Ethiopia is ranked 21th in the world in terms of barley production with a share of 1.2% of the world’s total production  and  the  second  largest  barley  producer  in Africa, next to Morocco and followed by Algeria (Abu and Teddy, 2014; FAO, 2014). Ethiopia is not only the largest producer   but   also  the  biggest  consumer  of  barley  in Africa. Hence, in relation to its dynamic nature and wide range of uses, barley is known as the “king of grains”. Unlike in the industrialized countries where barley is mainly used for animal feed and malting, barley is important for developing countries in terms of the lives and livelihood of smallholder farmers. At the national level, barley accounts for about 5.6% of the per capita calorie consumption as a main ingredient in staple foods and local drinks. It is also a substitutable crop for other cereals in the country and serves as a roof thatch for many highlanders (Berhane et al., 2011; CSA, 2014).

At the national level from the total area of cereals allocated in hectares, barley covered only 14.65% producing 13.37% quintals with the yield of 10.42 quintals per hectare. The total yield of barley has been increased by 4.99% between 2013/2014 and 2014/2015 and also by 5.2% in the year 2015/2016 (CSA, 2016). Furthermore, among the major cereals, barley is found to have experienced the highest annual fluctuation in area and yield. Hence, this fluctuation in barley yield and area shows that barley has received far less attention as compared to the other major cereals especially teff, maize, and wheat (Shahidur et al., 2015).

Rapid population growth multiplies the problems related to food and other fundamental human needs. Increasing food production is itself a complex process involving more intensive and extensive use of land and water, increased availability of basic agricultural inputs, appropriate agricultural policies and rural institutions and strengthened agricultural researches. However, if effort is made, the potential for increasing food production in every country in the world would be substantial (Aung, 2012). But, there is still yield gap between the farm households which are due to moistures stress, shortage of improved seeds, and degradation of soil fertility, insect pests, diseases, weeds and birds. This higher gap between yields of crops under farmer’s management clearly indicated that farmers having an opportunity to narrow this gap by increasing their crop production and earn higher yield.

Generally, in the case of Ethiopia, there are limited number of studies on efficiency of barley production (Hassena et al., 1999; Wadi’ah, 2012; Endalkachew et al., 2012) which focused on technical efficiency and that of malt barley rather than food barley. Even if technical efficiency being one component of economic efficiency, it may not provide plenty of information for decision makers and policy intervention at zonal and district level. Therefore, this study had analyzed the allocative and overall efficiencies of production and identifies factors causing inefficiencies of smallholder food barley producers. Particularly, in Meket district, barley is a major staple food and it takes the lion share in terms of the extent of production, food consumption, number of producers and area coverage relative to other major cereals grown in the district. However, its production was owned by small holder, a farmer which  produces  only  to survive their hand to mouth livelihood. Therefore, it is crucial to increase their volume of production and efficiency at least to secure their food needs. The general objective of this study was to analyze the economic efficiency of smallholder farmers in barley production, the case of Meket district, Amhara National Regional State, Ethiopia and to identify the determinants of inefficiencies of barley producers in Meket district.

 

Description of the study area

Meket district is one of the eleven districts in North Wollo Zone of Amhara National Regional State, Ethiopia which is located at 600 km north of Addis Ababa and bordered on the south by Wadla and Daunt districts, on the west by Debub Gondar Zone, on the northwest by Bugna districts, on the north by Lasta, on the northeast by the Gidan district and on the east by Guba-Lafto districts (https://en.wikipedia.org/wiki/Meket). There are four main agro-climatic zones in the district. These are the semi-arid lowlands less than 2,300 masl, the sub-humid midlands from 2,300 to 2,800 masl, the humid highlands 2,800 to 3,200 masl, and the very-humid high altitude plateau, which is over 3,200 masl, is often battered by frost and hail. The topography of the district is highland and it is suitable for barley production (Seid, 2012). Based on the 2007 National Census conducted by the Central Statistical Agency of Ethiopia (CSA), this district has a total population of 226,644 and currently according to the report of the Meket District Health Office (2016), the total population of Meket is estimated at about 263,567 of them 51.67% are male and 48.33% are female. This means that between 2007 and 2016, there is a population growth rate of 14% in the district (Figure 1).

