The Effect of Farmers Education on Farm Productivity. Evidence from Small-Scale Maize Producing Farmers in North Bench District, Bench Maji Zone

Master's Thesis, 2019

94 Pages







1.1. Background of the study
1.2. Statement of problem
1.3. Objective of the study
1.4. Significance of the study
1.5. Scope and limitation of the study
1.6. Organization of the paper

Chapter Two
Related reviewed literature
2. Introduction
2.1. Theoretical Framework
2.1.1. Education and Economic growth
2.1.2. Effect of Education on farmers productivity
2.1.3. The effect of agricultural extension on farm productivity
2.1.4. Determinant of farm productivity
2.2. Empirical studies
2.2.1. Evidence from developing countries
2.2.2. Ethiopian Evidence
2.3. Conceptual framework
2.4. Estimation of farmer education on farm productivity: Review of competing methodologies
2.4.1. Non frontier production function Approach
2.4.2. Frontier Production Function Approach

Chapter Three
Methodology of the study
3.1. Description of the study area
3.2. Research Design
3.3. Types, Sources and Methods of Data Collection
3.4. Target Population
3.5. Sample Size and Sampling Techniques
3.6. Method of Data Analysis
3.6.1. Descriptive Method
3.6.2. Empirical model
3.6.3. Definitions of variables and hypothesis setting
3.7. Statistical and specification test

Chapter Four: Results and discussion
4. Introduction
4.1. Descriptive results and demographic characteristics of farmers
4.1.1. Sex of respondents
4.1.2. Age structure of the respondents
4.1.3. Farm experience in maize production of respondents
4.1.4. Formal school attending
4.1.5. Educational Achievement
4.1.6. Land owner ship of respondent
4.1.7. Extension service contact
4.1.8. Utilization of Improved seed and chemical fertilizer
4.3. Production function regression
4.3.1. Basic production function regression
4.4. Effect of farmer education on farm productivity
4.5. Effect of education through input choice
4.6. Percentage gain per year of education

5.1. Conclusion
5.2. Recommendation


Annex - I: Survey questionnaire

Annex -II: Interview question

Annex -III: Correlation coefficient between Variable

ANNEX V: Testes for Multicolliniarity and Model specification

Annex IV: Econometrics results

Annex VI: percentage increase in output per year of education


Table 3.1. Sample size determination

Table 3.2. Number of sample from each Kebele

Table 4.1. Sex of sample respondent

Table 4.2. Age Group of sample farmers

Table 4.3. Farm experience of the sampled respondents

Table 4.4. Frequency distribution of farmers attending formal schooling

Table 4.5. Level of education completed by household head by sex

Table 4.6. Land ownership of the respondents

Table 4.7. Exposure to extension contact service

Table 4.8. The utilization of improved seed and chemical fertilizer

Table 4.9 variable definition and its mean value

Table 4.10. OLS and WLS estimate of Cobb- Douglas production function: (without education)

Table 4.11. The OLS and WLS estimate of Cobb- Douglas production function: with Education

Table 4.12. OLS regression coefficient of the effect of education on input choice


Figure 2.1. the conceptual framework

Figure 3.1. Map of the Study area


Abbildung in dieser Leseprobe nicht enthalten


The educational system in Ethiopia is differentiating by low participation rates, particularly in rural areas. The agriculture productivity and income of rural famer is increased by farmer education (both formal and informal education). The objective of this study is to examine the effect of farmer education on farm productivity of small-scale maize producing farmers. In order to achieve the objective of the study cross-sectional data has been collected from 200 maize producing farmers on the production level, farm size, farm input and equipment used, educational level, farm experience, gender, age, secondary occupation, etc. Semi-Structured questionnaire has been administered, and interview was conducted for selected farmers in order to collect the relevant data. Both descriptive statistics and econometrics model were used to analysis the data collected from household head. Cobb-Douglas production function model has been used to analysis the effect of farm education on farm productivity by including the education level as input of production. The main finding of the study was that higher education contributes to productivity. Hence, as educational level increase, output will increase with secondary school education. Thus, the return to higher education is having the highest return on agricultural productivity. Extension contact service is positive effect on farm productivity even though the coverage is very low. Thus, the study conclude that formal schooling opens the mind of farmers to awareness about the adoption of new farm technology , non- formal education propose the farmer offer on guidance and better method of farming , and informal education keeps the farmer together with each other on changing modernization and ideas. It recommended that to increase their productivity the farmers in the district should have required skills and knowledge in modern farming method and be able to know simple instruction on the use of modern farm inputs.

Key word: Cobb- Douglas production function model, Education, productivity



1.1. Background of the Study

In developing countries, agricultural growth is important for poverty reduction because most of people derive their livelihood from agricultural production. Thus, the means of making better agricultural production widely acknowledged as the main strategy for escaping poverty (Otsuka and Larson, 2013). Sumner ( 2012), note that Sub Saharan regions accounts for approximately 26 percent of people were in extreme poverty who live with the income of less than $ 1.25 per day, the sever problem is productivity is not significantly increased over the decades and its output has not kept speed with population growth (Teklewold, H., et,al, 2013) . Therefore improving the agricultural technology as a means of increasing farm productivity seems a crucial strategy.

