2.2. Efficiency Measurement Concept and Efficiency Analysis Methods
2.2.2.2. Applications of the DEA in Agricultural Studies
With the strong points in comparison to other methods, DEA is considered a popular technique in evaluating the technical, scale, allocative, economic, and
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energy use efficiency. Recently, DEA has been adopted by many reseachers to analyzed the efficiency on various crops in agricultural sectors.
In vegetable production, Haji (2008) employed DEA approach to analyze economic efficiency and marketing performance of vegetable production in the Eastern and Central parts of Ethiopia. The findings indicated that the economic efficiency of farms were not high in both regions of Ethiopia. To improve efficiency at the farm level, policy-makers should focus on developing extension services, training activities, and supporting credit loans for farms instead of only pay attention to introducing new technologies.
The input-oriented DEA method was also applied by Shrestha et al. (2016) to measure the efficiency level of 502 small vegetable farms in Nepal. The results of this study revealed that average efficient scores of farms were very low, with 0.85 for scale efficiency, 0.62 for technical efficiency, 0.50 for allocative efficiency and 0.30 for economic efficiency.
Hussaini and Abayomi (2010) adopted DEA technique to investigate the technical and scale efficiency of 192 vegetable farms in the North Central of Nigeria. The findings illustrated that the mean technical and scale efficiency were 0.93 and 0.82, respectively.
Bournaris et al. (2019) analyzed the efficiency of 98 vegetable producers.
The study reported that average efficient scores of farmers were 0.87, with the highest scores for eggplant production and the lowest efficient level for tomato crop.
In maize production, Karimov et al. (2014) estimated production and scale efficiency by applying the DEA model. The results indicated that some socio- economic factors such as education, extension contact, and credit access had a positive effect on technical efficiency of farms. Koc et al. (2011) employed DEA approach to measure the technical efficiency of maize growing farmers in Turkey. The findings revealed that the technical of maize farms in the East
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Mediterranean region of Turkey was 0.80. In other words, the technical efficiency of maize farms in Turkey could be improved by reducing 19% of inputs while the output quantity was constant.
With rice production, the DEA method applied by various authors to evaluate the efficiency of rice production in many countries in the world.
Ogunniyi et al. (2015) estimated the efficiency of 120 rice farms in Kwara State of Nigeria by using CRS and VRS- DEA model. The estimated results were 0.548 for technical efficiency under CRS, 0.681 for pure technical efficiency, and 0.844 for scale efficiency. The findings showed that the majority of rice farms in Kwara State (82.5%) operated their farms at increasing returns to scale, implying that these farms could improve their efficiency by increasing their farm size.
In addition, Zheng et al. (2018) used data of rice production in China during the period of 1995-2017 to evaluate the production efficiency by applying the DEA model. The study illustrated that both scale and pure technical efficiency had an impact on the overall efficiency of rice production.
Therefore, to improve the production efficiency of rice farms, the solutions should focus on changing their farm size.
Watkins et al. (2014) employed DEA to analyze the efficiency of 158 rice fields in Arkansas during the period from 2005 to 2012. The findings of the study revealed that average scale efficiency was the highest (0.920), followed by 0.803 for overall technical efficiency, 0.711 for allocative efficiency, and the lowest score for economic efficiency (0.622). Moreover, the rice fields which were operated at increasing returns to scale made a majority (48.7%).
Wardana et al. (2018) analyzed the technical efficiency of small rice farms in Indonesia by using the DEA approach. With 7 inputs and one output were used, the study indicated that the estimated efficiency of small rice farms was not high (0.41, 0.63, and 0.61 for overall technical efficiency, pure technical efficiency, and scale efficiency, respectively). The study showed that
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inefficient management and scale were the reason of inefficiency in rice production of small farms in Indonesia.
In Vietnam, Tung (2013) adopted the DEA model to analyze the technical and scale efficiency of rice production in the Mekong delta of Vietnam during the period of 1998-2010. The results indicated that there was an increasing trend in the technical efficiency of rice farms in Mekong delta from 0.48 (1998) to 0.61 (2010).
However, other than the SFA approach, the factors influencing the efficiency were not shown in the first when the DEA methodology was applied.
Thus, to determine the factors affecting the efficiency level, the Tobit regression model was used in the second stage because efficiency scores were bounded between 0 and 1. The summary of empirical studies on analysis the efficiency in agricultural production using DEA and Tobit regression model are presented in Table 2.2.
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Table 2.2. Summary of empirical studies used two-stage DEA on analyzing the efficiency in agricultural production
23 Table 2.2. Continue…
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Futhermore, using DEA approach to investigate the efficiency of energy use in agricultural production was applied by several authors in various crops in many countries around the world. Wakil et al. (2018) adopted DEA model to evaluate the energy efficiency in Nigeria by using cross-section data of 130 rice farmers. The study indicated that the energy saving of rice farms was computed by 8.02% compared to total actual energy inputs usage. In addition, the results showed that rice producers could be saved 2711.21 MJ ha-1 of energy inputs in their production while the output level was constant.
Kordkheili et al. (2014) estimated the energy efficiency of 86 orange farms by applying DEA approach. The findings revealed that the computed SE was the highest (0.97) compared to 0.96 and 0.92 for TE, PTE, respectively.
Besides, the study also indicated that in total saving energy, the energy saving of diesel fuel was the highest contribution, followed by chemical fertilizer and water energy inputs.
Khoshroo et al. (2013) employed the DEA method to estimate the energy efficiency of grape production in Iran. The estimated results indicated that there was a large difference between efficient and inefficient grape farms in using energy inputs such as chemicals, diesel fuel, and water for irrigation. In addition, the education level of farmers had a positive effect on energy-efficient of grape production farms.
Masuda (2018) used data from 2005 to 2011 to evaluate the energy efficiency of rice production in Japan using the DEA approach. The study revealed that the energy-efficient scores of larger farms found to be higher than that of the small ones, 0.988 and 0.732, respectively. The results suggested that increasing the farm size was a good way to improve energy efficiency because it was easy to adapt to high technology in rice production in Japan.
The summary of some empirical studies on analysis the energy efficiency of other crops was illustrated in Table 2.3.
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Table 2.3. Summary of empirical studies on energy efficiency in agricultural production using DEA approach
26 Table 2.3. Continue…