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Rural Development and Agricultural Extension Thesis and Dissertations
2019-09-20 ASSESMENT ON COMPETENCE OF AGRICULTURAL EXTENSION DEVELOPMENT AGENTS IN NORTH WEST ETHIOPIA
YOSEF, GETAHUN http://hdl.handle.net/123456789/9710 Downloaded from DSpace Repository, DSpace Institution's institutional repository
BAHIR DAR UNIVERSITY
COLLEGE OF AGRICULTURE AND ENVIRONMENTAL SCIENCES
DEPARTMENT OF RURAL DEVELOPMENT AND AGRICULTURAL EXTENSION
RURAL DEVELOPMENT MANAGEMENT PROGRAM
ASSESMENT ON COMPETENCE OF AGRICULTURAL EXTENSION DEVELOPMENT AGENTS IN NORTH WEST ETHIOPIA
By
YOSEF GETAHUN
JUNE, 2019
BAHIR DAR, ETHIOPIA i | P a g e
BAHIR DAR UNIVERSITY
COLLEGE OF AGRICULTURE AND ENVIRONMENTAL SCIENCES
DEPARTMENT OF RURAL DEVELOPMENT AND AGRICULTURAL EXTENSION
RURAL DEVELOPMENT MANAGEMENT PROGRAM
ASSESMENT ON COMPETENCE OF AGRICULTURAL EXTENSION DEVELOPMENT AGENTS IN NORTH WEST ETHIOPIA
MSc Thesis By
Yosef Getahun
Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science (M.Sc.) in Rural Development Management
June, 2019
Bahir Dar, Ethiopia
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APPROVAL SHEET
As members of the Board of Examiners of the Master of Sciences (M.Sc.) thesis open defense examination, we have read and evaluated this thesis prepared by Yosef Getahun (ID. No. BDU0906228PR) entitled “Assessment on Competence Level of Agricultural Extension Development Agents in North West Ethiopia”. We hereby certify that, the thesis is accepted for fulfilling the requirements for the award of the degree of Master of Sciences (M.Sc.) in Rural Development Management.
Board of Examiners
______Name of External Examiner Signature Date
______Name of Internal Examiner Signature Date
______Name of Chairperson Signature Date
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DECLARATION
As research advisors, we hereby certify that this thesis entitled “Assessment on Competence Level of Agricultural Extension Development Agents in North West Ethiopia” submitted in partial fulfillment of the requirements for the award of the degree of Master of Science (M.Sc.) in Rural Development Management” by Yosef Getahun is an authentic work carried out by him under our guidance. The matter embodied in this project work has not been submitted earlier for award of any degree or diploma to the best of our knowledge and belief.
Dessalegn Molla (PhD)______Name of Major Advisor Signature Date
Birgit Habermann (PhD______Name of Co-Advisor Signature Date
Tilaye Teklewoled (PhD______Name of Co-Advisor Signature Date
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Statements of the Author
I hereby declare that this thesis is my own work and it has not previously been accepted in substance for any degree and is not being concurrently submitted to any other institution elsewhere in candidature for the award of any degree, diploma or certificate.
Yosef Getahun______Name of Candidate Signature Date
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ACKNOWLEDGEMENTS
Oh Almighty God! Thank you for the wisdom and wellbeing that you provided for me. You gave me everything I needed to finish this work. I know I am nothing without you!
I gratefully acknowledge my advisors Dessalegn Molla (PhD), Birgit Habermann (PhD) and Tilaye Teklewoled (Phd) for their meticulous guidance. I have learned from each of you not only for this research but also for my future academic career. Without your constructive comments, this thesis would not have been possible. I also thank ICARDA-ARARI Joint Project for sponsoring this research.
I have to recognize the role of agricultural development agents, agriculture office experts, agricultural college department heads and academic vice deans who filled questionnaires and participated in focus group discussions and key informant interviews. They were willing to spend their time and provide information.
Finally, I acknowledge my wife for her priceless support and appreciation that pushes me forward even during difficult times. I also thank friends and class mates especially Atinkut Admas for encouraging me. Thank you all!
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Dedicated …
To my mother Sewasew Molla
To my father Getahun Aregaw
To my wife Mihiret Tadele and
To my daughter Yohana
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Abbreviations ADLI Agricultural Development Led Industrialization
AET Agricultural Education and Training
ATA Agricultural Transformation Agency
ATVET Agricultural Technical, Vocational Education Training
BSc Bachelor of Science
CSA Central Statistics Authority
DA Development Agents
ECOP Extension Committee on Organization and Policy
FTC Farmer Training Centers
GDP Gross Domestic Product
GPA Grade Point average
ICARDA International Centre for Agricultural Research in the Dry Areas
ICT Information Communication Technology
IMF International Monetary Fund
JICA Japan International Cooperation Agency
KM Kilometers
Mm Millimeters
SNNPR Southern Nations Nationalities and People Region
UNICEF United Nations Children’s Fund
WTO World Trade Organization
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Table of Contents Abbreviations ...... vii List of Figures ...... x List of Tables ...... xi ABSTRACT ...... 1 1. INTRODUCTION ...... 2 1.1 Background and Justification ...... 2 1.2 Statements of the Problem ...... 4 1.3 Objectives of the Study ...... 7 1.3.1 General Objective ...... 7 1.3.2 Specific Objectives ...... 7 1.4 Research Question ...... 7 1.5 Scope and Limitations of the Research ...... 7 1.6 Significance of the Study ...... 8 2. REVIEW OF RELATED LITERATURE ...... 9 2.1 Concepts and Definition ...... 9 2.2 Theoretical Perspectives ...... 14 2.2.1 Competence Theory ...... 14 2.2.2 Job characteristics theory ...... 16 2.3 Emerging Thoughts in Agriculture extension that affect curricula of training institutions ...... 19 2.4 Changing Roles of Extension Agents ...... 22 2.5 Empirical Studies ...... 23 2.5.1 Curricula of Training Institutes ...... 23 2.5.2 Competence Level of DAs ...... 26 2.5.6 Motivation of DAs ...... 27 2.5.3 Factors Affecting Competence and Motivation of DAs ...... 28 2.6 Conceptual Framework ...... 29 3 RESEARCH METHODS ...... 33 3.1 Description of the Study Area ...... 33 3.2 Data Type, Source and Methods of Data Collection ...... 34 3.3 Sample Size and Method of Sampling ...... 36 3.4 Methods of Data Analysis ...... 38 3.4.1 Qualitative Data Analysis ...... 38 viii | P a g e
3.4.2 Quantitative Data Analysis ...... 39 4 RESULTS AND DISCUSSIONS ...... 45 4.1 Demographic Characteristics of Sample DAs ...... 45 4.2. Review Recruitment criteria and Curricula of Agriculture Training Institutions ...... 48 4.2.1 Recruitment Process ...... 48 4.2.2 Findings on Curricula and Profile of Training Institutions ...... 49 What makes good DA? ...... 50 Curricula Related Findings ...... 52 4.2.3 SWOT Analysis ...... 55 4.3 Competence and Motivation levels of Agents in the Study Area ...... 56 4.3.1 Competence Level...... 56 4.4 Motivation Level of Development Agents ...... 74 4.5 Determinant Factors of Competency and Motivation ...... 75 4.5.1 Factors Affecting Competence of DAs ...... 75 4.5.2 Factors Affecting Work Motivation of DAs ...... 80 5. CONCLUSTION AND RECCOMENDATION ...... 82 5.1 Conclusion ...... 82 5.2 Recommendations ...... 84 References ...... 87 APPENDIXES ...... 93 Appendix 1: Secondary Resource Guide ...... 93 Appendix 2: Focus Group Discussion Guide for DAs on their Competency ...... 94 Appendix 3: Key Informant Interview with district (woreda) Agriculture Office ...... 96 Appendix 4: Key Informant Interview with ATVET Representatives ...... 97 Appendix 5: Questionnaire for DAs ...... 98 Appendix 6: Multicolinearity test ...... 105 Appendix 7: Distribution of competence scores ...... 106 Appendix 8: Pictures of FGD process ...... 107
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List of Figures Figure 2: Framework of Relationship among colleges, work and changing environment...... 30 Figure 4: Conceptual Framework ...... 32 Figure 5: The First one, Map of Ethiopia Showing the Relative Location of East Gojjam Zone and the second is districts of East Gojjam Zone ...... 34 Figure 5: Male and female composition among districts ...... 45 Figure 6: SWOT Matrix of Training Institution ...... 55 Figure 7: Descriptive Statistics result of six core competence areas ...... 57 Figure 8: ICT mean score by work experience ...... 61 Figure 9: Total competency of DAs by their education level ...... 62
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List of Tables Table 1: The Six Core Competence Areas ……………………………………………………………………………………. 13
Table 2: FGD participants selection criteria…………………………………………………………………………………… 37
Table 3: Number of respondents under each data collection method………………………………………….. 38
Table 4: Research Objective and data collection tool summary………………………………………..…………… 41
Table 5: Socio economic characteristics………………….…………………………………………………………………….. 46
Table 6: Likert Scale items Result (n=149)……………………………………………………………………….……………. 58
Table 7: one-way ANOVA result differences in competency level on type higher education……… .. 59
Table 8: mean comparison of General competence score by sex………………………………………………… 60
Table 9: ICT competency difference on work experience……………………………………………………………… 60
Table 10: Mean difference based on education level…………………………………………………………………… 62
Table 11: Mean score of planning competence…………………………………………………………………………… 67
Table12: Group Statistics: Mean competence scores comparison; Bsc holders and non-holders… 67
Table 13: Planning Competence of DAs……………………………………………………………………………………….. 69
Table 14: Planning Competence of DAs based on government criterion …………………………………….. 70
Table 15: Group Statistics: mean score of planning competence among DAs with education………. 70
Table 16: Implementation Competence of DAs (n=149)……………………………………………………………….. 71
Table 17: Mean scores of implementation competence between BSc holders and non-holders…. 72
Table 18: Monitoring and Evaluation Competence of DAs……………………………………………………………. 73
Table 19: Monitoring Competence of DAs’ based on government efficiency evaluation standard.. 73
Table 20: Communication Competence of DAs…………………………………………………………………………….. 74
Table 21: Subject Matter /Technical Competence of DAs (n=149) ………………………………………………….74
Table22: Technical Expertise based on government standard ……………………………………………………… 75
Table 23: ICT competence of DAs (n=149)……………………………………………………………………………………. 75
Table 24: ICT competency based on government standard…………………………………………………………. 76
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List of Tables (continued)
Table 25: Total competence of DAs (n=149)…………………………………………………………………………………. 76
Table 26: General Competence based on government standard…………………………………………………. 77
Table 27: Ten most frequently used words used by DAs to explain typical farmer………………………. 78
Table 28: Work Motivation level of DAs……………………………………………………………………………………… 78
Table 29: Comparison of results of work motivation level of DAs ……………………………………………….. 79
Table 30: Development Agents’ attitude towards their job…………………………………………………………. 79
Table 31: Factors that influence competency of DAs…………………………………………………………………… 80
Table 33: Factors Affecting Work Motivation of DA…………………………………………………………………….. 84
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ABSTRACT Agricultural extension development agents need to possess the necessary competencies in order to deliver quality service. This study is a comprehensive work that sought to assess competence and motivation levels of development agents, review the curricula of three agricultural training colleges and determine factors affecting competence and motivation of development agents. The study was conducted in Baso Liben, Gozamen, Debre Elias and Machakel districts of East Gojjam zone, Ethiopia. Different sampling procedures were applied to select development agents, government extension officers and agricultural college teachers who participated in the survey, focus group discussions and key informant interviews. Six core competence areas were identified based on the information drawn from literature review and the job description of development agents set by Amhara National Regional State Council. The six core competences are extension program planning, extension program implementation, monitoring and evaluation, communication, subject matter and Information and Communication Technology. On the scale 1 to 5 (lowest to highest), 149 development agents rated their own competence level for each competence area. The mean score results show that development agents scored above average on communication and program implementation competence areas with total mean score of 3.51 and 3.24 respectively. And they score below average on the rest four competence areas from which ICT was the least with 1.94 mean score. Regarding work motivation, proportion of development agents with low, medium and high motivation levels was 34.2%, 61.1%, 4.7% respectively. In addition, majority 53% of development agents want to leave the profession from which 20.8% are attending advanced education outside of agricultural profession. And based on regression analysis, independent variables like education level, relationship with co-workers, supervisor’s visit and evaluation have significant influence on competence of extension agents. On the other hand, qualitative findings indicate limitations of curricula of agricultural colleges and recruitment process extension agents. It was concluded that significant proportion extension agents lack core competencies and also majority of them are not interested to work as extension agents. In recommendation, curricula of training institutions need reformation to ensure alignment of education programs with expected competence areas. The recruitment process should be revised in order to get qualified agents.
Keywords: Agricultural Colleges, Agricultural Extension, Core Competence, Competence, Development Agents, Motivation
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1. INTRODUCTION
1.1 Background and Justification Ethiopia is the second most populous African nation next to Nigeria with a population size of over 94 million (CSA, 2017). The country’s economy is mainly dependent on subsistence agriculture which provides 46% of the Gross Domestic Product (GDP) and about 80% of employment. Different studies indicate that low productivity, food insecurity, lack of access to improved seeds, and poor information mechanisms are among the major challenges that the sector has faced for decades (Tully 2003; Kelemu et al 2014). The recent El Niño related drought put more than 10 million people in need of urgent food relief assistance (UNICEF, 2016). Thus, developing a strong and resilient agricultural system as well as developing well equipped practitioners are key to improving agricultural practice. In doing so, the Ethiopian government adopted agricultural extension as a national intervention strategy which is a major component of the country’s development principle called Agricultural Development Led Industrialization (Ministry of Finance and Economic Development, 2003; Ethiopia’s Agricultural Extension Strategy, 2017).
The agricultural extension system is essential in developing countries to complement traditional agricultural practices through research, information exchange, innovation and transfer of agricultural technologies (Asayehegn et al, 2012; Issahaku, 2014; Kassa et al, 2012). Various forms of agricultural extension services exist throughout the world. Their primary function focuses on facilitating learning and extending new technologies in non- formal educational settings to improve agricultural productivity as well as to increase farmers’ incomes. Moreover, agricultural extension services have profound roles for sustainable agricultural development. As environmental degradation, rapid population growth, and unfair trade relation continue to persist in most developing countries, effective extension system are required that takes in-to account the ecological, cultural, social and economic features which are the bedrock of sustainable agriculture. Studies conducted in Kenya and Nepal showed that the lack of proper agricultural extension services was one of the factors that contributed significantly to rural poverty (Davis and Place 2003; Suvedi and Ghimire 2015).
Several authors claim that the responsibility of putting agricultural policies and strategies on the ground as well as the responsibility of transferring technologies to farmers rest on the shoulders of development agents who are assigned in rural areas (Yohannes 2009;
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Kinfe et al 2012). Development agents are focal persons at local levels who are responsible to improve the living standard of rural households through the transfer of agricultural technologies and facilitating farmers’ access to inputs and credits. According to Anaeto et al (2012), development agents have various roles as being advisors, technicians, middlemen, consultants, advocators, planners and managers. Thus, competent extension professionals are the assets for agricultural extension services in particular and agricultural development in general. As extension agents link research centers and farmers, agricultural research agendas would remain largely academic (without solving filed problems) if extension workers failed to receive and provide accurate information both from farmers and research centers. Research focuses on the technical aspects of generating useful technologies, while extension focuses on the acceptance and adoption of those technologies by users (ibid).
Currently, the need and demand for extension professionals to demonstrate a higher level of professionalism are growing due to different factors such as dynamic agricultural systems, advancing science and technologies, changing socio-demographics, increasing globalization and growing competition for resources. All these factors demand agricultural extension professionals to be proficient in the technical aspects of their areas of expertise, as well as in the processes and delivery of the services (Suvedi and Ghimire 2015).
According to Agricultural Transformation Agency (ATA 2017), the government of Ethiopia is making a significant effort to increase the number of development agents and farmer training centers. The aim is to deploy three development agents and establish one farmer training center in each Kebele1. In order to support this strategy, the government established 25 agricultural technical and vocational training colleges that train development agents. Currently, Ethiopia has 12500 Farmer Training Centers (FTC) and 56000 development agents this is the highest number per farmer in Africa. The high number of development agents per farmer is expected to change the livelihood of smallholder farmers (ibid).
Although the Ethiopian government claims double digit growth of the agriculture sector in particular and national economy in general, various studies indicate that food insecurity and rural poverty are still major problems of the country where millions of people are still dependent on food aid programs every year. According to Asayehegn et al (2012),
1 Kebele refers to lowest administration unit or structure of government
3 | P a g e development agents work under difficult conditions that negatively affect their competency and motivation. Similarly, ATA (2017) identified key bottlenecks that lead to inadequate performance which included limited involvement of farmers in FTC management, insufficient resources in FTCs, absence of long term plans for sustainability, inadequate incentives to motivate DAs and limited knowledge and skill of DAs.
As the problems and challenges faced by agricultural sector change overtime, there is a need to adapt those challenges in extension systems, national policies, educational curriculums, and implementation modalities. It is important to assess competency gaps of development agents which are vital to adopt new changes. This research focuses on the assessment of development agents’ competence level in core competence areas.
1.2 Statements of the Problem Food demand is increasing globally due to rapid global population growth. Based on prediction made by Feed the Future (2015), world population will exceed 9.7 billion people by 2050 and food production should be increased by 60 percent to meet future food demand. Global forces such as new scientific discoveries, changing demographics, shifts in socioeconomic characteristics, rapidly changing consumption patterns, interdependence in global markets, environmental degradation and climate change are becoming dominant factors that affect daily lives of billions of people around the world. Agriculture is subjected to these changes and forces. Such changes could have positive impact if complemented by effective extension services. A challenge for agricultural extension rests in unleashing the creativity of millions of front-line extension workers to disseminate improved technologies and approaches in ways that benefit smallholder farmers and agribusiness operators across the world (Anandajayasekeram et al 2008).
As indicated by different scholar, extension agents should possess the necessary competencies to anticipate and deliver quality service (Awang 1992; Anaeto et al 2012; Issahaku 2014; ATA 2017). On top of that, most extension workers in developing countries work in harsh field conditions with limited facilities. Thus, only trained, motivated and competent staff members can work and succeed in such difficult conditions (Maddy 2002; Qamar 2005). Even a well-designed and quantitatively well-staffed extension system may fail to deliver expected outcomes if development agents are not sufficiently competent to fulfill their tasks. Therefore, it is important to define the skills, knowledge, motivation and attitude of extension professionals and to periodically assess extension education programs (Caffarella, 2002; Mulder, 2014). In addition, clear set job
4 | P a g e descriptions as well as job analysis systems should be established which are important to respond to the latest challenges of extension systems. However, job analysis systems are weak in most developing countries where core competencies of development agents are poorly defined and assessed (Suvedi and Ghimire 2015, Sarkar 2013).
In Ethiopia, the government has made significant efforts to increase quantity and quality of the extension service. In terms of quantity, promising results have been achieved where thousands of development agents were deployed to fasten agricultural technology dissemination at grass root level. In most cases, three development agents were assigned per Kebele, one each for crops, livestock and natural resource management expertise. Moreover, one farmer training center per Kebele has been established. In terms of quality however, different studies indicate that multiple programs have failed without achieving expected outcomes due to an ineffective extension service, low performance and motivation of development agents and other environmental factors which show a lot has to be done to improve the extension system (Gebremedhin et al cited in Kelemu et al 2012, Kassa et al 2012; Kinfe et al 2012; Haile and Abebaw 2012).