According to Ani (2007), improving the farmer's ability for rising agricultural productivity is a pre-requisite for social and economic development for rural areas. This is because agriculture forms the bedrock of economic activities in the rural area. Obviously, development, food security and poverty mitigation will not be truly achieved without rapid agricultural growth in developing country. Assisting the rural poor to enhance their livelihoods and food security in a sustainable manner is a great challenge. Broadly put, increases in agricultural productivity are central to growth, improved food security and alleviation of poverty in rural Africa (FAO, 2002).

Schult(1961) and Becker (1962) have been the main advocates of human capital as determinant of economic growth. According to his view, human capital is the capital good whose value depends on five main categories of investments in human beings: 1). Health, including nutrition, 2) migration, enhancing job opportunities, 3) on the Job training, 4) formal education, 5) study programs for adults , such as extension service in agriculture.

Education has been recognized to be a leading device for shaping people's life and making life important, even at adult age. It makes sense that there exist a positive relationship between education and human continued existence (Ani, 2007). Therefore, education becomes an appropriate way for agricultural development process and productivity of farmers. The farmer's ability to deal with the disequilibria induced by technological differences over time improves with education (Luh, 2009).

Hanushek,et,al., (2007) point out three mechanisms through which education may affect economic growth. First, education can increase the human capital (quality of labor) of the labor force, increasing labor productivity and thus transitional growth toward a higher equilibrium level of output (augmented neoclassical growth theories) (mankiew,et,al., 1992). Second, education can increase the innovative capacity of the economy, which encourages economic growth (endogenous growth model,) (Romar, P, 1990). Third, education can make possible the diffusion and transmission of knowledge needed to understand and process new information, which again promotes economic growth (Benhabib,J and Spiegal, M, 1994).

Highest agricultural productivity depends primarily on the education of the rural farmers to understand and accept the complex scientific changes that are difficult for the uneducated rural farmer. Hence, we cannot increase the productivity of the rural farmer without the provision of adult education (Onwubuya, E. , 2005). Education may enhance farm productivity directly by improving the quality of labour, by increasing the ability to adjust to disequilibria through its effect upon the adoption of innovations and in a rapidly changing technological or economic environment (shultz,W , 1964,1975). Thus, as educational level of farmers increases, the output increases having highest yields in agricultural productivity.

Extension service is one of the non-formal educations that have greater impact on farmer productivity. non-formal education gives the farmer better skills on that they train for particular task and better methods of farming, as well as offer them for innovation, ideas and permits the farmers to share experience with each other (Oduro,et,al., 2014). Agricultural extension service have been recognized as a complementary input for increasing farm productivity and it is connect the gap between the level of available technology and the technology adopted by farmers (Adhikari, 2016).

Asfaw & Admassie (2004) study notes those Ethiopian farmers have faced frequently varying input and output price under the new government. In addition, erratic weather, pests and crop disease all contribute to an environment in which farmers must adapt frequently in order to survive. As a result, there may be an efficiency advantage for farmers who are better prepared to predict and cope with disequilibria. Thus, even in the absence of innovation, farm productivity may be enhanced by investments in education. Since farming methods in Ethiopia are largely traditional, there appears to be little economic justification for Ethiopian farm households to invest in education. As technological innovations spread more widely within the country, the importance of formal schooling to farm production ought to become more apparent.

The various interrelationships between education (schooling), and farms productivity in North Bench district is not well known because of there is no empirical studies that have been conducted in this direction. This trend can be attributed to the qualitative nature of most of the human capital variables that affect agricultural farm productivity. Therefore, given the agriculture based economy of Ethiopia and the dominance of smallholder sub-sector, it is imperative to conduct a study which focuses on identifying the effect of farmer education on farm productivity. This paper aim is to identify the benefits of formal schooling and non-formal education on agricultural productivity of small-scale rural maize producing farmers in North Bench district, Bench Maji Zone.

1.2. Problem statement

An improvement in farm human capital through farmer education is important for enhancing farm productivity. Education is the basic tool that should be given to rural farmers in order to increase productivity and income from agriculture (Kotze, D.A., , 2003).

In order to increase productivity there is a need of knowledge and skill of farmers in farm production . Moreover, it is better to the farmers when they produce with modern and improved technology. Therefore, in this regarded the farmers education plays a greater role by providing them skill and knowledge about their production . So, Investing in human capital is necessary for raising farm productivity, which is a key to the improvement of living standards of farmers. However, in North Bench district most of the farmers were not attend the formal schooling as well as low attention to extension workers. Even the economy of district is dependent on agricultural sector. More than 94.3 % of the population who lives in rural areas, their livelihood depends on agricultural product (Agricultural & Natural resource office of district, 2017). Production in North Bench District is distinguished by low yield as well as returns to farm labor and land are low. This low level of productivity is arising due to several factors, among which the small size of farm-holdings, use of traditional farming system and low educational level and training. Thus, to identify the direction of human capital that will important for increasing farmer's productivities, it is significantly essential to investigate the effect of farmer's education on the farm productivity in the North Bench District.

So far, the relationship among farm productivity, and education and extension training is not well known because of there are no empirical studies that have been conducted in this way. Weir (1999) on his study drawn from the Ethiopian rural household survey which is collected from 18 peasent assocation from six region shows at at least four years of formal schoolling is siginificant effect on farm productivity. However, his study has geographically limited since Ethiopia has diverse in climatic zone , the results acquired from one region can not be widespread for the whole country. A study by knight (2004) on extarnality effect of education in rural Ethiopia from Ethiopian rural houshold survey of 1994 found that educated farmers are quikily copy the innovations and new farm practices. However, the above study dose not consider the farmers ability to imitate the technology form nigboors, since not in view of the non- formal and informal education of farmers. They also faild to examine the impact of educational level of farmers for the use of input effectively. Therefore, there is a need of further investigation on the effect of farmers education on farm productivity at district level.