 

 

where Ln=Logarithm to base e, βi=the unknown parameters estimated, i=1, 2, 3. . . n^th farmer, j=inputs of production used, Y_i=output of barley, X₁=land allocated for barley crop (ha), X₂=labor power (man-days), X₃=amount of barley seeds used (kg), X₄=oxen power (oxen days), X₅=quantity of fertilizer used in barley crop (kg), X₆ up to X₂₀ are the square and interaction terms of those inputs, ε=random composed error-term (V-U) and n=sample size

Technical inefficiency scores were estimated on hypothesized farm related, socioeconomic and institutional factors using a one stage estimation procedure in frontier model simultaneously with the production function. The technical inefficiency model was specified as using:

where Y_i is the barley output and Z_i is the different farmers specific, farm related and institutional variables that affect technical inefficiency.   

  

Estimating cost function is necessary to estimate allocative efficiency scores of the household farmers. Then, the total cost was regressed on each cost of inputs using trans-log functional forms of cost function as:

 

                                                                                             

                                                                                                        

where C_i=Minimum costs of the i^th farmers for the production of barley, C₁=rental price of land in birr per year, C₂=wage of human labor used in birr per man days, C₃=price of fertilizer used in birr per kg, C₄=price of oxen rent in birr per oxen days, C₅=price of seeds applied in birr per kilogram, lnY₆=output of  barley  in  quintals,  from C₇ up to C₂₇ are the square and cross product of cost of inputs, A=constant and ε=random composed error term (V+U).

Cost efficiency is the ratio of minimum cost and actual cost. If cost efficiency is less than one the farmer is said to be less cost inefficient while, if it is unity the farmer or producer is cost efficient. Since allocative efficiency is the reciprocal of cost efficiency and it is regressed on farm related farmer’s specific and institutional inefficiency factors as:

                                                                                         

where Y*=Allocative inefficiency scores

According to Bravo-Ureta and Pinheiro (1997), economic efficiency is the product of technical efficiency and allocative efficiency. In measuring the factors affecting economic inefficiency levels, OLS estimation technique could be applied. The estimating inefficiency scores are regressed on the same set of farm related, institutional and farmer’s specific factors that are assumed to be important determinants of inefficiency as allocative and technical inefficiency.

where U_i*=Economic inefficiency levels.

The inefficiency variables denoted as P₁ to P₁₃ in Equation 6 and Z₁ to Z₁₃ in Equation 9 and X₁ to X₁₃ in Equation 10 are: farming experiences of farmers in barley production (years), farmer education level (years of schooling), frequency of extension contact (numbers), amount of credit taken (Ethiopian birr), number of barley plots (number), total expenditure of households (Ethiopian birr), crop rotation (0= if they practice crop rotation, 1 otherwise), participation on non-farm income (0= if yes and= 1 if no), livestock holding (TLU), fertility status of the soil (0=fertile, 1=infertile), distance to market (kilometers), gender (0 if the household headed are male and 1 other wise) and distance of the plot from farmers home (walking minutes).

After all regression results, different post estimation tests or diagnostics were done including variance inflation factor, heteroscedasticity, omitted variable test and normality of the residuals for the models to ensure that the available data set meets the assumption of OLS regression and all these are presented under the Appendix.

 

 

Descriptive statistics of sample farm households

The mean education level of the sample households in the study area was 2.54 and it ranged from 0 to grade 10. This indicates that in the study area, there is low level of education and it requires different actions or assignments for the regional education bureau to expand adult education   for   the   farmers.  The   mean   frequency  of extension contact was nine times with a minimum of twice to a maximum of 18 times per barley production season. This reveals that there is high frequency contact between the farmers and the extension workers. Farmers have a long history and experience to cultivate barley in the study area. For this reason the average farming experience of the sample farmers in barley production was 33.62 years with a minimum of 5 years and maximum of 62 years. It was also found that, the mean number of plots allocated for barley crop was 2.21 with maximum of 4 plots located in different site. This study is in line with the arguments made by previous agricultural researchers, that is, in the highland there is land fragmentation and the sample households have more than two barley crops that come due to share cropping. In addition, on average, farmers walk relatively 1 h to reach their farm plot. This shows that there is a wide distance between the farmer’s residence and farm plot. Indeed, it affects the efficiency of farmers by decreasing the supervision of the plot by farmers. On average sample farm households own 3.29 TLU with a minimum of 0.815 TLU to a maximum of 15 TLU. Livestock could support crop production in many ways; it can be source of cash, draft power and manure that will be used to maintain soil fertility, then the study reveals there is no that much adequate livestock in the study area. 

