International Journal of Agriculture and Crop Sciences. Available online at www.ijagcs.com IJACS/213/5-18/264-27 ISSN 2227-67X 213 IJACS Journal Energy input-output and study on energy use efficiency for wheat production using DEA technique Mohamad Reza Moghimi 1, Behzad Mohammadi Alasti 1, Mohammad Ali Hadad Drafshi 2 1. Department of Acricultural Machinery, Bonab Islamic Azad University, Bonab, Iran. 2. Department of Agricultural Machinery, Urmia University, Urmia, Iran. Corresponding Author email:mohamadmoghimi1368@gmail.com ABSTRACT: This study applied a non-parametric method to analyze the efficiency of farmers, discriminate efficient farmers from inefficient ones and to identify wasteful uses of energy in order to optimize the energy inputs for wheat production in Gorve country, Kordestan province of Iran. The data used in this study was obtained through a face to- face questionnaire method in the surveyed region. The results indicated that total energy inputs were 42998.44 MJha -1. About 6% of this was generated by electricity fuel and 16% from total fertilizer. Two basic DEA models (CCR and BCC) were used to measure the TEs of the farms based on seven energy inputs and one output. The average values of TE and PTE farms were found to be 9.9 and 97.44, respectively. Moreover, energy saving target ratio for wheat production was calculated as 1%, indicating that by following the recommendations resulted from this study, about 4626 MJ ha -1 of total input energy could be saved while holding the constant level of wheat yield. Keywords: Data envelopment analysis, Management, Tomato production, Gorve country INTRODUCTION Energy use in agriculture has been increasing in response to increasing population, limited supply of arable land, and a desire for higher standards of living. Efficient use of energy in agriculture is one of the principal requirements for sustainable agricultural production. Improving energy use efficiency is becoming increasingly important for combating rising energy costs, depletion of natural resources and environmental deterioration (Dovì et al., 29).The development of energy efficient agricultural systems with low input energy compared to the output of food can reduces the greenhouse gas emissions from agricultural production systems (Dalgaard et al., 21). The energy input output analysis is usually made to determine the energy efficiency and environmental aspects. This analysis will determine how efficient the energy is used. Sensitivity analysis quantifies the sensitivity of a model's state variables to the parameters defining the model. It refers to changes in the response of each of the state variables which result from small changes in the parameter values. Sensitivity analysis is valuable because it identifies those parameters which have most influence on the response of the model. It is also an essential prerequisite to any parameter optimization exercise (Cbalabi and Bailey, 1991). In recent years, DEA has gained great popularity in agricultural enterprises. Heidari et al. (211) used DEA to assess the technical, pure technical and scale efficiency in greenhouse cucumber production in Iran. In this study the efficiency of farmers based on the constant and variable returns to scale DEA models was found to be.823 and.927, respectively. It was also concluded that DEA was a useful tool to improve the productive efficiency of greenhouse units. In another study (Reig-Martínez and Picazo-Tadeo, 24) DEA was applied to the data of variable costs for 33 citrus farms in Spain and the average efficiency of farms was reported as.711. Taki et al. (212b) studied the energy use patterns of cucumber production in Iran and found that the fertilizer application have the highest energy source in total inputs. Bahrami et al. (211) studied the productive efficiency for wheat production in Iran by means of data envelopment analysis (DEA). An advantage of DEA is that it does not require any prior assumptions on the underlying functional relationships between inputs and outputs. It is therefore a nonparametric approach.