For the current researcher there is no convincing empirical research done on the effect of formal and non-formal education on farm productivity of small-scale farmers. In addition, there is little evidence in the area to suggest that the agriculture sector's low education level is what affects its contribution to GDP. Therefore, conducting this study was creating to be highly significant to slight the existed knowledge gap and time gap. Thus, this study aim that to identify the possible benefits of schooling for households engaged in agricultural production and to quantify the effects of education on productivity of maize-producing farmers in North Bench District, Bench Maji Zone.

This study answers the following research questions.

- What are the major factors affecting farm productivity (output per hector) in maize production in the study area?
- What has been the effect of education and contact to agricultural extension service on the small scale rural maize producing farmers' productivity in study area?;
- What are the possible benefits of formal and non- formal education for farm productivity in the study area?
- What relevant recommendation can be made to inform policy on the effect of farmer education on farm productivity?

1.3. Objective of the Study

The overall objective of study is to examine the effect of farmer education on farm productivity of small-scale maize producing farmers in North Bench District, Bench Maji zone.

The specific objectives of the study include:

- To analyze factor affecting agricultural productivity of maize production in the study area;
- To estimate the impact of education and agricultural extension service on farmers productivity in the study area;
- To identify the possible benefit of formal and non- formal education for farm productivity in study area;
- To provide recommendation that will inform policy on the relationship between farmer education and farm productivity.

1.4. Significance of the Study

Ethiopia has focus on agricultural sector as the base of economic transformation. It is the key sector in the current governments' development strategy because Agricultural Development Led to Industrialization (ADLI) is the economic policy of the country. Social development as well as economic growth and development have also been linked with education (Asafu, 2010) . Thus, the topic of this research and its finding has regional and national importance. This paper is very important in examining the effect of farmer education on agricultural productivity and serve as the baseline for further study who investigate the related issues in the area. The result of the study is useful for identifying the problems that hamper agricultural productivity of farmers, and propose the remedial to the concerned government bodies for solution. It adds to the knowledge and understanding of the farmers by providing information in relation to the productivity. The study also intended to help farmers, the government, and the ministry of agriculture, NGOs, researchers, agricultural extension officers and adult education facilitators to understand the underlying factors that influence a maize producing farmer's productivity. The study helped farmers' to identify most favorite methods to improve their productivity. It was also hoped that farmer associations might also benefit from the findings of this study because the findings can be incorporated into teaching methods to improve the content and make agricultural education programmers meaningful to the needs of the maize farmers.

1.5. Scope and Limitation of the Study

The study was limited to North Bench district only due to the distance, availability and shortage of time. It is mainly focuses on the effect of farmer's education on farm productivity of small­scale rural maize producing farmers. The years of schooling attended by all household members is not used. Since some household members, such as young children and the elderly, participate less in agricultural production and decision-making. Thus, this study focuses only on the education level of household head. Similarly, limited financial resource affects the large coverage and wide scope of the study. Access to some centers and communities is limited due to poor infrastructure especially in the study area.

This study estimates only “worker effect” of education. Estimating the “allocative effect” was not possible because in estimating allocative effect, the dependent variable must be total farm output aggregated over at least two crops. There was limited empirical research in the area to get sufficient data to form basis for current research. Farmers are not meet in their home during collection of information from them. Most rural farmers cannot read and write, this makes the use of questionnaires difficult and interview schedule was use to draw out information from the respondent. There is no proper record where farmers list can be used for simple random sampling procedure.

1.6. Organization of the Paper

This paper consists of five chapters. The first chapters introduce the benefit of schooling in farm productivity. The second chapter surveys the literature on return to education in agriculture. In third chapter it covers the accessibility and suitability of data and methodology. The Result of study is presented in fourth chapter. Fifth chapter concludes the paper with the summery of finding and policy implication for possible solution to problem.



2. Introduction

The literature was reviewed in the area of general aspects of farm productivity over world and Ethiopia focuses on the estimation of private and social return to education, and impact of human capital. The core issues discussed include the importance of education to economic development and poverty reduction, and education as the source of technical change, especially increasing the agricultural productivity. A study also reviewed on the determinants of farm productivity. The last part is on the competing methodologies used in estimation of education on the farm productivity at household level data.

2.1. Theoretical Framework

2.1.1. Education and Economic growth.

Education is widely believed as an important role in economic growth. At aggregate level, there are strong theoretical reasons for linking the expansion of education to higher rates of economic growth. Solow(1956), argue that changes in national income are determined by changes in country's stock of physical and human capital. The new economic growth theories that are discover by Romer (1986) and Lucas ( 1988) confirm the human development that deriving force of all economic growth is people. In that theory increasing productivity is not in an exogenous factor but in endogenous, those related to the behavior of people responsible for accumulation of productive and knowledge. According to Panin (1999) the human capital model shows how education allows the whole production process to benefit from positive externalities. Educated people use capital more efficiently, so it turns into more productive. They are more likely to innovate, thus, to develop a new and better form of production. Moreover, they spread the benefit to their co- workers. Who learn from them are become more productive. Thus, raising the level of education can rise in the efficiencies of all factor of production (Panin, 1999).There is long run relationship between education and real GDP per capita (Nowak.A, 2016).

