. He further said that, if the technology is not cost - reducing, farmers are not likely to adopt it in future seasons unless policy options such as provision of credit facilities are effective.

Tura et al. (2010) analyzed the factors that explain adoption as well as continued use of improved maize seeds in one of the high potential maize growing areas in central Ethiopia.

Improving maize production is considered to be one of the most important strategies for food security in Mozambique. However, chemical fertilizers and improved maize varieties, i.e., hybrids and open pollinated varieties (OPVs) whose traits have been improved for selected characteristics such as drought tolerance, disease resistance, short maturity rate, increased yield per unit of land, and quality protein (Byerlee, 1994 ), are not yet widely adopted in Mozambique.

Amare et al. (2011) examined the driving forces behind farmers’ decisions to adopt improved pigeon pea and maize and estimated the causal impact of technology adoption on household welfare. Overall the analysis of the determinants of adoption identified inadequate local supply of seed, access to information, human capital, and access to private productive asset as key constraints for maize/pigeon pea technology adoption.

2.3. Participation of farmers in improved Maize technologies

According to Mmbando and Baiyegunhi (2016) institutional variables such as extension services and farmer’s membership of farmer-based organizations (FBO) are essential sources of information. Farmers get a lot of information with regard to production and marketing from extension officers and through a farmer-to-farmer network. Being a member of an FBO increases the probability of a farmer to adopt an IMV. Also, farmers with regular extension contacts have a higher likelihood of adopting an IMV than those with no extension contacts.

Credit may be an important factor in determining technology adoption. If a recommendation implies a significant cash investment for farmers, its adoption may be facilitated by an efficient credit program. If the majority of adopters use credit to acquire the technology this is a strong indication of credit’s role in diffusing the technology and participation of farmers in improved technologies (Assefa and Gezahegn, 2009).

2.4. Maize Production in Ethiopia

According to data collected by Central Statistical Agency 2013/2014 and 2016/17 the yield/ha of maize production with the area by hectare is compiled together in the following table:

Table 1: Maize Production in Ethiopia

Year Area in Yield Crop Hectares (Qt/Ha)

Maize 2013/2014 1,994,813.80 32.54 2016/2017 2,135,571.85 36.75 Percentage change 7.05 12.94

Source: CSA (2013/2014and 2016/2017)

According to the above table the yield of maize production per hectare and the used area by hectare throughout the regional of Ethiopian country were increasing respectively within two past years. Then the percentage change of yield of maize from 2013/14 to 2016/17 was increased by 12.94%. The area covered by maize production from 2013/14 to 2016/17 was increased by 7.05.

2.5. Maize production in Oromia Regional state

Maize is the first cereal crop produced in Oromia regional state and first ranked cereal crop produced when compare with other regional state of the country. The following table incorporate the Area in Hectares and Yield (Qt/Ha) together the data of maize production in Oromia Regional State collected by CSA two years (2013/2014 and 2016/2017) respectively.

Table 2: Maize production in oromia Regional state

Crop Year Area in Hectares Yield (Qt/Ha)

Maize 2013/2014 1,083,332.83 33.19

2016/2017 1,142,653.56 38.38

Percentage change 5.48 15.64 Source: CSA (2013/2014 and 2016/2017)

This source indicated that in Oromia region, the total area covered by maize in the production year of 2013/2014 Meher Season was 1,083,332.83 and 33.19 yield per Hectare of land and 2016/17 Meher Season was 1,142,653.56 and 38.38 yield per Hectare of land have been produced. Then the percentage change of yield of maize from 2013/14 to 2016/17 was increased by 15.64%. The area covered by maize production from 2013/14 to 2016/17 was increased by 5.48.

2.6. Maize production in East Wollega Zone Maize is also the first cereal crop produced in East Wollega zone. Table 3: Maize production in East Wollega Zone

Crop Year Area in Yield Hectares (Qt/Ha) Maize 2013/2014 124,707.64 40.03 2016/2017 135,191.93 44.65

Percentage change 8.41 11.54

Source: CSA (2013/2014 and 2016/2017)

The above table shows us that in East Wollega Zone the total area covered by maize in the production year of 2013/2014 Meher Season was 124,707.64 and 40.03 yield per Hectare of land and 2016/17 Meher Season was 135,191.93 and 44.65 yield per Hectare of land have been produced. Then the percentage change of yield of maize from 2013/14 to 2016/17 was increased by11.54%. The area covered by maize production from 2013/14 to 2016/17 was increased by 8.41%so when we are compared the maize production productivity per hectare of land with other cereal crops it has more potential of production for this zone and the land allocated to this cereal crop were the largest next to teff then the East Wollega zone oromia were the potential area for maize production.

Generally, East Wollega agro-ecology is comfortable for the production of cereal crops such as maize, sorghum, teff and finger millet and also for POF (pulse.oil and fababean) crops such as soya bean, haricot bean and other some crops.

2.7. Intensity of maize technology adoption

There have been few studies conducted to determine the rate of adoption of improved agricultural technologies in Ethiopia. Off-farm income has a positive but insignificant

effect on the adoption and intensity of use of improved maize seed. Extension services (AES) measured in number of visits per month by the extension agent to a farmer during the cropping season positively and significantly influenced the adoption and intensity of use of improved maize (Alene et al., 2000).

In analyzing the adoption of improved maize varieties, the dichotomous adopter or non- adopter classification may not give a complete picture. Even within adopters there is a wider range of variation in the intensity of maize area allocated to improved varieties. Some households allocate only limited share of their maize plots to the improved varieties while others are completely replacing the existing practices. To assess the intensity of adoption, they used the area of maize under improved varieties (Jaleta et al., 2013).

Those variables comprise educational attainment, household size, and distance from home to the farm plot, participation in demonstration fields, and membership of FBO, farm size, and previous income from maize crop. Many years in formal education is statistically significant and have a positive correlation with the intensity of IMV adoption. Thus, farmers with a relatively high level of education intensify the adoption of IMV than their counterparts with a low level of education. This is not amazing as many studies have reported a positive relationship between adoption of improved farm technology and farmers level of education (Ahmed, 2015; Deepa, Bandyopadhyay, &Mandal, 2015; Kebede &Tadesse, 2015). Household size had a significant and positive influence on the intensity of IMV.Farming in SSA; particularly in the study area is more intensive as mechanization remains rare.

Hence, having larger household size helps in the farm operations since IMV requires some farm cultural practices such as frequent weeding and application of pesticides. The results of this study agree with that of Sodjinou et al. (2015) who reported positive and significant effects of householdsize and adoption of organic farming. More extended distance from the farmer’s home to the farm plot has the potential to affect the farm business negatively as farmers may feel tired by the time they get to the farm or may have to spend extra money to commute from the house to the farm field. This is seen in the results of the study as the distance from farmers’ house to the farm plot has an inverse correlation with the intensity of adoption. The probability of farmers adopting and intensifying the IMV is higher in households with larger farm sizes than those with smaller farm sizes. This is

because farmers with larger farm sizes are usually into commercial farming and will usually plant IMV for profit maximization.

However, Lunduka, Fisher, and Snapp (2012) reported negative and significant effects of farmland holdings and opened pollinated variety of maize in Malawi. Previous income from maize farm did not meet our a priori expectation. The estimated results show that the probability of farmers intensifying IMV on their farmland is low for farmers who had more income from their maize farm in the previous season than those who had little income. This could partly be attributed to the fact that farmers who had more revenue in the last season might have diversified their income into other farm or non-farm business. Farmers participating in demonstration farms or on-farm trials have a higher probability of allocating a more significant proportion of their maize farmland to IMV compared to those who did not participate as indicated in the empirical findings. Through expression (demonstration) farms, farmers become aware of the attributes of IMV and acquire sufficient knowledge to make adoption decisions. Farmers learn more and become more sensitize through visuals and hands-on than hearing, hence the importance of demonstration fields.

These results go together those of Mmbando and Baiyegunhi (2016) and Gecho and Punjabi (2011). Finally, farmer’s membership of FBO variable is significant and positively related to the intensity of IMV adoption, implying that farmers belonging to FBOs adopt IMV more than the non-members of FBOs. Similar results were reported by Mmbando and Baiyegunhi (2016) in Tanzania, Ojo and Ogunyemi (2014) and Ugwumba and Okechukwu (2014) in Nigeria.

2.8. Farmers Perception on maize technology attributes

According to Jeffrey Pickens (2005), perception is the process that organizes and interprets by our sensory in order to give meaning about the environment. It is the set of processes by which an individual become aware of and interprets information about the environment. The person interprets the stimuli into something meaningful based on their past experiences. However, an individual interprets or perceives may be different from reality. Van den Ban and Hawkins (1998) defined perception is a process by which we receive information or stimuli from our environment and transform it into psychological awareness. However, all innovations do not diffuse at the same rate. Various innovations are objectively differ and probably are perceived as being different by farmer decision

maker. Thus, perception of differences would affect decisions to adopt or reject a particular innovation. Therefore, farmers receive and gather stimuli that indicate the attributes of improved maize technologies are superior over local and traditional one or not. Rogers (1983) has classified characteristics which may describe an innovation and individuals’ perception, which predict their rate of adoption. These characteristics of innovations are: relative advantage to current tool or procedure, compatibility with the pre- existing system, complexity or difficulty, trial ability (testability) and observably of its effects. These qualities interact and judged as a whole.