Intl J Agri Crop Sci. Vol., 5 (18), 264-27, 213 The goal of this study were to determine the efficiencies of farmers, rank efficient and inefficient ones, identify target energy requirement to know wasteful uses of energy from different inputs for wheat production in Gorve country, Kordestan province of Iran. MATERIAL AND METHODS Case study and data collection This study was conducted in Gorve country, Kordestan province of Iran. Data were collected through personal interview method in a specially designed schedule for this study. The collected data belonged to the 212/13 production year. Before collecting data, a pre-test survey was conducted by a group of randomly selected farmers. The required sample size was determined using simple random sampling method. The equation is as below (Mousavi-Avval et al., 211): N S n N D h h 2 2 2 N hs h where n is the required sample size; N is the number of total population; population in the h stratification; stratification, D 2 S h 2 d is equal to ; d is the precision, ( z 2 Nh (1) is the number of the is the standard deviation in the h stratification, S h2 is the variance in the h x X ) (5%) is the permissible error and z is the reliability coefficient (1.96, which represents 95% reliability). Thus the sample size was found to be 4. Consequently, based on the number of wheat producers in each village, the 4 farmers from the population were randomly selected. Energy equivalents of inputs and output The inputs used in the production of wheat were specified in order to calculate the energy equivalences in the study. Inputs in wheat production were: human labor, machinery, diesel fuel, total fertilizers, biocides, electricity and seed. The output was considered grain wheat. The energy equivalents given in Table 1 were used to calculate the input energy amounts. Based on the energy equivalents of the inputs and output (Table 1), the energy ratio (energy use efficiency), energy productivity, specific energy and net energy gain were calculated (Singh et al., 1997): (2) E E = r O E I Table 1. Energy equivalent of inputs and output in agricultural production. Inputs Unit Energy equivalents Reference A. Inputs 1. Human Labor h 1.96 (Mobtaker et al., 21) 2. Machinery h 64.8 (Kizilaslan, 29) 3. Diesel fuel l 56.31 (Heidari and Omid, 211) 4. Total fertilizer kg (a) Nitrogen kg 66.14 (Rafiee et al., 21) (b) Phosphate (P 2O 5) kg 12.44 (Mobtaker et al., 21) (c) Calcium kg 8.8 (Pimentel, 198) (d) Farmyard manure kg.3 (Heidari et al., 211) 5. Biocides kg 12 (Mobtaker et al., 21) 6. Electricity kwh 11.93 (Mobtaker et al., 21) 7. Seed kg 14.7 (Ozkan et al., 27) B. Output 1. Grain wheat kg 14.7 (Ozkan et al., 27) 2. Straw kg 9.25 (Tabatabaeefar et al. 29) O E = p P E I (3) N e = EO- E (4) I where E r is energy ratio; EO is energy output (MJ ha -1 ); production (kg ha -1 ) and EI is energy input (MJ ha -1 ); Ne is net energy (MJ ha -1 ). Ep is energy productivity (kg MJ -1 ); OP is output 265
Intl J Agri Crop Sci. Vol., 5 (18), 264-27, 213 The output-input energy ratio (energy use efficiency) is one of the indices that show the energy efficiency of agriculture. In particular, this ratio, which is calculated by the ratio of input fossil fuel energy and output food energy, has been used to express the ineffectiveness of crop production in developed countries (Unakitan et al., 21). An increase in the ratio indicates improvement in energy efficiency, and vice versa. Changes in efficiency can be both short and long term, and will often reflect changes in technology, government policies, weather patterns, or farm management practices. By carefully evaluating the ratios, it is possible to determine trends in the energy efficiency of agricultural production, and to explain these trends by attributing each change to various occurrences within the industry (Unakitan et al., 21). Data envelopment analysis In this study, a