Education as an investment in human capital has been considered as a growth factor to increase labor productivity, reduce income inequality and poverty (Amin & Awung, 2005). Arrow (1973) also reports that the productivity - adding human capital theory suggested that education adds to individual productivity and that leads to increase the market value of his/her labor.

Education is broadly expend as the act of acquiring knowledge, skills, values, attitudes and best practices (Asenso-Okyere,et,al, 2000). Education is also the source of technical knowhow and improvement on technical knowledge and, enhance of labor efficiency (Diwan, 1971). Economic benefit of schooling includes the potential to obtain employment or to generate income through self-employment, using skills learned in school (Knight, 2003) . According to Asenso ( 2000), education can be divided into two-border categories namely formal and informal education. Formal education has been recognized as the most effective way to develop the human potential. It represents that all forms of education that requires people to acquire skills through planned system or institution recognized by minister of education. Therefore, that formal education is divided as basic (primary and junior secondary), secondary, and tertiary education. Whereas, informal education largely deal with the education of adults or people not through the means that are rigid and do not follow formal class room education end in the award of certificate or degrees. To a large degree, informal means of education are aimed at making people either functionally literate or enable them acquire some skill or vocation. There are different informal education such as Adult education (non-formal education), artisanal training (apprenticeship), and extension education (Asenso-Okyere,et,al, 2000).

Weir (1999) point out that formal schooling not important only after new technologies have been adopted but education may also helps farmers to decide early adopter of innovation and the extent to which innovation will be used. There are at least three reasons for this event. First, those with schooling tend to be more prosperous and less in danger of starvation even if a potential innovation is unsuccessful. Second, educated farmers may be more likely to be contact by agricultural extension workers looking for model farmers to test innovations. Finally, literate farmers are better ability to acquire information about possible innovation and make rational progress of the risks involved in trying new inputs, crops or methods.

Understanding the impact of education on economic performance of a country requires taking in to account the specific institutional arrangement that comprise the structure of incentives and restriction at a certain moment. Education can encourage economic growth if it imitate positively on the individual income and the economy productivity (Marius. C, 2013). Future growth and social welfare will depend on knowledge intensive industries and services. Human capital is factor of production accumulated by the individual through education and it is causes the higher production (Dumciuvienea, 2015).

Education, particularly primary and lower secondary education contributes to poverty reduction by increasing the productivity of the poor labor (Lockheed., et, al., 1980; Moock, 1994; Philips, 1994 ; Villaume, 1977; world bank ,1995). As stated by Singh. ,et, al.,( 1986), education should result in an economies growing demand for flexible workers who can willingly to acquire new skills to support the sustained expansion of knowledge in the nation. In addition, higher education contributes to self- sustaining growth through the impact of graduates on the spread of knowledge (Becker, 1964). Workers that are more educated can know to deal more effectively with rapidly changing environments (Schultz, 1971, 1975; Mincer, 1974; World Bank 1991). Moreover, schooling may speed the modification in use of new technologies (Huffman W. E., 1974) . Most empirical studies Lockheed, Jamison and Lau, 1980; Moock, 1981; Philips, 1994; Weir, 1999) on the impact of farmer's education on farm productivity have recognized that primary education is very important in increasing productivity.

Education has also been identified as a driver of improved demographic and health outcomes, contributing to decreases in infant mortality and fertility over the second half of the 20th and early 21st centuries (Gakidou, 2010) , and (Barro, 2015). Education plays a central and significant role in economic growth. The higher is the quality of the education, the greater impact of education on the economic growth (Sylvie. K, 2017) . According to the World Bank (2001), education is central to its strategies for helping the counters' to reduce poverty and improves their living standard through sustainable growth and investment in people.

Education directly affects the economic growth insofar as it is essential to improve human capital. An economy's production capacity depends on different factors. These include physical capital, technology and the numbers of workers, as well as their quality. This quality is largely determined by human capital (the stock of knowledge, skills and habit). Thus, an increase in the workers educational level improves in their human capital and increasing the productivity of this workers and the economy's output (Canals, 2017). The primary device through which to increase human capital is education.

There are two different perspectives on education: one perspective views education as human capital development for economic growth and the other views it as a mechanism for social quality (Byrd, 2016). Human capital is like any other type of capital that can be invested in education and training to enhance benefits for an improvement in the quality and level of production. Therefore, human capital is most important factor in economic growth and social change (Todaro, 2015). The tertiary education is important for growth in countries above the certain technology level which themselves are innovation producers whereas lower level of education are important for countries which can only copy and imitate technological leaders (Ljungberg, J., & Nilsson, A. , 2009).

2.1.2. Effect of Education on farmers productivity

Agricultural education is the type of education that leads to achievement of practical skills and assist farmers in obtaining and developing skills that would be ultimately transferred to job opportunity in the society (Oduro,O, 2015). The productive value of education has two main effects on agriculture: “worker effect” and “allocative effect” (Welch, F, 1970). Worker effect means the farmers with more education are produce more output from a given level of input. It is seen as increased output per a unit change in education keeping all other factors constant. Hence, worker effect means additional output gain from a unit change in the education. Whereas, with allocative effect, a worker is able to acquire information about cost and characteristics of input and interpret the information to make decision that will enhance output. In his study conducted in Nepal, India (Pudasaini , 1983), discover that the allocative effect of education on productivity is more important than worker effect indicating that key way that education influences agricultural productivity is by improving the ability of farmers to take decision concerning the selection of input and combination of input for better output. He declares that there are three main ways through which education enhances agricultural productivity: Improvement in farmer's skills, enhancement of farmers' ability to utilize farm input, and improvement in managerial ability of the farmers.

