Economic efficiency of smallholder farmers in barley production in Meket district, Ethiopia

Abstract

This study analyzed the economic efficiency of smallholder farmers in barley production in the case of Meket district, Amhara National Regional State, Ethiopia. A cross sectional data collected from a sample of 123 barley producers during the 2015/2016 production season was used for the analysis. Two stages random sampling method was used to select sample respondents. The translog functional form was chosen to estimate both production and cost functions and OLS estimation method was applied to identify allocative and economic inefficiencies factors, while technical inefficiency factors were analyzed by using single stage estimation approach. The estimated stochastic production frontier model indicated input variables such as fertilizer, human labor and oxen power as significant variables that increase the quantity of barley output, while barley seed had a negative effect. The estimated mean levels of technical, allocative and economic efficiencies of the sample farmers were about 70.9, 68.6 and 48.8%, respectively which revealed the presence of a room to increase their technical, allocative and economic efficiencies level on average by 29.1, 31.4 and 51.2%, respectively with the existing resources. Among the hypothesized factors expected to affect technical, allocative and economic inefficiencies, extension contact and number of barley plots significantly and negatively affected all inefficiencies level. Besides, distance of residence from the nearest main market was found to have a positive and significant effect on all inefficiencies of sampled farm households. Hence, emphasis should be given to decrease the inefficiency level of those more inefficient farm households via experience sharing among the better of farmers and usage of improved or certified barley seed. Besides this, policies and strategies of the government should be directed towards increasing farmers' education, improve the system of input distributions and institutional facilities.

Economic Efficiency of Wheat Producers: The Case of Debra Libanos District, Oromia, Ethiopia

“…Computed marginal effect result indicated that a change in the dummy variable, fertility status of the soil (0 to 1) would increase the probability of the farmer being allocatively efficient by about 11.12% and the expected values of AE and EE by about 6.65% and 6.76% with an overall increase in the probability and levels of AE and EE by 11.12% and 6.76% respectively. This result is consistent with findings by (Getachew et al, 2017;Milkessa et al, 2019).…”

“…More specifically, a unit increase in the number of extension contact would increase the probability of a farmer being technically efficient by 0.02% and the expected values of TE and EE by about 2.18% and 1.38% respectively, and the overall efficiency of TE and EE by about 2.21% and 1.38% respectively. This result was in line with the finding of (Kifle et al, 2017;Getachew et al, 2017). Other studies, however, found that extension contact negatively affects efficiency since extension workers are only concerned with increasing output and have not new skills and information to support the farmers (Musa, 2015;Mustafa et al, 2017).…”

“…Likewise, livestock size (LIVESZE) entered the model pertaining to TE and EE with a positive coefficient at 1% of significant level supporting (Solomon, 2012;Mustafa et al, 2017;Getachew et al, 2017). This confirms the considerable contribution of livestock in reducing the cost of inputs in wheat production.…”

Review on Economic Efficiency of Smallholders Farmers in the Production of Sesame: The Case of Ethiopia

“…In another related study, Getachew W examined economic efficiency of smallholder farmers in barley production in Meket district, Ethiopia. The study was used Translog functional form and Ordinary Least Square (OLS) to analyze efficiency level and factor affecting efficiency level respectively [26]. However, according to the study conducted by ([13, 31, 33, 38, 27, 4, 41]) two limit Tobit model was appropriate to analyze factor affecting efficiency level over OLS.…”

“…Also, according to Getachew, Tobit regression approach is preferred over the OLS regression in the case of censored data. OLS regression also needs to ignore the censoring nature of the dependent variable or exclude the censored data from analysis [26]. Therefore, using Ordinary Least Squares (OLS) regression leads to serious specification errors in model structure and yields biased and inconsistent parameter estimates.…”