non-parametric method of DEA was employed to evaluate the technical, pure technical and scale efficiencies of individual farmers. In DEA, an inefficient DMU can be made efficient either by reducing the input levels while holding the outputs constant (input oriented); or symmetrically, by increasing the output levels while holding the inputs constant (output oriented) (Abdi et al, 213). The choice between input and output orientation depends on the unique characteristics of the set of DMUs under study. In this study the input oriented approach was deemed to be more appropriate because there is only one output while multiple inputs are used; also as a recommendation, input conservation for given outputs seems to be a more reasonable logic (Abdi et al, 213). The technical efficiency (TE) can be expressed generally by the ratio of sum of the weighted outputs to sum of weighted inputs. The value of technical efficiency varies between zero and one. The technical efficiency can be expressed mathematically as following relationship (Mousavi-Avval et al, 21): (5) where, u r, is the weight (energy coefficient) given to output n; y r, is the amount of output n; v s, is the weight (energy coefficient) given to input n; x s, is the amount of input n; r, is number of outputs (r = 1, 2,..., n); s, is number of inputs (s = 1, 2,.., m) and j, represents jth of DMUs (j = 1, 2,..., k). Pure technical efficiency is another model in DEA that introduced by Banker et al in 1984. This model called BCC and calculates the technical efficiency of DMUs under variable return to scale conditions. It can be expressed by Dual Linear Program (DLP) as follows (Charnes et al, 1978): (6) (7) (8) (9) where, z and u 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) can be improved at the same level of technologies (inputs) (Ajabshirchi et al, 211). The relationship among the scale efficiency, technical efficiency and pure technical efficiency can be expressed as (Abdi et al, 213): (1) In order to calculate the efficiencies of farmers and discriminate between efficient and inefficient ones, the Microsoft Excel spread sheet and EMS software were used. RESULTS AND DISCUSSION Analysis of input output energy and distribution of each input in wheat production The inputs used in wheat production and their energy equivalents with output energy rates are shown in the Table 2. The results revealed that 98.47 h of human labor and 21.64 h of machinery power per hectare were required to produce wheat in the research area. The amount of total fertilizers and biocides used for wheat growing were 471.44 and 2.25 kg ha 1, respectively. The total energy input for various processes in the wheat production was calculated to be 42998.44 MJha 1. Electricity has the highest share among all inputs (26135.93 MJha 1 ) because all the farms use the wells for irrigation and all of the wells consume electricity for 266
Intl J Agri Crop Sci. Vol., 5 (18), 264-27, 213 pumping water. Taki et al. (212a) concluded that the input energy for wheat production was to be 125674.8 MJha 1. The average inputs energy consumption was highest for irrigation. Energy use efficiency, energy productivity and net energy of wheat production in the Gorve country, Kordestan province is tabulated in Table 3. Energy use efficiency (energy ratio) was calculated as 2.28. In Iran, Taki et al. (212a), reported wheat output/input ratio as 1.63. The energy ratios for some crops are reported as 2.8 for wheat, 4.8 for cotton, 3.8 for maize (Canakci et al., 25). The energy productivity (grain) of wheat production was calculated as.13 kg MJ -1. The net energy of wheat production was found to be 54937.18 MJha -1. It indicates that in this crop production energy is gained (net energy is greater than zero). Table 2. Energy equivalent of inputs and output in agricultural production. Inputs (unit) A. Inputs 1. Human labor (h) Quantity per unit area (ha) 98.47 Total energy equivalent (MJ ha -1 ) 193. 