The effect of education on agricultural productivity can also be described as cognitive and non- cognitive as point out by (Appleton, S., & Balihuta, A. , 1996). A cognitive effect of education comprises basic literacy and numeracy that farmers achieve from education. Literacy enables farmers to read and understand information on inputs such as chemical fertilizers and pesticides among others. Numeracy allows for calculation of the right quantity of inputs to be combined to get the desired output. Similarly, Asadullah, M & Rahman, S, (2005) research conducted on 141 villages consisting of rice farmers with in Bangladesh, found out that schooling has positive effects on agriculture due to the skills of literacy and numeracy that give the farmers better understanding into agricultural issues. Concerning non- cognitive effects, there is a change in the attitude of farmers who attend school and this is because of discipline of formal schooling in terms of punctuality, teamwork, correctness, adhering to schedules and so on. on the other hand, non- cognitive effect on agriculture has not been widely studied and the inference of its effect on agricultural productivity are few as it is assumed that change in farmer's behavior as a result of education make them more susceptible to new ideas and modern practices. Education influences agricultural productivity either directly or indirectly. Indirectly, with the skills derived from education, farmers are able to engage in activities in the non- farm sector that serves as alternative source of income for agricultural activities (Appleton, S., & Balihuta, A. , 1996 ; weir,199).

The better educated farmer is quicker to observe profitable new process and product since the expected payoff from innovation is likely to be gerater and the risk likely to be smaller (Nelson,R& phelps,E, 1966). Lele ( 1990) has observed that an improvement in farm human capital through farmer education is essential for increasing agricultural productivity. Farmers who have acquired high education are likely to implement new technologies past than others are with low educational levels, and use inputs that make them more productive. Alene (2007) present that effect of education on productivity enhancing is positively under improved technology. Another important point is education effect is different on technology adopters and non- adopters (Alene, 2007).

The low earning from farms are partly the result of their relatively low level of human capital endowment and partly of labor market discrimination. Therefore, Education is critical for economic growth and poverty reduction and if the nation is to achieve high productivity and incomes from small and medium scale farms and eventually alleviate poverty, then the relationship between human capital development and productivity in agriculture should be explored fully (World Bank, 1995). Education is significant input in agricultural production and important input when the firm engaged in activities that involve more complex decision-making (Gallacher, 2001). Knowledge and skills delivery could be an integral part in farmer's capacity to generate higher growth in agricultural productivity (Betz, 2009).

The returns to education differ with the level of education and the type of education. A regards educational level, there are mixed evidence from literature as to whether primary or secondary education has most returns to agriculture but despite that it is generally agreed that returns on tertiary education is very minimal or non - existence (Appleton, S., & Balihuta, A. , 1996; Asadullah, M & Rahman, S, 2005; Reimers,et,al., 2012). Lockheed., et, al., (1980), argues that primary schooling is more criucial then secondary schooling for gricultural productivity because it gives farmer basic numercy and literacy. It was relized in their resreach that an additional year of primary scooling incearse agricultural productivity by 7.4% which has supported by Appleton, S., & Balihuta, A. ,( 1996) who gatherd that four years of primary schooling rasied productivity by 7% while completing primary schooling increase crop production by 13%. Pudasaini (1983) also stated that as education level incearse the the rate of productivity declines hence there is diminishing marginal productivity with regards to education. However ,these statements have been opposed by recent studies conducted by Reimers & Klasen (2012) on the sample of 95 developing, who descovered that returns to secondary education exeeds that of exceed that of primary education because it is not only the ablity to read and write that gives higer agricultural productivity but ability to do critical thinking in addition to application of knowldage gained. Secondary education can be side to enhance the allocative effect of education on agricultural productivity in addition to indirectly contiributing to productivity by providing a means to obtain non farm income that can be used in the acquisition of inputs (weir, 1999). Education is highly correlated with productivity . Specifically, workers with formal education are more productive than those with no formal education (Jones, 2001). education affects workers' productivity by enhancing individuals' ability to learn from experience (Marconi, 2012).

The role of education in farm production is improve the ability to learn tecnologies and to provide the productive capacity. To accumliate expertise in techenology is determined by the schooling of the agent that runs it (Mateos, 2017). The farmers increasing their productivity potential by developing and refining their capablites thourgh education. The more they know about the farming, the more valuable productivity gain (Radcliffe, 2018). Agricultural Education is significantly related to agricultural output. There is statistically difference between income and output of farmers with education and without education. Therefore the farmers ‘should be encouraged to participate in adult education schemes using incentives and agricultural policy should be in cycle on the farmer education (Okpachu, A. et,al, 2014).

2.1.3. The effect of agricultural extension on farm productivity

Formal and non- formal education can be seen as complementary in terms of increasing agricultural productivity (Lockheed., et, al., 1980). This means that formal schooling without help will not improve agricultural productivity if is not combined with extension services and mutual learning and sharing of ideas among farmers necessity the need for combination of both to improve productivity. There are technology gap and management gap between actual and potential productivity. Extension service is one of the most crucial systems that facilitate the access of farmers, value chain and market actors to knowledge and one of the channels to reduce the productivity differential (Anderson, 2004).

