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Economic Efficiency of Smallholder Farmers in Sesame Production: The Case of Haro Limmu District of East Wollega Zone, Oromia Region, Ethiopia
Yadeta Bedasa1
1 Department of Agricultural Economics, Wollega University, Shambu, Ethiopia
Abstract
This study examines the economic efficiency of smallholder farmers in sesame production. Specifically, the study examines levels of technical, allocative and economic efficiencies of sesame producer; and to identify factors affecting technical, allocative and economic efficiency of smallholder farmers in the study area. For this study both primary and secondary data were used; it was based on the data collected from 155 randomly selected farmers. Stochastic frontiers analysis with cobb-douglas function was fitted to estimate technical, allocative and economic efficiencies levels. OLS estimation method was applied to identify factors affecting technical, allocative and economic efficiency levels of the sampled farmers. The results show that the mean technical, allocative and economic efficiency score was found to be 67.7, 70.6 and 47.9%, respectively. The OLS regression model estimates result indicates that family size, education level, extension contact and credit access were positively influence technical efficiency; While, distance of sesame farm was negatively. Sex, livestock, education level, and non-farm activity were positively influence allocative efficiency; while, farm size and distance of sesame market were negatively. Sex, family size, educational level, credit access, livestock, extension contact, and non-farm activity were positively influence economic efficiency; while, farm size was negatively. The result indicates as there is a room to increase the efficiency of sesame producers in the study area. Strengthening the existing livestock production system, credit access, agricultural extension system and invest in the provision of basic education to smallholder farmers are advisable.
Keywords: Cobb-douglas, Efficiency, Haro Limmu, OLS
INTRODUCTION
Sesame is an important oilseed crop grown across the globe for the valuable edible oil and due to its economical value (World Bank, 20i8). Sesame is one of the key agricultural commodities grown in Ethiopia, and the most significant contributor to national economy. It is second largest foreign exchange earnings next to coffee. In addition, it uses as source of income for millions of population (FAS, 20i6; CSA, 20i3; ECX, 20i8). Northern and north western parts of country are areas where sesame seed is widely produced (FAO, 20i5; FAS, 20i6). Humera, Gondar and Wollega type sesame seeds are varieties produced in country that are well known on the world market (Boere et al., 20i5).
Despite its opportunities, there is still the inefficiency of the smallholder farmers in the production of sesame due to some problems that hinder its productivity (Abadi, 20i8). The Ethiopian sesame production is essentially full of challenges. It is believed that the producers lack the necessary input to improve their production and productivity; trade arrangements are not well organized; the necessary government policies and institutions, and the enforcement of regulations are either nonexistent or functioning too ineffectively to ensure a smoothly operating (Gelalcha, 2009). Sesame productivity is declining from 800 to 300 kg/ha in most parts of the country. The major reasons are the lack of knowledge and skill in land preparation and agronomic practices, weather uncertainties and pest outbreaks (Terefe et al. 20i2).
In Haro Limmu district, sesame is a major cash crop and it takes the lion share in terms of the extent of production, 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 livelihood. So, it is crucial to increase their volume of production and efficiency. This may be achieved through improving level of efficiency. Therefore, this study attempts to conduct an empirical research in sesame production to guide policy decisions, device appropriate interventions and integrated efforts to overcome inefficiency problem of sesame producer in study area.
RESEARCH METHODS
Description of Study Area
This study was carried out in Haro Limmu district. Haro Limmu is one of the districts in the Oromia Region of Ethiopia. It is part of the East Wollega Zone. The district was located at distance 165 km from zonal town called Nekemte and 488 km from capital city Addis Ababa. Today this district is sub divided into fifteen rural and two urban kebele. The agro ecology of district was lowland and highland with temperature ranging from 14-26 0C. The climatic condition of the district is almost partially warm zone (kola). Maize, sorghum, finger millet, haricot bean, sesame and ground nut are crops grown in the district. The total population for this district was 57,606 of whom 30,708 were men and 26,808 were women (Haro Limmu Finance and Economic Development office, 2019).
This image got removed by the editiorial team due to copyright reasons.
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Figure 1: Map of the study area.
Sampling Procedures and Sample Size Determination
A two stages random sampling procedure was employed to draw a representative sample. In the first stage, three kebeles were randomly selected out of the total kebeles. In the second stage, 155 sample farmers were selected by using simple random sampling technique from each kebele based on probability proportional to size. To obtain a representative sample size, the study employed the sample size determination formula given by Yamane (1967) as follow:
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Where, n is sample size, N is number of households in the district and taking 10,752, and e is the desired level of precision and taking e as 8%.