Several researches were conducted on performance, work motivation and job satisfaction of development agents. Kassa et al (2012) studied work motivation and job performance of DAs in fifteen zones of different regions from which two of them were in Amhara region. The study identified causes attributed to lower motivation levels which were lower salary, limited access to internal promotion, challenging nature of the profession itself, very high workload, duties and responsibilities irrelevant to the profession, and multiple chains of command. With regard to job performance, the research tried to measure self- reported and observed performance of DAs by using their job description activities rated in five points ranges from “very well doing” to “unable to do”. The rating was again analyzed as low, medium and high performance. And according to the study most of the DAs categorized themselves as medium and low performers.
Yohannes (2009) also conducted research on a similar topic aiming to identify factors influencing work motivation of DAs in Burji and Konso districts of SNNPR. According to the study, only 20% of DAs were found to be highly motivated while the remaining 80% were either a medium or low motivation level. The study further identified factors contributed for motivation of DAs such as advancement, recognition, attractive salary, work itself and fair organizational administration. Haile and Abebaw (2012) on the other
5 | P a g e hand tried to assess factors affecting time allocation of DAs on farmers’ lands. The results show that many variables that explain the perception of agents about their working environment significantly influenced their working time on farmers’ agricultural fields. In addition, ATA (2017) cited a study conducted by IFPRI in 2009 that indicated development agents had inadequate knowledge and skills to properly discharge their roles and responsibilities. Some of the key gaps and limitations included communication and facilitation skills, participatory approach and rural problem analysis, business plan and value chain development and marketing, conflict management, data collection, analysis and reporting. Similarly, most DAs in this study did not have sufficient technical knowledge and skills to provide hands-on training and demand-driven advisory services.
Finally, my research tries to fill gaps in literature at least in terms of four major reasons. First, most related studies were conducted prior to 2013. Since then the government of Ethiopia has made different adjustments and reforms but the changes occurred in competency and motivation of development agents during this time remain unknown. Second, in Amhara region, researches on similar topics were conducted in North Shewa, West Gojjam and Semen Wello Zones (Lakew 2011; Belay et al 2012). So, as the research was conducted in East Gojjam zone, it helped to see the context in new study area. Third, previous studies were focused on general job performance of development agents and performance can be influenced by environmental, psychological, institutional and demographic factors. In my research, I focus on competence of development agents which relate to basic skills, knowledge and attitude of development agents. Furthermore, my research also analyzed the competence of development agents in responding to current global changes in the extension system in general and the changing roles of development agents in particular. Finally, previous studies did not consider training institutions in their studies while this study tries to see curricula of training institutions in relation with competency of development agents. It aims at identifying which core competencies development agents lack in the study area.
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1.3 Objectives of the Study
1.3.1 General Objective The general objective of the study is to assess the competence of agricultural development agents in the study area.
1.3.2 Specific Objectives The specific objectives of the study were to:
1. Evaluate curricula of Agricultural Training Institutions. 2. Assess competence level of agricultural development agents in the study area. 3. Identify determinant factors that affect agricultural development agents’ competence and motivation.
1.4 Research Question The research tried to answer the following questions:
1. What are strengths and weaknesses of agriculture training institutions in teaching core competency areas to Development Agents? 2. How Development Agents get recruited? 3. What does the competence level of development agents look like? 4. What are determinant factors that affect competency and motivation of DAs?
1.5 Scope and Limitations of the Research This research was conducted in four districts of East Gojjam Zone Administration. It is a comprehensive document that sought to assess competence and motivation levels of development agents, review the curricula of three agricultural training colleges including recruitment process. As indicated by previous studies of Lakew (2011) and Belay et al (2012), development agents involve in a number of activities other than agriculture extension service including public administration, campaigns of other sectors, and other political assignments given by the government. These activities may require certain types of skills and knowledge. However, this research will be limited to assess only few but very important competence areas which are referred in this research as “core competences” (detail definition of core competence is presented in literature review section of this research). In reviewing Agriculture colleges, the study was limited to reviewing curricula of institutions only. It didn’t cover other profiles such as infrastructure, man power, budget, and organizational structures.
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1.6 Significance of the Study This study will make a significant contribution in indicating competency levels of development agents. The Ethiopian agricultural institutions can use competency gaps identified in the study as indicators to develop both short-term and long-term capacity building solutions that improve competency levels of development agents. The research will also give additional insights to development agents themselves on expected level competency and motivation as well as gaps in core competencies. Knowing areas where they are less competent will help them to update themselves in that regard.
Curriculum development and revision must be based up on empirical feasibility and need assessment findings that indicated gaps and improvement areas. So, this study can be an input for training institutions, government policy makers, and other stakeholders to improve on curricula of training institutions, competency level and work motivation of Development Agents and the extension system in general.
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2. REVIEW OF RELATED LITERATURE
2.1 Concepts and Definition Extension: the definition of extension system has changed over time. Traditional definitions include the one given by Addison (1972) stating that extension is a service that extends the educational assistance to rural people that is important in improving farming methods, increasing productivity, bettering their levels of living and lifting the social and educational standards of rural life. Fisher (1983) also defined extension as a system of education extending beyond the classroom to individuals on the farms and is available to every member of the family. Similarly, Moris (1991) defined extension as the mechanism for information and technology delivery to farmers.
However, traditionally agricultural extension systems were mainly top-down, supply- driven and extension agent-led with little participation of beneficiaries in the extension process. On the other hand, the rapidly changing context of agriculture due to globalization, population growth and climate change transformed the way knowledge is generated and applied (Ghimire 2016, JICA & ICARDA 2016). But traditional extension systems do not ensure demand driven agricultural development and participation of farmers. However, different scholars have emphasized the need for active participation of farmers in the extension process including in decision making and effective communication and collaboration among farmers, researchers and extension professionals rather than one-way top-down information flow (ECOP 2002; Swanson & Samy 2002; Dwarakinath 2006; Rivera et al. 2009).
For the purpose of this research a definition given by Christoplos (2010, p3) was used who defined extension to include:
“systems that should facilitate the access of farmers, their organizations and other market actors to knowledge, information and technologies; facilitate their interaction with partners in research, education, agri-business, and other relevant institutions; and assist them to develop their own technical, organizational and management skills and practices.”
This definition was adopted for this thesis because it counters the limitations of traditional definitions explained above. It emphasizes active participation of farmers and bottom-up
9 | P a g e approach to extension. The author also asserts that extension has to include competency areas involving technical knowledge, facilitation, brokering, coaching of different actors to improve market access, dealing with changing patterns of risk and protecting the environment. In addition, extension takes place within complex systems involving old and new service providers and information and communication technologies such as mobile phones, internet, radio and television.
Development Agents (DA): are employees of an extension organization (agriculture and rural development office) who are deployed at the community (Kebele) level under the supervision of the Kebele agriculture office head. DAs are frontline workers who work with farmers. According to ANRS Bureau of Agriculture guideline (2015), DAs are expected to execute extension programs such as conduction of socio-economic assessments, motivating farmers to development, community mobilization, conducting participatory annual plans, sharing best experiences, community meetings on weekly basis, strengthening farmer training centers, organizing demonstrations, providing technical support to farmers, promoting new technologies and inputs to create demand, prepare teaching aids, mainstreaming gender, classifying farmers based on their performance, provide modular trainings to farmers, and follow-up farming practices. This study focused on assessing competency levels of DAs and factors affecting their competence with regard to training institutions where DAs graduated from and other variables.
Competence: Traditionally, development of competencies has been reviewed based on job responsibilities. However, according to Langdon and Marrelli (2002), it is more significant to generate competencies based on the needed outcomes including human relations from the job. Stone (1997) described competencies as the application of knowledge, technical skills, and personal characteristics that are designed around the abilities individuals and groups need to give effective job performance and use in making human resource decisions. The definitions emphasized that competency is comprehensive of knowledge and skills which are vital to perform responsibilities on one hand and personal characteristics such as behavior, communication and positive attitude that smoothen human relations.
Another dimension of competence, as explained by Shavelson (2010), is its measurability. Competence is carried out under standardized conditions and it can be judged by some
10 | P a g e level or standards of performance as “adequate”, “proper” or “qualified”. In addition, it is something which can be improved through proper training, experience and supervision. Similar explanation was also given by Cernucca & Dima et al. (2007) explaining that competency is the quality of being adequately or well qualified, having the ability to perform a job.
When it comes to agricultural development agents, competence defines the behavioral characteristics of knowledge, skills, attitudes and judgment generally required of an extension agent for the effective, successful performance of an assigned job and/or task (Awang 1992). Although all the above definitions are relevant to conceptualize competence in general and development agents’ competence in particular, the concept of “core competency” is more relevant in this study because it gives more tuned explanations on basic knowledge, skills and attitude that extension professionals need. They refer to those skills and knowledge areas which are most relevant for the job.
Core Competencies: different scholars tried to identify basic competencies that development agents should possess to ensure significant contributions in the extension system and for sustainable agriculture development regardless of the type of extension approach. Although how competencies are defined differs from one scholar to the other, most of them focus on major functions of development agents which are planning, program implementation, evaluation, and information communication technologies.
Seevers, Graham and Conklin (2007) came up with the term “core competency” to describe the basic knowledge, skills, attitudes and behaviors that contribute to workers’ excellence in their respective professions (e.g., extension education and extension services). Likewise, Cooper and Graham, 2001; Scheer et al., 2006 identified nine areas of professional core competencies that adequately address the needs of demand-driven, decentralized, pluralistic and participatory extension systems. These are planning, coordinate and collaborate to implement, communication, good public relation, value diversity, use of ICT, evaluation to show results and update knowledge. Recently, Suvedi and Kaplowitz (2016) extended the concept by classifying core competencies in to two major categories as process skills and technical skills. Process skills are related with competencies needed to function and establish positive relationship with clients, facilitating group formation, resolving conflict and engaging stakeholders in program
11 | P a g e planning. On the other hand, technical skills refer to competency areas directly related with knowledge and practice of the science.
Regarding the Ethiopian context, the Ministry of Agriculture (2003; P30) described 25 responsibilities of development agents. Then, the Amhara National Regional State Council (2015) endorsed these responsibilities as job description of development agents. From the 25 responsibilities, 4 of them are activities outside of agricultural extension while the rest 21 are agricultural extension activities. Some of the major activities are conduct need and potential assessments, prepare annual plans, provide technical support to farmers, prepare work schedules, introduce new technology packages, provide trainings to farmers, label farmers based on their performances, monitor progresses, facilitate demonstrations in farmer training centers, organize experience sharing, prepare progress reports, and document best practices.
For this research, six core competence areas were identified based on the information drawn from recent literature specifically from Suvedi and Kaplowitz (2016). The six core competences are extension program planning, extension program implementation, monitoring and evaluation, communication, subject matter and Information and Communication Technology. And from 38 specific competencies listed by Suvedi and Kaplowitz under each core competence areas, 28 of them were picked for this research based on their relevance to execute the 21 agricultural extension responsibilities or activities set by Ethiopian government. The following table describes six core competence areas and 28 specific competencies defined for this research to measure competence level of agriculture extension development agents in East Gojjam Zone.
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Table 1: The Six Core Competence Areas
Program Program Monitoring and Communication Subject Learning using Planning Implementatio Evaluation matter IT n
1. Detailed 6. 11. Conduct 16. Respecting 21. Basic 26. Make good knowledge on Demonstrate monitoring and local social Bio use of Ethiopian team work evaluation in values Veterinary computers extension system extension Competenci goal and vision program es
2. knowledge of 7. Training 12. Design 17. Demonstrate 22. Animal 27. Make good federal and facilitation Data collection respectful Production use of regional and instruments / attitude towards Competenci information and extension presentation survey, all farmers es (Animal communication strategies and skills interview, /understand Nutrition, technologies packages FGD, individual Poultry and (ICTs)/access observation, situation of Dairy and use web- etc/ farmers based resources
3. Conduct Need 8. 13. Conduct 18. Demonstrate 23. Crop 28. Make use of Assessment Demonstrate Data analysis Good listening production internet for using assessment good /qualitative and skill /land email and tools professional quantitative preparation, exchange ethics pest and information disease control, seed selection
4. Conduct 9. 14. write report 19. Demonstrate 24. Apply Social mapping, Comprehend of monitoring explanation of basic tools identifying local life world of results /success technical issues for value resources farmers stories, lessons using local chain learned language approach /avoiding jargons/
5. Develop work 10. Manage 15. prepare 20. trace and 25. plan on weekly, conflicts genuine and resolve Demonstrate monthly and quality misunderstandin Soil annually basis performance gs conservation reports on works monthly, quarter and annually basis Source: Own development based on work of Suvedi and Kaplowitz (2016) and Amhara National Regional State Council (2015)
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2.2 Theoretical Perspectives
2.2.1 Competence Theory According to this theory, job scarcity is a driving force that pushes people to aspire being professionals by getting appropriate educational qualifications and get employed. However, getting diploma by itself should not be final goal rather a means to grasp necessary competence required to perform tasks. Educational institutions should equip students with standards in working situation (Mulder 2014).
According to Grant et al (1979), this disconnection between education and the labor market was the main cause of the competence movement which helped to educational institutions to rework their curricula to adjust themselves with hiring organizations and demands of society. The theory influenced higher education institutions that started to implement competence based education programs mainly in USA in late 1970s. They redesign their curricula in the direction of competence-based education. It was spreading in health, teaching, management and welfare professions at that time.
Grant et al (1979, P6) defined competence based education as follow:
“Competence-based education tends to be a form of education that derives a curriculum from an analysis of a prospective or actual role in modern society and that attempts to certify student progress on the basis of demonstrated performance in some or all aspects of that role. Theoretically, such demonstrations of competence are independent of time served in formal educational settings”
It shows the importance of competence in relation to performance, motivation, assessment, and performance improvement and education innovation which are all essential in establishing professional and practice-based learning of good quality.
Other scholars like Boyatzis (1982), Rosier (1994), Hampden & Tropenaars (2000) also developed core competency models for different professions that gave wider overview on core competencies needed under each profession. They emphasized that without competence, professionals would not be able to effectively function in their professional situation. But it does not mean that the focus is on the job rather on the person who use the competence both for effective performance and for effective relationship with the environment as specific actions and behavior are intersection of the competencies of the
14 | P a g e individual, the demands of the job, and the environment of the organization. According to proponents of the theory, competence is not limited on knowledge and skill required to perform a certain job rather it relates with overall characteristics of an individual who is expected to have effective interaction with the environment.
Competence Theory’s Point of View on Curricula of Higher Educations
Advocates of the theory emphasize that competence should be seen as being able to effectively interact with the social and intellectual environment and education institutions should be prepared for that. They argued that curricula need to be adjusted to essential competence domains, teaching and learning materials need to be in place, teaching staffs need to be prepared to implement these curricula, and educational tests need to be aligned to the curriculum and competence (McClelland, 1973; Grant et al, 1979; Karbasioun, Mulder and H. Biemans, 2007).
McClelland (1973) criticized the way educational institutions test education more for intelligence than for competence. He argued that intelligence tests do not predict job success. In addition, the work of Rosier (1994), also described the need of competence- based curriculum development in undergraduate colleges of various professions. They advocated for competence based education that demands a curriculum from analysis of prospective or actual role in modern society.
Competence Theory in Agriculture Extension Profession
As mentioned above, the concept of competence influenced the development of competence-based education across higher educations and professional learning. It was applied in various professions such as health, management, welfare, finance, information system, sales and marketing in USA and Europe since 1970s (Mulder, 2014).
In addition to the above listed professions, Karbasioun (2007) applied competency based approach to generate competence profile for agricultural extension professionals in Iran. He argued that various publications on competence-based trainings in other professions sparked interest of competence-based concept in vocational education and training (VET) including agricultural extension. This idea comes from the expectation that VET should enable learners to acquire the kinds of competencies needed for their actual professions. According to Karbasioun (2007), competency job profile describes the set of competencies particular to extension professionals that includes underlying characteristic of an employee
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(e.g. motive, trait, skill, aspects of one’s self-image, social role, or a body of knowledge) which results in effective and superior performance in a job. According to the researcher, competency modeling research contributed to uncover intrinsic and extrinsic difficulties that agriculture professionals face in the operation of their career. He also discussed evidences that indicated the need to improve their competencies in order to give effective output delivery.
2.2.2 Job characteristics theory Job characteristics theory started to emerge in 1960s and 1970s as response to the then attitude about the job that assumed jobs should be simplified in order to maximize production. The attitude was subjected to highly routinized and repetitive tasks which created work dissatisfaction. Due to this it was proposed that jobs should be enriched in ways that boosted motivation. This viewpoint laid to the emergence of Job Characteristics Theory.
Hackman and Oldham (1980), the proponents of the theory, came up with five core job characteristics. They are skill variety, task identity, task significance, autonomy and feedback that are believed to have effect on work related outcomes namely internal motivation, job satisfaction, performance quality, absenteeism and turnover. According to theorists, employees come to personal and work related outcomes through three psychological states that are experienced meaningfulness of the work, experienced responsibility of the outcome of the work, and knowledge of results of the work activities. Latter, they have added three moderators namely; Growth Need Strength, Knowledge and Skill, and Context Satisfaction that moderate the links between job characteristics and psychological states as well as between psychological states and the outcomes. Furthermore, the theory indicated the direct effect of job characteristics of employee’s work related attitudes and behaviors as well as individuals differences in need for development.
Most importantly, in addition to the theory, Hackman and Oldham created two instruments (Job Diagnostic Survey and Job Rating Form) that serve as assessment tools. Job diagnostic survey measures jobholders’ perceptions of the five core job characteristics, their psychological states, their growth need and outcomes while job rating form designed to assess perceptions of external observers such as supervisors. They have promoted work design should be done based on empirical findings. That means any shift or modification
16 | P a g e in a certain job setting should be backed by scientific evidences than to be mere decisions. The following figure presents Hackman and Oldham’s Job Characteristics Model:
Figure 1: Job Characteristics Model
Source: Hackman and Oldham (1980)
Important Variables of the Theory As mentioned above the theory created four related models (core job characteristics, critical psychological states, outcome and moderators) that have their own important variables. Core Job Characteristics As indicated above, there are five core characteristics of job. They are:
1. Skill Variety: it refers to the degree to which variety of skills required from the worker to perform variety of activities in certain job. The theory advocates the jobholder feel more meaningfulness in job that requires different skills and abilities than when the job is specific and routine.
2. Task identity: the degree to which the job requires the jobholders to identify and complete visible outcome from series of work. Employees give credit to the relevance of their job when they involve in entire process (or significant amount of the process) than just being responsible for piece of the work.
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3. Task Significance: to what level the job affects other people’s lives. The influence can be either in the immediate organization or the external environment. Workers feel more meaningfulness in a job that substantially improves well-being of others.
4. Autonomy: The degree to which the job provides the employee with significant freedom, independence, and option to plan out the work and determine the procedures in the job. In such cases, the jobholders experience greater personal responsibility for their own successes and failures at work.
5. Feedback: The degree to which the worker has knowledge of results. This is clear, specific, detailed, actionable information about the effectiveness of his or her job performance. When workers receive clear, actionable information about their work performance, they have better overall knowledge of the effect of their work activities, and what specific actions they need to take (if any) to improve their productivity.
Critical Psychological States
1. Experienced Meaningfulness of the Work: The degree to which the jobholder experiences the work as intrinsically meaningful and can present his or her value to other people and the external environment.
2. Experienced Responsibility for Outcome of the Work: The degree to which the worker feels he or she is accountable and responsible for the results of the work.