A number of studies results that extension has contributed to increased productivity and farm income (Huffman ,1976; Jamison, 1984; Birkhaeuser, 1991; Owens, 2003; Anderson, 2004). Jamison and Moock (1984) surveyed the rural household of two districts in Nepal related to three major crops; early Paddy, late paddy and wheat. They showed that the impact on wheat increased by 4% in 10% increase in extension contact. Owens et al.( 2003) also measured the impact of extension on the value of crop production per hectare with the number of extension contact in Zimbabwe and found that the value of crop production increases approximately by 15%, significantly. Evenson(2001), used the data set from 1982 for estimation and its information was on crop production, extension workers, schooling year of farmers, and farm inputs. The results indicated that extension service had a significant impact on four major crops including cotton, manioc, maize and potatoes in Kenya. However, its impact was not significant on minor crops such as sugar, peanuts, and tomatoes. Study by Patrick ., (1973) and Huffman (1976) also presented that extension had a positive effect on farm productivity generally.

Formal schooling does not necessarily increase farmer productivity but rather non-formal schooling. The illiterate farmer is able to learn new ideas and modern technology from neighboring educated farmers and from the mass media like radio and television; hence emphasis should be placed on non- formal education like extension rather than formal schooling (Elias, 2013). Non - formal education that has considered in terms of understanding, experience and extension service visits lead rather to significant increase in productivity than the year of formal schooling or educational level of a farmer. Therefore, Non -formal schooling increase agricultural productivity through the mutual learning among farmers and extension service (Feder, 1987).

Extension service is one of the critical components of rural development, and has been offered to contribute the reduction of hungry and poverty by increasing the acceptance of new technologies, and capacity of individual farmer (Nepal, 2016). The agricultural technical information distributes by extension enables the farmers to increase capability to adopt the new technologies and inputs. Thus, Household's head with access to extension has high gross farm revenue and profit, and agricultural extension has positive and significant impact in farm production (Yeyoung, 2017).

The key mission of extension agent is to support and encourage farmers to enhance their productivity (Adesoji, 2009). They are liable for translating the finding of research institutes to farmers and sending the agricultural challenges of farmers to research institute (Ajani, 2013). Therefore, the extension agent try to improve the livelihood of farmers by transferring research based knowledge to the agricultural sector (Rivera, 2011). In addition, an extension service influences farmer to adopt innovation as well as transferring technology and it is the one of the human development program that affects farm productivity (Akinbile, 2009).

2.1.4. Determinant of farm productivity

The factor determine farm productivity are access to land, access to input such as seed and fertilizer, labour, age , income , and education ( (Kilonizi, 2011). Reardon et al (1996) uncovered difference in patterns and determinants of farm productivity over agro climatic zones, types of technology, degradation of environment and level of improved inputs. They reported on the studies on the data from Burkina Faso, Rwanda, Senegal, and Zimbabwe. Generally, the main finding of the studies indicated that, the rate of growth in yields and returns per Labour -Day were low in the four study countries. They also identified productivity determent in the four study countries to include fertilizers, improved seed, animal traction, organic input and conservation investment. Other determents were farm size and land tenure, non-cropping income and well functioning input and output market. They recommended that to improve long-term food security in Africa, farmers must be able to follow the sustainable growth of farm production by the use of improved seed.

According to Kilonizi ( 2011), separately from social economic factors farm yields can be affected by two main factors namely natural and technical factors. Natural factors are physical and biological factors that affect agricultural production and they may environmental. They include climate, pests and diseases, soil and water. Drought and floods have increased in frequency and intensity in the immediate past three decades resulting in high crop failure and livestock death (Alila, 2006). The farmers not do much on natural factors apart from adopting explanatory approaches to reduce effects like adopting appropriate technologies. Technical factors are technologies use in production. They include cropping system, how the farmers improve their land soil fertility and knowledge contacted through extension service ( (Kilonizi, 2011). The use of inappropriate technologies by small-scale farmers is the main reason for low crop productivity and food insecurity (Buyu, 2002).

Tessema (2014), used the panel data from Ethiopian rural household survey (ERHS) found that the determinant of agricultural productivity in rural household do not much vary across labor productivity and land productivity. Cultivated area of land per unit of labor ratio, the number of household member size, the use of fertilizer , the use of extension service, the use of pesticides , the use of manure , age of household head are the main determinant of agricultural labor productivity . Similarly, labor-land ratio, use of fertilizer, the number of household member size, the use of extension service, the use of pesticide, the use of manure, the number of oxen used and age of the household head are the main determinants of agricultural land productivity. He use fixed effect model. He found that cultivated area of land per unit of labor is the most significant determinant of labor productivity. Fertilizer input and the number of household size were the most significant determinants of land productivity.

Strauss,, (1986) use household level data from Sierra Leone to test weather higher calorie intake enhanced family farm labour productivity. He estimated a farm production function using non-linear two stage least square (NLTSLS) developed by Amemiya (1983), accounting for simultaneity in input and calorie choice. He found that highly significant effect of calorie intake on labor productivity at farm level. In estimation procedure, both the calorie and calorie-squared coefficients were significant at more than the 0.01 level, with calorie consumption contributing positively to output. Nutrition (measured in calorie intake) is to be a major determinant of farm productivity. Because nutrition and health variables are known to be collinear with education (Strauss, and Thomas, 1995), this was excluded from this study.