Types of Data, Source and Methods of Data Collection
In this study, both qualitative and quantitative data collected from primary and secondary data sources. The primary data was collected using structured questionnaire that is administered by the trained enumerators before starting the actual data collection. Secondary data was collected by reviewing relevant sources such as documents of the office of agriculture of the district and other relevant organizations.
Method of Data Analysis
To address the objectives of the study, both descriptive statistics and econometric methods of data analysis were employed. Descriptive statistics like mean, percentage, frequency minimum, maximum and standard deviation was used. Also, after conducting the entire required hypothesis and make decision, a cobb-douglas functional form with stochastic frontier model was used to estimate production and cost function. The linear form of the Stochastic Frontier cobb-douglas production function for this study was defined as
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Where In denotes the natural logarithm; j represents the number of inputs used; i represents the ith farm in sample; Yi represent the observed sesame output of the ith sample farmer; Xij denotes jth farm input variables used in sesame production of the ith farmer; ß stands for the vector of unknown parameters to be estimated; The symmetric component (Vi) captures statistical noise and is assumed to be independently and identically distributed as N~(O, a 2v). The production function could also be estimated through an alternative form, called dual, such as cost or profit function.
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Where i refers to the ith sample household; Ci is the minimum cost of production; Wi denotes input prices; Yi* refers to farm output which is adjusted for noise vi and a's are parameters to be estimated. According to Sharma et al. (1999), the above cost measures are used to estimate the technical, allocative and economic efficient respectively. We can define the farm specific technical efficiency in terms of observed output (Yi) to the corresponding frontier output (Yi*) using the existing technology.
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The farm specific economic efficiency is defined as the ratio of minimum observed total production cost (C*) to actual total production cost (C).
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OLS estimation method was applied to identify factors that affect the efficiency level of the farmers, because the efficiency scores are not truncated or censored for a specific value. Therefore, ordinary least square estimation technique is applicable in this study. The estimating efficiency scores are regressed on the same set of farm related, institutional and farmer's specific factors that are assumed to be important determinants of efficiency as allocative and technical efficiency.
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Where Ui*=Economic efficiency levels. The efficiency variables are denoted as X1 to X12 with their coefficient ßl to ßi2 and Et where random term. After all regression results, different post estimation tests or diagnostics were done including variance inflation factor and 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
RESULTS AND DISCUSSION
The average family size of the sample households was found to be 3.58 man equivalents (Table
4) . With regards to the sex of respondents, the survey result shows, about i2.26% of the sample households were female headed and the remaining 87.74% were male headed households (Table
5) . According to the survey result, the average years of formal school of the sampled farmers was grade 4.48 (Table 4). More than 52.90% of the sample farmers reported that they participate at least in one of the aforementioned non-farm activities (Table 5). On average, the livestock holding of the sampled farmers in the study area was 6.i0 TLU (Tropical Livestock Unit) per household (Table 4).
Table 1: Descriptive statistics for continuous variables
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Source: Own computation (2019)
Table 2: Descriptive statistics for discrete variables
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Source: Own computation (2019)
Results of Econometric Analysis
Hypotheses test
Three hypotheses were tested. Firstly, the best functional form that can fit the data was selected by testing the null hypothesis which states that the coefficients of all interaction terms and square specifications in the Translog functional forms are equal to zero Ho: ßjj = 0 against alternative hypothesis which states that the coefficients of all interaction terms and square specifications in the Translog functional forms are different from zero Ho: ßjj 0. This test was made based on the value of likelihood ratio
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The log likelihood functional values of both Cobb-douglas and Translog production functions were -55.96 and -49.35 respectively. Therefore, the 2 value computed was 13.22 and this value is lower than the upper 5% critical value of X 2 at 15 degree of freedom 24.9 (Table 9). As a result, the null hypothesis was accepted and the Cobb-douglas functional form best fits the data.
The second test is to test the null hypothesis that the inefficiency component of the total error term is equal to zero (y = 0) and alternative hypothesis that inefficiency component different from zero. X test of (y = 0 ) provide a statistic of 28.26; which is significantly higher than the critical value of X 2 for the upper 5% at one degree of freedom (3.84). Rejecting the null hypothesis implies that the average response function estimated by OLS, which assumes all farmers are technically efficient is an insufficient representation of the data, given the stochastic frontier and the inefficiency effects model.