2. Machinery (h) 21.64 142.42 3. Diesel fuel (l) 72.95 417.81 4. Total fertilizers (kg) 471.44 756.26 (a) Nitrogen (kg) 77.97 5156.94 (b) Phosphate (P 2O 5) (kg) 34.98 435.13 (c) Calcium (kg) 9.99 87.95 (d) Farmyard manure (kg) 4587.5 1376.25 5. Biocides (kg) 2.25 27. 6. Electricity (m 3 ) 219.77 26135.93 7. Seed (kg) 26.75 3833.3 The total energy input (MJ) B. Output 1. Grain wheat 5537.5 42998.44 8141.25 2. straw 1787.5 16534.38 Total energy output (MJ) 97935.63 Table 3. Energy input output ratio and forms in wheat production. Items Unit Quantity Energy use efficiency 2.28 Energy productivity (For grain) kg MJ 1.13 Technical and pure technical Net energy MJ m 2 54937.18 Results obtained by application of the input-orientated DEA are illustrated in Table 4. The mean radial technical efficiencies of the samples under CCR and BCC assumptions are.9 and.97, respectively. This implies first, that on average, farms could reduce their inputs by 1% and 3% and still maintains the same output level, and second, that there is considerable variation in the performance of farms. Increasing the technical efficiency of a farm actually means less input usage, lower production costs and, ultimately, higher profits, which is the driving force for producers motivation to adopt new techniques. Energy savings from different energy inputs Table 5 show the obtained results from analyzing wheat farms by using input basis CCR model. Data of this Table are used for determining extra input and deficiency of efficiency. The specific quantity that each inefficient unit needs to decrease in order to become efficient is determined. In this table the DBU No.5 with the efficiency of 9.91% has to decrease 4.71 human labor, 6.95 diesel fuel and 1697.65 electricity to stand on the efficiency partition line. Mousavi-Avval et al (21) reported that on an average, about 9.5% of the total input energy for canola production in Iran could be saved. 267
Intl J Agri Crop Sci. Vol., 5 (18), 264-27, 213 CONCLUSION In this study, energy use of inputs and output in wheat production were analyzed for optimize energy inputs in Gorve country, Kordestan province of Iran. For this purpose, data were collected from 4 farms which were selected based on random sampling method. From the present study following conclusions are drawn: The average of energy input in wheat production was to be 42998.44 MJha -1, mainly due to electricity input (26135.93MJha -1 ) and total fertilizers (756.26 MJha -1 ). Energy use efficiency, energy productivity and net energy for wheat production were 2.28,.13 kgmj -1 and 54937.18MJha -1, respectively. Results of data envelopment analyses showed that on an average 4626 MJ ha -1 or about 1% from total energy input could be saved without reducing the yield. DEA has helped in segregating efficient farmers from inefficient farmers. It has also helped in finding the wasteful uses of energy by inefficient farmers, ranking efficient farmers by using the CCR and BCC models and ranking energy sources by using technical and pure technical efficiency. Table 4. Analyses of efficiency BCC and CCR for wheat farms DMU's E CCR E BCC 1 1. 1. 2 1. 1. 3 1. 1. 4 89.52 9.65 5 9.91 92.19 6 1. 1. 7 1. 1. 8 43.4 93.18 9 67.75 92.75 1 1. 1. 11 9.98 1. 12 75.1 1. 13 1. 1. 14 1. 1. 15 79.37 94.1 16 1. 1. 17 83.84 96.26 18 98.8 1. 19 85.45 1. 2 9.63 1. 21 9.63 1. 22 9.63 1. 23 67.71 98.4 24 87.37 96.96 25 1. 1. 26 1. 1. 27 1. 1. 28 74.3 1. 29 1. 1. 3 7.5 9.69 31 63.64 9.69 32 65.24 88.84 33 65.33 9.85 34 89.46 94.92 35 1. 1. 36 88.23 97.87 37 1. 1. 38 96.86 1. 39 83.7 89.86 4 94.9 1. Average 9.9 97.44 268