Bihon, ( 2015) using the cros-sectional data from farm household in tigrey regional state founds that the farm productivity in the regional state of tigray is mainly determind by the utilization of chimical fertilizer, irregation, improved seed and low land size under farm production. Dagnaw, (2008) also state that Poor agricultural policy, landlessness, land fragmentation, environmental degradation, population pressure, drought, famine, war, and political crisis have all determine the agricultural productivity of Ethiopia. In addition to that low socio- economic status, poor weather conditions, lack of basic infrastructure have undermined agricultural growth and reduced the labor-absorption potential of farming in Ethiopia (Dagnaw, 2008). Chemical fertilizers and improved verities of seed are critical agricultural input that helps the farmers to high agricultural yield (Bishaw, 2012). Education level of the farmers is the main determinant of the farm productivity (Bhaumik, 2011).

Abdurohman ( 2018) found that land holding size,ownership of oxen,agricultural labour input, educational level of farm household head, gender, the use of chemical fertilizer,number of dependents in the farm householde and access to credit is the main factor affect agricultural farm productivity. He also found that extension service is strogly siginificant effect up on the the farmers productivity.He recomand that strengthing different educational oppurtiunites and use modern agricultural technology leads the farmers to get high yeilds from their prodcution.

2.2. Empirical studies

2.2.1. Evidence from developing countries

Moock (1981) attempted to measure the worker effect of education in the production of the staple food crop in an area of Western Kenya characterized by small farms and considerable off- farm employment. He builds up a modified Cobb- Douglas production function, where a measure of education was integrated into the function as an explanatory variable. The effect of educational attainment on production was problematic. Men who had completed four or more years of schooling have nearly 2% productive in maize production, ceteris paribus. However, a negative coefficient (-0.111) for the category of respondent with 1-3 years of schooling, which he described as surprising, was reported in the study but he reported that exposure to extension services increase technical efficiency, a 10% increase in extension contact was associated with a 0.2% increase in yield of maize, other input remain constant.

For northern Nigeria, Alene,,( 2007) found significant positive effects for schooling, but only for farmers were using new technologies. They found no effect for those working with traditional technologies. The authors used a switching regression model to model a two-stage process where in the first stage, farmers can choose to adopt better cowpea varieties or not, and in the second stage cowpea production is modeled given adoption or not. Whether the household head had four or more years of education had a positive significant effect on the adoption of better technologies and it had a positive effect on cowpea production given that they adopted new technologies. Having four years of education increased cowpea production by 25.6% if they used improved cowpea varieties. The proportion of other household members who had completed primary schooling had no significant relationship with a adoption of better varieties or production.

In 1992 the World Bank conduct a survey for measuring the relationship between farmers education and their agricultural efficiency in low income countries and find that the farmers with basic education were more productive than farmers with no education. Yet, they are produce 8.7% more than illiterate farmers (Gasperini, 2000).

Eisemon, Schwille and Prouty(1990) conducted a survey , which has examined the effect of primary education on cognative skills of farmers in Kenya. They found that farmers who had been to school were able to construct casual models of event in nutural world and to demonistrate how humans could control these events in the natural world.they were also able to actively observe , dignose and correct comment on agricultural problem better than farmers with few year of schooling.

Elibariki (2008) using Cobb- Duglas production fuction and sthocastic fronteir model explineing the productivity variation among Smallholder Maize Farmers in Tanziana found that educational level of farmers, access to extension contact service, avialibality of capital and ablity to copy the new farm technology is the factors which causes variation among the small- scale farmers. He also found that educational level of farmers are siginificant effect up on farmers prodcutivity.

Using data covering 300 farmers of five villages in Irepodun local government Area of Kwara state , Awolola (1998) related three related socio economic factors with the use of agrochemicals such herbicides, pesticides, insecticides, and fertilizers. Based on simple cross tabulation, he found that the educational status, farm size, and income were positively related with farmer's use of agrochemicals. Furthermore, the study reveals that adoption of agrochemicals is related to the type of education a farmer has. The farmers who have secondary and post secondary education used more agrochemicals than illiterate and uneducated farmers did.

Kristin Davis, et al (2010) conducting on the impact of framers field schools on agricultural productivity and poverty in East African countries such as Kenya, Tanzania, and Uganda found that framers field school supports the farmers to facilitate their decision-making, solving problems and learning new technologies. They found that participation in farmer's field schooling increases income by 61 percent when pooling the three countries. Farmers field school's improves income and productivity overall, but differences were seen at the country level. Kenya, Tanzania, and at the project level (all three countries combined). The most significant change was seen in Kenya for crops (80 percent increase) and in Tanzania for agricultural income (more than 100 percent increase). A lack of significant increases in Uganda was likely due to Uganda's National Agricultural Advisory Services (Kristin Davis, 2010).

In the relationship between education and technological innovation, Cotlear (1986) stressed the role of non-cognitive aspects of education such as openness to new ideas that allowed farmers to employ new technologies. Using survey data from Peru, he estimated the effect of education on farm output using production function. He report that education affect production by developing analytical modes of solving problems by allowing farmers to think more abstractly and thus realize the relationship between technology and production.

In measuring level of farmer's education evidence from innovation adoption in Bangladesh, Hojo, (2004) find that education is a positive impact on the behavior of farmers for adoption of innovation. However, education level of the household head do not have any significant effect on the adoption of commercial vegetables, this is because of irrespective of the way of measuring the level of education.