According to Duvel (1975) perception is a key dimension in behavioral change process. Perception about the relative advantage of different attributes of high yielding maize varieties was assumed to have positive effect on adoption of high yielding maize varieties. Accordingly, farmers’ perception for higher yield potential, better price, resistance to diseases, shattering resistance and lodging, short maturity and stay for long period of high yielding wheat varieties were asked. Hence, better perception towards those attributes was expected to positively influence the adoption of high yielding maize varieties and market supply Habtemariam (2004) prove this hypothesis.

According to the Shiferaw et al.,(2009) factors related to the characteristics and performance of the technology and practices include food and cash generation functions of the product, the perception by individuals of the characteristics, complexity and performance of the innovation, its availability and that of complementary inputs, the relative profitability of its adoption compared to substitute technologies, the period of recovery of investment, local adoption patterns of the technology, the susceptibility of the technology to environmental hazards, etc were the criteria for select the improved agricultural technology.

Anne et al.,(2014) using a multivariate probit model on the perception of farmers variety attributes showed that improved varieties had desirable production and marketing attributes while the local varieties were perceived to have the best consumption attributes. Evidence further indicated that the major sorghum variety attributes driving rapid adoption are taste, drought tolerance, yield, ease of cooking, and the variety’s ability to fetch a price premium. Early maturity, a major focus of research was found to have no effect on the adoption decision. (The role of varietal attributes on adoption of improved seed varieties: the case of sorghum in Kenya)

According to AbaKemal et al.,(2013) most of farmers (98 %) in all PAs, except Sheki Sherera and Gora Silingo, identified the maturity period of cultivars as the second most important after yield, while plant height was ranked second to yield by 70% of the farmers in two PAs, Sheki Sherera and Gora Silingo (data not shown). Farmers of these two PAs strongly preferred intermediate plant height after yield and explained that short statured cultivars were more prone to attacks by either wild or stray domestic or wild animals such as dogs and porcupines than an intermediate or a tall variety. Conversely, tallness was not desirable because of the associated problem of lodging. On the other hand, most farmers who preferred early maturity as the second most important trait explained that they usually practice a relay cropping system whereby pulse crops, such as chickpea (Cicerarietinum L.) and grass pea (Lathy russativus L.), would be sown immediately after physiological maturity of maize and before the land dried out completely. Earliness is a relative term because the farmers preferred intermediate season cultivars to very short season cultivars. Marketability ranked fourth among farmer-preferred traits. During group discussions, farmers explained that a cultivar whose grains have a glossy (flint-textured) characteristics and hard endosperm types command better acceptability in local markets than dent- textured and chalky types, when sold as both green and grain maize. Farmers’ also considered local varieties to provide superior quality in the preparation of traditional beverages. But in terms of all other characters listed, local varieties were considered inferior to the improved cultivars. In general, farmers in all PRA areas were not concerned much about storability and feed quality in maize and ranked them low. Farmers argued that they had not seen a maize cultivar with resistance to storage pests or with special qualities as feed for animals.

2.9. Empirical studies on farmers’ adoption of improved maize varieties

Through demonstration farms, farmers become aware of the attributes of IMV and acquire sufficient knowledge to make adoption decisions. Farmers learn more and become more sensitize through visuals and hands-on than hearing, hence the importance of demonstration fields. These results complement those of Mmbando and Baiyegunhi (2016) and Gecho and Punjabi (2011). Finally, farmer’s membership of FBO variable is significant and positively related to the intensity of IMV adoption, implying that farmers belonging to FBOs adopt IMV more than the non-members of FBOs. Similar results were

reported by Mmbando and Baiyegunhi (2016) in Tanzania, Ojo and Ogunyemi (2014) and Ugwumba and Okechukwu (2014) in Nigeria.

According the result of Assefa and Gezahegn (2009) that younger farmers, famers with larger land size, farmer living closer to market, and farmers who had closer contact with the extension system are more likely to adopt new technology and use it more. The result underscores the need for research and extension programs to be sensitive to the needs of farmers when developing and disseminating technologies that are relevant to their agro‐ecologies.

According to Jaleta et al.,(2013) results by using Poisson, binary and multinomial Probit, Tobit and Heckman’s selection models show that household characteristics, availability of family labor, wealth status, social networks, and access to credit to buy seed and fertilizer, better soil fertility and depth, market opportunities (number of traders known in villages) affect the number of improved maize varieties known to farmers, their adoption and intensity of farm area allocated to improved varieties, and the use of freshly purchased hybrid and/or OPV maize varieties. Generally, institutional arrangements that strengthen farmers’ access to input and output markets and accumulation of wealth could enhance the knowledge and use of improved maize technologies for better productivity and household income.

According to Julius (2016) paper there are four results. First, the findings suggest that the adoption of improved maize varieties is determined by a whole range of factors that include land cultivated, education of the household head and the total asset holdings of the household. Second, the results show that the adoption of improved maize varieties is associated with higher levels of income, food security, child nutritional status and lower levels of poverty. Third, the counterfactual analysis applied in this thesis shows that if non- adopters had adopted improved maize varieties, they would have realized higher levels of welfare than they currently have. Fourth, the results show that adoption of improved maize alone has greater impacts on maize yields, but given the high cost of inorganic fertilizer that limits the profitability of adoption of improved maize, higher household incomes are associated rather with the adoption of multiple SAPs.

The paper done by Tura et al., (2010) analyzes the factors that explain adoption as well as continued use of improved maize seeds in one of the high potential maize growing areas in

central Ethiopia. Using a bivariate probit with sample selection model approach, the study provides insights into the key factors associated with adoption of improved maize seed and its continued use. The result revealed that human capital (adult workers, off-farm work and experience in hiring labor), asset endowment (size of land owned), institutional and policy variables (access to credit, membership in farmer cooperatives union) all strongly influence farmers’ decisions to adopt improved maize varieties, while continuous use of the seed is influenced by the proportion of farmland allocated to maize, literacy of the household head, involvement in off-farm work, visits by extension agents, farmers’ experience, household land size, and fertilizer usage. Accordingly, policies and interventions that are informed about such factors are required to accelerate adoption and continued use of improved maize seeds in order to increase farm yields and remedy shortage of food and fight food poverty and insecurity more effectively and more sustainably.

According to the paper written by James et al., (2014) Intensity of adoption of improved maize varieties varies continuously and this feature allows estimation of the dose response function. The dose response function was estimated using generalized propensity score useful for analyzing causal effects of continuous treatments. The results indicated an increasing dose response function between intensity of adoption and per capita food consumption expenditure.

2.10. Conceptual framework

Agricultural technology adoption patterns often vary from one smallholder farmer to another and this variation is due to the disparity in institutional and socioeconomic factors. As it was demonstrated by CIMMTY (1993) farmers’ decision to adopt or not to adopt new technologies can be influenced by the factors related to their objectives and constraints, these factors include farmers’ endowments which can be measured by farm size and assets ownership, size of the family labors, age, formal education and institutional support system available for inputs.

Adoption of technologies is the outcome of several interactions of farmers’ internal and external contexts. Demographic factors(House head age, House head education, House head farm experience, Family size), economic factors (owned livestock , owned oxen, farm income and off farm income), institutional factors (distance to nearest market, ,

frequency of extension contact ) and social factors (Membership of farmers’ cooperatives union) and farmers perception towards to improved maize on local maize are the main key variables that were expected to influence the adoption of improved maize varieties in the study areas were summarized in figure1.

Demographic factors

 House head age  House head education  House head farm experience  Family size

Social Factors Adoption of improved maize Institutional Factors

varieties Membership of farmers’  Extension services cooperatives union  Distance to market

Economic factors Perception  Farm income  perception of farmers  Off farm income towards improved maize on  Owned livestock local maize variety  Owned oxen

Figure 1: Conceptual framework Source: own sketch

3. RESEARCH METHODOLOGY

This chapter summarizes description of the study areas, data types, and source of data and method of data collection, sampling procedure and sample size. It also describes method of data analysis descriptive and econometrics.

3.1. Description of the Study Area

The study was conducted in Kiremu district of East Wollega zone. Kiremu district is one of the 17 administrative Woreda's in the zone. This district is bounded with Amuru Woreda of Horro Guduru Wollega zones in the East, Gida Woreda of East Wollega Zones in the West, Amhara Region in North, and Abe Dongoro Woreda of Horro Guduru Wollega zones in the South. Geographically the altitude varies from 750 up to 3020 meter above sea level. The district is classified into three agro ecological zones; namely, highlands (4.91%), Midlands, (53.17%) and lowlands (41.92%). Averagely the temperature is 280c. The capital town of the district is Kiremu which is about 140 KMs far from Nekemte Town and 458 from Addis Ababa. The total population of the district is 91,562. 21% of the population lives in urban and 79% in rural residents. Administratively the district is divided in to 19Kebeles.

Source: Ethio-GIS, 2019 Figure 2Map of Kiremu District

3.2. Data Types, Sources of Data and methods of Data collection

For this study both quantitative and qualitative data were collected. This study used both primary and secondary data. The data was collected from primary sources generated through structured questionnaire. Secondary data was collected from internet, through the desk review; the study assessed the existing literature on the perception of farmers’ on improved maize varieties and the factors affecting adoption and the intensity of use of improved maize varieties. The data was collected by the instrument Survey questionnaire and by FGD organizing together for both quantitative and qualitative data collection respectively. For FGD from three kebele three group were arranged by which one group contains 8 group, totally 24 sampled house hold were selected from the three kebele with the kebele experts for some of my qualitative data.

