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Analysis of technical efficiency of small holder wheat-growing farmers of Jamma district, Ethiopia

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Abstract

Background

A large majority of Ethiopians and the poor living in rural areas are deriving their livelihood from agriculture. The existence of inefficiency in production comes from inefficient use of scarce resources. The measurement of efficiency in agricultural production is important issue for agricultural development, and it gives useful information for making relevant decision in the use of these scare resources and for reformulating agricultural policies. Moreover, since social development is dynamic, it is imperative to update the information based on the current productivity of farmers. However, the productivity of agricultural system in the study area is very low. The aim of this study was to determine the level of technical efficiency of smallholder wheat producers and identify factors affecting technical efficiency of smallholder farmers in wheat production of Jamma district, Ethiopia.

Result

The maximum likelihood parameter estimates showed that wheat output was positively and significantly influenced by area, fertilizer, labor and number of oxen. The estimated mean levels of technical efficiency of the sample farmers were about 82%. The estimated stochastic production frontier model together with the inefficiency parameters showed that age, education, improved seed, training and credit were found to have negative and significant effect on technical inefficiency, while farm size was found to have positive and significant effect on technical inefficiency of wheat production.

Conclusions

We conclude that there is a room to increase wheat output from the existing level if farmers are able to use these input variables in an efficient manner. Hence, local government should provide necessary supports such as formal as well as informal education, training, credit, improved seed and timely supply of fertilizer.

Background

Agriculture in Ethiopia is dominated by smallholder farming households, which cultivated 94% of the national cropped area in 2013–2014 [1]. A large majority of Ethiopians and the poor living in rural areas are deriving their livelihood from agriculture. The proportion of the population of Ethiopia residing in rural areas in 2040 is predicted to be nearly 70%, when there will be 40 percent more rural residents [2]. Growth in agriculture was one of the major drivers of the remarkable economic growth recorded in Ethiopia in the last decade [3]. The major grain crops grown in Ethiopia are wheat, teff, maize, barley, sorghum and millet. Out of the total grain production, cereals account for roughly 60% of rural employment and 80% of total cultivated land [4]. In the crop production sub-sector, cereals are the dominant food grains. The major crops occupy over 8 million hectares of land with an estimated annual production of about 12 million tons [5]. The potential to increase productivity of these crops is very high as it has been demonstrated and realized by recent extension activities in different parts of the country. However, population expansion, low productivity due to lack of technology transfer and decreasing availability of arable land are the major contributors to the current food shortage in Ethiopia [6]. According to CSA [5], Ethiopian population will exceed 126 million by the year 2030. This increase in population will impose additional stress on the already depleted resources of land, water, food and energy. In Ethiopia, agricultural production and productivity is very low and the growth in agricultural output has barely kept pace with the growth in population. The high potential areas of Ethiopia can produce enough grains to meet the needs of the people in the deficit areas. However, the inefficient agricultural systems and differences in efficiency of production discourage farmers to produce more [6]. Gains in agricultural output through improvement of efficiency levels are becoming particularly important nowadays. The opportunities to increase farm production by bringing additional forest land into cultivation or by increasing the utilization of the physical resources have been diminishing. In addition, eliminating existing inefficiency among farmers can prove to be more cost effective than introducing new technologies as a means of increasing agricultural output and farm household income [7]. The smallholder farmers in the northeastern Ethiopia are poor, individual land holding ranges between 0.5 and 2.5 hectares, large family sizes, land productivity is low, and household food requirements are not fully met. The smallholder cereal-based farming systems have also remained traditional and non-commercial oriented. Thus, the system is unable to sustain the ever-increasing population with food and energy demands. Therefore, an ever-increasing population pressure and environmental degradation followed by declining productivity and expansion of marginal agricultural lands necessitates farmers either to use modern technologies or need to use resources efficiently in order to optimize outputs in the northeastern Ethiopia [8]. According to previous researches in Ethiopia (for example [6, 9, 10]), there also exists a wide cereal yield gap among the farmers that might be attributed to many factors such as lack of knowledge and information on how to use new crop technologies, poor management, biotic, climate factors and more others [11, 12]. Because of the scanty resources that are on ground, recently it is getting importance to use these resources at the optimum level which can be determined by efficiency searches [13]. Thus, increasing crop production and productivity among smallholder producers requires a good knowledge of the current efficiency/inefficiency level inherent in the sector as well as factors responsible for this level of efficiency/inefficiency [14]. Despite its potential, Jamma district’s agricultural productivity is declining [15]. Therefore, the need for the efficient allocation of productive resources cannot be overemphasized. However, in areas where there is inefficiency, trying to introduce new technology may not bring the expected impact, unless factors associated with inefficiency among farmers are indentified and acted upon. The existence of inefficiency in production comes from inefficient use of scarce resources. The measurement of efficiency in agricultural production is important issue for agricultural development, and it gives useful information for making relevant decision in the use of these scare resources and for reformulating agricultural policies. Moreover, since social development is dynamic, it is imperative to update the information based on the current productivity of farmers. However, the productivity of agricultural system in the study area is very low. The poor production and productivity of crop and livestock resulted in food insecurity. The specific objectives of the study were to: (1) estimate the level of technical efficiency in wheat production of smallholder farmers in the study area and (2) identify factors affecting the level of technical efficiency in wheat production among farmers in the study area.

