INTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, DINDIGUL Volume 2, No 2, 2011

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1 Application of data envelopment analysis (DEA) to improve cost efficiency of barley Hassan Ghasemi Mobtaker Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran ABSTRACT In this study a non parametric method of data envelopment analysis (DEA) was applied to analyze the cost efficiency of farmers, in order to optimize the input costs for barley production in Hamedan province of Iran. Data were collected using face to face questionnaire from 67 farms in central region of Hamedan province. The DEA method was used based on seven input costs including human labour, land rent, seed, water for irrigation, fertilizers, biocide and machinery and single output of barley yield. The technical, pure technical and scale efficiencies of farmers were calculated as 0.82, and 0.834, respectively. Total optimum cost requirement was found to be about 879 $ ha ; showing that 0.09% of input costs could be saved if the farmers follow the results recommended by this study. Moreover the contribution of water for irrigation input from total cost saving was 63.77% which was the highest share followed by chemical fertilizers and machinery input costs. Optimization of input costs use improved the benefit/cost ratio, productivity and net return by.6%,.27% and 0.25%, respectively. Keywords: Data envelopment analysis, Optimization, Cost saving, Technical efficiency; Barley.. Introduction Barley is a common staple in human and animal diets. Part of the grass family, barley grows in over 00 countries and is one of the most popular cereal crops, surpassed only by wheat, corn and rice. Although barley is fairly adaptable and can be grown in many regions, it is a tender grain and care must be taken in all stages of its growth and harvest. Barley serves as a major animal fodder and as a component of various health foods. It is used in soups and stews (Anonymous, 200). Barley is one of the major crops grown in the Hamedan province and is grown once in a year during the spring season. In Hamedan province it established in autumn (September and early October) and it is harvested in the late spring (early Juan). The average yield of barley in this state is about 4850 kg ha (Mobtaker et al., 200). Agricultural productions use large quantities of locally available non commercial energy, such as seed, manure and animate energy, as well as commercial energies, directly and indirectly. Efficient use of these energies helps to achieve increased production and productivity and contributes to the profitability and competitiveness of agriculture sustainability in rural living (Singh et al., 2002). In addition to technical analysis, economical and energy analysis as well as environmental analysis are among the important necessities in agricultural systems. As the first time, energy analysis in agriculture began seriously since 970s due to the intense increasing of oil products prices, and researches were carried out on characterizing of the amount of energy consumption and substituting energies as well as better production methods. 578

2 There are a lot of tools and multiple criteria decision models used for evaluation of manufacturing and service systems such as multi attribute utility theory, expert systems, mathematical programming, analytical hierarchy process outranking, simulation and scoring models, etc (Onut and Soner, 2007). Data envelopment analysis (which is used in this study) is one of these models. Data Envelopment Analysis (DEA) optimizes the performance measure of each production unit or decision making unit (DMU). DEA defines the efficiency in three different forms: technical efficiency, pure technical efficiency and scale efficiency. It results in a revealed understanding about each DMU instead of depicting the features of a mythical average DMU as in parametric analysis (Chauhan et al., 2006). Some researchers have been carrying out on economical analysis for crops production. Yilmaz et al., (2005) calculated input and output cost for cotton production in Turkey. Cost analysis showed that net return per kilogram of seed cotton was insufficient to cover the costs of production in the research area. The profit cost ratios and productivity was 0.86, and.59 respectively. Erdal et al. (2007) made an economical analysis for sugar beet production in Tokat province of Turkey. They indicated that the total production cost was $ ha, and gross production value was $ ha.the highest energy cost items were labour, land renting, depreciation and fertilizers. In Turkey, input and output cost was analyzed for two types of dry apricot production farms in Malatya. Results of this study showed that the profit cost ratios of the farms were. and.9. Calculated net returns were 44.5 $ ha and $ ha in the investigated farms (Esengun et al., 2007). Mousavi Avval et al. (20b) employed the DEA technique to analyze the efficiencies of apple producers in Tehran province of Iran. The results of economical analysis showed that the total costs of production could decreased from to $ ha ; also the benefit to cost ratio and productivity improved from.24 to.34 and 2.52 to 2.74, respectively. Mohammadi et al., (20) investigated the effect of optimization of energy on improvement of input costs and energy indices in kiwifruit production. The results of economical analysis showed that optimization of energy decreases the total costs of production by 4.9%; and the benefit cost ratio and productivity increased by 5.5% and 5.9%, respectively. Based on literature, although many studies have conducted on energy use in agricultural crops(mousavi Avval et al., 200; Taheri-Garavand et al., 200; Namdari, 20; Pishgar Komleh et al., 20), there isn t study on optimization of input costs for barley production in Iran. So, this study focuses on the application of DEA to benchmark and rank the technical efficiency of barley based on the amount of input costs use, and barley yield. 2. Materials and Method The research was done in Hamedan Province which is located in the west of Iran; within / and / north latitude and / and / east longitude. In this research the DEA approach was used to analyze the data for optimizing the performance measure of each production unit or each barley farm. The data used in this study, has collected form 67 barley farms in central region of Hamedan province. The data included amount of input costs used in barley production such as human labour, diesel and machinery rent, fertilizers, biocide, water for irrigation, land rent and seeds, and the yield as an output. Table showed the inputs and output costs used in barley production. As can be seen from table, there was a wide variation in the quantity of input costs and output for barley production; indicating that there is a great scope for improving the cost efficiency of barley production in the region. 579