3.3. Sampling procedures and Sample Size

This study implemented two- stage sampling procedures to collect the required primary data. In the first stage, out of 19 kebeles in Kiremu district three kebeles were selected using simple random sampling. Secondly, stratified random sampling method was employed to identify sample households for inclusion in the study. To this effect, list of adopter households was obtained from district agricultural office (district agricultural office,2018) and from development agents at each sample kebeles and then households in the area were categorized into 2 strata, that is 1291 adopter of improved maize households, and 1223 non-adopter households. Finally, sample of adopters and non-adopters were selected by using simple random sampling. The sample keeping the proportion to each kebeles were selected by using Yamane (1967) sample size formula and 7% Precision Level Where Confidence Level is 95%.

n = ()

n = 2514 =189 2514 (.)

Where: n is the sample size, N is the population size, and e is the level of precision.

In general, using the above sample size and the total number of household from the selected Keble’s, the proportion and the number of sample households have been summarized in the following table.

Table 4: Sample distributions of HHs in the study area

Kebele Total households Sampled house hold Total sample

Adopter Non adopter Adopter Non adopter

Gudina Jeregna 613 545 46 41 87

Chefe Soruma 266 266 20 20 40

Burka Soruma 412 412 31 31 62

Total 1291 1223 97 92 189

3.4. Method of Data Analysis

The study used various categories of data analysis methods, such as descriptive statistics, and econometric models to analyze the data.

3.4.1. Descriptive statistics

Descriptive statistics analysis was used to clearly compare and contrast different characteristics of the sample households along with descriptive statistics such as ratios, frequencies, percentages, means and standard deviations to analyze the collected data

3.4.2. Econometric analysis

Following data collection, the collected data were coded, edited and made ready to data entry. Based on objectives of this study, both descriptive and inferential statistics; Double- hurdle econometric model was applied for data analysis.

The double-hurdle model

This model assumes farmers faced with two hurdles in any agricultural decision making processes (Cragg, 1971). Accordingly, the decision to participate in an activity is made first and then the decision regarding the level of participation in the activity follows. In this study, thus, double-hurdle model was chosen because it allows for the distinction between the determinants of adoption and the level of adoption in maize production through two separate stages. This model estimation procedure involves running a probit regression to

identify factors affecting the decision to participate in the activity using all sample population in the first stage, and a truncated regression model on the participating households to analyze the intensity of use of improved maize, in the second stage. In our case, we were applied the first stage of double hurdle model to examine the factors determining the decision to adopt improved maize and it is analyzed by a means of the probit.

According to Burke (2009), double hurdle model is useful because it allows a subset of the data to pile-up at some value without causing bias in estimating the determinants of the continuous dependent variable in the second stage, hence you can obtain all the data in the remaining sample for the participants. Thus, in double hurdle model, there are no restrictions regarding the elements of explanatory variables in each decision stages. That means it is possible to separately analyze the determinants of adoption of improved maize decision and the level of adoption decisions. Due to this separablity, the estimates of adoption decisions can be obtained by a means of probit regression and that of the level of adoption decision can be analyzed by use of a truncated regression. According to Burke (2009), the separablity in estimation may not be mistaken for separablity in estimation is possible.

Then to derive the likelihood function, we begin in the first stage (adoption decision) where households are identified according to whether they are adopters or not, using probit analysis. To do so, let Pi denote a binary indicator function taking value “1” if farmers adopt improved maize in 2017/18 production year and “0” otherwise. Further, let Qi denote the amount of land allocated in the specified production year. Then we can derive the likelihood function for the standard double hurdle model as follows:

L = L(, ) = [1 − Ф()Ф ]

Ф(ρ (′β [( )] (1)

Where Ф denotes the standard normal CDF, is the univariate standard normal PDF, and σ is the variance of error terms. The first portion (top line) is the log-likelihood for a probit, while the second portion (bottom line) is the log-likelihood for a truncated regression, with truncation at zero value of the continuous dependent variable in the second stage (the amount of land allocated in the survey year, in our case. Therefore, the log-likelihood from

the Cragg type double hurdle model is the sum of the log-likelihood from a probit and a truncated regression. More useful, is the fact that these two component pieces are entirely separable, such that the probit and truncated regression can be estimated separately (Burke, 2009).

Double hurdle model is the modification of Tobit model and Heck man model because it is more flexible. There were no missed data or no selection bias according to the Heck man Model output in appendix table 5 the Lambda value which is equivalent with inverse mill’s ratio p-value was insignificant and the tobit model structure cannot handle the situation in which adoption decision and land allocated may be a separate decisions, influenced by different variables or by the same variables but in different ways. Because of this all reasons we used the double hurdle model.

Empirical model specification

Based on the above backgrounds, the linear probit model can be specified as the follows:

P(Yi = 1) = βo + βiXi + e (2)

Where P is the probability of an individual farm household to adopt improved maize varieties in the specified survey production year (2017/18), βi is the vector of parameters to be estimated, Xi is the vector of exogenous explanatory variables expected to influence the adoption decision probability and е is the error term.

In the second stage of double-hurdle model we examine factors affecting the intensity of use improved maize varieties, conditional on adoption decision, which is implemented using the truncated regression analysis. Thus, it involves the truncated regression that can be specified as:

Y = Y ∗ if Y ∗> 0 = 1 (3) Y=0 other wise From this, we can specify the reduced form of the truncation model as: Y = βo + βoiZi + Ui (4) Where Y is the observed amount land allocated for maize production , Y* is the latent variable which indicates the level of maize adoption is greater than zero, βi is the vector of parameters to be estimated, Zi is the vector of exogenous explanatory variables and Uis the error term. The empirical model used in this study assumes that the total amount of land

allocated for improved maize in the survey year of (2017/18) is a linear function of continuous and dummy explanatory variables.

3.4.3. Definition of Variables and Working Hypothesis

Dependent variables

Dependent variable for first double hurdle model

Adopter and non- adopter categories was identified based on the adoption of improved maize variety. In this study, the data (2017/18) on area allocated to improved maize varieties and the continues use of improved maize varieties for long period of time up to present were used to categorize the two groups. Adopters (participants) are those that allocated land to improved maize varieties for two or more years while non adopters (non- participants) are those who did not allocate land for these varieties at all. It is equal to one if the farm household has adopted the varieties and zero otherwise.

Dependent variable for second double hurdle model

Land allocated for improved maize: It is a continuous variable, which refers to the land allocated for improved maize varieties. It was used in the 2nd hurdle model as dependent variable to analyze the factor affecting the intensity of use of improved maize. It is measured in ha.

Independent variables

The independent (explanatory) variables which are expected to determine the adoption decision of the farm households in this study are categorized into three. They are: The socio-cultural factors: such as age, education, family size, farm experiences which were hypothesized to influence agricultural technology adoption significantly. Economic factors: such as owned livestock, number of owned oxen, farm income, off farm income and the Institutional factors: such as distance to market center, extension visits, and X1,..., Xi, are factors that promote or prevent farm households’ from adopting improved maize technologies. They are explanatory variables in the equation above described as follows:

1. Age of the household head: It refers to the age of the household head in years and it is a continuous variable. Age is important household related variable that has relationship with adoption. It is also assumed to be a determinant of adoption of new technology. Older

farmers are assumed to have gained knowledge and skill over time and are better able to evaluate technology information than younger farmers. According to Alene etal.,(2000) and Yenealem etal., (2013)result the estimated parameter for age of the farmer (AGE) is statistically insignificant and has the expected negative sign. 2. Education level of the house hold: It is well expected that farmers with more education are aware of more information, and be more efficient in evaluating and interpreting information about innovations than those with less education. According to Alene et al., (2000) level of Education of the head of the household has a positive and significant influence on the adoption and use of improved maize variety. It is measured by number of years of schooling of the head of the households and hence a continuous variable.

3. Family size: It is a continuous variable which indicate the number of person living in the house of the farmers. It is expected that as the size of the house hold increase the adoption of new technology increase provided that number of dependent family members in a household is less. According to Alene et al.,(2000) household family size has a positive influence on the number of hectares of land planted to improved variety of maize. This indicates the family with large number is more involved in adopting the new technology during their farm production effort.

4. Farm income: The farm income refers to the total annual cash income of the family from the sale of crops, livestock and livestock foodstuffs after family requirement. This is to be main source of wealth for purchasing agricultural inputs. Thus, households with relatively advanced level of farm income are more likely to purchase or exchange improved technologies. It is measured by the amount of Ethiopian birr obtain from sale of farm products Asfaw et al., 2010), as cited in Afework and Lemma, (2015).

5. Off-farm income: Non/Off-farm income represents the amount of income the farmers earn in the year on other than on-farm activity. It is the amount of income (in Birr) generated from activities other than crop and livestock production. These include petty trading, charcoal selling, firewood selling and others. It is expected that the availability of off-farm income is positively related with adoption decision since households engaged in off-farm activities are better endowed with additional income to purchase initial seeds or other essential agricultural inputs. According to Alene et al., (2000), Off-farm income has a positive but insignificant effect on the adoption and intensity of use of improved maize seed.

6. Owned livestock exclude oxen: It is continuous variable which is expected to affect the decision of the farm households positively. This is because as the asset becomes larger the household gets more money and materials and equipments to practice the new technology of production. According to Yenealem et al., (2013) result shows that those farmers with large number of tropical livestock units are more likely to adopt improved maize varieties than those who own small number of TLU.