3. Knowledge of Results of the Work Activities: The degree to which the jobholder knows how well he or she is performing.
Moderators
1. Growth Need Strength: it is the strength of employee’s need for personal accomplishment, learning and development. It has dual purpose in moderating the relationship between core job characteristics and psychological states as well as between psychological states and outcomes.
2. Knowledge and Skill: it refers the level of knowledge and skill the worker possesses which can moderate between job characteristics and outcomes because adequate knowledge and skill lead to experiencing the critical psychological states and better
18 | P a g e outcomes. On the other hand insufficient knowledge and skill discourage the psychological states and result in more negative outcomes.
3. Context Satisfaction: The context of the job also affects employees’ experience. The theory suggests that when workers are satisfied with the context (things around them) such as their managers, salary, co-workers, and job security, they respond more positively their jobs and less positively when they are not satisfied due to the undesirable work context.
Generally, proponents of the theory (Kini and Hobson cited in Yohannes, 2009) the theory recommended the following to hiring organizations and managers in order to enhance motivation and performance of their employees: - Providing their employees with a variety of skills to perform the job - Providing some sort of autonomy to employees to do their tasks - Provide constant feedbacks to employees In summary, the two theories (competence theory and job characteristics theory) are adopted for this research because they give important view points on major components of this study. Competence theory helps to see whether what agricultural training institutions teach align with actual job responsibilities of development agents, how colleges and hiring organization define competence of development agents, their perspectives on what makes a good development agent, how they identify competence gaps, and methods they apply in order to revise their curriculum to fill identified gaps. Job characteristics theory gives additional insights from characteristics of job viewpoint. It helps to identify variety of skills that development agents should possess, to review their tasks, to check whether development agents receive clear and detailed feedbacks from their supervisors and to the effect of feedback on their competence and motivation.
2.3 Emerging Thoughts in Agriculture extension that affect curricula of training institutions There are new thoughts in agriculture extension that promote reinvention of the roles of extension programs and curricula of training institutions in order to adopt emerging problems and challenges that farmers and agriculture sector are facing. Globally, the scope of agricultural extension services has been widening due to the growing need to adapt changing contests. Agriculture sustainability, globalization, market liberalization, environmental degradation, development of new approaches are some of the factors that require change in extension service in general and roles of extension agents in particular.
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Leeuwis (2004) argued that there is a need to rethink agriculture extension due to the challenges that farmers are facing. Some of the challenges such as sustainability, ecosystems and natural resource management issues are arisen more recently while other challenges like food insecurity, poverty and low income have been with us for long time. The author explained complexity of challenges by showing two contradicting realities; in one hand, the demand for food will likely to increase significantly in the near future due to world population growth and on the other hand, there is degradation of natural resources. Environmentalist and ecologists raised concerns such as soil degradation, erosion, water pollution, excessive use of chemicals, waste of water, decreasing ground water tables, destruction of natural habitats for wildlife, and limited animal welfare. This had led to a call for agriculture to become less exploitative and more ‘sustainable’, which means that agriculture will have to be carried out to make the best use of available natural resources and inputs, and regenerate conditions for future production (e.g. soil fertility, resilience of the ecosystem, water availability).
There are different schools of thought on the specific technical, social, economic and ethical criteria and characteristics that should be used to assess and describe sustainable agricultural practice. However, regardless of using different parameters by different scholars, sustainable agriculture and natural resource management represent important challenges for primary agriculture, agro-industries and service institutions. According to Roling and Wagemakers cited in Leeuwis (2004), sustainability goes beyond biophysical and ecological terms because the state of hard system depends crucially on interactions between multiple human beings which they called it the soft system. They raised illustration of hydrological state of water catchment area as example. One cannot properly understand water catchment area in hydrological terms only without taking into account the practices of water users such as their way of irrigate their land, make wells, plough their land, water laws and regulations and other social and organizational circumstances. Thus, when one wishes to improve the situation of water catchment area from a sustainability point of view, it will require activities that improve not only the hard system but also soft system.
According to Roling and Jiggins (1998), at local level, sustainable agriculture requires different types of agricultural knowledge from that previously developed by research institutes and disseminated by extension organizations. Sustainable agriculture require farmers to manage and co-ordinate ecological processes and cycles carefully. In crop-
20 | P a g e protection, for example, it is no longer sufficient merely to apply a number of preventive sprayings according to a standard recipe. Instead, a balance must be maintained between pests and their natural predators, and keeping the ecosystems in which the latter exist. The management of this kind of balance requires that farmers have a good insight into complex ecological processes and interconnections. In short, the nature of the requisite knowledge could be described as complex, diverse and local. Much of this knowledge is not readily available and needs to be developed and/or adapted ‘on the spot’ with close co-operation between farmers, researchers and extension change agents.
Generally, scholars like Leeuwis and Roling argued that, if agricultural branches are to become more sustainable, farmers and other stakeholders will have to take into account and link inherently complex knowledge regarding both global and local processes and circumstances. The emergence of new practices and forms of co-ordination depends in essence on joint learning and negotiation between stakeholders.
Globalization and market liberalization are other factors that require adaptation in extension systems and competency of development agents. Due to huge changes in communication and transport technologies, the exchange of goods, people and ideas has become much easier and more widespread than before. Even the most remote rural areas often have numerous direct or indirect connections with the wider world economy. As argued by neoclassical economic theory, free market has become most efficient means to allocate scarce resource. International institutions such as WTO and IMF also promoted principles of free market economy by influencing national policies and multinational trade agreements. The emerging world market provides both constraints and opportunities for agriculture in developing countries.
Such types of changes in local and global contexts demand competent human resources in agricultural extension services with updated knowledge and skills. Extension professionals should possess core competencies such as knowledge, skills, attitudes and behaviors that help them attain excellence in their professions and to adapt new changes in agriculture. Training and education centers also have significant impact in equipping extension agents with core competencies. Therefore, agricultural training institutions themselves need to be competent in teaching the core competencies that extension students need (Suvedi and Ghimire 2015).
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2.4 Changing Roles of Extension Agents Agricultural systems and practices are changing across the world, and producers’ needs are changing, too. Awareness of farmers on new technologies and improved practices is increasing significantly throughout the world. They demand quality services such as credit, quality seed, and access to market information to be demand driven. These challenges put pressure on extension professionals to be more knowledgeable, skillful and able, not only in technical subject matter but also in process skills (Suvedi and Kaplowitz 2016). Due to this, scholars recommend shifts in roles of development agents that require new skills and knowledge. Some of the changes include:
From teachers to coaches: Habermann and Hassan (2017; P4) described the role of development agents should not be to change the awareness and attitude of farmers that consider farmers have little expertise knowledge. They promote the role of development agents to shift from teaching coaching so that they guide farmers to discover their own potential that lead them to live more autonomy and responsibly.
Being Brokers in era of Participation: Development agents are expected to serve as mediators between government organizations (or donor NGOs) and the farmers. This requires considerable creativity and skills as they juggle sometimes with contradicting interests. On one hand they have to fulfill interests of their employers which can be certain type of development program or innovation. And on the other hand, they have to work and maintain credibility with their immediate clients or farmers who may have different priorities (Hilhorst 2000). Another paradox is participation approach followed by many countries. It starts from the idea that people are capable, knowledgeable and active. But in reality, most participatory projects are dominated by outsider experts (Leeuwis 2004).
Communication has vital role: communication process in general and communication skill of development agents in particular is another important factor emphasized by different scholars. According to Habermann and Hassan (2017), communication in general takes place in two levels. The first level is about facts, procedures and contents which contain 30% of communication. The second level is about emotions and relationships which involves 70% of the communication. Thus, the largest part (70%), of our communication takes place on the second level of communication which includes processes, feelings, nonverbal behavior, way of speaking, etc.
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This implies that one should go beyond contents and procedures in order to disseminate certain information. Scholars argue that development agents need comprehensive skill of communication that considered not only the subject matter but also nonverbal behavior, tone of voice, speed of speech, and others that are related with feelings and emotions.
Similarly, Leeuwis (2004) argued that although development agents play different roles as development workers, marketing facilitators, communication managers, trainers, mediators and process facilitators, their work mainly centers on the deliberate use of communication to stimulate change. Because of this, authors prefer the nomenclature to be communication specialists or communication workers than extension workers.
2.5 Empirical Studies Different studies were conducted globally on agricultural colleges and development agents including their competency, job performance, motivation, and roles of agricultural training institutions. However, studies on national level focused on evaluating factors affecting adoption of new varieties or technologies and other impacts of agriculture extensions. Regarding development agents, studies were focused on work motivation, job satisfaction and performance of DAs. There was no study dedicated neither on curricula profile of training institutions as well as competency of DAs. This made it difficult to present empirical evidences that show national context of training institutions and competency of development agents. Therefore, studies conducted on job performance and motivation of DAs in Ethiopia and competency and curricula related studies conducted in other countries are presented below.
2.5.1 Curricula of Training Institutes Agricultural Education and Training provides a range of educational activities with the primary aim of achieving human resource development throughout the rural economies of almost all nations (Wallace and Nilsson, 1997). Similarly, Agricultural Technic, Vocational and Training (ATVET) Colleges in Ethiopia have also similar responsibilities in covering the learning needs of agriculture extension systems. Wallace and Nilsson (1997) conducted the role of agricultural education and training in improving the performance of support services for the renewable natural resources sector in sub-Saharan Africa contexts. They indicated that recent researches in sub-Saharan Africa were able to generate a number of successful innovations but couldn’t attain their goals because agricultural education and training were unresponsive to change their patterns. After reviewing Agricultural Education and Training (AET) institutes in Kenya, Malawi,
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Mozambique, Zimbabwe and Uganda, they have indicated that institutes are not able to catch up to the rapid changes occurring in rural economies. Factors for such failure include lack of strong policy framework, failing of donor and government support, disruption of training programs, loss of experienced teaching staff, and low recruitment of women as teachers and trainees.
They forwarded things to be done to improve the design and management of AET, and to strengthen the policy framework through which support and direction are channeled. There is also a need to enhance the interactions between AET institutes and the formal schools sectors, as well as AET institutions’ linkages with local communities, NGOs and other intermediary organizations. In this case, research results can be easily disseminated and new ideas (including new skills and knowledge DAs need) can be easily incorporated.
Wallace and Nilsson (1997) identified five most common problems that exist in most agricultural training institutions. The firs is within many countries there is a lack of a clear policy framework for AET and inadequate mechanisms to coordinate the several agencies involved, particularly the ministries responsible training and for education. Second, AET institutions are often isolated from extension and research services, and from rural communities themselves. Third, identification of training needs (including rural labor market studies) is often lacking, or the results are not fed into curriculum design processes. Fourth, curricula rarely adjust to emerging issues (e.g. sustainability, gender, farmer participation in research and extension, changing career patterns), or to local variability. Participation in curriculum review and the evaluation of training by key stakeholders (including researchers and extension workers, farmers, agribusinesses) is still uncommon. And fifth, many institutions lack the entrepreneurial leadership necessary for improvement.
The above critical findings clearly indicate that in sub-Saharan Countries, training institutions face problems such as lack of policy frame work, being isolated from research services, leadership inefficiency and curricula related problems. In order to mitigate these problems and to make training institutes to meet emerging requirements, Wallace and Nilsson (1997, P3) forwarded recommendations such as offering more transferable skill, providing part-time farming and rural enterprise niches, incorporating new perspectives and reaching more vulnerable groups.
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As indicated under new thoughts section of this research, the recommendations forwarded look very critical in equipping agricultural colleges respond to emerging roles of development agents. In addition to training institutions, the following improvement areas were also recommended by the scholars:
Clearer policy frameworks for AET are needed in most countries, to provide for coherence between government bodies (including Education ministries), donors, NGOs, training institutions and community organizations. Acceptance of new learning paradigms is essential to permit the incorporation of indigenous knowledge, more holistic and multidisciplinary approaches to problem solving and greater emphasis on experiential learning. Existing/new curricula need to be reformed through involvement of all the key stakeholders, including `client' groups, and by embracing issues such as gender, environment, sustainability and participatory development. AET can be strengthened by the adoption of new learning modes and mechanisms, including various forms of distance learning, reformed library services and applications of IT. The results of research also need to be fed more strongly into AET institutions. The paper emphasized that curricula should be responsive to the changing demands through active participation of stakeholders during curricula development including local farming communities. Curricula development should be viewed as an ongoing process with regular evaluation and feedback from ex-trainees and periodic reviews in order to deliver relevant up-to-date knowledge and skills. In current society skills such as entrepreneurial ability, language competence, use of computers and IT, communication and management should be included in curriculum of agricultural training colleges. Gender is another priority that needs to be included in the curricula reforms because women are underrepresented in AET and extension (ebid).
Garton, Dyer and Ball (2002) studied criteria set by agricultural colleges for admission as predictors of students’ academic performance. They have investigated admission criteria as possible predictors of academic performance and retention. The criteria include examination, high school core grade point average (GPA), and high school class rank. In addition, students’ preferred learning styles were investigated as a possible predictor of academic performance and retention. The study found that substantial positive correlation
25 | P a g e between high school GPA and academic success agricultural colleges measured by freshman cumulative GPA (r=.61), with high school class rank (r=.52) and moderate correlation with entrance exams (r=.47).
2.5.2 Competence Level of DAs After conducting researches in Cambodia, Nepal, Malawi and India, Suvedi and Kaplowitz (2016) tried to determine the essential competencies that effective front-line extension workers should possess. They classify responsibilities of agricultural extension workers in to two categories as process skills (also called functional competencies) and technical skills. Process skills are related with competencies needed to function and establish positive relationship with clients such as networking with local organizations, facilitating group formation, resolving conflict and engaging stakeholders in program planning. On the other hand, technical skills refer to competency areas directly related with knowledge and practice of the science. For instance, identifying the causal organism of maize disease, testing the soil acidity and interpreting the results, and conducting a method demonstration on how to perform artificial insemination on dairy cattle are examples of technical competencies. A good development agent needs to possess both process and technical skills. That means, being knowledgeable and/or intelligent only does not indicate that a person is an effective and efficient worker as worker’s performance is a function of his/her knowledge plus skills and attitudes. Hence, extension professionals should not be judged solely on how knowledgeable they are in their technical subject area of expertise but on how skillful and able they are in delivering services to their clients. The scholars identified four competencies that development agents should acquire which are observation, communication, conflict resolution and human how skills. Suvedi and Kaplowitz (2016) identified 38 competencies under the core competence areas which are program planning, program implementation, program evaluation, and communication and information technology.
Similarly, Seevers, Graham and Conklin (2007) came up with the term “core competency” to describe the basic knowledge, skills, attitudes and behaviors that contribute to workers’ excellence in their respective professions (e.g., extension education and extension services). Likewise, Cooper and Graham, 2001; Scheer et al., 2006 identified nine areas core competences that can adequately address the needs of demand-driven, decentralized, pluralistic and participatory extension systems. They are planning, Coordinate and
26 | P a g e collaborate to implement, communication, Good public relation, value diversity, use of ICT, evaluation to show results and update knowledge.
Ghimire (2016) conducted study to assess core competency of agricultural extension professionals in Nepal. He identified 8 core competencies namely;
1. Program planning 2. Program implementation 3. Communication skills 4. Educational and informational technology 5. Program evaluation 6. Personal and professional development 7. Diversity 8. Technical subject matter expertise He collected data from 344 extension workers and he was able to determine their competence level by using coefficient variation technique. He was also able to compare results of each competence area to identify competence areas with highest and lowest scores. In addition, the researchers also showed differences in level of competencies among demographics. For example, extension organizations in Nepal classified in three organizations (Department of Agriculture, Department of livestock, and NGOs). And one- way analysis of variance (ANOVA) was calculated to examine the differences in level of competencies among respondents from these three organizations. Similar comparisons were made among people with different education levels (Ibid, Pp 138-140).
2.5.6 Motivation of DAs When we see Ethiopian context, there was no any research conducted on competence level of development agents as previous studies focused on their performance and motivation. As the studies themselves indicated, performance is output or result which is affected by both internal and external factors. Performance of development agents may be affected by factors outside of their control such as being overburdened over nonprofessional assignments and lack of basic materials to perform their job (Yohannes, 2009; Lakew, 2011; Belay et al, 2012). In other words, well competent DAs are also subjected to these factors. So it was difficult to review empirical studies that are focused on competency of
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DAs due to this those studies on performance are discussed below to get some overview on study topic.
Yohannes (2009; pp41-43) studied work motivation level of development agents in southern region of Ethiopia by using five points Likert-type scales. He find out that most (57.1%) of development agents have medium level of motivation followed by 22.7% with low motivation while the remaining 20.7% of DAs were highly motivated. Belay et al (2011; p22) also conducted similar research by taking respondents from all regions that helped to see national level picture of development agents’ motivation. The research indicated that 29.3% of DAs had low motivation while 67% fall in medium category and only 4% were highly motivated.
In determining performance level of development agents, works of Lakew (2011) and Belay et al (2012) showed that majority of development agents had medium performance level. Lakew (2011; p29) studied performance of development agents in Eastern Harerege of Oromia region and 23.5% were in low performance while 51% medium and remaining 25.5% were highly performing. Whereas in Belay et al (2012) study, 9% were low in their performance while 58% were medium and remaining 33% had high performance level.
2.5.3 Factors Affecting Competence and Motivation of DAs Study conducted by Khalil (2008) to determine the relationships between competencies, organizational commitment and job satisfaction with job performance of extension workers in Yemen. The results of their study revealed a positive relationship between leadership development competency and extension workers’ job performance. They further reported program evaluation competency predicts extension workers’ job performance. Some research found interpersonal skills to be the best single predictor of job performance ratings (Wayne et al., 1997). Similarly Ferris et al. (2001) reported that social skills as the single strongest predictor of performance rating dimensions of task performance, job dedication and interpersonal facilitation, as well as for an overall rating of performance.
In Lakew (2011) study, factors that affect job performance of development agents include agricultural background, marital status, distance from home to work area, quality of supervision, interpersonal relationship, working condition, recognition and the work itself. According to his research, physical working condition and the facilities around the
28 | P a g e working area had positively and significantly affect the performance of DAs at less than 1% significance level.
Regarding determinants of motivation, Yohannes (2009) found out variables such as advancement, recognition, attractive salary, the work itself, fair organizational administration, achievement, and perception on distance from home as independent variables that affect work motivation of development agents. According to the study, three most significant independent variables found to exert relatively high influence or significantly influence work motivation of DAs were recognition, the work itself and organizational administration.
2.6 Conceptual Framework This research examines the level of core competencies and determinant factors among agricultural development agents in Ethiopia. Similar previous studies tried to assess performance level of extension agents determined by other factors as well such as additional nonprofessional assignments given to DAs, absence of transportation, and lack of resources. But this research completely grounded in the competency based approach to human resource management which indicates gaps in core competencies so that they can be improved to enhance organizational outputs.
This study also relates competency of DAs with curricula of training institutions arguing that education programs in training institutions should be designed to equip DAs with integrated sets of knowledge, skills and attitudes which are necessary to effectively deliver expected job results. They should have both process skills and technical competencies. In addition, training institutions should adapt changing roles of DAs due to changes in agriculture system by incorporating new knowledge and skills in their education programs. This requires assessing the competency of development agents and conducting curriculum revision periodically. Such trend is important to identify gaps in competency and help to design training and education to address identified gaps.