3 Table : Amounts of inputs and output costs in barley production Item (unit) Total input costs ($ ha ) SD Max Min Inputs. Human labour Diesel and machinery rent Land rent Total fertilizers Biocides Water for irrigation Seeds Total production costs Output. Barley Technical efficiency is basically a measure by which DMUs are evaluated for their performance relative to other DMUs in a sample (Mohammadi et al., 20). It is also called global efficiency and can be expressed generally by the ratio of sum of the weighted outputs to sum of weighted inputs (Nassiri and Singh, 2009). The value of technical efficiency varies between zero and one. For calculated technical efficiency (TE) Eq. () was used (Nassiri and Singh, 2009; Mousavi Avval et al., 20b): TE j u y + u y u y j 2 2 j n nj r= = = m vx j + v2x2 j vmxmj n s= u r s y v x rj sj () where, u r, is the weight given to output n; y r, the amount of output n; v s, the weight given to input n; x s, the amount of input n; r, number of outputs (r =, 2,..., n); s, number of inputs (s =, 2,.., m) and j, represents jth of DMUs (j =, 2,..., k). To solve Eq. (), Linear Program (LP) was used, which developed by Charnes et al. in 978: Maximize n θ = u r y ri (2) r= n Subjected to (i) u y v x 0 r= r ri m s= s sj (3) m s= v s x sj = (4) u 0, v 0 and (i and j=, 2, 3, 4,, k ) (5) r s where, θ is the technical efficiency and i represents ith DMU (it will be considered as fixed in Eqs. (2) and (4) while j increases in Eq. (3)). The above model is a linear programming model and is popularly known as the CCR DAE model which assumes that there is no significant 580

4 relation between the scale of operations and efficiency (Avkiran, 200). So the large producers are just as efficient as small ones in converting inputs to output. Pure technical efficiency is another model in DEA that was introduced by Banker et al. in 984. This model called BCC and calculates the technical efficiency of DMUs under variable return to scale conditions. Pure Technical efficiency could separate both technical and scale efficiencies. The main advantage of this model is that the scale inefficient farms are only compared to efficient farms of a similar size (Bames, 2006). It can be expressed by Dual Linear Program (DLP) as follows (Mousavi Avval et al., 20b): Maximize z=uy i u i (6) Subjected to vx i = (7) -vx+uy - u o e 0 (8) v 0, u 0 and u o is free in sign (9) where, z and u 0 are scalar and free in sign. u and v are output and inputs weight matrixes, and Y and X are corresponding output and input matrixes, respectively. The letters x i and y i refer to the inputs and output of ith DMU. Scale efficiency shows the effect of DMU size on efficiency of system. Simply, it indicates that some part of inefficiency refers to inappropriate size of DMU, and if DMU moved toward the best size the overall efficiency (technical) could be improved at the same level of technologies (inputs) (Nassiri and Singh, 2009). The relation among the scale efficiency, technical efficiency and pure technical efficiency can be expressed as (Chauhan et al., 2006): Technical efficiency Scale efficiency = (0) Pure technical efficiency The results of standard DEA models separate the DMUs into two sets of efficient and inefficient ones; so many units are calculated as efficient and can not to be ranked. Also in DEA because of the unrestricted weight flexibility problem, it is possible that some of the efficient units be better overall performers than the other efficient ones (Adler et al., 2002). To overcome this problem and achieve a complete ranking of efficient farmers, the cross efficiency ranking method was used which developed by Sexton et al. (986). In this method the results of all the DEA efficiency scores can be aggregated in a matrix, called cross efficiency matrix. In this matrix E ij, the element in the ith row and jth column, represents the efficiency score for the jth farmer and was calculated using the optimal weights of the ith farmer which is computed by the CCR model. In general, the efficient farmers can be ranked according to their average cross efficiency score (Angulo Meza et al. 2002; Chauhan et al. 2006; Zhang et al. 2009). In the analysis of efficient and inefficient DMUs the cost saving target ratio (CSTR) index was used which represents the inefficiency level for each DMUs with respect to cost use. The formula is as follow (Hu and Kao, 2007): ESTR ( Cost Saving T arg et ) ( Actual InputCost ) j j j = () 58