7. Extension service: Extension service helps the farm households to understand the importance of the modern technology and enhance the accuracy of implementation of the technology packages. Alene et al., (2000); Yenealem et al., (2013) and Milkias and Abdulahi (2018) Extension services (AES) measured in number of visits per month by the extension agent to a farmer during the cropping season positively and significantly influenced the adoption and intensity of use of improved maize. In this study this variable was treated as continues variable. Measured in number of visits per year by the extension agent to a farmer during the cropping season of 2017/18.

8. Distance to a nearest market center: It is distance to nearest input and output market center places and it is continuous variable which is measured in minutes. The nearest to market, the more likely to contribute in modern farming activities that adoption of improved of maize varieties. Distance is expected to influence adoption of chickpea technologies negatively (Asfaw et al., 2010), as cited in Afework and Lemma, (2015).

9. Farm experience: It is a continuous variable measured in years of maize production. Experience in a particular farming area or with a given crop may not be strictly correlated with age (CIMMYT, 1993). Experience of the farmer is likely to have a variety of influences on adoption. Experience will improve the farmer’s skill on the production of maize. Higher skill increases the opportunity cost of not growing the traditional enterprise. A more experienced grower may have a lower level of uncertainty about the innovation’s performance (Chilot et al, 1996; Abadi et al, 1999), as cited in Mulugeta, 2009). Tewodros (2016) indicated that farm experience affect adoption and intensity adoption of improved varieties positively. Therefore, it is expected that the farm experience is positively related to adoption and intensity of use of improved maize varieties.

10. Membership to farmer cooperatives union: membership to cooperatives represents whether a household is member to cooperatives or not. Cooperatives worldwide are

committed to the concept of mutual self-help. This makes them natural tools for social and economic development, and provides significant additional benefit to communities and social systems. Formal as well as informal associations, such as indigenous cooperation groups, enforcing widely agreed standards of behavior, and uniting people with bonds of community solidarity and mutual assistance (Tura et al., 2010).It is a dummy variable, which takes a value of 1 if the farm household were membership of farmers’ cooperatives union and 0 otherwise.

11. Number of oxen owned: It is continuous variable to number of oxen owned by the households who own oxen have superior chance to produce more. This is because oxen ownership allows undertaking farm activities on time and when required. Therefore, it is expected that possession of oxen size increases the probability of adoption of improved maize and thereby increase farm income According to Jaleta et al.,(2013) households with more number of oxen for plowing know more number of hybrid maize and Solomon (2012) on his study the impact of livestock development program on farm household cash income: the case of UmbulloWacho integrated watershed, Dore Bafano Woreda in Southern Ethiopia, households who do not own oxen are more like to participate in livestock development program.

Table 5 Summary of dependent and independent variables, their definitions and expected effect dependent Variables Definitions of variables Unit of measurement Expected sign Adoption of improved maize Dummy, household participation 1 if adopted IMVs’ and varieties in adoption of improved maize 0 other wise varieties Land allocated for IMV Continues, the amount land ha allocated for IMV Independent Variables Definitions of variables Unit of measurement Expected sign Age Age of household head Years - Family size Number of persons per No + household Education Continuous, number of years of years + schooling of the HHH Total income from farm Log of Farm Income birr + Total income from non/off farm Log of off Farm Income birr + Membership of farmers Membership to farmers 1=Yes 0= No + cooperatives union cooperatives union, dummy Number of livestock owned Number of livestock owned Tropical Livestock unit + Number of oxen owned Number of oxen owned No + Distance to market Distance of farmers house from minute - market Farming experience Maize farming experience of years + farmer Extension Contact with extension agents No of extension contact + per year Source: Own definition

4. RESULT AND DISCUSSION

This section consists of two sub-sections. The first one is description of sample households’ characteristics and the second subsection is econometric methods.

4.1. Descriptive Results

In this chapter the overall findings of the study is presented under different sections. Next to description of status of adoption and intensity of use of improved maize varieties, the influence of different personal, demographic, social, economic, institutional and psychological factors on adoption and intensity of use of improved maize discussed consecutively. In this section of analyses descriptive statistics such as mean, percentage, t- test and chi-square test were employed using STATA 13 software programs. In this study, adopters of a technology refer to farmers who are used improved maize varieties and those who are more productive by allocating proportion of their land for improved maize varieties and those farmers who experienced growing of local variety considered as non- adopters.

4.1.1. Land allocation and production of improved maize varieties

The mean area planted by improved maize varieties was about 0.66 hectare for adopters. The Study indicated that the average size of cultivated land holding of adopter households was 2.15 hectares with standard deviation of 0.637 and they allocated about 30.70 % of their farm lands for improved maize production. The maximum area allocated for improved maize varieties was 1.5 hectare and minimum land allocated to improved maize varieties was 0.25 hectare.

Table 6: Yield and area of land allocated to improved maize varieties

Description Mean max min Std Total land cultivated(ha) 2.15 3.53 1.01 0.637 Area of improved maize (ha) 0.66 1.5 0.25 0.359 Total production of maize (Qt) 26.25 60 10 12.98 Source: Own Survey 2019

4.1.2. Adoption of improved maize varieties

In this study, adoption decisions refer to use of improved maize varieties. A farmer is defined as an adopter if he/she uses at least one of the improved maize varieties, otherwise is a non-adopter. Based on their use of improved maize varieties farmers were classified as adopters and non-adopters. As results, a farm household is adopter of improved maize varieties if he/she used at least one variety of improved maize varieties during the cropping season. Under normal conditions, improved maize varieties are preferred by smallholder farmers in the study area which have better yield potential, shattering resistance, disease resistance and marketability. There is some maize varieties in use and tend to stay with farmers due to resisting crop diseases and other ecological characteristics of varieties and few of them were discarded from production due to poor disease resistance and environmental problems. The resistant high yielding maize varieties such as Shone (75.26 %) have been widely demonstrated to farmers and adopted with associated cultural practices in the study areas.

Table 7: Types of improved maize varieties adopted by smallholder farmers improved Maize varieties Freq. Percent Shone 73 75.26 BH660 16 16.49 Owner Limmu 8 8.25 Total 97 100 Source: Own Survey result, 2019

4.1.3. Descriptive Statistics for Continuous Variables

The descriptive and inferential results presented on Table 8 show that there was statistically significant difference between adopters and non-adopters in terms of distance to market, number of oxen, TLU, Education level the house hold, Family size, Frequency of extension visit and farm income in favor of the adopters. The descriptive and inferential result of each variable is interpreted as below:

The mean of the family size of household head was about 4.49 for adopters and 2.65 numbers for non-adopters of improved maize varieties. The t-test result indicated there

was significant difference between the average adopters and non-adopters for improved maize varieties sample farmers at 1% significance level.

The average livestock ownership (exclude oxen) of adopters of improved maize varieties was 9.36 and for non-adopters 5.19.The implication is that adopters have more access to financial capital by selling their livestock to purchase improved seed from suppliers. This result suggests that, those farmers who owned more livestock have better chance to use improved seed technology.

The education level of the household’s head is expressed in terms of years of schooling results indicate that the average number of years of education for the head of households in the years. Adopting households have significantly more years of education (3.94 years) than non-adopting households (2.43 years) suggesting that there is a positive correlation between adoption and the number of years of formal education. Education is very important for the farmers to understand and interpret the information coming from any direction to them. The t-test indicated that, from sample farmers the mean differences for a year of schooling were found to be at 1% significant level between adopter and non- adopter of improved maize varieties.

The average frequency of extension contact in a year was 32.85 for adopters and 19.78 for non-adopters of improved maize varieties. Extension access is a necessary catalyst to technology adoption as it is the major source of agricultural information in Ethiopia. The t- test indicated that, from sample farmers the mean differences for frequency of extension contact were found to be at 1% significant level between adopter and non-adopter of improved maize varieties. Farmers who have a frequent contact with extension agents have more information that would influence farm household’s demand for new technologies.

Adopting households have significantly shorter distances to the village market 41.26 minutes than non-adopting households 45.2 minutes. The findings suggest that farmers with access to markets have a higher probability of adopt improved maize varieties than those that with limited access to markets. The t-test result showed that the near market distance mean difference between the two groups is significant at 5% level.

The farm income is the amount of income (in Birr) generated from activities of crop and livestock production by the house hold then the average income generation from farm activities by transforming it to Log form was 8.88 for adopters and 7.13 for non-adopters.

The availability of farm income is positively related with adoption decision since households engaged in farm activities are better endowed with additional income to purchase initial seeds or other essential agricultural inputs. The t-test indicated that, from sample farmers the mean differences for farm income generation were found to be at 1% significant level between adopter and non-adopter of improved maize varieties.

The average oxen ownership of adopters of improved maize varieties was 5.47and for non- adopters 3.74. The implication is that adopters have more access to productivity by using their oxen for plough purposes. This result suggests that, those farmers who owned more oxen have better chance to use improved seed technology. The t-test result showed that the oxen owning mean difference between the two groups is significant at 1% level.