The third hypothesis tested was that all coefficients of the inefficiency model are simultaneously equal to zero Ho = 50 = 51 = 52 = ••• S12 = 0 against the alternative hypothesis, which states that all parameter coefficients of the inefficiency model are not simultaneously equal to zero. Therefore, by using the formula in Equation (4.1), the X value obtained was 155.02, which is higher than the critical X 2 at 12 degree of freedom 21.03. As a result, the null hypothesis is rejected in favor of the alternative hypothesis that explanatory variables associated with inefficiency model are simultaneously not equal to zero.
Table 3: Generalized LR test of hypotheses for parameters of SPF
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Source: Own computation (2019)
The Estimation of production and cost functions
The dependent variable of the estimated production function was of sesame output (kg) and the input variables used in the analysis were area under sesame (ha), oxen (pair of oxen-days), labor (man-days in man-equivalent), quantity of seed (kg) and amount of chemical herbicide (liter). Four variables (land, labor, oxen and seed) had positive and significant effect in determining the variation of sesame output (Table 10). Land and labor are found to be statistically significant at 1% significance level implying that increasing the level of these inputs would increase sesame output in the study area. Moreover, the coefficient for land used was 0.461, which implies that, at ceterius paribus, a 1% increase in the area of land allocated for sesame production, results in 0.461% increase in sesame output. This result is consistent with the findings of Tolesa et al. (2019); Asfaw et al. (2019); Assefa A et al. (2016) and Sisay et al. (2015).
Labor also appeared to be an important factor, with coefficient of 0.198. This implies that a 1% increase in labor enhance sesame output by about 0.198% at ceterius paribus. This result is also in line with the empirical results of Getachew et al. (2018), Mustefa et al. (2014), Bekele et al. (2013), Mekonen et al. (2015), Wudineh et al. (2016) and Sisay et al. (2015). Similarly, the coefficient of production with regard to seed use was 0.138 and significant at 5 % significance level. It is further indicated that, a 1% increase in the quantity of seed used for sesame production, holding all other inputs constant, results in 0.14% increase in sesame output. This result is also in line with the empirical results of Asfaw et al. (2019), Mustefa et al. (2017). Also, the coefficient of production with regard to oxen use was 0.177 and significant at 10 % significance level. It is further indicated that, a 1% increase in the power of oxen used for sesame production, holding all other inputs constant, results in 0.18% increase in sesame output. This result is consistent with the findings of Getachew et al. (2018).
Table 4: Estimates of the Cobb-douglas frontier production function
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Source: Own computation (2019)
Table 5: Estimates of the Cobb-douglas frontier cost function
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Source: Own computation (2019)
The dual frontier cost function derived analytically from the stochastic production frontier shown in table 11 is given by:
lnCi = 1.2 + 0.523lnw1i + 0.153lnw2i + 0.071lnw3i + 0.092lnw4i + 0.095lnw5i + 0.068Y* Where Ci, is the minimum cost of production of the ith farmer, Y ' refers to the index of output adjusted for any statistical noise and scale effects and W represents for input costs.
Efficiency scores and their distribution
The Technical efficiency (TE) ranged from 0.148 and 0.928 with the mean TE of 0.677. The average TE index of 0.677 suggests that an average sesame farmer in the study area still has the capacity to increase TE in sesame production by about 32.3% to achieve the maximum possible level or the average farmers could decrease inputs by 32.3% to get the output they are currently getting, if they use inputs efficiently. Similarly, the mean AE and EE of sample households were 70.6 and 47.9%, respectively. Unlike TE and EE there was high average AE score. This result is consistent with study of Mekonen et al. (2015); Desale (2017) and Hika et al. (2018). Similarly, the mean allocative efficiency of farmers in the study area was 70.6% with a minimum of 0.318 and a maximum of 0.916. It indicated that Sesame producer households can save 29.4% of their current cost of inputs if they use the right mix of inputs given their prices. Hence, a farmer with average level of allocative efficiency would enjoy a cost saving of about 22.93% derived from (1 - 0.706/0.916)*100 to attain the level of the most efficient farmer.