Therefore, this research was designed to assess profile of agricultural training colleges (including selection process of DAs) as well as development agents’ perceived responses of their competency level by assuming they are able to articulate their level against presented competence areas. Based on this, as illustrated in Figure 2, admission process of agricultural colleges, what DAs learn in agricultural colleges, how they get hired, and demands of agriculture system (and farmers) should align each other. Competencies can
29 | P a g e be developed through education and training. When conducted effectively, training and education can make individuals competitive and their services efficient. Therefore, it is important to assess effectiveness of education programs. In-service and pre-service training are keys to producing competent agricultural extension professionals who are capable of addressing extension problems. For this to happen, education and training should be tailored to per field needs.
Figure 2: Framework of Relationship among colleges, work and changing environment
Source: Own description
The figure illustrates how admission process, education programs and work environment are related to one another in preparing development agents with core competencies. It enables to see the whole picture of issues interrelated with competency of development agents. The admission process refers to selecting best qualified people who are ready to acquire basic knowledge, skills and attitude required by the job. Moreover, education programs of agricultural colleges and what DAs actually expected to perform after graduation should align each other. And both colleges and work environment should adapt to the changes happening globally and in local contexts that affect agriculture system.
In addition to the above own analytical framework, the one developed by Ghimire, Survedi, Kaplowitz and Richardson (2017), was also applied as conceptual framework for
30 | P a g e competency assessment. It emphasize on the development and assessment of core competencies is learning process that helps organizations a standard for training, development, and learning activities for development agents to prepare for the future, adapt to changes, and makes service more efficient. As shown in next figure, scholars developed stage that follows seven cyclical steps to be followed in order to improve competency level of extension professionals. Identifying gaps in core competencies is the next step based on empirical evidences which again enables to update curricula and provide training with ultimate objective of strengthening extension services.
Figure 3: Conceptual framework for competency assessment
Source: Ghimire, Survedi, Kaplowitz and Richardson (2017)
The figure illustrates comprehensive seven cyclical stages to be followed from core competency identification up to extension services strengthened. However, this research is based on the first three stages anticipating that it will be input to concerned stakeholders who are responsible to the rest of the stages. The findings of this study will lead to identifying ways to acquire core competencies by revising and updating pre and in-service extension and training curricula. In addition, such type of periodic review of the extension programs would help identify new core competency areas that need to be addressed, which completes the cycle.
Finally, another framework was formulated that enables to see relationship independent variables and dependent variables together. It was developed based on previous empirical
31 | P a g e study of Belay (2011 and personal observation. As indicated in the below figure, institutional, personal, and psychological factors are assumed to have impact on work competency and motivation of Development Agents.
Personal Factors Psychological Factors - Sex of DAs - Age of DAs - Work experience in current kebele - Work experience - Transfer opportunity - Education level - Advance education - Passion to agriculture profession Psychological /Attitudinal Factors - Job significance /brought change - Relationship with coworkers - Training opportunity - Attitude towards farmers - Recognition - Promotion Institutional Factors - Attitude towards farmers - Perception on salary - Type of training institutes DAs attended - Work condition - Supervisor’s visit & support
- Selection criteria and recruitment - Relationship with coworkers process - Relationship with farmers - Job Position - Supervisor’s visit - Comment /feedback of supervisor
Work Motivation of DAs Six Core Competence Areas of DAs
Source: Own analysis Figure 4: Conceptual Framework
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3 RESEARCH METHODS
3.1 Description of the Study Area East Gojjam is one of zonal administrations of Amhara Regional State located in northwestern parts of Ethiopia. It shares border with Oromia Region in the south, West Gojjam Zone in the west, with South Gondar Zone in the north, and with South Wollo Zone in the east. The bend of Blue Nile River defines its eastern and southern boundaries. It covers an area of 14004 square kilometers and it is divided in to 16 district (locally called Woreda) administrations. And the zone’s total population is estimated 2.4 million people (CSA 2017). Debre Markos is zonal capital town located 300 kilometers from Addis Ababa. This research was conducted in four districts /woreda/ of East Gojjam Zone. They are Gozamen, Debre Elias, Machakel and Basoliben.
Debre Elias is one of the districts in East Gojjam zone which is located 342 kilometers to the North-west from the capital Addis Ababa and 42 km from zonal capital, Debre Markos town. It is bordered by Gozamen district to the east, Denbecha town to west, Machakel district to the north and Oromia region to the south. Total land area of the district is estimated about 112961.89 hectare. The district is divided into 16 Kebele administrations. The attitude goes from 700 to 2217 meters above sea level and annual rainfall is 1500 mm. As far as agro-climate zone is concerned, 51% of the area is classified as kola and the remaining 49% is woinadega. The total population of the district is 99,259 people (male 49213, female 50046) from which 84802 people (85.4%) live in rural areas (Debre Elias Woreda Administration 2016).
Gozamen is located in a geographical location of 10°1′ 46″ and 10° 35′ 12″ N latitudes and 37° 23′ 45″ and 37° 55′ 52″ E longitudes and at a distance of 300 km from Addis Ababa. The district is bordered by Aneded and Debay Tilatgin in the East, Machakel and Debre Elias in West, Sinan district in North, Baso Liben district and Abay River in the South. It has an altitudinal difference of 1200-3510 meter above sea level with three agro-climatic zones namely; Dega, Woina-dega and Kola. The maximum and minimum average temperatures are 25°C and 11°C respectively and the average annual rainfall is 1628 mm. Agriculture is the mainstay of farmers in the district which is characterized by mixed crop and livestock production systems. The most important crops grown in the district are cereals like wheat, teff, maize, barley and oats; Pulse crops such as horse beans and
33 | P a g e chickpeas are produced and Oil seed crops. The district is divided in to 25 Kebele administrations and the total population is estimated about 149,498 people.
Machakel district was another study area located at the distance of 25 kilometers from Debre Markos. It has 24 rural and one urban kebele administrations. Amanuel is the major town of the district. The total area of the district covers 2250 square kilometers and the altitude ranges between 1200 to 3200 masl. The agro ecology classified in to three zones where 50% is midland, 48% as low land, and the remaining 2% as highland. The total population is estimated around 130,898 people.
Baso Liben woreda is located in southern most point of East Gojjam zone. It is bordered by Abay River in the south (which separates it from Oromia Region), Gozamen district in the northwest, Aneded district in the northeast. The district has total population of 138,332 people with area of 1118.56 square kilometers. And Yejube is the major town of the district.
East Gojjam Zone
Four Districts
Figure 5: The First one, Map of Ethiopia Showing the Relative Location of East Gojjam Zone and the second is districts of East Gojjam Zone
Source of Maps: World Map
3.2 Data Type, Source and Methods of Data Collection Different procedures were applied in this research to collect primary and secondary data by using qualitative and quantitative data collection methods. Primary qualitative and
34 | P a g e quantitative data were collected from development agents, Zone and Woreda agriculture office extension work unit leaders, and training institution department heads. Primary quantitative data were collected from sampled development agents regarding the variables hypothesized to have influence on competence of DAs including education background, work experience, transfer, education-work relatedness and other factors using self- administered questionnaires. After the questionnaire was developed, it was translated in to Amharic in order to make it easier for a self-administrative survey. The questionnaire includes self-rating questions where DAs rated their competency and motivation levels using scales. Then, pre-testing was conducted on 19 randomly selected DAs in Gozamen district, and the questionnaire was refined to avoid ambiguities, redundancy and other technical editorial issues.
The survey was conducted in Gozamen, Debre Elias, Machakel and Baso liben districts. The researcher explained the objective of the research and gave page by page orientation to sampled DAs on how they could respond to structured questionnaire. The researcher then stayed around to clarify any ambiguities until they had finished.
In addition to quantitative data, qualitative data were also gathered from DAs, ATVET teachers, district and zone agriculture offices. Key informant interviews and FGDs were used to gain qualitative data on the current curricula, the actual practice of teaching and possible improvements of the education system for DAs. Furthermore, these data were useful to reach and in-depth understanding of the work situation of DAs including their responsibilities, expected competencies, and major challenges.
Three FGDs were conducted with DAs who were selected using purposive sampling. FGDs were needed to understand their perspectives on what makes a good DA, how their knowledge and skill contribute to perform their jobs. FGD checklist was developed in advance which consist of questions and discussion topics. Female and male group discussions were held separately because females may not be comfortable to express their ideas freely while males are in the same room due to cultural reasons. Rather sitting around the table and discuss issues, more flexible approach was applied where each participant was given flip chart and stick-notes to write her/his ideas and share their views to the rest of participants by posting on the wall. The process helped the researcher to ensure active participation of all participants.
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Key informants were defined based on their job positions’ relation with the topic under study. The researcher selected agricultural colleges’ academic deans and head of departments as resource persons for interview questions on curricula of agricultural colleges. In addition, agricultural extension experts and unit heads in zonal and district offices were considered to be resource persons regarding competence and motivation of DAs. Then, key informant interviews were conducted to get exhaustive information from department heads in colleges and district agriculture office extension unit heads. Open ended questions were used during the interviews. Up on consent, interviews were tape recorded.
For secondary data, extension related reports at district and Kebele levels, hiring and benefit guidelines, Efficiency Evaluation Reports (EER), and curricula of training institutions were reviewed. The primary data were used to measure perceived competence level of development agents in terms of the core competence areas. The information from district agriculture offices was used to assess the selection and hiring processes used to hire development agents, as well as to understand the current status of incentives and the impacts that they have brought. Finally, data from training institutions were used to see the strengths and weaknesses of agricultural colleges in order to equip development agents with both technical and process skills.
3.3 Sample Size and Method of Sampling Two stage sampling procedure was used to identify study areas and respondents. First the four districts of East Gojjam zone, namely Gozamen, Machakel, Baso liben and Debre Elias were selected purposively as they are pilot centers of ICARDA’s Improving Agricultural Extension Systems for Wider Adoption of Technologies Project. The 3 ATEVETs Kombolcha, Woreta and Mertolemariam were also selected purposively because most DAs graduated from these colleges. Then nonprobability sampling techniques were adopted for qualitative research while a probability sampling technique was used for the survey.
Non probability Sampling
For qualitative researches, the people to be studied are selected based on their relevance to the research topic rather than their representativeness so nonprobability sampling is used in qualitative research (Kreuger and Neuman, 2003). Non-probability sampling techniques were applied to select key informant and focus group discussion respondents. Academic
36 | P a g e vice deans and department heads were purposively selected from Woreta and Mertolemariam Agricultural colleges for key informant interview. Purposive sampling technique helps to reach people with most knowledge and experience on the study topic. Regarding curricula of training institutions, department heads and academic deans were selected as ideal key informants. Department heads from animal science, plant science, natural resource management, and basic science departments as well as academic vice deans were interviewed. In addition, agricultural extension unit leaders from Gozamen, Machakel, and Debre Elias agriculture offices as well as East Gojjam Zone agriculture office agricultural extension expert were selected with criterion of having rich information on the topic. The following table shows criteria used to select participants for FGDs.
Table 2: FGD Participants Selection Criteria no Selection criteria # of participants from two Remark woreda Male Female Total 1 With more than 10 years of work 2 2 4 experience as DA 2 10 month college study 2 2 4 3 2 years college study /diploma 2 2 4 4 3 years college or university study 2 2 4 /degree 5 With less than 1 year of work 2 2 4 experience 6 DA who works in the remotest kebele 2 2 4 7 DAs with experience of 2 to 5 years 2 2 4
Probability Sampling
Probability sampling procedure was used in order to select development agents who participated in the survey. The sampling design provided a road map regarding sampling unit, source list, sample size, and sampling procedures.
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List of development agents deployed in the four districts serve as sampling frame to select sample respondents for the survey. Simplified Yemane formula is used to calculate sample size of development agents. The formula is shown below
n =
Where, n is statistically acceptable sample size, N is population size, and e is level of precision (Israel 2003). Based on this, there are 238 development agents in the four districts and at 95% confidence level, and e = .05, the sample size will be 149. The formula was used due to two major factors. First, it is simple and easy to use and second the sample size determined through the formula was similar with other previous researches conducted on DAs performance, motivation, job satisfaction and time allocation (Yohannes 2009; Belay et al 2012; Kele et al 2014). These researchers selected 100 to 181 development agents as their samples. Finally, 149 DAs were selected by using systematic random sampling technique.
Totally, data were collected from 188 people from whom 149 of them participated in survey while the remaining 39 participated in KI interview, and FGD.
Table 3: Number of respondents under each data collection method
Data Name of districts Names of training institutions Total collection Gozamen Machakel D/elias Baso Zone Woreta m/lemariam methods FGD 18 10 28 KI 1 1 1 1 5 2 11 Survey 37 39 37 36 149 Total participants 188
3.4 Methods of Data Analysis
3.4.1 Qualitative Data Analysis Qualitative data are usually in text forms, written words, phrases, and images that explain people’s action, behavior and social events. There is no single widely accepted qualitative data analysis technique as different disciplines apply different ways of analysis. Qualitative information collected through key informant interview and focus group
38 | P a g e discussion techniques were transcribed and then analyzed by using coding and categorization methods. In doing so, similar ideas were highlighted with same color and will have same code. Based on this, concepts will be generated based on categorization of frequently mentioned ideas. Then, the relationship among concepts was examined in terms of sequence, similarity and oppositional character. Moreover, the data were triangulated with quantitative data and secondary sources.
In order to review training institutions, SWOT (Strength, Weakness, Opportunities, and Threats) analysis was used. According to Austin Community College (2019), a SWOT analysis refers to “a subjective assessment of data that is organized into a four- dimensional SWOT matrix, similar to a basic two-heading list of pros and cons.” This enabled the generation of a comprehensive understanding on the strength, weakness, opportunity and threats of agricultural colleges.
The four-dimensions are:
Strength: in what way the institutions excel?
Weakness: What aspects of institutions are not addressed well and will impede their progress?
Opportunities: What factors are taken as advantage that might enhance the quality of training institutions in the future?
Threats: what external factors could negatively affect the futures of training institutions?
3.4.2 Quantitative Data Analysis For quantitative data, encoding, processing and analysis of data were done by using SPSS software version 16.0. Descriptive statistics such as frequencies, percentages, means, and standard deviations were used to summarize the data about the demographic characteristics of the respondents like sex, education, and work experience. In addition, Chi-Square test was used to observe differences among the four districts in terms of competency level of DAs. It was also be used to assess the significance of differences among districts.
In addition, the degree of relationship between curricula of different educational programs and core competences possessed by DAs was analyzed by using Spearman’s rank correlation coefficient. The association between these two variables is expressed in a range
39 | P a g e from (-1≤ r≥ 1). It shows both the direction and degree of relationship between the two variables under study. The type of correlation can be either positive or negative. The positive correlation is when the values of the two variables changing in the same direction and it will be negative if the values change in opposite directions. The value of “r” shows the strength of relationship as -1 means there is perfect negative correlation, 1 means perfect positive correlation and 0 means there is no correlation between the two variables. The Spearman’s correlation is compute as: 6 (di) 2 r 1 n(n 2 1)
Where, r is rank correlation coefficient, di is difference of rank between paired item in two series and n is total number of observation. This correlation was also be applied to see the relationship between other independent variables with dependent variable.
Econometric Method
Ordered Logit Model
Due to the ordinal nature of dependent variable, ordinal logistic regression analysis used to assess determinants of competency and motivation of DAs. The response categories are ordered but they do not form an interval scale because although there is some sort ordered ranking among categories, the difference among adjacent categories cannot be treated as the same. For example, in five scale models from very low, low, medium, high and very high, we can’t say the distance between very low and low is the same as low and medium. So, if the dependent variable is non-interval, it cannot be treated with linear regression rather with ordered logit model. The method helps to estimate the relationship between competency levels of development agents with other independent variables.
The ordered logit model specified as:
∑
y* = is the unobserved and thus can be thought of as the underlying tendency of an observed phenomenon. ɛ = is the random term which is assumed to it follow a certain symmetric distribution with zero mean such as normal or logistic distribution. What we do observe is:
40 | P a g e y = 1 if y* ≤ μ1 (=0) y = 2 if μ1 < y*≤ μ2 y = 3 if μ2 < y* ≤ μ3 (2) y = j if μj-1< y* Where y is observed in j number of ordered categories, μs are unknown threshold parameters separating the adjacent categories to be estimated with βs. The general form for the probability that the observed y falls into category j and the μs and the βs are to be estimated with an ordinal logit model is
( ∑ )
In addition to ordinal regression method, multiple linear regression was also conducted for total sum score of competence variable which was considered as continues variable. Then predictor variables having significant effect are discussed.
Multicollinearity and heteroskedasticity tests were performed to check on econometric problems. Contingency Coefficient was used to perform muticollinearity test between independent variables. In summary, the following table summarizes what kind of data collection and analysis methods were administered for each research objective.
Table 4: Research Objective and data collection tool summary
Objective Data collection Tool Data analysis
1. Review recruitment 1. Document review - SWOT Analysis criteria and curricula of 2. Key informant - Data Triangulation Agriculture Training interview Institutions in the study 3. FGD area. 2. Assess actual 1. Survey - Qualitative data analysis competency level of 2. Key informant interview methods agents in in the study area 3. document review - Descriptive statistics
4. FGD 3. Define determinant Survey and FGD Descriptive statistics factors that affect DAs Correlation competency and Orderd logit regression motivation Categorization and
Coding Triangulation
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3.5 Identification of Variables
Dependent Variables:
1. Competence of DAs: As indicated in the conceptual framework, the dependent variable is competency of development agents in the study area. It was described based on core competency areas derived from two major sources. The first one is responsibilities of DAs set by the regional government and the second one is core competencies identified by Suvedi and Kaplowitz (2016). As indicated in the theoretical framework of the study competence comprises vital knowledge, skill, and attitude that development agents should possess to deliver extension system. Six core competency areas were developed and were measured by development agents themselves as perceived competency. Each competency area was rated on a 5 level scale (1= Very low 2= low 3= moderate 4= high 5 = very high) and finally changed in to low, medium and high categories.
Very low: very low refers to the fact that the job holder is unable to do or perform a given task due to the lack of knowledge and/or skills required to perform the given task.
Low means that the job holder possesses only partial knowledge and skills to perform the task. It represents competency level that needs improvement
Medium: refers to the fact that the job holder reaches satisfactory level in most of the competence areas but sometimes may face difficulties.
High: means that the job holder can perform the task without any difficulty and sometimes with excellence /commendable/
Very high: the job holder is doing very well and executes the tasks beyond the expected.
General Competence was also measured as continues variable by using total sum score of all likert-type scales. Based on this the variable is measure from 28 (lowest possible score) to 140). This used to run ANOVA tests and multiple linear regression.
2. Motivation of DAs: it is another dependent variable which was summarized in to three categories as low, medium, and high
Independent Variables:
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1. Types of Training Institutions (Recodedinst): it represents where DAs attended their education and those who attend in government institutions assumed to have better competency level than those who attend in private colleges. Although they assigned to same job position, development agents pass through different education programs. For this research, institutions are measured as “private” and “public” 2. Specialization (specializereco): it is a dummy variable measured whether DAs trained more “general” or “specialized” field of studies. 3. Work Experience of DAs (Experrecoded): it refers to experience of DAs measured in number of years categorized in four groups. They are less than two years, from 2 to 5 years, from 5 to 10 years and above 10 years. DAs with more years of experience expected to have better competency hence the variable is hypothesized to have positive relationship with competence. 4. Age (Agecatego): refers to age of respondent DAs which measured in to three groups. They are 25 years and under, from 26 to 30 years and 31 years and above. Older DAs are expected to be more competent than younger DAs as they acquire process skills from life experience. 5. Sex (SEX): is a dummy variable (takes a value of 1 if the respondent is male and 0 otherwise). The variable is expected to have a positive relation with competency. 6. Education Level (Edurecoded): it is a dummy variable measures education level of DAs measures whether DAs graduated on B.Sc. or level based programs. 7. Experience in one duty station (KAexp): it is continues variable measured on number of years that DAs stayed in their current duty station (Kebele). Those who stayed longer in a single kebele to have high competence and motivation than the rest. 8. Access to Capacity Building Trainings (Opportunity): it is a categorical variable measures access to capacity building trainings that focus on enhancing core competences. 9. Perception on Salary (salary): it measures perceptions of development agents whether they receive fair amount of salary and incentives for the work they do. 10. Relationship with farmers (rlshpfar): it is categorical variable that defines relationship of development agents with farmers from very low to very high. 11. Relationship with coworkers (rlshpcowr): it is categorical variable that defines relationship of development agents with their coworker measures on scale from very low to very high.