Table 8: Descriptive statistics of continuous independent variables

Variable Mean across adoption categories Adopter Non adopter t test (N=97) (N=92) Age 47.37 47.97 0.47 Familysize 4.49 2.65 -8.39*** Farming experience 20.80 19.56 -0.89 Education 3.94 2.43 -4.70*** Extenservice 32.85 19.78 -6.43*** Distmarket 41.26 45.2 2.57** Lnoffarmin income 5.17 5.12 -0.12 Ln farm income 8.83 7.13 -13.92*** TLu(exclude oxen) 9.36 5.19 -6.94*** Number of oxen 5.47 3.74 -6.78*** Source: own survey 2019, *** and **indicates that significance level at 1% and 5%respectively

4.1.4. Descriptive Statistics for Dummy Variables

The descriptive and inferential statistics results presented in Table 9 show farmers cooperatives to members ship of farmer union 59.79% of them were members of cooperatives farmers union. Compared to non-adopters, adopter households has got

satisfied with their joining of membership of farmer cooperatives union needs for fertilizer and improved seed purchases.

Table 9: Descriptive statistics of Dummy/ discrete Independent Variables

Adoption category Variables Adopter % Non adopter % χ2 value (N=97) (N=92) Membership of farmer 11.84*** cooperatives union Yes 58 59.79 32 34.78 No 39 40.21 60 65.22

Source: own survey 2019, *** indicates 1% of significance probability level

4.1.5. Major crops produced

As presented in table 10, in the study areas, maize is the dominant crop produced with mean 30.20 quintals for adopters and 4.60 for non-adopters and it is the basis of livelihood in the study areas. Around 35.34% of the lands of the sampled house hold are allocated for maize production. The second dominant crop produced is teff with mean of 10.84 and 3.22 quintals for adopters and non-adopters respectively. It is also the basis of livelihood in the study area. Sorghum is also the dominant crop produced with mean of 3.66and 3.23 quintals for adopters and non-adopters respectively. Finger millet, wheat, Nug and Barley is also the major crop produced in the study areas with mean of 3.03 and 3.49, 3.15 and 1.86, 3.72 and 2.86, 1.75 and 1.33 quintals for adopters and non-adopters respectively. The result of t- test revealed that there is significant mean difference between adopters and non-adopters farmers in terms of amount of maize produced at 1% and 5% significance level respectively. But the mean of finger millet for adopter and non-adopter is not different so because of this t value is not significant.

Table 10: Major crops produced by sampled households (Qt)

Crops Mean across adoption categories Adopter (N=97) Non adopter (N=92) t test Area(ha) Mean(Qt) std Area(ha) Mean(Qt) std Maize 0.77 30.20 13.68 0.10 4.60 2.36 -16.65 Teff 0.60 10.84 2.48 0.18 3.22 2.74 -20.08 Wheat 0.15 3.15 2.54 0.09 1.86 3.95 -2.69 Nug 0.31 3.72 2.96 0.24 2.86 2.89 -2.02 Barley 0.09 1.75 1.76 0.07 1.33 1.44 -1.80 Sorghum 0.12 3.66 5.77 0.11 3.23 4.83 -0.54 Finger millet 0.12 3.03 4.86 0.13 3.39 4.66 0.51 Source: Own Survey 2019

4.1.6. Sources of Improved maize varieties

According to sample respondents about 95.88% obtained improved maize varieties from government supply. About 2.06% respondents obtained improved maize varieties from market while about 2.06% respondents obtained from research center.

Table 11: Sources of seed for improved maize varieties source of seed Freq. Percent Cum. Research Center 2 2.06 2.06 Government supply 93 95.88 97.94 Purchase from market 2 2.06 100.00

Source: Own Survey 2019

4.1.7. Descriptive Statistics for Perception of Farmers for Improved Maize Varieties on Local Maize

Farmers’ perception of certain technology is the interwoven result of technical and socioeconomic factors. Farmers’ knowledge and beliefs about the technology can originate from different sources of information and experiences. They consider the consequence of using the improved maize on local one from different angles. Technical, economic and social

factors influence and/or determine the possibility and the extent of use of the new ideas and practices.

Similarly, in this study, there is a need to consider the perceived nature of the improved maize varieties. Therefore, farmers’ perception towards improved maize varieties on local maize were assessed in terms of their evaluative perceptions on their yield characteristics, drought resistance characteristics, early maturity characteristics, shattering resistance characteristics, marketability characteristics, disease resistance characteristics, and non- logging characteristics by giving value for each characteristics out of 15 value for total characteristics then the farmers valued each characteristics of improved maize varieties on local maize varieties >10 out of 15the value of the characteristics of improved maize varieties on local maize varieties were categorized under highly perceived, the farmers valued each characteristics of improved maize varieties on local maize varieties 5-9 out of 15 the value of the characteristics of improved maize varieties on local maize varieties were categorized under moderately perceived, the farmers valued each characteristics of improved maize varieties on local maize varieties <5 out of 15the value of the characteristics of improved maize varieties on local maize varieties were categorized under less perceived. This is the way by which I categorized my sampled house hold under three scales of the evaluative comparisons of perception. The value of the scale for the positive statements of evaluative perception on improved maize production were assigned 3,2,1 for highly perceived, moderately perceived, less perceived; respectively.

In order to get insight on farmers’ decisions of new technology use, looking at their perceptions about each attributes of a given expertise is very important. Hence, knowledge of respondent farmers’ evaluative criteria as regard to expertise attributes is needed. These include: yield characteristics, drought resistance characteristics, early maturity characteristics, shattering resistance characteristics, marketability characteristics, disease resistance characteristics, and non-logging characteristics.

Accordingly, about 0%, 1.03%, 0%, 0%, 1.03%, 3.09% and 1.03% adopter respondents and about 4.35%, 10.87%, 3.26%, 30.43%, 3.26%, 5.43% and 5.43% non-adopter respondents were perceived that the traits yield, drought resistance, early maturity, shattering, marketability, disease resistance, and non-logging of the improved maize varieties are less perceived to the local ones respectively. About 0%, 16.49%, 19.59%, 0%, 0%, 0% and 18.56% adopter respondents and about 81.52%,86.96%,76.09%,

47.83%,17.39% and 94.57% non-adopter respondents were perceived that the traits yield characteristics, drought resistance characteristics, early maturity characteristics, shattering resistance characteristics, marketability characteristics, disease resistance characteristics, and non-logging characteristics of the improved maize varieties are the same or moderately perceived to the local ones while About 100%, 82.47%,80.41%,100%,98.97%,93.81%and80.41adopter respondentsandabout14.13%, 13.04%, 23.91%, 21.74%, 75.00%, 77.17% and 0% non-adopter respondents were perceived that yield characteristics, drought resistance characteristics, early maturity characteristics, shattering resistance characteristics, marketability characteristics, disease resistance characteristics, and non-logging characteristics of the improved maize varieties are highly perceived to the local one respectively.

Table 12: Perceptions of sampled house hold about improved Maize varieties on local maize variety

Adoption category characteristics Adopter % Non adopter % χ2 value (N=97) (N=92) yieldcharac 128.14*** Less perceived 0 0% 4 4.35% Moderately perceived 4 4.12 % 75 81.52% Highly perceived 93 95.88 % 13 14.13% droughtreschar 91.46*** Less perceived 1 1.03% 10 10.87% Moderately perceived 16 16.49% 70 86.96% Highly perceived 80 82.47% 12 13.04% Early mature char 61.06*** Less perceived 0 0% 3 3.26% Moderately perceived 18 19.59% 67 76.09% Highly perceived 79 80.41% 22 23.91% Shattering char 108.44*** Less perceived 0 0% 28 30.43% Moderately perceived 4 4.12% 44 47.83% Highly perceived 93 95.88% 20 21.74% Market char 25.30*** Less perceived 1 1.03% 3 3.26% Moderately perceived 0 0% 20 21.74% Highly perceived 96 98.97% 69 75.00% Disease resistant 11.74*** Less perceived 3 3.09% 5 5.43% Moderately perceived 3 3.09% 16 17.39% Highly perceived 91 93.81% 71 77.17% Logging resistant 125.96*** Less perceived 1 1.03% 5 5.43% Moderately perceived 18 18.56% 87 94.57% Highly perceived 78 80.41 0 0% Source: own survey result 2019

Therefore the most perceived preference attributes of improved varieties of maize are yield characteristics, Shattering resistance characteristics’, disease resistance characteristics and market characteristics averagely.

4.2. Econometric Analysis

An econometric (double hurdle) model was used to determine the influence of various personal, demographic, socio-economic and institutional variables on adoption and intensity of use of improved maize varieties.

The estimates of parameters of the variables expected to influence adoption of improved maize varieties are displayed on Table 5. Eleven explanatory variables of which 1 are dummy variables and 10 variables are continuous were taken to the model for analysis. The impact of these variables on the adoption decision and intensity of use of improved maize varieties are discussed below:

4.2.1. Determinants of adoption of improved maize varieties

Education: Level of Education of the head of the household has a positive and significant at 1% significance level, indicate that adoption and use of improved maize varieties with each additional year of schooling increasing the probability of adoption improved maize by 2.76 percent. Similar results were reported by Alene et al., (2000) and Ahmed (2015) as their result the more educated farmers were adopted improved maize varieties than those who had no education on improved maize varieties.

Family size: found to be positive and significant at 1% significance Level, indicate that each additional of family size increases the probability of adoption of improved maize varieties by 5.85 percent. Similar results were reported by Milkias and Abdulahi (2018) but Contradicting with the research finding of Ahmed (2015) as their result the family size had contribution on adoption of improved maize.