The mean total expenditure of the sample households was 1279.92 birr within the range of 1000 and 5000 birr. The mean amount of credit obtained from different sources was 1775.61 birr and ranges from 0 to 7000 birr. The mean distances from the farmers residence to nearest market was 5.31 km and ranged between 2 and 12 km.

Sample of respondents composed of both male and female household heads. Out of the total sampled household  head   farmers,   about   80.49%   were   male headed and the remaining 19.51% were female headed households. As shown in Table 1, about 19.51% of the sample households did not practice crop rotation, while the remaining 80.49% adopted the practice of crop rotation. Based on their perceptions, about 24.39% of the respondents classified the fertility status of their barley plot on average as infertile class, while about 75.61% respondents perceived it as fertile. The same table also shows that majority (which is 58.54%) of the farmers had participated on different non-farm income activities while about 41.46% had not any source of non-farm income.

 

 

Descriptive results of barley output and input usage

Table 2 shows that the mean barley output of the sample household per average land coverage by barley in the study area in the 2015/2016 production season was relatively 17 quintal with a minimum of 4 quintal to a maximum of 45 quintals. Generally, the average inorganic fertilizer application for the production of barley among the respondent was 40.52 kg and allocated on average 1.49 ha of their farm plot for barley production. The sample households apply only local barley seed with an average of nearly 83.5 kg for their land covered by barley in the production season. On average, a total of 54.1 man days and 35 oxen days were needed to perform all related activities of farming starting from the beginning land preparation up to collection of outputs in harvesting time.

 

 

Results of econometric models

Hypothesis testing

The  first  hypothesis in Table  3,  was  that selecting the appropriate functional form which fits to the data set by using likelihood ratio test. The most commonly functional forms reviewed in most previous researches were Cobb-Douglas and Trans-log. Then by applying the likelihood ratio test statistics which is  the null hypothesis was rejected which implies that the trans-log functional form adequately represented the data set.

 

 

The second hypothesis was conducted to decide whether the production function without considering non-negative random error term best fits the data set or not. H0=γ=0 and H1=γ>0. The gamma (γ) parameter is defined as the ratio of the unexplained inefficiency error term (δu²) to the total sum of errors (δu²+δv²). Since, the value of gamma is 63% which indicated that there was technical inefficiency. This mean that in the study area barley production is more affected by those factors under the control of the farmers than other variables beyond the control of the farmers like climate related factors.  Therefore, identifying this inefficiency variable is more needed and included under the OLS estimation procedure.

Thirdly, the null hypotheses is a model without explanatory variables of inefficiency effects, while the alternative hypothesis says the full frontier model with explanatory variables are supposed to determine inefficiency. Therefore, explanatory variables of technical inefficiency can determine variation in production of barley output in the study area.

Estimation of production function

The dependent variable in estimation of stochastic production function was barley outputs produced in quintals analyzed on the five major inputs with their square and interaction terms. The major five inputs were land under barley, amount of local barley seed, quantity of fertilizer, human labor and oxen power. The stochastic frontier model estimates both the trans-log functional forms of production function and variables of technical inefficiency simultaneously by using the first stage estimation approach.

As  shown  in  Table  4,  looking from the output  of  the model, seed had negative and significant effect on the output of barley at 5% level of significance. The negative effect might be due to the reason that, the farmers may have applied only local seed and does not apply any improved and certified seed. Fertilizer is one of the necessary inputs to improve barley output by providing required nutrients and it was significant at 5% level of significance. Hence, a farmer who increased the application of fertilizer up to the recommended rate (that is, 100 kg of UREA and DAP) in turn can earn more output of barley. Labor also had a positive sign and significantly affected barley output at 1% level of significance. In order to increase their barley output, farmers must use more family or hired labor for performing different farming operation on the field. In most developing countries like Ethiopia, oxen are the main source of draft power to perform activities like plowing and sowing crops. In line with this, oxen power had a significant and positive effect on farmers barley output in the study area.

