INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES Volume 1, No 6, Copyright 2010 All rights reserved Integrated Publishing Association

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1 INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES Volume 1, No 6, 2011 Copyright 2010 All rights reserved Integrated Publishing Association Research article ISSN Study on Energy use Pattern and Efficiency of Corn Silage in Iran by using Data Envelopment Analysis (DEA) Pishgar Komleh.S.H. 1, Omid. M 2, Keyhani.A 3 1 MSC Student, Department of Agricultural MechanizationEngineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran. 2 Associate Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran. 3 Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran. s.hassan.pishgar@gmail.com ABSTRACT Energy ratio and technical efficiency are the ways to explain the efficiency of farmers. This study was conducted to determine the energy use efficiency for corn silage production in Iran. For this purpose, data were collected from farmers by using a face to face questionnaire. Energy indices, technical, pure technical and scale efficiencies were calculated by using Data Envelopment Analysis (DEA) technique for three groups of farms (<5ha, between 5 and 10ha and >10ha). The results revealed that the highest energy ratio (2.8) and least specific energy (2.9MJkg 1 ) belonged to large farms. Total energy input and output were calculated as and MJha 1, respectively, whereas machinery with a share of 42% was the highest consumer followed. The average value of TE, PTE and SE was 0.92, 0.98 and 0.93, respectively. It was specified that medium and large farms scored highest TE and SE. The contribution of saving energy for machinery was the highest and followed by chemical fertilizer and diesel fuel with shares of39.41%, 28.48% and 16.43% respectively. The total energy savings calculated to be 40% of total input energy. The Karl Pearson s correlation coefficient (r) revealed that 81% and 79%of farmers were efficient under CCR and BCC. Keywords: Corn silage, Energy use, Energy ratio, Technical efficiency, Data envelopment analysis, Scale efficiency. 1. Introduction Different crops are used as silage in the world. Corn silage is a popular forage crop that is used for ruminant animals because of high yield, digestibility, palatability, storage ability and etc. (Schroede, 2004;Wheaton et al., 1993).The annual production of corn silage was 9.2 million tons in 2008 worldwide with the average yield of almost 88 tons per hectare(food and Agriculture Organization, 2007). United States of America is the highest corn silage producer with 3.9 million tons followed by Mexico, Nigeria and France (Food and Agriculture Organization, 2007). The annual production of forage crops (except alfalfa and clover) in Iran in 2008 production year was about 9411 t. The harvested area was almost hawith the average yield of 36 t.ha 1.In all provinces Tehran ranks firstinforage crop production (except alfalfa and clover) with share of 25% (Anon., 2008). Agriculture is known as both a producer and a consumer of energy. It uses large quantities of locally available non commercial and commercial energies as direct and indirect forms, such as seeds, manure and animals, diesel fuel, electricity (mostly for irrigation), fertilizer, Received on December, 2010 Published on March

2 biocides, chemical fertilizers, and machinery. Efficient use of energies helps to achieve increased production and productivity and contributes to the economy, profitability and competitiveness of agriculture sustainability in rural areas(ozkan et al., 2004; Singh et al., 2002).In order to meet the ever increasing demand for food production, energy use in agriculture production has become more intensive. However, more intensive energy use has brought some important human health and environment issues forcing humans to make more efficient use of inputs to maintain a sustainable agriculture production (Ibrahim et al., 2005).Energy requirements in agriculture are divided into two categories as direct and indirect. Direct energy is required to perform various tasks related to crop production processes such as land preparation, irrigation, intercultural, threshing, harvesting and transportation of agricultural inputs for farm produce (Singh, 2000). This type of energy is directly used at farms and on fields. Indirect energy, on the other hand, includes those types of energies used in manufacturing, packaging and transportation of fertilizers, pesticides, seeds and farm machinery to the warehouse (Kennedy, 2000). Heichel(1982) studied energy consumption for forage production systems (corn silage, alfalfa and oat). Phips et al. (1976) compared the energy output input ratio for forage maize and grass leys. The results indicated 4.8 and 2.7 for forage maize and gross, respectively. Despite of few studies conducted on corn silage, other crops have been targeted from energy consumption point of view (Mandal et al. 2002;Singh et al., 1998; Mohammadi et al., 2008; Franzluebbers and Francis, 1995; Singh et al., 2000; Ghasemi et al., 2010). In general, two competing approaches for the measurement of efficiency are the parametric stochastic frontier model and non parametric Data Envelopment Analysis (DEA). In Hatirli et al.(2006) and Mohammadi et al.( 2010) studies, relations between energy inputs and yield of greenhouse tomato and cucumber production were determined, respectively using parametric Cobb Douglas (CD) production function. The main advantage of the CD over DEA is its ability to allow measurement error. However, Omid et al. (2010) studied the productive efficiency of selected cucumber greenhouses in Iran by means of DEA. A main advantage of DEA is that it does not require any prior assumptions on the underlying functional relationships between inputs and outputs. DEA technique for data of paddy producers in Punjab State was applied by Nassiri et al. (2009) to calculate the technical, pure technical and scale efficiencies for farmers from category wise and zone wise point of views. The results indicated strong correlation (0.98 and 0.99) between energy ratio and technical efficiency in both categories. Zhouet al. (2008) presented a literature review on the application of DEA to energy and environmental (E&E)studies that expressed the widely use of DEA technique. Banaeian et al. (2010) applied DEA technique for walnut farmers in Hamadan province, Iran. The results indicated that 13 walnut producers were producing at an efficient scale, whereas 24 producers were inefficient. The results suggest that If producers follow the input package recommended by the study, 7745MJha 1 of total input energy could be saved. Malana et al. (2006) studied the benchmarking productive efficiency of selected wheat areas in Pakistan and India taking water for irrigation, seeds and fertilizer as inputs. Chauhan et al. (2006) applied DEA to distinguish the efficient farms from inefficient ones regarding energy use in rice production in alluvial zone in the State of West Bengal in India. The results indicated that 11.6% of total input energy could be saved if the farmers follow the recommendations. Nowadays, DEA technique has gained great popularity and application in energy and environmental (E&E) modeling. 1095