Intl J Agri Crop Sci. Vol., 5 (18), 264-27, 213 DM U 4 5 8 9 11 12 15 17 18 19 2 21 22 23 24 28 3 31 32 33 34 36 38 39 4 Table 5. Slack and surplus energy consumption in each of wheat farms with CCR model (MJha -1 ) Efficiency Human labor Machinery Diesel fuel Fertilizers Biocides Electricity Seed 89.52 56 174.2 72.36 178.14 9.91 4.71 6.95 1697.65 43.4 54.85 23.57 67.75 247.52 198.19 9.98 133.71 877.73 666.38 75.1 56.79 174.19 358.4 169.47 447.94 79.37 338.29 23.45 83.84 35.29 44.53 691.94 17164.81 98.8 697.13 679.25 191.25.1 713.6 85.45 135.45 9.63 83.36 323.48 93.54 436.23 1228.36 9.63 88.4 489.88 93.54 436.23 2125.39 9.63 75.7 49.41 81.47 436.23 12841.3 67.71 29.39 456.6 1697.49 87.37 47.26 53.84 575.38 9792.26 74.3 52.93 464.22 8664.51 7.5 21.47 228.43 9124.55 63.64 6.2 94.93 45.98 65.24 53.43 65.6 14676.3 65.33 5.11 178.18 15248.19 89.46 61.7 324.8 37.41 3337.98 88.23 21.82 575.26 26735.73 96.86 6.94 465.39 792.74 2528.74 83.7 13.52 151.92.3 94.9 58.98 2.66 389.35 69.51 REFERENCES Abdi R, Taki M, Jalali A. 213. Study on energy use pattern, optimization of energy consumption and CO2 emission for greenhouse tomato production. International Journal of Natural and Engineering Sciences 7 (1): 1-4. Ajabshehichi Y, Taki M, Abdi R, Ghobadifar A, Ranjbar I. 211. Investigation of energy use efficiency for dry wheat production using data envelopment analysis (DEA) approach; Case Study: Silakhor Plain. Journal of Agricultural Machinery Engineering. 1(2): 122-132 (In Persian). Bahrami H, Taki M, Monjezi N. 211. Optimization of energy consumption for wheat production in Iran using data envelopment analysis (DEA) technique. African Journal of Agricultural Research. 6(27): 5978-5986. Canakci M, Topakci M, Akinci I, Ozmerzi A. 25. Energy use pattern of some field crops and vegetable production: case study for Antalya region, Turkey. Energy Cbalabi ZS, Bailey BJ. 1991. Sensitivity analysis of a non-steady state model of the greenhouse microclimate. Agric. For. Meteorol 56: 111 127. Charnes A, Cooper WW, Rhodes E. 1978. Measuring the efficiency of decision making units. Eur J Oper Res. 2: 429 444. Conversion and Management. 46: 655 666. Dalgaard T, Halberg N, Porter JR. 21. A model for fossil energy use in Danish agriculture used to compare organic and conventional farming. Agric. Ecosyst. Environ 87: 51 65. Dovì VG, Friedler F, Huisingh D, Klemes JJ. 29. Cleaner energy for sustainable future. Journal of Cleaner Production. 17: 889 895. Heidari MD, Omid M, Mohammadi A. 211. Measuring productive efficiency of horticultural greenhouses in Iran: A data envelopment analysis approach. Expert Systems with Applications. 39: 14-145. Heidari MD, Omid M. 211. Energy use patterns and econometric models of major greenhouse vegetable productions in Iran. Energy. 36: 22 225. Kizilaslan H. 29. Input output energy analysis of cherries production in Tokat Province of Turkey, Applied Energy. 86: 1354 8. Mobtaker HG, Keyhani A, Mohammadi A, Rafiee SH, Akram A. 21. Sensitivity analysis of energy inputs for barley production in Hamedan Province of Iran. Agriculture, Ecosystem and Environment. 137: 367 372. Mousavi-Avval SH, Rafiee S, Jafari A, Mohammadi A. 21. Improving energy use efficiency of canola production using data envelopment analysis (DEA) approach. Energy. 36: 2765-2772. Mousavi-Avval SH, Rafiee S, Jafari A, Mohammadi A. 21. Improving energy use efficiency of canola production using data envelopment analysis (DEA) approach. Energy. 36: 2765-2772. Mousavi-Avval SH, Rafiee S, Jafari A, Mohammadi A. 211. Optimization of energy consumption for soybean production using Data Envelopment Analysis (DEA) approach. Applied Energy. 35: 2156-2164. Ozkan B, Fert C, Karadeniz CF. 27. Energy and cost analysis for greenhouse and open-field grape production. Energy. 32: 15 154. Pimentel D. 198. Handbook of Energy Utilization in Agriculture. CRC Press, Boca Raton, FL. Rafiee S, Mousavi-Avval SH, Mohammadi A. 21. Modeling and sensitivity analysis of energy inputs for apple production in Iran. Energy. 35: 331-336. 269
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