This study found mean economic efficiency level of sample households was 47.9% with minimum and maximum efficiency scores of 9.7% and 75.3% respectively. The mean shows that an economically efficient household can reduce his/her sesame production cost by 52.1%. The mean levels of efficiencies were comparable to those from other similar studies in Ethiopia. Accordingly, Mekonen et al. (2015) found mean TE, AE and EE of 67.1, 67.25 and 45.14% respectively for sesame producers in Selamago district, southern Ethiopia. In addition, Hika et al. (2018) found mean TE, AE and EE of 75.16%, 72.95% and 53.95% respectively for sesame seed producer farmers in Babo-Gambel district of west Wollega zone, Ethiopia. Furthermore, Tolesa et al. (2019) found mean TE, AE and EE of 71.65%, 70.06% and 49.89%, respectively for maize seed producer farmers in Gudeya Bill district of Oromia region, Ethiopia.
Table 6: Summary statistics of efficiency score of sample households
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Source: Own computation (2019)
The distribution of the technical efficiency scores showed that about 22.58% of the sample households have technical efficiency score of 80 to 90%. But there were also some households whose technical efficiency score levels were limited to the range of 20 to 40%. On average, households in this cluster have a room to enhance their sesame production at least by 60%. Out of the total sample households, only 7.74% have technical efficiency score of greater than 90%. This implies that about 91.71% of the households can increase their production at least by 8% (Figure 5). The allocative efficiency distribution scores indicated that about 1% of sesame producers operated above 90% efficiency level. The distribution of economic efficiency scores implies that 23.87% of the household heads have an economic efficiency score of 50-60%. This also indicates the existence of substantial economic inefficiency than technical and allocative inefficiency in the production of sesame during the study period in the study area (Figure 5).
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Figure 5: Frequency distribution of technical, allocative and economic efficiencies scores
Determinants of efficiency differentials among farmers
OLS estimation technique was employed to identify factors influencing technical, allocative and economic efficiencies. Before explaining the model, a test on multicollinearity was made. The VIF was found to be low (a maximum VIF of 1.07). This shows that there is no problem of multicollinearity in the data set. The OLS regression model estimates result indicates that the coefficient for sex of the household head was significant and positively affected allocative and economic efficiencies of farmers at 1% significance level, as it was expected. It indicated male headed households operating more efficiently than their female counterparts. Moreover, a change in the dummy variable sex from (0 to 1) would increase the farmers being allocatively and economically efficient by about 9.6% and 6.4%. The result is consistent with that of Wudineh et al. (2016); Bacha et al. (2019) and Asfaw et al. (2019).
As expected, educational level of the household head has a positive and significant effect on AE and EE at 1% and TE of sesame production at 10% level of significance. More educated households have relatively better capacity for allocation of inputs and their decision-making capacity. Moreover, the computed result revealed that, a one-year increase in educational level of the household head increases the farmer being technically, allocatively and economic efficient by 0.4, 0.9 and 0.9% respectively. These results are consistent with the findings of Asfaw et al. (2019); Saulos (2015) and Mustefa et al. (2014).
Frequency of extension contact has significant and positive effect on technical and economic efficiencies at 1% and 5% significance level respectively. This indicates households who receive more extension contacts by extension workers appear to be more efficient than their counterparts.
Furthermore, the computed result shows that, a unit increase in the number of extension contact would increase a farmer being technically and economically efficient by 1.9 and 1.4%, respectively. This result was consistent with the research done by Desale (2017); Musa et al. (2013) and Getachew et al. (2014).
As it was hypothesized the coefficient of participation in non-farm activity was positive and significant influence allocative and economic efficiency at 5% and 1% significance level respectively. Participation in non-farm activity affect efficiency positively for the reason that the income obtained from such activities could be used for the purchase of agricultural inputs. In addition, the computed result revealed, a change in a dummy variable participation in non-farm activity from (0 to 1), would increase the farmer being allocatively and economically efficient by about 4.3 and 4.1%, respectively. Getachew et al. (2018) and Kadiri (2014) also reported similar results for Nigerian rice farmers.
The coefficient of farm size for allocative and economic efficiency is negative and statistically significant at 5% significance level as it was expected. Moreover, as the number of farm size operated by the farmer increases, it may be difficult to manage those plots. Furthermore, the computed result indicated, a unit increase in the number of farm size would decrease the farmer being allocative and economic efficient by 2.7 and 2.2%, respectively. This result is similar with the findings of Endriase et al. (2013).