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12. Supervisor’s Comment (comment): it is assume clear and objective feedback of supervisor after follow-up will have positive impact to improve DAs competency and the variable measured their perception to what level that supervisors feedbacks are clear and objective. 13. Recognition to good performance (recognition): it is measured how often DAs recognized to their good performance. 14. Supervisor’s evaluation (supervisr): categorical variable that measures how clear and objective the evaluations are.
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4 RESULTS AND DISCUSSIONS This chapter deals with the findings of the study such as results of descriptive, econometrics as well as qualitative analyses relevant to the research questions and objectives. The descriptive analysis was used to describe the general personal and demographic characteristics of the sample DAs including sex, age, place of birth, educational background, and work experience. Mean, ranges, total sum score, and standard deviations were employed to obtain the results. In addition, descriptions of qualitative findings were also presented.
4.1 Demographic Characteristics of Sample DAs The total number of sampled DAs for the survey was 149 and they were selected from four study districts (Baso =36, D/Elias = 37, Gozamen = 37, and Machakel =39). In terms of sex, from 149 respondents, majority (72.5%, n=108) of them were males while the remaining 27.5% (n=41) were females. This figure is close to general population composition of study area that from total 238 DAs who work in the four districts, 71 of them (29%) are female (East Gojjam Zone Agriculture Office, 2018). As shown in below chart, the profession is highly dominated by men in Machakel and D/Elias districts while Baso and Gozamen districts had relatively better representation of female DAs.
Sex Composition of DAs in the Study Area
male female total
39 36 37 37 29 30 24 25
12 12 8 9
Baso Elias Gozamen Machakel
Figure 5: Male and female composition among districts
Source: own survey result
In terms of age, respondents’ age ranges between 20 to 38 years and the mean is 26.4 years (median 26.00, mode 26) while standard deviation was 3.32. Further, the age of respondents was divided in to three groups; 25 years and under, 26 to 30 years and 31
45 | P a g e years and above. Based on this, 38.9% fell in to the first group, 51.7% in the second, and 9.4% in the third category. This indicates that most development agents are in young ages. Similar result was reported by Lake (2011) who conducted research on performance of development agents in Kombolcha district of Oromia region that mean of age was 25.85 years. Similarly, in Yohannes’s (2009) work done in SNNP region, the mean age of development agents was 29 years.
The results mentioned above indicated that most development agents in the study area are in younger ages and it is different from statistics of other countries. For example, in Nepal, according to Ghimire (2016), 79% of extension professionals were above the age of 35 years from which 43.9% were even above the age of 51 years. This shows most of them were adults who lead more settled life inside the community acquiring life experiences. In addition, study conducted by Awang (1992) in Malaysia indicated that from 361 extension professionals, only 8.1% of them were between 26 to 30 years (no one indicated and age of 25 and younger) whereas about 61.3% of them were above 36 years. Similarly, Agunga (2017) conducted similar study in nine African countries including, Ghana, Tanzania, Botswana, Cameroon, Senegal, Malawi, South Africa, Uganda, and Nigeria where most extension workers were predominantly middle-aged who worked for more than a decade.
Table 5: Socioeconomic characteristics
Socioeconomic Characteristics (n=149) N % Work Experience in < 2 years 40 26.9 Extension 2- 5 years 67 45 5-10 years 33 22.1 >10 years 9 6 Highest Education BSC 48 32.2 Level Based 101 67.8 Type of college or Private college 21 14.1 university Public/government 128 85.9 Current Position Crop production 53 35.6 Animal Science 38 26.5 Natural Resources 45 31.2 Irrigation dev’t 13 8.7 Birth place of DAs Rural 128 85.9 Urban 21 14.1
Source: own survey result
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Regarding highest education, in addition to the above classification as BSc and level based, their educational background was further classified in to 5 education levels namely; BSc Degree, Level 3&4 or Diploma, Level 1 & 2 or Certificate and Short Term Training (3to 7 months). Based on this, from total 149 sampled DAs, 48 of them (32.2%) were BSc graduates, 95 (63.8%) level 3&4 graduates, 3 (2%) were level2 while the rest 3 (2%) attended short training. In terms of specialization, DAs studied 11 different fields of studies which are Plant science (25.5%), Crop Production (12.8%), Animal Science (21.5%), Livestock (0.7%), Poultry (0.7%), Dairy (0.7%), Beekeeping (1.3%), Natural Resource Management (22.8%), Forestry (1.3%), Horticulture (4.7%), and Small Scale Irrigation Management (8.7%).
Like age, work experience of respondents was found to be low when compared to other countries. Their experience ranges between 1 month to 16 years and standard deviation was 3.1091 whereas the average work experience was 4.4 years. As indicated in above table, 71.9% of respondents are below five years of work experience and only 6% of them work as DA for more than 10 years. When we see case of Nepal, 75.92% had more than 10 years of work experience from which 27% of them work as extension professional for more than 30 years. To see African context, in work of Agunga (2017) who conducted the role of extension workers in nine African countries (Ghana, Tanzania, Botswana, Cameroon, Senegal, Malawi, South Africa, Uganda, and Nigeria), that respondents’ worked between 5 months to 38 years with an average work experience of 12 years. When we see work experience by districts, D/Elias (n=37) is the least in terms of experienced DAs where 37.8% (n=14) of respondents were below 2 years of experience and 51.4% (n=19) of them worked between 2 years to 5 years followed by Baso district with 27.8% with below two years and 52.8% with 2 to 5 years of work experience. In explaining why development agents have such a smaller work experience, one of the key informants explained that most development agents consider the job as transient job. They always aspire to get office based positions in agriculture or other jobs outside of the profession. This aspiration is unachievable due to their low high school results and the only place that they will get accepted is in agricultural colleges. So, they join agricultural colleges to be DAs by targeting graduating in the colleges will qualify them to pursue their education in other professions. Due to this, it is very rare to get most experienced DAs due to the high turnover. In terms of their birth place 128 of them (85.9%) were born and raised in rural areas while the rest 21 (14.1%) were born urban areas.
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4.2. Review Recruitment criteria and Curricula of Agriculture Training Institutions The central theme under this objective was to assess the recruitment process of DAs done by districts and over all curricula of training institutions; to look at how these two processes are related to each other; and to identify if there was a significant mismatch between what DAs studied and what they were expected to do while working as DAs. The results obtained to answer this objective were based on qualitative data gathered through focus group discussions, key informant interviews and document reviews.
4.2.1 Recruitment Process According to key informant interviewees from district agriculture offices and according to documents reviewed, there are two ways of recruitment of DAs. The first way is high school graduates selected by government. These students then get free education in government agriculture colleges to study diploma /level based certificate/ and they are then assigned as DAs when they graduate. The second way is for people who studied agriculture fields either privately or in public universities by their own. Agriculture offices announce vacancies for vacant positions and hire best qualifier. For the first case, there are two major criteria. They are: 1. Their willingness to study and work in assigned community after they graduated. 2. Those who fulfill minimum high school result mostly above 2.0 grade ten national examination. Fulfilling these criteria is the only requirement used to select applicants. Based on key informants in colleges, the training institutions have no role in the recruitment process except for accepting the batch of fresh students selected by agriculture office. Applicants will not sit for entrance exam. In addition, most of the time selection process is held so late after other sector screening is completed (health extension, teaching and TVET and private colleges). So, only those who cannot get in anywhere else choose to be DAs. Key informants reported that what motivates applicants is because they have no any other alternative to pursue their college level education. So, they use their DA diploma to learn other disciplines. So, being DA is a transition profession till they get what they need. It indicated by Garton, Dyer and Ball (2002) that admission criteria such as entrance examination, high school grade point average and high school rank have positive correlation with academic performance of students in agricultural colleges. That means those who have good GPA will most likely to be best performers in colleges. However,
48 | P a g e these quality maintaining selection requirements are not being applied in Ethiopian agricultural colleges. The second recruitment method is hiring people through vacancy announcement. In this case, people already graduated by their own. So when there is vacant position district agriculture office will inform to civil service office to hire someone for the vacant position. In order to hire a person who fulfill the required competency, civil service office will make vacancy announcement (there is civil service guideline). Interested applicants will go through screening process and those who fulfill minimum requirement will sit on written exam and interview. The exams are prepared by related department woreda agriculture office experts and they always are derived from manuals. For example, based on most recent recruitment in Machakel district for plant science position, questions were related with seed amount, fertilizer amount, and how to use chemical. The exam was prepared by plant science department. And all the questions were focus on technical competencies where, 90% is written exam and 10% is interview. It was confirmed by extension unit head that there is very rare chance of extension department/unit staff to engage in hiring process of DAs because exams are about technical expertise. So, applicants are only examined on their technical expertise and no extension related competencies will be part of examination. Different scholars argue that extension agents’ competence should be in accordance with the task areas in which they will be assigned to operate in order to perform successfully (Androulidakis and Siados; 2003, Scheer et al., 2006; Seevers, Graham and Conklin, 2007; Suvedi and Kaplowitz, 2016 ). So, using only technical expertise as hiring criterion may not ensure general competence of candidates because DAs are expected to be equipped with integrated sets of knowledge, skills and attitudes which are necessary to effectively deliver expected job results. As mentioned in theoretical framework of this research proponents of competence theory criticize the way educational institutions test education more for intelligence than for competence. They argue that intelligence tests do not predict job success because competence is not limited on technical knowledge (McClelland, 1973; Hampden & Tropenaars, 2000).
4.2.2 Findings on Curricula and Profile of Training Institutions Three training institutions (Woreta, Mertolemariam and Kombolecha) were selected for curricula review. In doing so, three major issues were reviewed. The first one was issue related with specialization and how different departments are related. Second, how the
49 | P a g e training institutions shape attitude of DAs and finally matching between courses given in training institutions and actual job descriptions of DAs. Since 2007, Woreta college has been giving level based (level 2 to 4) programs in 3 departments; namely plant science, animal production and natural resource management. But currently due to government direction for colleges to specialize on one or two filed of studies, the college has stopped giving natural resource management (which is given in Mertolmariam College). In addition to level based programs, colleges also give BSc affiliation program with universities. BSc programs include Animal Health, Animal Science, Natural Resource Management, Cooperatives, Plant Science, Water Resource and Irrigation Management. Woreta College affiliated with Gondar, Bahir Dar and D/Tabor universities while Mertolemariam University affiliated with D/markos University. Curriculum development and revision of level based programs is done by Federal TVET while BSc programs are done by affiliated universities. Departments in training institutions have no role neither in developing nor revising curriculums. The colleges’ authority is limited on module modification when there is necessary. According to key informants in training institutions, curriculum of level based students is designed by Federal TVET and Ministry of Agriculture. When there is a need of revision, colleges may report/inform to regional agriculture office, and the region report to ministry of agriculture then report to Federal TVEt. After the revision the feedbacks follow the same line. But it was confirmed by all respondents that there has never been a case of following such process to revise certain curriculum. Because teachers are mostly very busy to complete courses and had no time to pause and think about challenges.
What makes good DA? It was observed that there has been difference among training institutions in defining what makes a good DA. There was no uniformity in definitions given by different departments in the same institutions and among institutions as well. Responses given to competencies that make good development agents differ from one department to the other. For comparison, five definitions given by different departments are presented below. The first three were given by animal science, plant science, and basic science departments in Woreta College of Agriculture while fourth one was given by Mertolemariam agriculture college and the last one was by Gozamen District Agriculture office. According to head of animal science department, most important competencies that make good DAs explained as:
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“There are three domains of competency that DAs should acquire. They are; knowledge, skill and attitude. A good DA should have adequate technical knowledge, able to implement that knowledge in practice (skill) and also have good attitude on the work he/she is doing.” The definition indicated that competency is comprehensive of three domain areas that are knowledge, skill and attitude and a good DA should acquire all the domains. In other words, to be called as good DA, one should have scientific knowledge, basic skills to implement the knowledge, and good attitude toward his/her own work and farmers he/she works with. On the other hand, the following explanation was given by plant science department: “A good DA is the one who fulfill or fit with the knowledge, practical skill and attitude expected from the profession. DAs work with farmers so if one should possess both theoretical and practical expertise that the profession requires. In addition, he/she should be interested to work. Those without these qualities may not be considered as competent DAs. So, a good DA should demonstrate practical skill of conducting for instance soil analysis and other plant protection aspects” Although similar with above definition, this one focuses on theoretical and practical aspects of competencies that development agents should acquire. And the third definition was given by Basic Science Department; “A DA is the one who knows extension methods that used to diffuse the extension packages. And a DA who is able to mobilize the community using different mobilization skills.” This one is more on how to diffuse extension packages to communities and mobilization skills of DAs. According to Mertolemariam college a good DA is “A good DA is the one how have better knowledge than farmers, has good attitude towards farmers. A person who adopts their living condition. Who has good relationship and have good communication.” And extension unit of Gozamen district agriculture office defined what makes a good DA as: “A good DA always know the reason why he/she is hired and strive to achieve the objective. Accountability is also main quality of good DA. A person who is ready to serve farmers with humility and honesty. A person who can execute assignments effectively. Dedicated to the tasks. “
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The above definitions indicate that respondents have different notions on what makes a good DA. It indicates the limitation in setting clear framework on ultimate objective that DAs should reach on at the end their training. There is a mismatch between training institutions and district offices in defining competencies that make good DAs. This by itself may affect quality of education that training institutions provide having different perspectives on what makes good DA and qualities that competent DA should possess. The mismatch is not only between colleges and districts but also among colleges themselves.
Curricula Related Findings The first finding was related with the modular approach that training institutions are applying. In the modular approach, DAs take one course at a time in blocked system and move to the next. It requires one course to be completed in 7 weeks. Department heads reported that they find it difficult to cover both theoretical and practical aspects of courses within the given period of time especially in plant science, where practical lessons include showing planting, growing and harvesting which is unrealistic to manage in seven weeks. It is also the same thing for breeding as well. The second finding was related with measurement and evaluation systems that training institutions apply to evaluate DAs at the end of each course. According to academic vice deans, there is no grading system in level based curriculum. They are rather classified as competent and non-competent. According to respondents, this contributed to decrease sense competition among students. In addition knowing that they will not fail in exams no matter what their results are affects them to be less committed to study. They will not fail because they will get pass mark and all are “C”. In BSc program there is grade system of A, B, C, D, F. Student study hard for good grades this is not the case in level based program all will pass. So, they are not required to study hard. The third finding was courses do not cover all areas of extension system. It was reported by district agriculture offices that the training doesn’t make development agents ready to address actual challenges in agriculture sector. It doesn’t teach how DA’s can help farmers in rural areas.
The fourth finding was plant science, animal science, and natural resources department representatives assumed that all soft skills (process skills) are given by Basic Science Department. But the assumption was in contrary with response of basic science department heads who responded that skills such as communication are not well covered
52 | P a g e by courses which is related with curriculum. There are important topics/study areas which are not covered such as gender and communication. The fifth finding is major difference between former and current curricula and their impact on competency of development agents. By comparing between former “Generalist DA curriculum” and current “specialized DA curriculum”, one of the key informants in agricultural colleges said the following “My perception is there is difference between current DAs and former ones in terms of job performance. Current DAs are less competent in terms of technical skill and commitment. There are different reasons for this. The first reason is the curriculum problem. As I mentioned students do not encourage to study because all students above 50% will be rated as “Competent”. But it was not the case in pervious times where DA’s study hard not to fail or terminated. This helped the former DAs to grasp the knowledge. The second reason is nowadays DAs specialized on specific subject matter but assigned to do more diversified and general assignments. For example Natural Resource Position may be filled by DA graduated with soil science, or land use management. The education background is highly specialized to the position. The same is true for animal science position which might be done by a person graduated with beekeeping is expected to advise farmers about all animal science issues. But formerly generalist agents were assigned who have general knowledge. The third reason is false reporting which has now becomes huge critics of current DAs. Reports are duplicated and falsified, undone activities reported as achieved and unaddressed farmers reported as benefited. The fourth reason is the criteria to join agriculture institutions decreased from time to time. Previously, people with good GPA selected and join agriculture related departments but now the reverse is true. DAs selected after all other areas completed. Those who couldn’t make it to teachers colleges and health extension positions recruit for DA.” Another finding is time allocation between theoretical and practical sessions. According to basic science department, major courses cover 84 hours from the total 100 while common courses (Rural Sociology and Agricultural Extension, Business and Entrepreneurship, Project Planning, Introduction to Economics, Introduction to Computer Application, Basic Mathematics, English) cover 16 hours. In addition, practical exercises are for major courses while common courses are given in traditional lecture method. This indicates that most of the sessions are theoretical and courses related with process skills are limited only with traditional lecture methods. The upgrading program is given in interruption and disconnected way that DA’s learn one semester and go back to work. It is not continued. After taking one semester, they wait for a year. Some courses are related to one another but DA’s already forget the first one due to
53 | P a g e time span. Even level based also given in long term. First they certified with level one then level two and three. Each time they return back to work and comeback after year time. As competence theory proposes, the disconnection between education and labor market is the main cause of competency crisis. Similarly, the above findings indicate that it is difficult to say education programs given to development agents are competence based. The findings point to the training institutions and actual jobs are disconnected. Findings Related with Training Institutions themselves As indicated above, training institutions have very limited role in curriculum revision. In addition to this, the following findings were identified that have negative impact on equipping development agents. The first finding was that the training institutions do not have their own visions, goals and values. Although they are colleges, they work without strategic plans that show what they aspire to reach after certain period of time. They are not allowed to expand by opening new departments and increase number of students. In addition, unlike other training institutions, agricultural colleges do not use academic calendar. According to Garton, Dyer and Anna (2002), it is almost universal that universities establish criteria in selection of students for admission. Their study indicated that there is positive correlation between admission criteria and academic performance of students. However, in Ethiopian context, training institutions have no any control in admission process as DAs are solely recruited by agriculture office. Key informants questioned that, although they completed secondary education, DAs may not be qualified for college education if they pass through recruitment process that teachers and health colleges use. In addition, colleges do not follow academic calendar because class start dates for new batch of students is decided solely by agriculture office and it sends prior notification letter to colleges to be ready to accept new batches in few weeks. According to key informants, this may happen in any month of the year. So, the findings indicate that training institutions are passive in decision making regarding that should join the colleges, when will new batch will start, how to review the curriculum, what departments to be opened or closed, how many students to accept, all these are decided by another body. Employment terms and capacity of teachers was another finding that may have effect on producing good and competent DAs. Regarding teachers’ profile, from total teachers, about 19% MSc holders while, 73 % were BSc graduates and the remaining 8% were Diploma graduates. It was learned that most of BSc courses in Mertolemariam college
54 | P a g e given by guest lecturers who come from Debre Markos University. Respondents in Woreta College of Agriculture also mentioned that employment term is contract based renewed in every two years which affect teachers work motivation negatively and they aspire to transfer to other colleges with permanent employment. Even teachers capacity is another challenge. “When we study in universities, we were filled with theories” said key interviewee department head. “So we face skill gap to show for our students.”