5 where cost saving target is the total reducing amount of input costs that could be saved without decreasing output level and j represents jth DMU. The minimal value of cost saving target is zero, so the value of CSTR will be between zero and unity. In order to calculate the efficiencies of farmers and discriminate between efficient and inefficient ones, the Microsoft Excel spread sheet and Frontier Analyst software were used 3. Result and discussion The results of BCC and CCR DEA models are illustrated in Fig.. The results revealed that, among the total of 67 farmers considered for the analysis, 4 farmers (6.9%) had the pure technical efficiency score of. Also, from the pure technically efficient farmers 6 farmers (23.88%) had a technical efficiency score of. From efficient farmers 6 were the fully efficient farmers in both the technical and pure technical efficiency scores; indicating that they were globally efficient and operated at the most productive scale size of production; however, the remaining of 25 pure technically efficient farmers were only locally efficient ones; it was due to their disadvantageous conditions of scale size. From inefficient farmers 2 and 7 have their technical and pure technical efficiency scores in range of It means that the farmers should be able to produce the same level of output using their efficiency score of their current level of input costs when compared to its benchmark which are constructed from the best performers with similar characteristics. From efficient farmers 7 ones had a scale efficiency of unity. Figure : Efficiency score distribution of barley producers The summarized statistics for the three estimated measures of efficiency are presented in Table 2. The results revealed that the average values of technical, pure technical and scale efficiency scores were 0.82, and 0.834, respectively. Moreover the technical efficiency varied from 0.53 to, with the standard deviation of 0.84, which was the highest variation between those of pure technical and scale efficiencies. The wide variation in the technical efficiency of farmers implies that all the farmers were not fully aware of the right production techniques or did not apply them at the proper time in the optimum quantity. 582

6 Mohammadi et al. (20) applied DEA technique to determine the efficiencies of farmers in kiwifruit production in Iran. They reported that, the technical, pure technical and scale efficiency scores were as 0.942, and 0.948, respectively. In another study, the efficiency of soybean production was analyzed and these efficiency indices were reported 0.853, 0.99 and 0.926, respectively (Mousavi Avval et al., 20a). Table 2: Average technical, pure and scale efficiency of barley farmers Particular Average SD Min Max Technical efficiency Pure technical efficiency Scale efficiency In this study efficient farmers were ranked according to their average cross efficiency scores. For this purpose, the CCR model (2) was used to calculate the cross efficiency scores in each cell of cross efficiency matrix. The average and standard deviation of cross efficiency scores for 0 truly most efficient farmers are showed in Table 3. The results revealed that farmers No. 28, 32 and 34 with the average cross efficiency scores of 0.899, and had the highest average cross efficiency scores, respectively; therefore, these farms can be used as terms of benchmarking and establishing the best practice management. Table 3: Average cross efficiency (ACE) score for 0 truly most efficient farmers base on the CCR model Farmer No. ACE SD Farmer No. ACE SD The optimum cost requirement and cost saving of various farm inputs for barley production based on the results of BCC model are given in Table 4. The results revealed that the total production cost in optimum condition was $ ha ; so the total cost of production in present condition (978.6 $ ha ) could be saved by 0.09%. This mean that by following the recommendations resulted from this study, on average, about $ ha of total production cost could be saved while holding the constant output level of barley yield. The results of ESTR calculation showed that, 22.63% from total fertilizer, 22.04% biocides, 2.3% from water for irrigation and 6.4% from diesel and machinery rent cost consumption could be saved. In the last column of Table 4 the shares of the various sources from total input costs saving are presented. Results revealed that the highest contribution to the total cost saving was 63.77% for water for irrigation cost. This indicates that in the case of water for irrigation cost, there is a greater scope to decrease the production cost. It was followed by fertilizers and diesel and machinery rent cost by.72% and 8.78% respectively. Also the shares of land rent and biocides costs were relatively low, indicating that, they have been used in the right proportions by almost all the farmers. 583