Methodology

Description of the study area

The study was carried out in Jamma district. It is located in the northeastern part of Amhara National Regional State, South Wollo Zone, Ethiopia, lying between 10°23′0″ and 10°27′0″N latitude and between 39°07′0″ and 39°24′0″E longitude. The district has an altitude that ranges from 1600 to 2776 m above sea level. The district is bordered on the southeast by Qechene River which separates it from North Shewa Zone, on the west by Kelala, on the north by Legahida, on the northeast by Wore Ilu, on the south by Mida, on the east by Gera Mider and Keya Gebrieal. The district capital town, Degolo, is about 260 km away from Addis Ababa and 110 km away from the zonal city of South Wollo Zone, Dessie [15, 16].

Data types, sources and methods of data collection

Both primary and secondary data as well as quantitative and qualitative data were employed for this study. In the study, cross-sectional household data of 2016–2017 main harvest cropping season were used. Data for input (such as land, human labor, oxen labor, fertilizer and seed amount) were used, and output of wheat production was collected from the specified period of time. Data on input use and outputs were collected in local units and converting into standard units. In addition, primary data were collected by interviewing the selected wheat-producing farmers and variables that cause variation in production efficiency like age, education, household size, extension contact, gender and the like. In addition, socioeconomic variables such as demographic data, credit access, livestock holding, wealth indicators and institutional data were collected. On the other hand, data related to wheat production trend, input supply and extension services were collected to clarify and support analysis and interpretation of primary data.

Sampling technique and sample size

In order to select sample households, three-stage sampling technique where combinations of purposive and simple random sampling techniques were used to select the district and sample household heads. Out of the 20 rural districts in South Wello Zone, Jamma district was purposively selected due to long-year experience in wheat production and extent of wheat production in South Wollo Zone. This information is obtained from South Wello Zone Agricultural Office. In the first stage, out of the three agroecologies of the district, weyina dega was selected purposively due to the major wheat production part of the district. In the second stage, out of the total weyina dega kebeles, three kebeles were selected by simple random sampling. In the third stage, 149 sample wheat-producing farmers were selected using simple random sampling technique from each selected kebeles based on probability proportion to size sampling technique.

Specification of the empirical model

Stochastic production frontier is the most appropriate technique for efficiency studies which have a probability of being affected by factors beyond control of decision-making unit. This is because of the fact that this technique accounts for measuring inefficiency as a result of these factors and technical errors occurring during measurement and observation. Wheat production at the study area is likely to be affected by natural hazards, unexpected weather conditions, pest and disease occurrence which are beyond the control of the farmers. In addition, measurement and observational errors could also occur during data collection. So as to capture effects of these errors, this study used stochastic frontier model.