4.2.3 SWOT Analysis SWOT is an assessment tool that provides an overall view on most important factors influencing performance of a certain program, unit or institution. In most cases, it is used as self-assessment tool to measure how one’s unit or program is performing as part quality improvement plan that includes developing recommendations and action plans, These recommendations and action plans take into consideration many different internal and external factors that maximize the potential of the program’s strengths and opportunities, while minimizing the impact of its weaknesses and threats. The following figure illustrates SWOT matrix of training institutions under study. Findings listed in the matrix were rated by participants.
Figure 6: SWOT Matrix of Training Institution
Source: own summary from KII and document review
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Generally, the results of this research on agricultural college has some similarity with findings of Wallace and Nilsson (1997) who identified most common problems of agricultural colleges in Sub-Saharan countries such as agricultural colleges being isolated from extension and research services, curricula rarely adjusted, and inefficient leadership system.
4.3 Competence and Motivation levels of Agents in the Study Area
4.3.1 Competence Level Measuring competency level of DAs was the second objective of this study. As indicated in section three, questionnaire was developed which consisted items measuring 6 core competence areas. Five interrelated Likert Scale items were developed for each competence area except for ICT competency which had 3 items. A Likert scale is composed of a series of Likert-type items that are combined into a single composite score/variable during the data analysis process. As combined items are used to provide a quantitative measure of a character, in this case DAs rated themselves. Each item had five points (Likert-type scale) where 1= very low; 2= low; 3= medium; 4= high; and 5= very high. The answer of DAs against each question used to compute their competency levels. The six core competence areas are: 1. Agriculture Extension Program Planning Competence 2. Extension Implementation Competence 3. Extension Monitoring and Evaluation Competence 4. Extension Communication Competence 5. Subject Matter Expertise competence 6. Education and Information Communication Technology Descriptive statistics calculated to examine the levels of core competencies revealed that respondents perceived to have low to high level of competencies in all core competencies. On the scale of 1 to 5 (the lowest and the highest), as shown in below chart, respondents indicated themselves as having the highest level of competency in Communication (M =3.51, SD = 0.598), followed by program implementation skills (M = 3.24, SD = 0.66), Monitoring and Evaluation (M=2.89, SD=.583), Technical Subject matter expertise (M = 2.88, SD = 0.574), Program planning (M = 2.77, SD = 0.542), and the least ICT (M= 1.94, SD= 0.724). The scale was 1 to 5
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5
4 Program planning 3.51 Implementation 3 3.24 Monitoring and 2.89 2.88 evaluation 2.77 communication
2 Subject matter 1.94 expertise ICT 1 Mean score of six core competency areas
Figure 7: Descriptive Statistics result of six core competence areas
Source: own survey result As the figure shows, development agents perceived they have lowest competency on ICT related competencies such as use of computers followed by planning competency that requires knowledge on policy documents and strategies, ability to conduct need assessment using tools, and prepare workable plan. Moderate competence is observed on monitoring and subject matter competences. And higher competence was observed on program implementation and communication. Regarding specific knowledge and skills under each competence area, DAs scored least competence level on ability to conduct need assessments, ability to use computers and ICT. As indicated in yellow shaded columns of table 6, proportion of DAs with low competence level in these 3 competencies exceeds 50%. For instance, 52.3% of DAs reported that they have low and very low competence level on their ability to conduct assessment using different need assessment tools. Similarly, 86% of them responded that their skill to use computers is low. On the other hand, DAs respond higher level of competence on professional ethics, listening skill, and knowledge and respect for local values of people. Each item’s result presented in table 6.
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Table 6: Likert Scale items Result (n=149)
Compet Likeret items Percentage of 5 likert points ency Very low low Medium high v.high areas Ext. Being familiar with ext system goal 7.4% 24.8 55.7 10.7 1.3 plannin Knowing extension strategies 4.7 33.2 54.4 7.4 1.3 g Ability to use need assessment tools 6.7 45.6 38.3 9.4 Ability to identify indigenous resources and 2.0 26.8 55.7 14.1 1.3 knowledge Prepare workable plan .7 16.8 61.1 17.4 4.0 Implem Demonstrate team work 2.0 13.4 54.4 25.5 4.7 entation Training facilitation and presen skill 2.0 15.4 55.7 21.5 5.4 Demonstrate good prof. ethics 4.7 38.3 47.7 9.4 Comprehend life world of farmers .7 14.8 56.4 20.1 8.1 Manage conflicts 1.3 21.5 52.3 19.5 5.4 Monito Conduct monitoring and evaluation in 2.7 17.4 58.4 20.1 1.3 ring extension program and Develop/Design Data collection instruments 6.7 38.3 45.0 9.4 .7 Evaluat / survey, interview, FGD, observation, ion Conduct Data analysis /qualitative and 2.7 37.6 47.7 10.7 1.3 quantitative write report of monitoring results /success 1.3 26.8 48.3 20.8 2.7 stories, lessons learned prepare genuine and quality performance 2.7 10.7 57.7 22.1 6.7 reports Commu Respecting local social values 2.0 38.3 47.0 12.8 nication Demonstrate respectful attitude towards all .7 5.4 37.6 45.0 11.4 farmers /understand individual situation of farmers Demonstrate Good listening skill 8.1 32.9 47.7 11.4 Demonstrate explanation of technical issues 14.8 56.4 24.8 4.0 using local language /avoiding jargons/ trace and resolve misunderstandings 7.4 51.0 31.5 10.1 Subject Basic Bio Veterinary Competencies 24.2 37.6 32.2 5.4 .7 matter Animal Production Competencies (Animal 8.1 34.2 38.9 15.4 3.4 Nutrition, Poultry and Dairy Crop production /land preparation, pest and 4.0 8.7 47.0 30.9 9.4 disease control, seed selection Apply basic tools for value chain approach 4.0 28.2 52.3 14.1 1.3 Demonstrate Soil conservation works 4.7 6.7 47.7 31.5 9.4 ICT Make good use of computers 43.0 43.0 12.1 .7 1.3 Make good use of ICTs access and use web- 23.5 45.0 24.2 5.4 2.0 based resources Make use of internet for email and 33.6 45.6 16.8 4.0 exchange information Source: own survey result
4.3.1.1 Level of Competency by Demography Training Institutions One-way analysis of variance (ANOVA) was calculated to examine the differences in level of competencies among respondents who passed through three different education
58 | P a g e programs (Government agricultural colleges, government universities and private colleges). And the result show that development agents who attended in government universities have higher level of competency in all competency areas than the other two groups. And those who graduated from government agricultural colleges were better than those who came from private colleges except for ICT competency. Table 7: one-way ANOVA result showing differences in competency level on type higher education Core Competency Higher education institutes F-value P-value Government Government private colleges Agricultural universities (n=21) colleges (n=88) (n=40) Mean Std. Mean Std. Mean Std. Deviatio Deviatio Deviatio n n n Planning Competency 2.70 .457 3.06 .519 2.51 .697 9.777 .000 Implementation 3.13 .514 3.54 .637 3.09 .677 7.990 .001 competency Monitoring & 2.84 .494 3.13 .720 2.66 .518 5.552 .005 Evaluation Communication 3.48 .538 3.75 .684 3.22 .517 6.266 .002 Competence Subject Matter 2.77 .517 3.21 .596 2.7 .531 10.281 .000 Expertise competence ICT 1.78 .610 2.25 .921 2.06 .534 6.685 .002 Source: own survey result Age: Respondents were categorized in to three age groups (25 years and younger; 26 years to 30 years; 31 years and above). By considering this categorical independent variable with three distinct categories, one-way ANOVA was used to test whether there are significant differences in the mean scores on the dependent variable (total sum score of competency level) across the three groups. And the result shows there is no significant difference. Similarly, there was no statistically significant difference between sex groups as well. Similar result was reported by Yohannes (2009) and Lakew (2011). Sex
As the below table indicates, there was no significant competency difference based on sex of development agents.
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Table 8: mean comparison of General competence score by sex
Group Statistics t-value p-value
Sex of DA N Mean Std. Std. Error Deviation Mean
General male 108 2.9072 .45366 .04365 1.532 0.128 competence
female 41 2.7821 .42174 .06586
Source: own survey result
Work Experience: Work experience was another variable categorized in to four levels as; 1 day to 2 years; 2 years to 5 years; 5.1 years to 10 years; and above 10 years. By taking this independent categorical variable as a factor one-way ANOVA was applied over the six core competencies. As the result, there was no significant mean difference found except in one competency area which was Information Communication Technology (ICT). As the below table indicates, there was significant variation among work experience categories in terms of their ICT competency level (F=5.507, P=0.001) somewhere in the groups. The significance value of homogeneity of variance assumption test was .088 which indicated that the assumption of homogeneity was not violated. Table 9: ICT competency difference on work experience
Work Experience Std. F P-value Category N Mean Deviation Minimum Maximum zero to 2 years 40 6.98 2.281 3 11
2.1 to 5 years 67 5.42 2.251 3 14
5.1 to 10 years 33 5.42 1.437 3 9
Above 10 years 9 5.33 1.732 3 8
Total 149 5.83 2.173 3 14 5.507** 0.001
Source: Own survey, 2018; **= the mean difference is significant at 0.05 level.
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To find out where the differences lie, post-hoc test was used which helps to see multiple comparisons. Based on this, the least experienced group is significantly different from the next two groups. In addition, as the following figure shows, development agents with less than 2 years of work experience had higher ICT competency than the other groups and there is dramatic drop in the next groups. As the qualitative data indicates, one of the factors for this was ATVETs had no computer and internet facilities up until recently. For instance, according to academic vice dean of Woreta College of Agriculture, the college has got internet access one year ago. So, recently graduated development agents had better exposure to computers and internet than their seniors.
Figure 8: ICT mean score by work experience
Source: own survey result
Education Level Educational Background was another variable computed to see mean difference in competency level among DAs attended different education programs. They were categorized as: BSc graduates; level 3 and 4 completed; level 1 and 2 completed; and finally who attended 3 to 7 month short trainings. This helps to see whether DAs who passed through different programs /curriculum/ may differ in their competence as well. And one-way ANOVA indicated that there is statistically significant mean difference between the groups (F=7.657, P=.000). The following table and figure show descriptive and mean differences.
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Table 10: Mean difference based on education level Highest N mean Std. Minimum Maximum F P Education Deviation category B.Sc 48 89.12 13.922 65 119
Level 3&4 95 79.42 10.992 51 104
Level 2 3 80.00 5.568 75 86
Short 3 73.33 8.963 63 79 training Total 149 82.44 12.725 51 119 7.657 .000
Source: own survey result
Figure 9: Total competency of DAs by their education level
Source: own survey result
In addition, when we see each competency area, there was significant difference on planning competency. The mean score of competence was used to see if there is significant difference among different groups. Based on this, the following table shows planning competence mean score comparison between BSc holders and level based graduates. And the result shows that there is significant difference between the two groups that mean of BSc graduates was 3.0292 while level based graduates’’ mean was 2.6653 (p=0.000 and t=3.969).
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Table 11: Mean score of planning competence
Group Statistics
Education level of DAs N Mean Std. Deviation Std. Error Mean
Mean B.Sc degree 48 3.0292 .50527 .07293 t=3.969 score of P=0.000 planning items Level 3, 4 or 95 2.6653 .52385 .05375 Diploma
Source: own survey result
Table12: Mean competence scores comparison between Bsc holders and non-holders
Std. Error Education level of DAs N Mean Std. Deviation Mean
Mean score of planning B.Sc degree items 48 3.0292 .50527 .07293
Level 3, 4 or Diploma 95 2.6653 .52385 .05375
Mean score of BSc degree 48 3.4667 .61794 .08919 implementation Level 3, 4 or Diploma 95 3.1411 .57083 .05857
Mean score of BSc degree 48 3.1042 .67665 .09767 monitoring and evaluation Level 3, 4 or Diploma 95 2.7979 .50063 .05136
Mean score of BSc degree 48 3.6958 .64970 .09378 communication Level 3, 4 or Diploma 95 3.4000 .54186 .05559
Mean score of subject BSc degree 48 3.1500 .57686 .08326 matter Level 3, 4 or Diploma 95 2.7705 .53153 .05453
Mean score of ICT BSc degree 48 2.1806 .86146 .12434
Level 3, 4 or Diploma 95 1.8421 .63179 .06482
Source: own survey result
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4.3.1.2 Discussion on level of each core competency Three different analysis procedures were applied to determine competency level of development agents. The approaches enable to see the scenario from different viewpoints The procedures are: 1. Garcia (1989) classification of coefficient of variation (CV%) using mean and standard deviation: it is widely used method in agricultural surveys that researchers used to measure the variability of their experiments (Scapim et al., 1995; Amaral et al., 1997; Carvalho et al., 2003). So, classification was done based on, CV% ≤ – SD rated “low”; – SD < SV% ≤ + SD rated medium; CV% > + SD rated high, where is the sampling mean while SD is standard deviation. Simply, here DA’s competency level was determined as low, medium and high based on the deviation from mean score distribution.
2. Using government efficiency evaluation scale: development agents’ performance is measured by their supervisors on biannual basis. Their result will be converted to 100% and rated based on the rating standard; <50% low, 50% to 79% medium, 80 to 100% high (the third category split in to two as 80%-94% high and above 94% as very high). Based on this, the standard was used in this study as well by computing total sum score of items and the result under each competence area was converted to 100% and then categorized based on government performance rating system.
3. Using total mean score: as shown in figure 8, mean score of each item as well as total mean score of competencies is used to compare differences among different groups. In this way data is generated by computing total mean score of items classified in one group and see mean and standard deviation.
Using three techniques to determine competency levels of development agents widen the chance to see the scenario in different ways. Based on this, results of six competence areas are discussed below:
A. Planning Competence
Planning competence involves knowledge and skills of development agents that are necessary to produce workable planning. It includes adequate knowledge of national agricultural extension goals and strategies need assessment conducting tools, social mapping techniques and ability to prepare weekly, monthly and annual plans. Based on Garcia’s proposal, planning competency of development agents was categorized as low,
64 | P a g e medium and high based on the deviation from mean score distribution. As indicated in the below table, the mean score was 13.86 while standard deviation was 2.71 and the minimum and maximum scores were 7 and 22 respectively. Based on this, those who score below 11 were categorized as low; 11-17 as medium; above 17 as high. As the result 14.8% (n=22), 77.2% (n=115), and 8.1% (n= 12) of DAs had low, medium and high planning competency respectively.
Table 13: Planning Competence of DAs
Competence Score Frequency Percentage Mean SD Category Low <11 22 14.8% Medium 11-17 115 77.2% 13.86 2.71 High >17 12 8.1% Total 149 100% Source: own survey result
The result indicated most (77.2%) development agents rated themselves having medium level of planning competence which consists of basic expertise vital for effective planning. However, it is not quite enough to get in to realistic competence level of DAs because it is determined by standard deviation and mean. In another word, DAs giving similar score will affect the result by hiding the real competence level of DAs. So, there is a need to fill this limitation by using other ways of determining competency of DAs.
In order to overcome the limitation mentioned above, Extension organization efficiency evaluation scale was applied to categorize planning competency of DAs. The score was converted in to percentage and then categorized as low (below 50%), medium (50% to 69.99%) and high 70% and above.
Table 14: Planning Competence of DAs based on government criterion
Category Planning competence Count Percentage Low 75 50.3% Medium 62 41.6% High 12 8.1% Total 149 100% Source: own survey result
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The result presented in table 6 gives different result than the previous one where it categorized most development agents’ planning competence in low level. In previous analysis, development agents’ competence was computed among themselves using mean and standard deviation. But here their score is computed against a certain standard. Therefore, most development agents are below expected level in their planning competence. Finally, total mean score was applied to see it there is any difference of planning competency among different groups. The following table presents total mean score among BSc graduates and level based graduates.
Table 15: Group Statistics: mean score of planning competence among DAs with different educational background
Education level of DAs N Mean Std. Deviation Std. Error Mean Mean BSc degree t=3.969 score of 48 3.0292 .50527 .07293 P=0.000 planning items Level 3, 4 or Diploma 95 2.6653 .52385 .05375
Source: Own analysis from survey, 2019
The result shows that there is significant difference between development agents with BSc degree and level based graduates (diploma or certificate holders). Based on the score from 1 to 5 (very low to very high), mean score of BSc degree holders was 3. 03 while it was 2.67 below degree graduates (diploma and certificate). This indicates that competence level of development agents increases as their education level increases.
B. Extension Implementation Competence
Similar approach was applied here as well where three different ways used to determine implementation competence of development agents. Development agents’ ability to implement extension program was measured by considering important competencies such as team work, training facilitation and presentation skills, professional ethics, comprehension of life world of farmers, and manage conflicts. After computing total sum score of items designed to measure implementation competence, mean and stand deviation to categorize DAs’ score as low, medium and high. The minimum score was 9 while the maximum score was 25. The mean and standard deviation were 16.32 and 3.17 respectively. Based on this, DAs’ implementation competence is presented next table:
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Table 16: Implementation Competence of DAs (n=149)
Competence Score Frequency Percentage Mean SD Category Low <13 22 14.8% Medium 13-19 103 69.1% 16.32 3.17 High >19 24 16.1% Total 149 100% Source: own survey result
Based on this categorization, implementation competence of most development agents fall at medium level. However, based on human resource professionals’ Bell Curve, 10% is the highest possible threshold for low level of employees in one organization. It is considered that normal distribution of employees is low <10%, average 70%, and high 20% (Bhatia, 2016). Although it is debatable among professionals to use Bell curve as ultimate standard, it is still widely acceptable method that organizations adopt to categorize their employees. And HR professionals recommend it is unhealthy if there is existence of beyond 10% of low performing staff in one organization that need urgent staff development. So, based on this, 14.5% of DAs were fall on low level of competency. Similar result was generated when the score categorized based on government efficiency evaluation scale which confirmed 14.8% (n=22) in low level of implementation competence. This enforces to give more attention because the measurement is done to core competencies necessary to perform primary tasks of development agents.
In addition to the above parametric analysis, mean comparison score was also computed to see implementation competence difference between groups.
Table 17: Mean scores of implementation competence comparison between BSc holders and non-holders.
Std. Error Education level of DAs N Mean Std. Deviation Mean
Mean score of BSc degree 48 3.4667 .61794 .08919 implementation Level 3, 4 or Diploma 95 3.1411 .57083 .05857
Source: own survey result
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C. Extension Monitoring and Evaluation Competence This is another core value that development agents are expected to master in order to monitor and evaluate extension programs. The competence requires ability to design data collection instruments, conduct data analysis, prepare quality reports, and write success stories and/or lessons. Based on the deviation from mean score distribution, monitoring and evaluation competence level of development agents were categorized in to three as low, medium and high. The mean score was 14.46 and standard deviation was 2.917 and minimum and maximum scores were 7 and 23 respectively.
Table 18: Monitoring and Evaluation Competence of DAs (n=149)
Competence Score Frequency Percentage Mean SD Category Low <11 20 13.4% Medium 11-17 112 75.2% 14.46 2.917 High >17 17 11.4% Total 149 100% Source: own survey result
The result indicates that development agents have better competence level of monitoring and evaluation than planning and implementation. But still, 13.4% have low competence level which is higher than Bell curve threshold. The figure increases when it is computed based on government performance efficiency standard that low competent DAs reached 36.9% (n=55).