7 Table 4: Optimum cost requirement and cost saving for barley production Input Optimum cost requirement ($ ha - ) Cost saving ($ ha - ) Cost saving (%). Human labour Diesel and machinery rent Land rent Total fertilizers Biocides Water for irrigation Seeds Total production costs Contribution to the total cost savings (%) Mohammadi et al. (20) reported that optimization of energy, decreases the total costs of production by 4.9%. In another study the results of economical analysis showed that by optimization of energy the total costs of apple production could be decreased from to $ ha (Mousavi Avval et al., 20). The improvements of economical indices for alfalfa production are presented in Table 5. The benefit/cost ratio was calculated as.98 and 2.2, in present and optimum conditions, respectively, showing an improvement of.6%. Also, net return and productivity in target conditions were found to be $ ha and 5.53 kg $, respectively. Several investigations have been done in economic analysis of crops production and benefit/cost ratio was concluded, as 2.37 for orange,.89 for lemon and.88 for mandarin (Ozkan et al., 2004),.88 for potato (Mohammadi et al., 2008), 2.09 for canola (Unakitan et al., 200). Table 5: Improvement of economical indices for barley production Items Unit Present quantity Optimum Difference (%) Benefit/cost ratio Net return $ ha Productivity kg $ Total production $ ha Total production $ ha In Table 6 the pure technical efficiency (PTE) and actual cost requirement from different inputs for individual inefficient farmers are showed. Also in Table 7 the optimum cost requirement are showed. Using this information, regarding the better operating practices, it is possible to advise a producer for following his/her target cost requirement from different inputs to reduce the input costs levels to the target values while achieving the output level presently achieved by him. So dissemination of these results will help to improve benefit/cost ratio of farmers for barley production in the surveyed region. In the last column of Table 7 the ESTR percentage for 26 inefficient farmers are presented. As it can be seen, for inefficient farmers, ESTR ranges from 4% (farmer No. 64) to 43% (farmer No. 45), with the 584

8 average of 7%, Indicating that between inefficient farmers, No. 64 and 4 were the best and the worst inefficient ones, respectively. Table 6: The source wise actual cost for inefficient farmers in the barley production DMU PTE Actual cost ($ ha ) Labour Machine Land rent Fertilizer Biocide Water Seed Ave S.D (based on BCC Model) Table 6: The source wise target cost for inefficient farmers in the barley production (based on BCC Model) Optimum cost ($ ha ) CSTR DMU Labour Machine Land rent Fertilizer Biocide Water Seed (%)

9 Ave S.D Conclusion The objective of this study was to apply the non parametric method of data envelopment analysis (DEA) to analyze the cost efficiency of farmers and discriminate efficient farmers from inefficient ones for barley production in Hamedan province of Iran. This method was done based on seven input costs including human labour, land rent, seed, water for irrigation, fertilizers, biocide and machinery and single output of barley yield. The following results were concluded by this study:. Among the total of 67 farmers, considered for the analysis, 23.88% and 6.9% were found to be technically and pure technically efficient, respectively. 2. The technical, pure technical and scale efficiencies of farmers were calculated as 0.82, and 0.834, respectively. 3. Total optimum cost requirement was found to be about 879 $ ha ; showing that 0.09% of input costs could be saved if the farmers follow the results recommended by this study. 4. Optimization of cost use improved the benefit/cost ratio, productivity and net return by.6%,.27% and 0.25%, respectively. 586

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