Stochastic frontier analysis was simultaneously introduced by Aigner [17] and Meeusen and Van der Broeck [18]. The stochastic frontier approach splits the deviation (error term) into two parts to accommodate factors which are purely random and are out of the control of the firm. One component is the technical inefficiency of a firm, and the other component is random shocks (white noise) such as bad weather, measurement error, omission of variables and so on.

The model is expressed as:

$$\ln Y_{i} = \beta_{0} + \sum {\beta_{i} \ln X_{ij} + \exp^{ei} }$$
(1)

where ln = denotes the natural logarithm, i = represent the ith farmer in the sample, Yi = represents yield of wheat output of the ith farmer (Qt), Xij = refers to the farm inputs of the ith farmer, ei = viui which is the residual random term composed of two elements vi and ui.

The vi is a symmetric component and permits a random variation in output due to factors such as weather, omitted variables and other exogenous shocks.

Selection of the functional form

Another issue surrounding parametric frontiers relates to the choice of functional form. Among the possible algebraic forms, Cobb–Douglas and the translog functions have been the most widely used functional forms in most empirical production analysis studies. Each functional form has its own advantage and limitations. Some researchers argue that Cobb–Douglas functional form has advantages over the other functional forms in that it provides a comparison between adequate fit of the data and computational feasibility. It is also convenient in interpreting elasticity of production, and it is very parsimonious with respect to degrees of freedom. So, it is widely used in the frontier production function studies as stated in Hazarika and Subramanian [19].

In addition, due to its simplicity features, the Cobb–Douglas functional form has been commonly used in most empirical estimation of frontier models. This simplicity, however, is associated with some restrictive features in that it assumes constant elasticity, constant return to scale for all firms/farms and elasticity of substitution are equal to one [20]. In addition, the Cobb–Douglas functional form is also convenient in interpreting elasticity of production and it is very parsimonious with respect to degrees of freedom. Therefore, that is why Cobb–Douglas functional form was used in this study.

The technical efficiency of wheat production in Jamma district was measured by considering the output obtained per household head as the dependent variable. The output of wheat from the 2015–2016 production year was measured in quintals. The independent variables were the inputs (factors) of production used in the same production year. Accordingly, the relevant inputs which were considered were as follows:

  • Y = the total amount of wheat produced in quintal by the ith farmer;

  • X1 = the total number of oxen power used for wheat production in oxen-days by the ith farmer;

  • X2 = the total labor (family and hired) in man-days used for wheat production by the ith farmer;

  • X3 = the total quantity of wheat seed in kilogram used for wheat production by the ith farmer;

  • X4 = the total amount of fertilizer in kilogram applied for wheat production by the ith farmer;

  • X5 = the total area covered by wheat in hectares of the ith farmer;

The Cobb–Douglas form of stochastic frontier production is stated as follows:

$$\ln Y = \beta 0 + \sum\limits_{j = 1}^{5} {\beta_{j} } \ln X_{ij} + V_{i} - U_{i}$$
(2)

where for ith farmer, Y is the total quantity of wheat produced, x is the quantity of input j used in the production process including oxen labor, human labor, land, quantity of seed and quantity of fertilizer, Vj is the two-sided error term and Uj is the one-sided error term (technical inefficiency effects).

The inefficiency model was estimated as the equation given below.

$$\ln Y \, = \, \delta 0 + \, \sum\limits_{n = 1}^{13} {\delta_{n} Z_{ni} } \,$$
(3)

Zi is the variable in the inefficiency model.

The technical inefficiency (ui) could be estimated by subtracting TE from unity. The function determining the technical inefficiency effect is defined in its general form as a linear function of socioeconomic and management factors.