Table 19: Monitoring Competence of DAs’ based on government efficiency evaluation standard.
Monitoring competence category Monitoring Count Percentage Low 55 36.9 Medium 77 51.7 High 17 11.4 Total 149 100 Source: own survey result
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D. Extension Communication Competence
As development agents liaison between extension organization and communities, communication skill is a core element that enable them to establish positive relationship with coworkers and community members. They are expected to deliver information to farmers effectively without creating any ambiguity. They are also expected learn local knowledge, lessons and challenges from farmers. To do so, they should respect local social values, demonstrate respectful attitude towards all farmers /regardless of their difference/, demonstrate good listening skills, resolve misunderstandings. The following table describes communication competence level of DAs by using deviation from mean score as determinant factor. The minimum and maximum score were 10 and 25 respectively while the mean score was 17.56.
Table 20: Communication Competence of DAs (n=149)
Competence Score Frequency Percentage Mean SD Category Low <15 19 12.8% Medium 15-21 114 76.5% 17.56 2.992 High >21 16 10.7% Total 149 100% Source: own survey result E. Specific Subject Matter competence This refers to competence level of DAs in their technical expertise such as basic Bio Veterinary Competencies (Anatomy, Physiology and Biochemistry), animal Production Competencies (Animal Nutrition, Poultry and Dairy), Crop production (land preparation, pest and disease control, seed selection), Knowledge of basic concepts and tools for value chain approach, and Soil conservation works (acidity treatment, compost preparation, fertilizer usage). Based on the total sum score of five likert-type items, the minimum and maximum scores were 7 and 23 respectively. The mean score was 14.40 while standard deviation was 2.869. Based on this, their competence was classified in to three as low (14.1%), medium (73.1%), and high (12.8%).
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Table 21: Subject Matter /Technical Competence of DAs (n=149)
Competence Score Frequency Percentage Mean SD Category Low <11 21 14.1% Medium 11-17 109 73.1% 14.40 2.869 High >17 19 12.8% Total 149 100% Source: own survey result
When the classification is made based on government performance efficiency classification standard, proportion of DAs with low competency raised to 39% while medium competency proportion decline to 47.7% and high competency remain the same. There is also significant difference in technical competency based on educational background of DAs.
Table22: Technical Expertise based on government standard
Technical Count Percentage Low 59 39.6 medium 71 47.7 High 19 12.8 Total 149 100 Source: own survey result
F. Education and Information Communication Technology
Competency of ICT is newly emerging competence area which is becoming important in globalized world. However, DAs were found to be the least competent from the six core competence areas. The minimum and maximum scores were 3 and 14 respectively. Based on mean and standard deviation classification, 28.2% were in low competence category while 62.4% and 9.4% were in medium and high category.
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Table 23: ICT competence of DAs (n=149)
Competence Score Frequency Percentage Mean SD Category Low <4 42 28.2% Medium 4-8 93 62.4% 5.83 2.173 High >8 14 9.4% Total 149 100% Source: own survey result
As shown in table 24, based on government standard of efficiency rating standard, low competent development agents reached 90.6% (n=135). This huge figure is indicative that DAs are not using computers and internet for their work as well as for their personal development.
Table 24: ICT competency based on government standard
ICT Count percentage Low 135 90.6 Medium 8 5.4 High 6 4.0 Total 149 100 G. General Competency of Development Agents
Overall competency level of respondents was also measured using total mean score and total sum score by merging the six competencies. As shown in table 25, minimum and maximum scores of general competence were 51 and 119 respectively while the mean score was 82.44 and standard deviation was 12.725.
Table 25: Total competence of DAs (n=149)
Competence Score Frequency Percentage Mean SD Category Low <69 14 9.4 % Medium 69-95 115 77.2% 82.44 12.725 High >95 20 13.4 Total 149 100%
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By using government efficiency and performance rating standard, 27.5% (n=41) of development agents had low general competence while 59.7% and 12.8% of them had medium and high competency level respectively.
Table 26: General Competence based on government standard
Total Competence Count Percentage Low 41 27.5 medium 89 59.7 High 19 12.8 Total 149 100 H. Attitudes of DAs towards farmers and their work Along with the six core competencies discussed above, development agents’ attitude of DAs towards farmers was also assessed. Focus group discussion participant DAs were asked to explain “a typical farmer” in three words and most of the words they used to explain farmers were negative. The following table shows most frequently used words that DAs used to explain farmers. Table 27: Ten most frequently used words used by DAs to explain typical farmer S/no A typical farmer is: Frequency of the Category word used
1 Lazy 5 Negative 2 Resistant to change 4 Negative 3 Laggard 3 Negative 4 Hard working 3 Positive 5 Doubtful 3 Negative 6 Dependent /expect from others 2 Negative 7 Productive 2 Positive 8 Innovative 2 Positive 9 Early Adopter 2 Positive 10 Careless and difficult to DA 2 Negative Source: Summary from FGD As Table 27 shows, six out of ten words indicated negative attitudes and the top three words are all negative. Based on secondary resources and KII, what DAs learn about adoption category may contribute for this. DAs thought at colleges how to classify farmers based on farmers’ willingness to adopt certain technology.
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Based on KII in colleges, the categories are: 1. Innovative farmers- very active farmers or model farmers 2. Early adopters- next to innovative farmers 3. Early majority 4. Late majority 5. Farmers who don’t accept the technology who are called lazy farmers or laggards. Development agents have developed negative perceptions of farmers due to what they have been thought at colleges to label farmers and to use words such as “lazy” and “laggards” to refer farmers who do not want to accept a certain technology. In explaining the power of labeling, Moncrieffe and Rosalined (2007) argue that traditional top-down labeling approach bears the risk of devaluing personal understandings, and de- emphasizing cultural contexts, increase stigmatization which provide inadequate strategy or method of intervention. In addition, the work of Habermann and Hassan (2017; P4), who described the role of development agents in Ethiopia, also complements this result. The scholars argued that farmers considered as having little expert knowledge and blamed not to adopt technologies regardless of their reasons. Regarding attitudes towards their job, findings of quantitative and qualitative studies include; Based on survey analysis, 53% of respondents are not happy to continue working as development agents and they are interested to change their profession from which 20.8% of them responded that they are perusing advanced education outside of agricultural profession. In addition, regarding the work, 61.4% of respondents believe that most of the tasks they do are aligned with their educational qualifications while 38.6% of them believe that tasks they are performing are not related with their educational qualification. In addition, during focus group discussions, DAs were asked to list out their major roles and responsibilities and as the result they have mentioned roles such as transfer new technologies and making farmers to use them, conduct awareness creation trainings, monitoring and supervision works. This indicates that DAs are still in previous mindsets about their roles that emphasize on top down and technology transfer approaches. Although the country’s policy framework promotes the extension system to follow pluralistic, participatory and demand driven approaches, DAs did not mention a single responsibility related with learning from farmers, partnership and other characteristics of demand driven and participatory approaches.
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4.4 Motivation Level of Development Agents Mills et al. (2006) define work motivation as “the individual's desire to direct and sustain energy toward optimally performing, to the best of his or her ability, the task required in order to be successful in a work position.” Employee motivation is one of the key factors determining the success of any organization. Work motivation of DAs as rated by them is presented in table below. Table 28: Work Motivation level of DAs
Motivation Frequency Percent Mean Stan. Deviation Category Low 51 34.2 Medium 91 61.1 1.70 .551 High 7 4.7 Total 149 100 Source: own survey analysis As shown on the table, majority of development agents (61.1%) were in medium category of work motivation while another significant proportion (34.2%) was in low motivation level. And only 4.7% were rated having high work motivation. Similar report was reported by Belay et al (2012) on research conducted to assess motivation level of development agents at national level. During that time, development agents with high, medium and low competency levels were 67%, 29% and 4% respectively. Yohannes (2009; P42) also find out that 22.7% of development agents (in SNNP region) were low motivated while 57.1% were medium and 20.7 % were highly motivated. Although there are methodological, sample size and study area difference among these researches, the results indicate that actions taken in the last decade to increase motivation of development agents did not bring anticipated results rather proportion DAs with low motivation is increasing from time to time. The following table summarized results of the three findings. Table 29: Comparison of results of work motivation level of DAs Motivation level Yohannes (2009) Belay et al (2012) Yosef (2018) Low 22.7% 29% 34.2% Medium 57.1% 67% 61.1% High 20.7% 4% 4.7% Sample size 140 181 149 Study Area Southern Ethiopia National level Northwest Ethiopia
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Source: Summary of own survey with other studies (Note: there is sample size and study area difference among three studies) Development agents’ attitude towards the job was another proxy indicator that used to measure their work motivation. As the below table shows, majority of them (53%) want to leave the job while 47% of them want to stay in profession. In addition, 37.6% of development agents did not think that their effort brought any change in living condition of farmers that they work with while 59.7% think there effort brought some change and 2.7% think they have brought significant changes. Table 30: Development Agents’ attitude towards their job Proxy indicators for work motivation Do you want to stay in this Frequency Percent profession? Vali No 79 53.0 d Yes 70 47.0 Total 149 100.0 I feel that my efforts brought changes in living condition of farmers in my work area Never 56 37.6 Somehow 89 59.7 Significantly 4 2.7 Total 149 100.0
4.5 Determinant Factors of Competency and Motivation
4.5.1 Factors Affecting Competence of DAs Multiple linear and ordinal regression methods were used to determine factors that affect competence of development agents. The predictor variables were examined to predict general competency of development agents. The explanatory variables included in the regression analysis were relationship with co-workers, relationship with farmers, education level, sex, specialization in field of study, work experience, supervisors’ clear and objective visit and evaluation, age of DAs, types of training institutions where DAs attended, and current position. Based on the analysis, from 10 independent variables, 7 of them had significant influence on competence level of development agents.
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Table 31: Factors that influence competence of DAs /Multiple linear regression Variables Coefficients Std. Error T Sig. /Estimate
(Constant) 7.425582297 4.482992491 0.015339137
Sex of DA 0.167145768 2.099335343 2.26103097** 0.025321789 Relationship with 0.221996995 1.293218584 2.594233587** 0.010503648 coworkers Relationship with 0.175680687 1.542896629 2.013144009** 0.046044448 farmers Supervisors visit 0.140448486 0.839912309 1.939311627* 0.054503731 and evaluations Education level 0.286054446 1.962968445 3.955039516** 0.000121809 recoded * Institution recoded 0.129357404 2.878495545 1.637977179 0.103704607 Age of DA -0.060625701 1.99158532 -0.613494627 0.540559221 categorized Work experience -0.165544304 1.442713647 -1.707844533* 0.089913782 recoded Specialization catg 0.123765083 2.620588209 1.721398711* 0.087419397 Position -0.073214302 0.788657411 -1.032657068 0.303570758 ANOVA model fit measure =0.591754 P=.000
***, **, * represent significant at 1%, 5%, and 10% level of significance respectively
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Table 32: Factors that Influence Competence of DAs /Ordinal Regression for
Estimate Std. Error Wald Sig. Threshold [Totalcat = 1] .814 2.882 .080 .778 [Totalcat = 2] 8.615 3.159 7.436 .006 Location [Sex=0] -.039 .610 .004 .948 [Sex=1] 0a . . . [rshpcowr=1] -5.485 5.380 1.039 .308 [rshpcowr=2] -2.408 1.437 2.809 .094 [rshpcowr=3] -2.234 1.093 4.180 .041 [rshpcowr=4] 3.200 .814 15.465 .000 [rshpcowr=5] 0a . . . [rlshpfar=1] 0a . . . [rlshpfar=2] -.996 1.631 .373 .541 [rlshpfar=3] -2.493 1.066 5.473 .019 [rlshpfar=4] .266 .761 .122 .727 [rlshpfar=5] 0a . . . [Specializrec=1.00] -.093 .831 .012 .911 [Specializrec=2.00] 0a . . . [suprvisr=1] -3.833 1.680 5.204 .023 [suprvisr=2] 1.429 1.582 .816 .366 [suprvisr=3] 3.381 1.663 4.133 .042 [suprvisr=4] 3.788 1.591 5.669 .017 [suprvisr=5] 0a . . . [Position=1] .155 1.480 .011 .917 [Position=2] -.046 1.458 .001 .975 [Position=3] -.630 1.508 .175 .676 [Position=4] 1.494 1.642 .827 .363 [Position=5] 0a . . . [Recodedinst=1] -2.839 1.027 7.641 .006 [Recodedinst=2] 0a . . . [Agecatego=1] -.754 1.318 .327 .567 [Agecatego=2] -1.515 1.176 1.660 .198 [Agecatego=3] 0a . . . [Exprecoded=1] 2.028 1.984 1.044 .307 [Exprecoded=2] .765 1.978 .150 .699 [Exprecoded=3] -.308 1.887 .027 .870 [Exprecoded=4] 0a . . . [Edulevel=1] 5.965 1.749 11.639 .001 [Edulevel=2] 5.030 1.656 9.226 .002 [Edulevel=3] 4.257 2.413 3.111 .078 [Edulevel=4] 0a . . . Link function: Logit. Model fitting info = .000 Pseudo R-Square = .592 Source: own analysis
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Sex of DAs: the variable has positive and significant influence at less than 5% level of significance on competency. Male DAs were found more likely to be competent than females. There might be different issues that explain this variance. For example, during key informant interview, academic vice dean explained how difficult the campus is for “mother students” who brought up their infants with them. They couldn’t use free dormitories given by colleges as they are shared by other students as well rather they forced to rent houses nearby and pay for care takers. This affects their education as they miss classes very often. Another possibility is the one raised by female FGD participants about attitude of farmers towards females. They said it easier for males to get acceptance by community members as little attention is given for women. Finally, as the upgrading program requires back and forth of DAs, it probably be difficult for women to maintain work life balance than men. Relationship with Co-workers: the variable has positive and significant influence of competency at less than 5% level of significance. The variable is accounted for 22% of variation in competency level. The positive association suggests that the likelihood of being competent increases when development agents relationship with their co-workers changed to positive. As mentioned in literature review part of the study, competency is not just only expertise knowledge or technical ability. It has also functional part that helps to function including building relationship with clients, networking with local organization, conflict resolution, and mobilization (Suvedi and Kaplowitz (2016). So, establishing positive relationship with co-workers lays foundation for development agents to enhance their process skills. Relationship with Farmers: like the previous variable, establishing good relationship with farmers also found to have positive and significant influence on competency of development agents at less than 5% level of significance. It also covers 17% of variance in competency of DAs. Two major explanations could explain this. First, as reviewed in literature review part of this research, it is becoming clear in recent agricultural extension systems that information exchange should be two way between farmers and research. Top down approaches that framed farmers as having little expertise knowledge and ignorant to new technologies are now outdated. Recent approaches promote non-nominal participation of farmers in researchers, and decision making of extension system. Based on this reality, development agents who establish good relationship with farmers will have higher chance to learn from farmers, discover local knowledge, rationale behind decisions of farmers and able to dig out problems and find solutions together. They have a chance to learn and
78 | P a g e grow in many aspects of both process skills and technical competencies. Second, having good relationship with farmers is also a key to build trust, transparency and accountability between a development agent and farmers where farmers feel free to genuinely evaluate and comment the development agent who is working with them. Supervisor’s visits and evaluation: the variable has positive influence on competency of DAs with less than 10% level of significance. It explains 14% of variance on dependent variable. Based on the analysis development agents who receive clear and objective feedback from their supervisors were more likely to be competent than those who do not have such chance. As narrated in theoretical review part of the study, job characteristics theory indicated that feedback is one of the five variables of Job Characteristics Model. It refers to the degree to which the worker has knowledge about her/his performance. This is clear, specific, detailed, actionable information about the effectiveness of his or her job performance. When workers receive clear, specific, detailed and actionable information about effectiveness and performance, they have better overall knowledge of the effect of their work activities and what specific actions they need to take (if any) to improve their productivity (Hackman and Oldham,1980) . Education level: it is the only variable with less than 1% level of significance. It is the highest education that development agents attended. It accounted 28% of variance in competency of DAs. Those DAs who have BSc. Degrees were found to have better competency than level based graduates. This could be mainly due to curricula differences that BSc graduates take more courses both in theory and practice than level based graduates. As mentioned in discussion of objective one, there are numerous problems related with curricula and performance of agricultural colleges. Although it is expected to observe difference between BSc. and diploma (level ) holders, the variance is significant as they work at the same job position. Work Experience: it has negative and significant influence on competency at less than 10% of significant level. The negative association suggests that likelihood of competency level declines as year of work experience increases. There are two logical explanations for this. First, as discussed under second objective of this study, most development agents are found in young ages. And there is no significant competence difference with changes in age except for ICT competence. Due to this, it is difficult to see the impact of long years of work experience. Second, from the six core competencies, younger DAs (25 years and
79 | P a g e younger) were found to be more competent on ICT competencies. The combination of these two reasons created the negative impact of the variable. Specialization: it is the last significant variable having positive and significant influence on competency of development agents at less than 10% of significance level. According to the result it accounted for 12% of variation in core competences. Based on this development agents who graduated from more or less general field of studies such as crop science, animal science and natural resource management were better in their competence than those who graduated from narrowly specialized fields like dairy, poultry, beekeeping, and horticulture.
4.5.2 Factors Affecting Work Motivation of DAs In order to determine work motivation of development agents, 14 independent variables were examined from which 2 of them had significance influence. The variables were transfer opportunity, advance education, interest to stay in agriculture profession, perception on impact of job, access to training opportunity, recognition, promotion, perception on salary, access to incentive packages, relationship with co-workers, relationship with farmers, work condition, comment, and supervisor’s visit. Table 33: Factors Affecting Work Motivation of DA Estimate Std. Wald Sig. Error Threshold [motivtn = 1] 5.019146683 1.2531643 16.04145519 0.0000619 Location KAexp 0.002153997 0.1288336 0.000279532 0.986660619 Transfer -0.05394577 0.1091098 0.24444819 0.621011696 Advaedu -0.16039497 0.4550331 0.124249751 0.724470231 Stayagri 0.839108336 0.180664 21.57212803 3.407681583 Change 0.303558902 0.232879 1.699124666 0.192402491 Opportnty -0.21836781 0.1683517 1.68245028 0.194599186 recognition 0.105938203 0.1843742 0.330145108 0.565573656 Promotion -0.1766174 0.1594699 1.226618802 0.26806561 Salary 0.565889643 0.1961775 8.320804383 0.003919356 Incentive -0.21716206 0.2010812 1.166339257 0.280154703 Rshpcowr 0.163515457 0.2501897 0.427148235 0.513391594 Rlshpfar 0.423915841 0.2981246 2.021918368 0.155043159
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Workcond -0.16071097 0.210314 0.583921726 0.444779222 Comment 0.628525568 0.1748874 12.91602988 0.00032578 Suprvisr 0.129642047 0.1729076 0.562164612 0.4533894 Link function: Logit. Model fitting: .000 Pseudo R2 : 68.811 Source: own survey analysis Perception of Salary: the variable has positive influence on work motivation of development agents with less than 5% of level of significance. Those who strongly agree that they are being paid fair amount of salary for the work they do are more likely to have high motivation. Comment of supervisor: the variable has positive significant influence of work motivation of development agents at less than 1% level of significance. Based on the result, those who receive constructive comments from their supervisors are highly motivated than others.