In their study of the relationship between farmer education and farm efficiency in Nepal the role of schooling, extension services and cognitive skills, Jamison and Moock (1984) found that farmers who could read, write, and understand numbers could allocate inputs more efficiently and thus increased productivity. In general, the study found that direct returns to education were stronger in developing countries than in developed countries. Jamison and Moock (1984) posited that this could simply reflect shortages in minimal skills in developing countries.

Lau,,.(1991) studies on the effect of primary education on the economic growth in East Asian and Latin Americans found that primary education is significantly affected the economic growth in twenty -two east Asian and Latin American countries. They also found that secondary education affect economic growth in fifty - four East Asian, Latin American, African and Middle Eastern countries.

Okpachu, Oche and Obijesi (2014) study on the impact of education on agricultural productivity of small scale female farmers in Yobe state of Nigeria. They asserte that there was sigificance difference in out put of female who have particpate in adult education schem and non - particpant iin adult education schem. They conclued that for rural women to enhance agricultural productivity and other economic activites agricutural education schem is important. Therefore, femal farmers encouraged to particpate on adult education and also Government agricultural and rural development policies should be in tandem with the new paradigm-shift on gender.

Lockheed, Jamison and Lau (1980) reviewed and summarized the findings of 18 studies containing thirty- seven data sets from thirteen developing countries (primarily in Asia) and found that most reported significant positive effects of education upon output, even if the results were mixed. They concluded that four years of primary education increased the productivity of farmers by 8.7 percent overall and 9.5 percent in countries go through modernization. However, for the group of studies concerned with the effects of education in traditional agriculture, the increase in output due to four years of schooling was only 1.3 percent on average. Education increased the ability of farmers to allocate resources efficiently; enable them to improve their choice of inputs; and enabled farmers to estimate more accurately the effect of those inputs on their overall productivity. They concluded that as the education level of an individual increased, so did the individual is able to perform more tasks that are complicated or to adapt to changing condition or tasks.

Philips (1994) reviewed additional 12 studies using 22 data sets (with more recent data and greater representation of Latin America), and was able to confirm the general trends noted above. He considered the average increase in output due to an additional four years of schooling was 10.5 percent, with the relevant figures for traditional versus modern farming system at 7.6and 11.4 percent , respectively. However, his survey was significantly geographical divers e to show that the effect of schooling were strongly in Asia than in Latin America, irrespective of the degree of modernization. This may have implication for the assumed applicability of Asian findings to Africa, though too few studies using Africa data was included to draw strong conclusion.

Using a panel of 95 developing and emerging countries, together with the newest educational attainment datasets Barro and Lee (2010), show that there is indeed a positive impact of educational attainment on agricultural productivity that is robust to changes in the control variables and in the econometric methods applied. Furthermore, distinguishing between different levels of education reveals that only primary and secondary schooling attainments have significant positive impacts on agricultural productivity.

Dadhi Adhikari and Naresh Nepal (2016) examine the impact of agiricultural extension on farm prodcutivity in Nepalese agriculture using the siwching regression model, using panal data set obtained from two wavea of Nepal living starnderd survey , find that there is a siginificant difference in the farm productivity between farmers who recive the extension service and those who have not access to extension contact.

A study approved out by Colclough (1980) to establish the relationship between primary schooling and economic development asserted that the benefits of primary schooling arise from cognitive and non - cognitive behavioral changes encouraged by the schooling experience even in system of very low quality. It also declare that evidence from fertility and farm productivity studies suggested that individual behavioral changes that resulted from schooling were stronger when literacy is widespread than when it concentrated. He assumes that an interactive effect existed between individual and community attitudes and values, which significantly strengthen the economic and social case for universalizing access to primary schooling.

Yeyoung, (2017) study on the effect of agricultural extension and education on farm productivity evidence from Mbale District in Uganda found the agricultural extension service in Mbale district had a significantly positive impact on bean and rice production, gross farm revenue, and profit except for maize output. On the other hands, formal education in the district mostly had a negative or insignificant effect on each production function, this nagetive effect is because of if the eduaction level of faremrs in the district increase the leave the farm sector and shift to non farm sector.

A study by Masuku,,.(2013) estimate the effect of farmer education and managerial ability on food crop production Nigeria. They found that farmer's education has positive impact on the farmer's technical efficiency and also education play important role in increasing technical efficiency of food crop production because farmers are capable of adoption of innovation which could enhancing the food crop production. They asserted that education is significantly affecting technically inefficiency of food crop production especially in the area of training by the state agricultural extension service. Abbasian and Hussain ( 2011) studies on the impact of educated farmer on agricultural product founds that educated farmers reveals the highst cofficient and siginificant at 5%.

2.2.2. Ethiopian Evidence

In Ethiopian context, there is limited empirical evidence available on the effect of education in Ethiopian agriculture. Much of the research may be criticized on the ground of poor measurement of education variables and small sample size. However, variety of data sets and methods have been used in this context, providing some approaching into the effect of education on productivity and efficiency in Ethiopia.


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The Effect of Farmers Education on Farm Productivity. Evidence from Small-Scale Maize Producing Farmers in North Bench District, Bench Maji Zone
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Habtamu Solomon (Author), 2019, The Effect of Farmers Education on Farm Productivity. Evidence from Small-Scale Maize Producing Farmers in North Bench District, Bench Maji Zone, Munich, GRIN Verlag,


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