It can be defined in the following equation:

$$U_{i} \, = \, \delta 0 + \, \sum\limits_{k = 1}^{13} {\delta_{K} Z_{jk} } \,$$
(4)

where ui is the technical inefficiency effect and δk is the coefficient of explanatory variables. The Zi variables represent the socioeconomic characteristics of the farm explaining inefficiency of the ith farmer. As a result, the technical inefficiency was explained by the following determinants:

Zi1 = Age of the household head (years); Zi2 = sex of the household (a dummy variable. It takes a value of 1 if male, 0 otherwise); Zi3 = household size (total numbers of family member who lives in one roof); Zi4 = education (number of years of schooling of the farmer); Zi5 = farm size measured by hectare; Zi6 = land fragmentation (it includes the number of locations of different plots); Zi7 = distance to wheat plot from residence measured in km; Zi8 = number of livestock measured by TLU; Zi9 = training (a dummy variable. It takes a value of 1 if yes, 0 otherwise); Zi10 = extension contact (frequency of extension service during the farming season); Zi11 = off/non-farm income (total amount of off/non-farm income in birr); Zi12 = credit (total amount of credit received during the production season); Zi13 = improved seed (a dummy variable. It takes a value of 1 if yes, 0 otherwise).

Results and discussion

Maximum likelihood estimation of parameters

The ML estimates of the parameters of the frontier production functions are presented in Table 1. The coefficients of the input variables were estimated under the full frontier production function (MLE). During the estimation, a single estimation procedure was applied using the Cobb–Douglas functional form. The result of MLE gave the value of the parameter estimations for the frontier model and the value of \(\delta\)2. Moreover, it gave the value of log-likelihood function for the stochastic production function. The maximum likelihood estimates of the parameter of SPF functions together with the inefficiency effects model are presented in Table 1.

Table 1 Maximum likelihood estimate for Cobb–Douglas production function

Out of the total five variables considered in the production function, four (land, labor, oxen power and fertilizer) had a significant effect in explaining the variation in wheat production among farmers. The coefficients of production function variables were positive. The coefficients of land and labor were significant at 1% level of significance, the coefficient of oxen power was significant at 5% level of significance, and the coefficient of fertilizer was significant at 10% level of significance. This informs that they were significantly different from zero, and hence, these variables were important in explaining wheat production in the study area. The positive production elasticity with respect to land, fertilizer, oxen and labor implies that as each of these variables increases, wheat output will increase. On average, as the farmer increases area allocated to wheat, amount of chemical fertilizer application, labor and oxen power for the production of wheat by 1% each, he/she can increase the level of wheat output by 0.365, 0.057, 0.030 and 0.002%, respectively.

Summing the individual elasticity yields a scale elasticity of 1.31. This indicates that farmers are facing increasing returns to scale (Table 1) and depicts that there is potential for wheat producers to increase their production. In other words, they are not efficient in allocation of resource and this implies that production is inefficient; moreover, there is a room to increase production with an increasing rate.

Determinant of technical efficiency

The focus of this analysis was to provide an empirical evidence of the determinant productivity variability/inefficiency gaps among smallholder wheat farmers in the study area. Merely having knowledge that farmers were technically inefficient might not be useful unless the sources of the inefficiency are identified. Thus, in the second stage of this analysis, the study investigated farm and farmer-specific attributes that had impact on smallholders’ technical efficiency.

Accordingly, the negative and significant coefficients of age of the household head, education, improved seed, training and credit indicate that improving these factors contribute to reducing technical inefficiency. Whereas, the positive and significant variable such as farm size, affect the technical inefficiency positively that is increases in the magnitude of these factors aggravate the technical inefficiency level. The implications of significant variables on the technical inefficiency of the farmers in the study area were discussed (Table 2).