Farm income: found to be positive and significant at 1% significance Level, indicate that each additional amount of farm income by one birr increases the probability of adoption of improved maize by 9.50 percent. This indicates that, those farmers who have more farm income were more risk takers to try new technology such as improved maize adoption. The result of this research is identical with (Asfaw et al., 2010), as cited in Afework and Lemma, (2015).

Number of oxen own: found to be positive and significant at 1% significance Level. Owning oxen is crucial for farming activity. Those farmers who have more oxen had higher probability to prepare their land for different improved varieties and can use their

cultivable land more properly, thereby to adopt new technology more rapidly. The probability of adoption of the package significantly affected by number of oxen owned at 1% significance level and each unit increase of the number of oxen farmers owned increases the probability of adoption of improved maize by 3%.This indicates when the number of oxen owned increases farmers’ adoption of improved technologies, particularly improved maize varieties will increase. This result is similar with the result of Solomon (2012) and Jaleta et al., (2013) that as the number of owned oxen were adopted improved maize varieties than those who had no oxen.

Livestock (TLU excluding of oxen): Livestock holding was positively and significantly affect the adoption of improved maize varieties at 10% level of significance, this means that as the number of livestock holder farmers increase by one unit the probability of adoption of improved maize varieties are increased by 1.2% implying that farmers with more livestock holding are more likely to devote significant amount of produced improved maize varieties than those households with less livestock holding. This result is lined with Yenealem et al., (2013) result that indicate those farmers with large number of tropical livestock units are more likely to adopt improved maize varieties than those who own small number of TLU

Contact with extension agents: found to be positive and statistically significant variable in determining adoption decision at 1 percent level which implies an increase in contact with extension agent increases probability of adoption of improved maize varieties production by 0.42 percent. This is due to the fact that, frequency of contacts with extension agents increases the probability of acquiring up-to-date information on the new agricultural technologies. The finding of this research result was also lined with the research result reported by Milkias and Abdulahi (2018) and Yenealem et al., (2013) as their result the more the extension contact the farmers were adopt improved maize varieties more.

Table 13: Marginal effect estimates of1st Hurdle (Probit) model

Variable Estimated Std. Err. Marginal effect P>z coefficient agehhd 0.0205 0.0201 0.0025 0.303 education 0.2221 0.0711 0.0276 0.001*** familysize 0.4701 0.1571 0.0584 0.001*** farmingexperience 0.0167 0.0171 0.0020 0.324 numberofoxen 0.2419 0.0973 0.0300 0.008*** TLu 0.0979 0.0587 0.0121 0.090* extenservice 0.0338 0.0114 0.0042 0.002*** distmarket -0.0211 0.0158 -0.0026 0.171 lntotfarminc 0.7643 0.1813 0.0950 0.000*** lnoffarmincome -0.0181 0.0511 -0.0022 0.723 membshipfrmccoop 0.2957 0.3244 0.0367 0.361 Log likelihood -42.022867 LR chi2(11) 177.83 Prob> chi2 0.0000 Pseudo R2 0.6791

Source: Model output, *, *** represents 10% and 1% level of Significance respectively

4.2.2. Factors determining the Intensity of use of improved maize adoption

This section focuses on factors determining the intensity of farmers’ maize production participation, conditional on decision to produce improved maize varieties. Truncated regression is used in this case, which is the second stage of the double-hurdle model, to analyze the problem.

Education: Level of Education of the head of the household has a positive and significant at 1% significance level and influence positively the adoption of improved maize varieties by increasing the amount of land allocated for improved maize varieties. This finding indicates that with each additional year of schooling increasing the land allocated for adoption of improved maize varieties by 0.03ha.Similar results were reported by Alene et al. (2000) and Ahmed (2015) as their result the more educated farmers were allocated land for improved maize varieties than those who had no education on improved maize varieties.

Number of oxen own: found to be positive and significant at 1% significance Level. The result of this decisions point towards with one addition of number oxen increases the land allocated for improved maize varieties by 0.06ha. Those farmers who have more oxen had more productive to prepare their land for different improved varieties and can use their cultivable land more properly, thereby to adopt new technology more rapidly. The productivity (production) of improved maize had significantly affected by number of oxen owned. This also implies that households who have more assets are likely to adopt more than farmers who have less. This finding is also the same with the result of those authors Solomon (2012) and Jaleta et al., (2013) that as the number of owned oxen were increased the productivity of farmers increased as well.

Farm income: found to be positive and significant at 1% significance Level, indicate that each additional amount of farm income by one birr increases the land allocated for improved maize varieties by 0.25ha. Amount of farm income obtained with-in a year was one explanatory variable in this analysis. This indicate that, those farmers who have more farm income more risk takers to try new technology such as improved maize adoption. The result of this research is identical with Alene et al. (2000) and the result of (Asfaw et al., 2010), as cited in Afework and Lemma, (2015) on adoption of chick pea technologies.

Livestock (TLU): Livestock holding positively and significantly related to intensity of use of improved maize varieties at 1% level of significance, this means that as the number of livestock holder farmers increase by one unit the amount land allocated for improved maize is increased by 0.03 ha implying that farmers with more livestock holding are more likely to devote significant amount of produced improved maize varieties than those households with less livestock holding. A household with large livestock holding can

obtain more cash income from the sales of animal products. This income in turn helps smallholder farmers to purchase farm inputs. This result is lined with Yenealem et al., (2013) result that indicate those farmers with large number of tropical livestock units are more likely to adopt improved maize varieties than those who own small number of TLU

Membership in farmers’ cooperatives union: Participation in cooperative society had positive influence on intensity of use of improved maize varieties at 5% level of significance. Organizing of farmers to be a member of cooperative society would facilitate access to credit, access to extension information and access to market. This implies Strengthening and expansion of rural cooperatives is paramount importance to enhance adoption of improved maize production package. The significant relationship between being member of a cooperative society and adoption is an indication for the importance of rural financial institutions in supporting agricultural production particularly oil crops farming. Cooperative members were found to be better in access to and use of credit services. This finding is confirmed with Tura et al., (2010).

Table 14: estimated coefficient of 2nd Hurdle (Truncated regression) model

Variable Estimated coefficient Std. Err. P>z agehhd -0.0015 0.0033 0.654 education 0.0302 0.0113 0.008*** familysize 0.0086 0.0188 0.644 farmingexperience 0.0028 0.0033 0.389 numberofoxen 0.0621 0.0209 0.003*** TLu 0.0323 0.0092 0.001*** extenservice -0.0007 0.0020 0.713 distmarket 0.0049 0.0031 0.108 lntotfarminc 0.2512 0.0456 0.000*** lnoffarmincome -0.0077 0.0085 0.368 membshipfrmccoop 0.1224 0.0562 0.030** Log likelihood 11.015959 Number of obs 97 Wald chi2(11) 97.80 Prob> chi2 0.0000

Source: Model output, *** and **represents 1%& 5% level of Significance respectively

5. SUMMARY, CONCLUSIONS AND RECOMMENDATIONS

5.1. Summary

This study was conducted in Kiremu District of Oromia Regional state, which is located about 458km away from Addis Ababa. In this area, maize is an important crop, which serves as source of cash and used for home consumption. New technologies that include improved varieties have been introduced by government institutions such as district agricultural office, agricultural research centers and other non–governmental organization. However, adoption of improved maize varieties, intensity of use and the perception farmers for improved maize seed varieties were not well studied in the study area.

The objective of this study was to provide empirical evidence on factor affecting adoption and intensity of use of improved maize and to identify the farmers’ perception for improved maize varieties on local maize variety. For this study, a total of 189 respondents were interviewed using structured interview.

The descriptive results show that there was statistically significant difference between adopters and non-adopters in terms of distance to market, number of oxen owned, TLU, frequency of extension visit, education of level of the house hold, family size and the econometric analysis result shows that six variable were significantly and positively affect adoption of improved maize varieties and also five variables were affect significantly and positively the intensity of use of improved maize varieties

5.2. Conclusion

Descriptive statistics such as mean, standard deviation, percentages, and frequency, used to describe different categories of sample units with respect to the continues independent variables, dummy independent variables and for identifying the perception of farmers for improved maize varieties on local seed by their characteristics of yield, drought resistance, early maturity, shattering, marketability, disease resistance, and non-logging by using the categories of comparisons less perceived, moderately perceived and highly perceived. Moreover, inferential statistics such as chi-square test (for categorical variables) and t-test (continuous variables) were used to compare and contrast different categories of sample units with respect to the desired characters so as to draw some important conclusions. Double hurdle was used to analyze factor affecting adoption and intensity of use of improved maize varieties.

The descriptive and inferential results show that there was statistically significant difference between adopters and non-adopters in terms of distance to market, number of oxen owned, TLU, frequency of extension visit, education of level of the house hold, family size and farm income. Regarding to membership of farmers cooperatives union 59.79% of adopters were members’ of farmers’ cooperatives union.

Education, family size, farm income, TLU, number of oxen, and frequency of extension contact affect adoption of improved maize varieties positively and Education, farm income, number of oxen, membership of farmers’ cooperative union and TLU also affect the intensity of use of improved maize varieties positively and significantly.

Regarding to perception of farmers for improved maize varieties on local seed are their yield characteristics, Shattering characteristics’, disease resistance and market characteristics averagely.

5.3. Recommendations

Based on the findings of the study the following recommendations are suggested for the improvement of the livelihood of the smallholder maize producers in the study area.