The coefficient of family size was positive and has statistically significant on TE and EE at 5 and 10% significance level respectively. Hence, the farmers who have more available labor were better managers; therefore, they produced closer to their production frontier. A possible reason for this result might be that a larger household size guarantees availability of family labor for farm operations to be accomplished in time. Moreover, the computed effect of family size showed that a one person change in the number of family in man equivalent would increase the farmer being technically and economically efficient by 1.1 and 0.9%, respectively. The result is consistent with that of Mustefa et al. (2014) and Tolesa et al. (2019).
The coefficient for livestock holding was positive and has a significant influence on AE and EE at 10 and 1% level respectively. The result reveals that having the largest number of livestock holding helps to shifts cash constraint, provide manure and to satisfy all needs of farmers in the study area.
Each unit increase in the value of TLU would increase the farmer being allocatively and economic efficiency by 0.5%. This finding was consistent with the result obtained by Bekele (2013); Mustefa et al. (2014); Getachew et al. (2018) and Wudineh et al. (2016).
The coefficient distance of farmer's home from nearest to plot is negatively and significantly influences TE at 1% levels of significance. This relation may be because farmers living near the production site follow up whole day their sesame plot that enables to better manage farms and save time of work which leads to better achievement of their efficiency. This implies that as the distance of the plot from home increases, TE decreases. A unit increase in the distance of plot from home would decrease the farmer being technically efficient by 0.1%. This finding was consistent with the result obtained by Desale (2017); Musa et al. (2013); Getachew et al. (2018); Bekele et al. (2013) and Musa et al. (2015).
Also, the coefficient distance of farmer's home from nearest to market is negatively and significantly influences AE at 5% levels of significance. This implies that as the distance of the market from home increases, AE decreases. A unit increase in the distance of farmer's home from nearest to market would decrease the farmer being allocatively efficient by 0.1%. This result is also consistent with research done by Ahmed (2013).
The result also indicated that credit used has a positive sign and statistically significant effect on both TE and EE at 1% levels of significance. This suggests that on average households who use credit tend to exhibit higher levels of efficiency. Moreover, a change in the dummy variable representing the uses credit by the household ordered from 0 to 1 would increase the farmers being technically and economically efficient by about 10.7% and 5.6%, respectively. This result is also consistent with research done by Mekonen et al. (2015); Musa et al. (2013); Ahmed (2013) and Tolesa et al. (2019).
Table 7: Source of technical, allocative and economic efficiencies
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Source: Model output (2019).
CONCULSION AND RECOMMENDATION
The study result also shows that there was significant amount of variation in efficiency among farmers. Accordingly, the mean values of technical, allocative and economic efficiencies were 67.7, 70.6 and 47.9%, respectively. This implies that farmers can increase their sesame production on average by 32.3% if they were technically efficient. Similarly, sesame producers can reduce current cost of inputs, on average, by 29.4% if they were allocatively efficient. The result also indicated that if these farmers operate at full efficiency levels, they on average could reduce their costs of production by 52.1%. Furthermore, there is a considerable room to enhance the level of technical, allocative and economic efficiency of sesame producing farmers in the study area.
The study result suggested that interventions aiming to improve efficiency of farmers in the study area could need. Also, less efficient farmers are advised to share an experience from the most efficient farmers to increase their efficiency level. The study recommends proper extension services with equipped skills may assist farmers to be better decision makers of their farms that ultimately increase the level of efficiency.
Government and other stakeholders could have designed appropriate policy to provide adequate and effective basic educational opportunities to the rural population. Furthermore, the establishment of sufficient rural finance institutions and strengthening of the available microfinance institutions could need to assist farmers in terms of financial support.
The study result suggested that government could increase the efficiency of smallholder farmers via the development of road and market infrastructure that reduce distance of farmer's home from nearest to plot and from nearest to market. Also, farmers could have to get inputs easily and a communication channel has to be improved to get better level of allocative efficiency.
Concerned stakeholders and government organizations are advised to identify the different possible types of non-farm activities and support with the necessary knowledge and skills of the various types of non-farm activities that could improve their efficiency statutes of smallholder farmers. Moreover, they could need to design appropriate policy and strategies for improving livestock production systems which in turn would enhance the efficiency of smallholder farmers.
The study recommends policy to promote the application of best farm practices on small land holdings. Also, especial emphasis could have to given for female headed households and this would in turn help them to improve their efficiency level in sesame production.
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