Table 2 Maximum likelihood estimates of technical inefficiency determinants

Age of farm household heads

The age of the household is the proxy for the experience of the household head in farming. The result indicated that age of the household heads influenced inefficiency negatively at 5% level of significance. This suggested that older farmers were more efficient than their young counterparts. The reason for this may probably be that the farmers become more skill full as they grow older due to cumulative farming experiences [21]. Moreover, increase in farming experiences leads to a better assessment of the important and complexities of good farming decision making including efficient use of input. This result was consistent with the arguments by Mesay [11] and Alemu [6], and they indicated that, since farming as any other professions needs accumulated knowledge, skill and physical capability, it is decisive in determining efficiency. The knowledge, the skills as well as the physical capability of farmers are likely to increase as their age increases.

Education

Education enhances the acquisition and utilization of information on improved technology by the farmers. In this study, education measured in years of formal schooling; as expected, the sign of education was negative effect on technical inefficiency at 1% level of significance. This implying that less educated farmers are not technically efficient than those that have relatively more education. This could be because educated farmers have the ability to use information from various sources and can apply the new information and technologies on their farm that would increase outputs of wheat. In general, more educated farmers were able to perceive, interpret and respond to new information and adopt improved technologies such as fertilizers, pesticides and planting materials much faster than their counterparts. This result was in line with the findings of Tefera [22], Ali and Khan [23], Hailemariam [24], Fantu [25] and Alemu [6] who stated that an increase in human capital will augment the productivity of farmers.

Farm size

It is measured as total land cultivated by the farmer including those rented and shared in. In this study, it was hypothesized that farm size affects inefficiency positively. As the farm size of a farmer increases, the managing ability of him/her will decrease given the level of technology, and this leads to reduce the efficiency of the farmer. Accordingly, the estimated result coincides with the expectation and that coefficients of this inefficiency variable found positive and statistically significant. That means total area cultivated by a household affected technical inefficiency level positively and significantly at 1% level of significance. This shows that a household operating on large area is less efficient than a household with small land holding size. This might be because an existence of increased in area cultivated might entail that the farmer might not be able to carry out important crop husbandry practices that need to be done on time, given his limited access to resources. As a result, with the increase in farm holding size the technical inefficiency of the farmer might increase. This finding was in line with the results obtained by Sultan and Ahmed [26].

Improved seed

Use of improved seeds negatively and significantly affected farmers’ technical inefficiency in wheat production at 1% level of significance. Thus, production of wheat through the use of more of improved wheat seeds was more efficient compared to using local seeds. This was in agreement with the findings of Sultan and Ahmed [26]. Moreover, the negative sign of the estimated coefficients had important implications on the technical inefficiency of the wheat farmers in the study area. It means that the tendency for any wheat farmers to increase his production depends on the type and quality of improved seed available at the right time of sowing.

Training

Training is an important tool in building the managerial capacity of the household head. Household’s head that gets training related to crop production and marketing or any related agricultural training is hypothesized to be more efficient than those who did not receive training. Training of farmers on wheat crop was important because it could improve farmers’ skill regarding production practices and related aspects. A number of farmers in the study areas received training on wheat for few days mainly on production practices and importance of using improved package. The dummy coefficient of training was negative and significant in the technical inefficiency model of wheat production at 10% level of significance. This implied that technical inefficiency effect decreases with farmers having training on wheat. It may also be concluded that farmers with training on wheat tended to have lower inefficiency effects than farmers without training. That is, farmers with training were technically more efficient than farmers without training. This result is in line with the arguments by Beyan [27] and Michael and James [28] who indicated that training given outside locality relatively for longer period of time determined inefficiency negatively and significantly.

Credit

It is an important element in agricultural production systems. It allows producer to satisfy their cash needs induced by the production cycle. Amount of credit increases farmers’ efficiency because it temporarily solves shortage of liquidity/working capital. In this study, amount of credit was hypothesized in such a way that farmers who get more amount of credit at the given production season from either formal or informal sources were expected to be more efficient than those who get less amount of credit. In this study, amount of credit affected inefficiency of farmers negatively and significantly at 5% level of significance. This implies that credit availability shifts the cash constraint outwards and thus enables farmers to make timely purchases of inputs that they cannot afford otherwise from their own resources and enhances the use of agricultural inputs that leads to more efficiency. The empirical studies conducted by Musa [29] and Biam [30] found positive and significant relationship between credit and farmers’ technical efficiency which was in line with this study.