Education has a significant positive impact on adoption and intensity of use of improved maize varieties. Hence, strengthening adequate and effective basic educational opportunities to the rural farming households in general and to the study areas in particular is required. In this regard, the regional and local governments need to strengthen the existing provision of formal and informal education through facilitating all necessary materials. Such as:-Constant visiting site or demonstration site, preparing manual by their language and the other that is going with their farming practice and demonstration site.

The family size has a significant positive impact on adoption of improved maize varieties this indicate that the study area were used the human capital (labour force) for farming activity and family size directly contributes to labour forces to farming activities but the recommendation to use the increased family size is contradicted with the use family planning, therefore, the government should substitute the technologies used in terms of family size such as tractors, harvester technology, thresher technologies and etc for different agricultural technology practice to minimize the human capital because it is not recommended to increase the family size.

Government should make sure rural transportation and infrastructures are improved to make them passable in all seasons in order to make many producing areas accessible to input and output market and contribute to timely input delivery. Strengthening the knowledge of farmers’ on the modern agricultural production by proper linking the extension services with farmers especially those smallholder maize producers by involving them in experimentation of innovations such as dissemination of those innovations to their fellow farmers which will motivate them to adopt the new agricultural technologies.

From the finding of the study farm income has a positive effect on adoption and intensity of use improved maize varieties; therefore, scaling up and diffusion of improved maize varieties in the study area should be broadened and the income of small holder farmers were increased through their participation on farm activities. Increasing of small holder farmers’ income had positive effect on the adoption and intensity of use improved maize varieties through supporting of the ability of farmers to buy improved seed and others input. Thus, it is recommended that encouraging households’ participation on farming activities by creating favorable conditions and better opportunities for smallholders.

Organizing of farmers to be a member of cooperative society would facilitate access to credit, access to extension information and access to market. This implies Strengthening and expansion of rural cooperatives is paramount importance to enhance adoption of improved maize production. Therefore, the government should encourage farmers to form an association of maize producers which will help them to find market for their products at profitable rate.

The livestock play very important economic and socio-cultural roles for the wellbeing of rural households, such as food supply, source of income, asset saving, source of employment, soil fertility, livelihoods, transport, agricultural traction, agricultural diversification and sustainable agricultural production. Then Strengthening the existing livestock production system through providing improved health services, better livestock feed (forage), targeted credit and adopting agro-ecologically based high-yielding breeds and disseminating artificial insemination in the areas improve adoption and intensity of use of improved maize varieties..

Farmers have their own preference criteria for adoption among the available high yielding maize varieties. The finding of this study suggested that farmers in the area seek specific

varietal attributes, such as yield potential, tolerance to disease, shattering, and market characteristics. Information about the benefits of improved maize varieties should be important for smallholder farmers for priorities improved maize varieties. Therefore, the district agricultural office and extension system has to give more attention to farmers’ priorities and needs related to agriculture.

Furthermore, this research did not focus on the assessment of the impact of adoption of improved maize varieties on the income of smallholder farmers therefore; further research on this subject should be done to explore issues that were not captured by this study.

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7. APPENDICES

Appendix I: conversion factor Appendix table 1: Conversion factors used to calculate Tropical Livestock Units (TLU)

No Animals TLU-equivalent 1 Calf 0.20 2 Heifer & Bull 0.75 3 Cows 1 4 Camel 1.25 5 Horse 1.10 6 Donkey 0.70 7 Ship & Goat 0.13 8 Chicken/poultry 0.013 Source: Strock et al. (1991)

Appendix II: Multicollinearity test Appendix table 2: VIF

Variable VIF 1/VIF lntotfarminc 1.85 0.540466 TLu 1.74 0.575759 familysize 1.64 0.607906 numberofoxen 1.33 0.749469 extenservice 1.19 0.837163 farmingexp~e 1.12 0.891329 education 1.11 0.899595 agehhd 1.09 0.920687 distmarket 1.09 0.921447 lnoffarmin~e 1.05 0.953111 Mean VIF 1.32

Appendix table 3: Result of 1st hurdle and 2nd hurdle together

Number of obs = 189 Wald chi2 (11) = 56.58 Log likelihood =-31.006908 Prob> chi2 = 0.0000 variable Coef. Std. Err. z P>z [95% Conf.Interval] Tier1 agehhd .020516 .0200878 1.02 0.307 -.0188553 .0598872 education .2221266 .0710683 3.13 0.002 .0828352 .3614179 familysize .4701522 .1571199 2.99 0.003 .1622028 .7781016 numberofoxen .2419505 .0973742 2.48 0.013 .0511006 .4328004 farmingexperience .0167529 .0171625 0.98 0.329 -.0168851 .0503908 TLu .0979829 .0587369 1.67 0.095 -.0171393 .2131051 lntotfarminc .7643512 .181373 4.21 0.000 .4088666 1.119836 extenservice .0338409 .0114385 2.96 0.003 .0114219 .0562598 distmarket -.0211267 .0158234 -1.34 0.182 -.0521399 .0098865 lnoffarmincome -.0181215 .0511551 -0.35 0.723 -.1183836 .0821406 membshipfrmccoop .2957489 .3244575 0.91 0.362 -.3401761 .9316738 _cons -10.4212 2.193681 -4.75 0.000 -14.72073 -6.121663 Tier2 agehhd -.0015069 .0033631 -0.45 0.654 -.0080985 .0050847 farmingexperience .0028875 .0033551 0.86 0.389 -.0036884 .0094634 education .0302161 .0113445 2.66 0.008 .0079813 .0524509 familysize .008681 .0188083 0.46 0.644 -.0281825 .0455445 lntotfarminc .2512537 .0456542 5.50 0.000 .1617731 .3407344 numberofoxen .0621361 .020983 2.96 0.003 .0210102 .103262 TLu .0323118 .0092944 3.48 0.001 .0140951 .0505286 extenservice -.0007533 .0020491 -0.37 0.713 -.0047694 .0032627 distmarket .0049976 .0031066 1.61 0.108 -.0010912 .0110864 lnoffarmincome -.0077369 .0085917 -0.90 0.368 -.0245763 .0091025 membshipfrmccoop .1224674 .0562917 2.18 0.030 .0121377 .2327971 _cons -2.379755 .6379603 -3.73 0.000 -3.630134 -1.129376 sigma _cons .2451464 .0199262 12.30 0.000 .2060919 .284201

Appendix table 4: Heckman model out put

Number of obs=189 Censored obs = 92 Uncensored obs= 97 Wald chi2 (11) =68.24 Prob>chi2 =0.000

variable Coef. Std. Err. z P>z [95% Conf.Interval] tlandallocimmaize agehhd -.0011948 .0030895 -0.39 0.699 -.00725 .0048605 education .0316659 .0106984 2.96 0.003 .0106973 .0526344 familysize .0117014 .0182815 0.64 0.522 -.0241298 .0475325 farmingexperience .0035509 .0028601 1.24 0.214 -.0020549 .0091566 numberofoxen .0587227 .0180899 3.25 0.001 .0232672 .0941783 TLu .0310488 .0085131 3.65 0.000 .0143635 .0477342 lntotfarminc .223319 .0462721 4.83 0.000 .1326274 .3140106 extenservice -.0001064 .0019434 -0.05 0.956 -.0039153 .0037026 distmarket .0035144 .0027042 1.30 0.194 -.0017857 .0088145 lnoffarmincome -.005851 .0078088 -0.75 0.454 -.0211559 .0094539 membshipfrmccoop .1151662 .050135 2.30 0.022 .0169034 .213429 _cons -2.091329 .6310055 -3.31 0.001 -3.328077 -.8545805 adoptionimv agehhd .020516 .0200877 1.02 0.307 -.0188553 .0598872 education .2221265 .0710683 3.13 0.002 .0828353 .3614178 familysize .4701522 .1571197 2.99 0.003 .1622032 .7781012 farmingexperience .0167529 .0171625 0.98 0.329 -.0168851 .0503908 numberofoxen .2419505 .0973741 2.48 0.013 .0511007 .4328003 TLu .0979829 .0587369 1.67 0.095 -.0171392 .213105 lntotfarminc .7643512 .1813729 4.21 0.000 .4088668 1.119836 extenservice .0338409 .0114384 2.96 0.003 .0114219 .0562598 distmarket -.0211267 .0158233 -1.34 0.182 -.0521399 .0098864 lnoffarmincome -.0181215 .0511551 -0.35 0.723 -.1183836 .0821405 membshipfrmccoop .2957489 .3244573 0.91 0.362 -.3401758 .9316735 _cons -10.4212 2.19368 -4.75 0.000 -14.72073 -6.121664 mills _lambda .1013602 .1075016 0.94 0.346 -.1093391 .3120595 rho 0.43122 sigma .23505187

Appendix III: The questionnaire used for the survey Adoption of Improved Maize Varieties: the Case of Kiremu Districts, East Wollega Zones, Oromia Regional state, Ethiopia

MSc Thesis Research

Survey Questionnaire

Prepared by Alemayehu Keba (MSc Student, Jimma University)

Instruction: Please introduce yourself before starting the interview, the institute you are working in and explain the objective of the survey. Please ask each question patiently until the farmer get the point. Fill the answers to the question accordingly to the farmer’s response.