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5. CONCLUSTION AND RECCOMENDATION
5.1 Conclusion Development agents are expected possess competencies in many diverse areas and it is the responsibility of agricultural colleges and extension organizations to identify core competencies required in the work environment and train them accordingly. But the study indicates that there is little similarity among definitions of DA competency given by agricultural colleges’ teachers and district experts. The definitions differ among departments in colleges and from one individual to the other in district offices. In additions, definitions given on non-technical competencies were vague which shows they are not articulated well. Based on this, it is possible to conclude that there is no concrete understanding on which core competence areas are expected from DAs. And this has its own negative effect on individual capability and hinders effort of extension organization to meet future demands.
Both negative and positive features were identified on curricula of training institutions. Affiliation BS.c programs that agricultural colleges give in partnership with nearby universities has vital role to strengthen colleges where teachers get more advanced work exposure. However, grading system, distinctive feature of upgrading program, time period given to finish a certain course in modular approach, focus given to technical skills than process skills, and specialized programs are identified as major bottlenecks of curricula of agricultural training colleges. The current grading system of ATEVT programs does not promote competition among students as it is determined as “pass and fail” principle. Due to this, students are not motivated to study for better grades. Fail means a burden on the teacher not on students that he/she should provide makeup classes and examine students until they get passing mark. In such case it is difficult to make sure DAs catch all the required knowledge and skills needed in their work career.
The research finding also indicates that there is significant competence difference between BS.c graduates and level based graduates in all competence areas. However, roles of DAs with different educational qualifications are not differentiated rather they are assigned in same job position and expected to deliver similar outcomes.
Regarding training institutes, it is almost universal that colleges and universities set criteria such as entrance examinations, high school GPA, and high school class ranks, during admission. And researches indicate that entrance examinations and high school
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GPA found to be the best predictors of academic performance of students in agricultural colleges that students with better score of entrance exam and better GPA were found more likely to be more successful than others. But ATVETs in the study area do not entertain such privilege because all the selection process is done by bureau of agriculture. In addition, in most countries, it is common to revise curriculum in every 5 to 10 years, but major review of the curriculum never happened in the last 5 years. So, ensuring alignment of the curriculum and learning outcomes with the emerging global trends of 21st century is under question.
Demographic characteristics indicate that majority of DAs in the study are males. And most DAs are in young ages with less than five years work experience. Due to the high turnover, most DAs have short and intermediate years of work experience. Only one person (from 149 respondents) had more than fifteen years of experience. This status is very low when compared to Asian and African countries where there are DAs who worked up to fifty years. In terms of education, most of them are level based curricula which have limitations in equipping core competence areas.
Regarding their competence level, from six competence areas, development agents scored below average on four of them namely program planning, monitoring and evaluation, subject matter expertise and ICT. In addition, based on bell curve appraisal approach of human resource professionals, maximum proportion of employees in an organization with low competency should not exceed 10%. But in this research, low competency was observed in all core competence areas that exceed the benchmark. This indicates need of critical diagnostics of competency development and design training and development programs appropriate for overcoming the weaknesses as significant proportion of DAs lack core competences that are basic to perform their responsibilities.
The research also discovered that the way agricultural colleges teach to classify farmers based on their willingness to adopt new technologies affected DA’s attitude towards farmers. As qualitative study shows, the most repeatedly word used by DAs to explain a “typical farmer” was the term “laggard/lazy”. Findings of this research also indicate that similar terminology is used by agricultural colleges to teach DAs to classify farmers based on their willingness to accept certain technology. DAs have been thought to label farmers who do not accept a technology as “laggards”. This way of teaching may mislead development agents to associate failure of certain technology with farmers’ laziness. Due
83 | P a g e to this, they may not see and search the rational decisions of farmers why farmers do the way they do. The survey result of this research also supported this finding that establishing a good relationship with farmers was found to be predictor variable that determine the competency level. Development agents who reported having positive relationship with farmers were found more competent than those who do not have good relationships. They are in better position to learn from farmers and to implement farmer-led approaches than those who have negative attitude that consider farmers as laggards, ignorant and with little knowledge.
In addition to relationship with famers, there were other sis predictor variables that had significant influence on competency of DAs. They were; education, relationship with co- workers, sex, supervisors’ visit and evaluation, and field of study (specialization). All of them had a positive significant relationship.
Regarding work motivation of development agents, although most of the DAs found in medium level of competence, significant proportion had low motivation and insignificant proportion reported having high motivation. When compared with previous studies conducted on motivation level of DAs, the figure indicates that there is a negative trend of motivation which is decreasing from time to time. In addition, perception on salary and constructive comments of supervisors were found to be predictor variables that had statistically significant positive relationship with motivation of DAs. In addition, most development agents do not actually want to be DAs. They rather want the DA training as qualification to do other degrees outside of agricultural extension profession.
5.2 Recommendations Based on the empirical findings, the researcher forwards the following recommendations:
The competence assessment indicate that development agents need additional training on four competence areas especially they have very low competence on ICT. It is recommended employer organization to prepare short term trainings to enhance their computer and ICT skills. Clearer policy frameworks are needed that ensures coherence among ministry of agriculture, Federal ATEVET, ministry of education, Agricultural colleges and community members. This will help to create alignment between what colleges teach, what development agents are required to perform extension programs and what farmers demand.
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Curricula of training institutions need reformation based on at least four major factors. First, there is a need to identify core competence areas that are basic to perform extension programs. And agricultural education programs should be aligned to these competencies. This will help to create some standards over what DAs should possess both in terms of technical and process skills. Second, there is a need to assess the negative impact of teaching DAs to label farmers and consider them as “laggards”. They use this term to shift blames for failed programs and the rational farmers who do not accept a certain technology considered because of their laggardness. This type of traditional top-down labeling approach has a risk of devaluing personal understandings and increase stigmatization that results failure of extension intervention. So, the curricula should address such type of classification of farmers in to groups by shifting to focus more on individual level contexts. Third, grading system of ATEVT program does not promote competition among students as it is determined as “pass and fail” principle. Fourth, the upgrading program time frame is another issue that needs to be reconsidered. DAs learn only one semester and return back to work for rest of the year and rejoined again. Based on qualitative study it created inconveniences in maintaining work life balance, catch up interrelated courses, and difficulty in English language skills. Unlike other colleges, they do not engage in admission process as DAs are selected by agriculture offices (non-academic staff). In this process, the likelihood of getting more qualified students may be affected. Colleges also do not have their organizational goals and strategic plans. Accepting new batch of students is also decided by agriculture office due to this, colleges do not follow academic calendars. In order to improve this, the Amhara Agriculture Bureau should empower the colleges by allowing them to exercise some sort of power in deciding in admission processes, formulating their plans, and follow academic colanders. The study reveals that establishing good relationship with farmers is a predictor variable that determines both competency and motivation of development agents. So, extension organization needs to assess training needs to fill gaps on basic human relationship and interaction skills. This is also related with attitude of DAs towards their job as well as towards farmers. Development agents mostly think that they are there to teach farmers and deliver new technologies without considering the other way around. Most on-job trainings focus on technical expertise.
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However, enhancing DAs’ abilities to establish good relationships with farmers should also be parts of trainings. As evident by studies conducted in the last ten years, level of DAs motivation is decreasing from time to time. In addition, based on the analysis, perception on salary and constructive comments of supervisors were predictor variables that influence work motivation of DAs significantly. In addition, majority of DAs have no interest to work as DAs. So, in order to change this, there is a need to implement evidence based applicable motivational packages. There is also a need to check on the way supervisors forward feedbacks to DAs.
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APPENDIXES
Appendix 1: Secondary Resource Guide Secondary information will be collected from district Agriculture office and ATVETs. Data sources will be reports, evaluation sheets, manuals, course outlines and other related documents that will provide information on the following areas:
1. National ATVET Strategy and Operational Standards regarding expected DA competency areas. 2. Rate of DA turnover in the last five years from district offices 3. Major reasons of turnover from district 4. Performance assessment and recruitment documents from district 5. Course outline and textbooks in ATVETS 6. Teachers’ profile in ATVETs 7. Criteria for recruiting DAs /minimum competencies required during selection from district 8. Supervision and support mechanisms? 9. What are major gaps of competency areas of DAs in terms of their job description
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Appendix 2: Focus Group Discussion Guide for DAs on their Competency FGD will be conducted with DAs who work in two of four study areas (Gozamen and D/Elies Woreda). Total of 16 DAs will participate in FGD in two separate discussions. Based on their consent, their voice will be recorded and latter will be transcribed. In order to make the transcription easier, numbers will be given from 1 to 8 for each participant and when every time the facilitator gives chance for participants to talk, he will call them by their number. After transcription, the information will be translated in to English. Finally most repeated concepts will be categorized together by giving codes to each for analysis. Different tools such as card games will be used to help participants to actively participate and exhort out what they know about issue of discussion. DAs will write their ideas on cards and post them on the wall and then cards will be categorized based on similarity. FGD participants will be purposively selected as per the following criteria:
Guiding Questions are:
1. Brainstorming Greeting and Introduction /ትውውቅና እና መግቢያ ወይም አለማውን ማሳዎቅ What motivated you to become DA? 2. Competency Think about all types of DAs and what services you provide to your clients. Write at least 3 activities/services you give on 3 different cards. Please describe what makes a good DA. Write 3 basic competencies that make a good DA on three different cards and post them on the wall. Let us discuss on their relevance. Then let us put similar competencies together under a certain topic. Finally we will rank them. In what way does your present knowledge and skill contribute to your job performance? Now give me an example of a DA who is in your opinion less competent, in which areas, and why is that the case?
Please describe the major challenges in your communication with farmers when it comes to technical issues? What do you think farmers could not understand sometimes? Let us divided in to two subgroups and identify three major challenges on flipchart.
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How can the social, personal and technical competences of DAs be improved by yourself, and by others (and who are “the others!)? 3. ATVET Related Question What has your training at the ATVETS contributed to your basic personal, social, methodological and technical competences - Personal: confidence, self-perception, self-image, - Social: ability to see connections, ability to communicate, rhetorical skills and communication techniques, ability to lead group work, act as team member, - Methodological: know how to organize learning processes, facilitate and moderate, competence to organize own work, prepare field visits, attitude for work, information management, ability to solve problems - Expert knowledge: ability to advise and offer solutions based on knowledge. 4. Selection Process and Motivation
What do you think were the criteria that lead to your selection as DA? What
Please describe existing motivational incentives for DAS. How they contribute to motivate DAs? How these can be improved. Use flip charts What can be done to increase your work motivation?
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Appendix 3: Key Informant Interview with district (woreda) Agriculture Office Extension department heads at Woreda level will be interviewed:
1. Background questions? Year experience, education etc? 2. How do select DA? What kind of criteria you use? Why do you think is that important? 3. How you define competency of DA’ 4. What mechanisms do you know to enhance DA’s competency? - 5. What are major gaps of competency areas of DAs in terms of their job description? 6. What do you think are major factors for these specific gaps of competency you mentioned of DAs in terms of: - Personal - Social - Methodological - Expert knowledge and skills 7. Can you tell me good achievements done by government in your woreda to motivate DAs 8. What government directions or administrative issues that may affect DAs motivation? In what way? Is it a good think or bad thing 9. What is your experience with DA’s turnover? Is it high or low? Why is that 10. What differences you observed among DA’s trained with different programs?
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Appendix 4: Key Informant Interview with ATVET Representatives ቃለ-ምልልስ ከ ATVET ተወካዮች ጋር ቃለመጠይቅ 1. Background questions. Work experience, educational background, etc. 2. What mechanisms are in place to update, modify and adjust course curriculum of DAs training? . የትምህርት ስልጠና ኮርስ ስርዓተ ትምህርቱን ለማሻሻል, ለማስተካከል እና ለማስተካከል ምን አይነት ስልቶች ተቀምጠዋል? 3. Which of your courses focus on communication skills and other process skills /የትኞቹ ኮርሶችዎ በግንኙነት ክህሎቶች እና ሌሎች የሂደት ክህሎቶች ላይ ያተኩራሉ/ 4. What are your major gaps of your institution /eg in terms of facility, teachers’ profile, etc/ that constraint effective teaching-learning process? /በተቋምዎ, በአስተማሪዎችዎ ወዘተ / ወዘተ / ውጤታማ የማስተማር-ማስተማር ሂደቶች ውስጥ ያሉ ዋና ዋና ክፍተቶችዎ /ምን ምን ናቸው? 5. Recent studies indicated that that quality of education in ATVETs is decreasing. What do you think are major factors? በቅርብ ጊዜ የተደረጉ ጥናቶች እንደሚያመለክቱት የትምህርቱ ጥራት በ ATVET ውስጥ እየቀነሰ መጥቷል. ዋና ዋና ምክንያቶች ምንድነው ብለው ያሰቡት? 6. What mechanisms are there with woreda and zone agriculture offices to get feedback in order to make what DAs learn in college and their job descriptions compatible? በወረዳና በዞን ግብርና ጽ / ቤቶች ግብረመልስ ለማግኘት ግብረ መሌስ ለመቀበል ምን አይነት ስልቶች አሉ?
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Appendix 5: Questionnaire for DAs Survey instrument was developed based on competencies drown from review of literature and from recent job description of DAs. The instrument has three major parts namely; personal background, competency and motivation. Competency part again subdivided in to 6 subsections. They are:
Agriculture Extension Program Planning Agriculture Extension Implementation Extension Monitoring and Evaluation Communication Specific Subject matter competency Education and Information Technology Personal Background
1. Sex 0. Male 1. Female 2. Age in years ______3. Work Experience as DA ______years 4. Area of specialization 1. Crop Production 2. Natural Resources 3. Animal Health 5. What is your highest level of Education?
1. BA/BSc Degree
2. Diploma (10 +3, 10+4, or advanced diploma)
3. Certificate (10+2, or 10+1)
4. Short training /7 month training/
6. Where did attended your education? Select ( ) one that applies. ____Mertolemariam ) ____Woreta
____Kombolcha
____ Other government ATVET
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_____Government University
_____ Private college
7. Are you currently working in your area of specialization?
1. yes 0. No
8. Are you currently pursuing advance education outside of your profession?
1. yes 0. No
9. Place of birth
1. rural 2. Urban
10. How long have you been woking in your current duty station /Kebele or village/ ------years.
11. How many times did you transfer/change work area? ……
Job Competency Would you please genuinely rate your competency with respect to the following on the scale?
(1= Very low 2= low 3= moderate 4= high 5= very high)
Very low: very low refers to the job holder is unable to do or perform a given task due to the lack of knowledge and/or skills required to perform the given task.
Low means the job holder possess only partial knowledge and skill to perform the task
Moderate refers to the job holder has adequate level of competency but sometimes may face difficulty
High means the job holder can perform the task without any difficulty and sometimes with excellence
Very high: the job holder is doing very well and executes the tasks beyond expected well it is extra ordinary
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The questionnaire based on two major sources. The first one is responsibilities of DAs set by the government and the second one is core competencies identified by Suvedi and Kaplowitz (2016). Based on this six broad competency areas were identified.
1. Agriculture Extension Program Planning Competencies DAs should be: What is your level of knowledge or skill to perform this task Very Low avera High Very low ge high 1 Familiar with goal, vision and mission of agriculture extension system 2 Knowledgeable about national livestock development strategies, programs, and policies 3 Able to conduct assessments to identify needs, constraints and potentials of farmers 4 Able to identify, document and use effectively indigenous resources and knowledge 5 Prepare workable plan on annually, seasonal, monthly and weekly basis
2. Extension Implementation Competencies DAs should be able to: What is your level of knowledge or skill to perform this task Very Low avera High Very low ge high 1 Demonstrate teamwork skill to achieve extension results 2 Training facilitation and presentation 3 Demonstrate good professional ethics 4 Ability to comprehend social world of farmers such as social relationships, roles, their needs family history, their view point of seeing
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things such as a new technology, why do they do the way they do, etc 5 Competence in conflict resolution techniques 3. Extension Monitoring and Evaluation Competencies DAs should be able to: What is your level of knowledge or skill to perform this task Very Low avera High Very low ge high 1 Conduct monitoring and evaluation in extension program 2 Design data collection instruments such as survey, interview, FGD, observation, etc 3 Conduct data analysis /Analyze data (qualitative and quantitative), interpret data, and write evaluation report 4 Share monitoring results through success stories, lesson learned and monitoring reports 5 Prepare genuine and quality monthly, quarterly, and annual progress reports of their works.
4. Extension Communication Competencies DAs should be able to: What is your level of knowledge or skill to perform this task Very Low avera High Very low ge high 1 Respect and consideration of social values /way of greeting, tone, dressing, taboo, etc/ while communicating with clients 2 Have a respectful attitude towards farmers and the ability to understand the individual situation of each farmer.
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3 Possess good listening skills and listen to all clients and stakeholders with equal respect. 4 Understand and explain complex and technical issues in a comprehensible manner. 5 Ability to trace and clear misunderstandings 5. Specific Subject Matter competencies DAs should be familiar with: What is your level of knowledge or skill to perform this task Very Low avera High Very low ge high 1 Basic Bio Veterinary Competencies (Anatomy, Physiology and Biochemistry 2 Animal Production Competencies (Animal Nutrition, Poultry and Dairy ) 3 Crop production /land preparation, pest and disease control, seed selection 4 Knowledge of basic concepts and tools for value chain approach 5 Soil conservation works /acidity treatment, compost preparation, fertilizer usage/ 6. Education and Information Technology DAs should be able to use: What is your level of knowledge or skill to perform this task Very Low avera High Very low ge high 1 Microsoft windows /MS word, excel, power point / for data entry, typing, editing, printing and 2 Use internet to refer and download latest journals, research reports and new innovations on agriculture and extension system 3 Internet for email and/or social media to send and receive messages
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DA Motivation
Totally 14 items are prepared to measures motivation related with motivational factors such as satisfaction, attitude, recognition, promotion and working condition.
Instruction: Would you please genuinely rate your motivation with respect to the following on the scale?
Scale: 1= strongly disagree; 2= disagree; 3= undecided; 4= agree; 5= strongly agree S/n Motivational Factors 1 2 3 4 5 1 I have a chance to do things for which I am most qualified 2 I often think to stay in Agriculture profession /attitudinal/ 3 I feel that my effort has brought change in living condition of farmers in my working Kebele /attitudinal, satisfaction/ 4 I am given opportunity to improve my competency through training / 5 I receive recognition from my organization for my good performance (Recognition) 6 There is clear set of criteria for promotion in my organization (promotion) 7 I feel I am being paid a fair amount for the work I do /salary/ 8 I receive monetary and in-kind incentives often /incentive/ 9 I have good relationship with co-workers /relationship/
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10 I have good relationship with farmers 11 I have the necessary tools and equipment I need to perform my responsibilities /work condition 12 I receive constructive comment and support from my supervisor 13 Supervisors visit and evaluations are clear and objective 14 I am motivated working as DA 15 I find farmers are eager to change and to adapt new technologies
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Appendix 6: Multicolinearity test Two ways of testing multiconlinearity test. They are:
- VIF / Variance Inflation Factor/… for continues independent variable. It is not applied for this research because ind variables were not continues. - Contingency coefficient was used as independent variables were discrete or categorical variables.
. correlate Agecatego Exprecoded rlshpfar rshpcowr Recodedinst Sex suprvisr Edurecoded Specializrec Position
(obs=149)
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Appendix 7: Distribution of competence scores
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Appendix 8: Pictures of FGD process
Fig 1 and 2: Female and male focus groups in Goazamen woreda
Fig 3 and 4: DAs explanations of what makes a good DA and typical farmer
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