 

 

 

Technical, allocative and economic efficiencies scores of barley producers

The result of frontier model in Table 5 revealed that, the mean technical efficiency of the sample household farmers during the 2015/2016 production season was 70.9% and it ranged from 14 to 95%. This indicated that, there is a wide efficiency gap among the sample barley producers in the study area. This indicates that, farmers had opportunities to decrease all the current input usage by 29.1% without decreasing the output of barley produced.

 

 

The  economic,  technical  and  allocative  inefficiencies levels were regressed on the hypothesized institutional, farmers specific and farm related variables that bring inefficiency disparity among the barley growers. The technical inefficiency  variables  were estimated by using one stage estimation approach of frontier model, while allocative and economic inefficiency variables were regressed by using OLS estimation technique. The same estimation technique is also used by Sharma et al. (1999), Arega and Rashid (2003), Komicha and Öhlmer (2007) and Susan (2011).

Detailed interpretation and discussion of the statistically significant variables in Table 6 would be presented as the following.

 

 

Soil fertility: It had significant and negative influence on economic inefficiency level at 10% levels of significance. It means that, as compared to those individuals having infertile lands, the economic inefficiency of farmers having fertile land had decreased by 32% holding other factors constant. Therefore, such policies to increase and maintain soil fertility of land must have a negative effect on   inefficiency   of   barley  production.   The  result  was similar with the findings of Alemayehu (2010), Musa (2013) and Hailemariam (2015).

 

 

 

 

 

 

The main aim of this study was show the degree of variation and identification of important variables that bring disparity of inefficiency among farmers. As expected, the result of the study clearly shows the presence of inefficiency variation among the farmers and identifies which variables strongly affected their inefficiency level and also showed that there is a huge opportunity to improve their efficiency level and increase barley output if appropriate measures are taken. Hopefully, this would help for concerned bodies and policy  makers  to intervene through different policies and strategies which minimize the inefficiency variation among them and bring all barley producers closer together at fully efficient level as much as possible with the available resource at their hand with the given technology and input at its disposal.

Based on the significant variables which affected all technical, allocative and economic inefficiencies level simultaneously, the following recommendations are forwarded. Since, appropriate actions and policies on these variables had a strong implication to narrow the gap of inefficiency variation among farmer, policy-makers who know why some farmers are relatively more inefficient than others will help to choose the most likely array of development programs for improving institutional and socio-economic factors contributing towards decreasing inefficiency.

Frequency of extension contact of farmers with extension agents significantly and negatively affected all inefficiencies level. Hence, strengthening the extension service and make it easily accessible to farmers is important. Since, development agents had a pivotal role to disseminate new production information, technologies and inputs from the research field to farmers on ground. Therefore, special emphasis and motivation should be given for those personnel so as to improve the efficiency level. This is possible by building the capacity of development agents or extension workers by providing practical attachment training with the current agricultural production.

The second important variable that requires serious emphasis was the number of barley plots. It had a negative effect on economic inefficiency. This means that, farmers having more barley plots were less inefficient than farmers having small number of barley plots. The policy which addressed this issue must be, farmers equally allocate their available inputs and resources used for cultivation of barley in each plots with rigorous use and as much as possible increase the productivity of inputs especially labor and land.

Thirdly, the result confirmed that education level of the household head had a positive effect on economic inefficiencies which indicated that formal education is probably not helped to decrease their inefficiencies. Therefore, it calls special emphasis to increase knowledge and education related to agricultural practices itself by upgrading the managerial ability of farmers or farmer’s education through youth training center and practical attachment training. It is also better to fill the knowledge gap among farm households by creating awareness and knowhow about the application of inputs and different farming system in the study area.

Finally, the other variable which had a positive effect on all inefficiency levels was the distance of the home from the nearest main market. The result suggested that policy makers would significantly decrease the inefficiency of sample farmers via the development of road and market infrastructure that reduce home to market distance.

 

The authors have not declared any conflict of interests.