1. General information

1.1. Questionnaire no: ______1.2.Date of interview (DD/MM/YYY):______

1.3. Zone: ______1.4. District: ______

Peasant Association (Kebele) ______

Name of respondent______

Name of Enumerator______

2. Household Characteristics

2.1). Name of household head: ______

2.2). Sex of household head: 1. Male 0. Female

2.3). Age of household head (year): ______

2.4. What is the house you owned and live in?

1) Grass roofed and muddy wall

2) Corrugated tin roof and muddy wall

3) Corrugated tin roof and Block wall

4) Other (please specify)______

2.5. Marital status of household head 1.Married 2.Single 3.Divorced 4.Widowed

2.6. Educational level of household head (in grade):______

2.7. Farming experiences of household head, since he started farming maize (in year):______

2.8. Religion:

1) Orthodox Christian 2) Muslim

3) Protestant 4) others (specify): ______

3. Demographic Characteristics

3.1. Number of family members by sex and age Composition

No By age category By Sex category

Male Female 1 Below 10 years 2 13-14 years 3 14-16 years 3 17 - 35 years 4 35 -50 years 5 Above 50 years 6 Total 3.2. How many of your family members do permanently work on farm activities: _____

4. Socio economics Characteristics

4.1. What is the source of income for your household in order?

1. Crop cultivation 2.Animal husbandry 3. Crafts man 4. Employed (salary)

5. Trading 6. Other (please specify) ______

4.2. Landholding status (ha)

4.2.1. Total landholding: ______

4.2.2. Total cultivable land: ______

4.2.3. Land allocated for IMV production in 2009/2010 E.C: _____

4.3. What are the main uses of maize grain for you in 2009/2010 E.C?

Use of maize By kilogram By% from annual production For consumption For sale Source of livestock feed Improving soil fertility For other purpose

5. Cultivation practices for maize

5.1. What is the farming culture that you implement in cultivation maize?

Practice used by farmers Frequency of land preparation Planting time Seed rate per hectare Fertilizer rate per hectare

1.DAP

2. UREA

5.1.1. Planting method (1.row planting 2. Broadcast) =______

5.1.2. Weeding frequency=______

5.1.3. Harvesting time=______

5.2. List the major problems in maize production?

1. Lack of seed 2.lack of fertilizer 3.Disease 4. Lack of rain fall 5.Lack of market 6. Other specify______

6. Livestock production 6.1. Do you practice rearing livestock? 1. Yes 0. No 6.2. If yes fill the table below Class of Amount sold last Total price livestock Number year(2010 E.C) Unit price Local Improved Total Local Improved Local Improved Cows Oxen Heifers Bulls Calves Sheep Goats Donkeys Horses Mules Poultry

7. Adoption status of maize production practices 7.1. When you heard about improved maize varieties for the 1st time in years? ______7.2. From whom/where you did first heard about the improved maize varieties 1. Development agent 2.Research Center 3.Neighbors 4. Farmers’ organizations 5.Others (specify) 7.3. Have you ever used improved seed varieties of maize on your farm? 1. Yes 0.No 7.4. If yes, when did you start planting? ______year (in E.C) 7.5. If yes for Q#7.3 how much land did you allocated for improved maize varieties for the last three years?

No Name of varieties 2007/08 E.C 2008/09 E.C 2009/10 E.C

Area(ha) Yield Area(ha) Yield Area(ha) Yield

7.6. Type of improved maize varieties used. 1. Shone 2. BH660 3.Owner Limmu

7.7. If yes, for Q#7.3, where do you get these seed?

1. Research Center2. Government supply3. Purchase from market4. Supply of development partners (e.g. NGO)5. Other source (please specify)______

7.8. Have you ever used fertilizer (DAP and UREA) on your farm? 1. Yes 2. No

7.9. If your answer is yes for Q #7.7, fill the following table

Type of Utilized per cropping Purchase Price S.N Fertilizers Quantity season/2009/10/ per packet 1 DAP 2 UREA

7.10. Why you are using improved maize varieties (multiple answers is possible)?

1. Improving yield performance 2. Reducing cost of production 3.Offsetting environmental effect 4. Increasing income 5.improving soil fertility 6.food security 7. Other (please specify…) ______

7.11. If you say no for Q#7.3, why you are not in a position to use these improved technology inputs?

1. High purchase price 2.Acecebility problem 3.Incopatible weather condition 4.Lack of information 5. Fear of risk 6. Other: ______

7.12. Do you face any challenge in adoption process of farm input fertilizer and improved seed?

1. Yes 0. No

7.13. If your answer is Yes for Q#7.13 what are the major challenges that affect the use of these farm inputs (multiply answer is possible)?

1. Lack of improved seed 2. Lack of fertilizer 3.Disease 4.Lack of information about these technology 5. Others (specify).______

7.14. Do you think the improved maize is better than local varieties in terms of the following traits (mark for the better one in the table below)?

Traits Maize characteristics Local Improved Yield Color Taste Drought resistance Maturity period Disease resistance Storability Other (please specify...... )

8. Extension Service

8.1. Did you consulted by DAs in the last cropping Season (2009/10 E.C)? 1. Yes 0.No

8.2 If your answer is yes, for the question Q#8.1, how many days did DA contacted you in 2009/10 cropping season for purpose of maize production and mgt?______

8.3. If yes for Q#8.1 how the DA did helped you?

1. Practical assistance at farm 2.Demonistartion 3.Training at FTC

4. Others (please specify) ______

8.4. Have you ever attended any demonstration or field days arranged by DAs or research center on maize? 1. Yes 0.No

8.5. Have you ever participated in training on maize production? 1. Yes 0. No

8.6. Which institution was your first source of information about improved maize varieties and fertilizers? 1) BOA 2) Other farmers 3) Research center 4) NGOs (specify)______5) Relatives 6) other (specify) ______

9. Market service and price

9.1. Do you have market for maize? 1. Yes 0. No

9.2. Did you sell your maize crop during the 2009/10 E.C year? 1. Yes 0.No

9.3. If yes, where do you sell your crop?

1. at farm gate 2. Village market 3.District market 4. Secondary market

5. Tertiary market 6. Other specify: ______

9.4. At what season do you usually sell maize product? 1. Right at harvest 2. Latter after harvest

3. Any time I face problem 4. Other (specify):______

9.5. Distance to the nearest market center (in minute.) ______

9.6. Distance to the all-weather road (in minute.) ______

10.) Uses of crop produced

10.1) what are the major crops you cultivate in your farm for 2009/10 cropping season? Please fill the requested information here below

Crop Unit Amount produced Amount to be used for Seed Food Sale Price Maize Teff Wheat Nug Barely Sorghum Finger millet

11. Participation on off/non-farm income

11.1. Did you participate on off farm activities last year? 1. Yes 0.No

11.2. Did you participate on non-farm activities last year? 1. Yes 0.No

11.3. Total income from off farm activity by birr: ______

11.4. Cash income from livestock by products

Total Type of Quantity Quantity Unit product Unit produced sold (Q) price(P) (P*Q) Milk Eggs Butter Cheese Others(specify) Total 11.5. Total income from farm income=______

12. Credit availability and use

12.1. Do you have access to credit for you farming operation? 1. Yes 0. No

12.2. If yes, from where and how much did you obtained in last cropping season (2009/10) E.C?

Source of credit Amount Interest rate Microfinance Cooperative/union Bank(specify) Traders Iqub/Iddir NGOs (specify)

12.3. If yes for Q #12.1 for what purpose did use credit you got?

1. To pay school fee 2. To pay tax 3.To buys agricultural inputs 4. To cover house hold expenditure

5. To buy livestock 6.Others (specify)______

12.3. If no, what are your sources of finance for farming operations?

1) Crop sales 2) Livestock sales 3) Off-farm activities 4) Others (specify) ______

12.4. How far is from your home to credit office (in Km) ______

12.5. Do you have any problems in getting credit? 1. Yes 0. No

12.6. If yes, what is the nature of your credit problems?

1.) Bank loans not available 2.) Do not have required collateral

3.) Loans from informal sources not available 4.) Repayment terms are unfavorable

6.) Interest rates are too high 7.) Others (specify) ______

13. Are you a member of farmers’ cooperative union? 0. No 1. Yes

13.1. If yes for Quest.13 for what purpose you are joined the member?______

13.2.If no for Quest.13 why you are not joined the member?______

14. Perception of farmers on improved maize technology attributes

14.1 Characteristics of improved maize varieties as compared to local variety

(1= less perceived 2= moderately perceived 3= highly perceived) (Mark x for theselectedone)

(

Comparison of Characteristics of Value given for each No improved maize on local maize Characteristics by each 1=(0-4) 2=(5-9) 3=(10-15) respondent out of 15 1 Yield

2 Drought resistance

3 Earl maturity

4 Shattering

5 Marketability

6 Disease resistance

7 Logging

15. Cost –benefit analysis

15.1. How much did you get from maize production in 2009/10 E.C from improved varieties?

No varieties Area(ha) Quantity obtained Price per Total in quintal(Q) quintal (P) profit(p*Q) 1 2

15.2. How much did you spent for maize production in 2009/10 cropping season on improved varieties?

No Input Cost of input Price of input Total input per unit per unit cost 1 Land rent(ha) 2 Seed(Kg) 3 Herbicide(L) 4 Pesticide (L)

15.3. How much did you spent for operation of maize production in 2009/10 cropping season on improved varieties?

No of Operation No of day Working hour Wage rate per day Total cost workers Land preparation(Oxen and labor) Planting (Oxen and labor) Weeding Herbicide pesticide application Harvesting Threshing Sack cost Cost of transportation to Market Total