Conclusions and recommendations

The implication of this study is that technical efficiency of the farmers can be increased through better allocation of the available resources especially land, oxen power, labor and fertilizer. Thus, local government or other concerned bodies in the developmental activities working with the view to boost production efficiency of the farmers in the study area should work on improving productivity of farmers by giving especial emphasis for significant factors of production. Moreover, age should be considered in increasing resource use efficiency and agricultural productivity. This is because results showed that younger farmers are technically more inefficient than older ones. It implies that there should be policies to improve resource use efficiency of younger farmers and encourage them to be in farming activities by providing them incentives. However, this should not be at the expense of older ones.

Training determined technical efficiency positively and significantly in wheat-producing farmers. Provision of training for farmers to improve their skills in use of improved seed, resource management, post-harvest handling and general farm management capabilities will increase their farm productivity. In addition to strengthening the practical training provided to farmers, efforts should be made to train farmers for relatively longer period of time using the already constructed farmers’ training centers and agriculture research demonstration centers. The amount of credit received was found to positively and significantly influence household technical efficiency level. This could imply that households needed external financial sources to solve their own financial constraints. Therefore, the regional government should intervene to strength the operation of rural saving and credit institutions at village level and creates awareness for those farmers to improve their saving habits so as to improve their asset formation. Total farm size was negatively and statistically significantly related to technical efficiency in wheat production. Hence, smallholder farmers have a limitation of resources which are used for agriculture production on his/her available farm land in the given operation calendar, his/her production performance affected negatively. This in turn improves the production of the farmers due to using better technology which shifts the production frontier outward. Therefore, it would be better if the regional government or concerned body facilitates such machinery services on either credit bases or cooperative rendering rental service.

Those farmers that are more educated are relatively more technically efficient than less educated ones, in the study area. Education is fundamental in improving the technical efficiency of farmers. Therefore, the regional governments need to strengthen farmers’ access to education that could be implemented through expansion of farmers training center or expansion of formal and non-formal education in the area. Improved wheat seed had a significant and negative effect on technical inefficiency of wheat production. Hence, researchers and extension agent should improved the awareness of farmers to use improved wheat seed and efforts should be made to access different types of high-yielding wheat seed before the starting of sowing date.

Abbreviations

ACSI:

Amhara Credit and Saving Institution

CSA:

Central Statistical Agency

HHH:

household head

GDP:

gross domestic production

KAs:

Kebele Administrations

ML:

maximum likelihood

MLE:

maximum likelihood estimation

MASL:

meter above sea level

NBE:

National Bank of Ethiopia

OLS:

ordinary least square

SFA:

stochastic frontier analysis

SPF:

stochastic production frontier

TE:

technical efficiency

TLU:

total livestock unit

WOA:

Woreda Office of Agriculture

UN:

United Nations

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Authors’ contributions

MD initiated the research, wrote the research proposal, conducted the research, did data entry and analysis and wrote the manuscript. MD was did analysis, methodology, writing, reviewing and editing of research proposal and manuscript. MD read and approved the final manuscript. The author read and approved the final manuscript.

Acknowledgements

I would like to convey my heartfelt thanks to Amanuel Moges for multidimensional support and continuous encouragement in my endeavor. I extend my profound appreciation to the farmers of Jamma district for their hospitality who willingly participated in the survey and spent many hours explaining their livelihoods, which I never forget.

Competing interests

The author declares that he has no competing interests.

Availability of data and materials

The author want to declare that he can submit the data at whatever time based on your request. The datasets used and/or analyzed during the current study will be available from the corresponding author on reasonable request.

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Not applicable since the study involved wheat crop.

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Correspondence to Moges Dessale.

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Keywords

  • Jamma district
  • Stochastic frontier analysis
  • Technical efficiency
  • Wheat