3 This study was conducted to determine the energy use efficiency for corn silage production and to compare input energy use in all farms. The aim of this study was to benchmark productive efficiency of corn silage farms in Tehran province of Iran by means of DEA. 2. Materials and methods 2.1. Data collection and energy equivalent Tehran province with total farming area of 172,915 ha is located within 34 52' and 36 21' north latitude and 50 10' and 53 10' east longitude. The wheat crop with 51,104 ha, has the highest farming area followed by forage crops (except alfalfa) with 45,478 ha with share of 26%(Anon., 2008).Data were collected from 40 farms in Tehran province (Karaj city) by using a face to face questionnaire introduction year of The size of sample was determined using Eq.(1)(Yamane, 1967): where istherequiredsamplesize; isthenumberoftotalpopulation; isthenumberofthepopulationinthe stratification; isthestandarddeviationinthe stratification, isthevarianceinthe stratification, isequalto ;d istheprecision, (5%)isthepermissibleerrorand isthereliabilitycoefficient(1.96,whichrepresents the95%reliability). Table 1: Energy coefficient equivalents of inputs and output in corn silage production Inputs(unit) Energy equivalent (MJ unit 1 ) Reference A. Inputs 1. Machinery Tractor and self propelled(kg a * ) 9 10 (Kitani 1999) Stationary equipment (kg a * ) 8 10 (Kitani 1999) Implement and machinery(kg a * ) 6 8 (Kitani 1999) 2. Labor (h) (Kitani 1999) (Yaldiz 1.96 et al. 1993) (Zangeneh et al. 2010) 3. Diesel fuel(l) 47.8 (Kitani 1999) 4. Fertilizer N (kg) 78.1 (Kitani 1999) P 2 O 5 (kg) 17.4 (Kitani 1999) K 2 O (kg) 13.7 (Kitani 1999) 5. Seed(kg) 100 (Kitani 1999) 6. water for irrigation(m 3 ) 0.63 (Yaldiz et al. 1993) B. output 1. dry matter corn silage(kg) 8 (Robinson 2001) a * : economic life of machine(year) 1096

4 The permissible error in the sample size was definedtobe5%for95%confidence interval, and the sample size was calculated as 38 farms, so 40 farms were selected randomly. Machinery, human labor, diesel fuel, chemical fertilizers, water for irrigation and seed were considered as inputs and the output was the yield of corn silage as a forage production. Observation revealed that no biocides were used in the studied region. To calculate the energy use per hectare, the amount of each input was multiplied by the energy coefficient equivalent (Table1). Farms were classified into three group sizes as small (<5ha), medium (between 5 and 10ha) and large (>10ha).To the grouping was carried out according to the frequency of farm sizes in the region. The energy ratio (energy use efficiency) (Eq.(2)) and specific energy (Eq.(3)) were calculated based on the energy equivalents of inputs and the output (Table1)(Mandal et al., 2002; Zangeneh et al., 2010): 2.2. Data Envelopment Analysis (DEA) models DEA is a non parametric analysis method to measure the relative efficiency of a homogeneous number of organizations that essentially perform the same tasks (Cooper et al.,2006). It is used to empirically measure productive efficiency of Decision Making Units (or DMUs). Non parametric approaches have the benefit of not assuming a particular functional for the frontier; however, they do not provide a general relationship (equation) relating the output to the input (Charnes et al. 1994; Omid et al.,2010). Charnes et al. (1978) extended an optimization model (CCR) with Constant Returns to Scale (CRS). Later, Banker et al. (1984) developed the model with Variant Returns to Scale (VRS) and the CCR model (Charnes, Cooper and Rhodes) transformed into the BCC model (Banker, Charnes and Cooper).There are three kinds of efficiencies in DEA: overall technical efficiency (TE CCR ), pure technical efficiency (TE BCC ) and scale efficiency (SE) (Nassiri and Singh, 2009) Technical Efficiency Technical efficiency can be expressed by the ratio of sum of the weighted outputs to sum of weighted inputs and mathematically can be shown as (Cooper et al.,2004; Nassiri and Singh, 2009): where 'x'and'y'areinputandoutputand 'v' and u areinputandoutputweights,respectively, 's' and 'r' are numbers ofinputs(s =1,2,...,m) and outputs(r =1,2,..,n)and 'j' representsjthdmus(j =1,2,...,k). 1097

5 The technical efficiency value changes between zero and one. To solve Eq. (4), following linear program (LP) was developed by Charnes et al.(1978), which was called CCR model(nassiri and Singh, 2009): where'h' isthetechnicalefficiencyand 'i' representsithdmu(itis fixedineqs.(2)and(3)while 'j' increasesineq. (4). In this study inputs were fuel, labor, machinery, seed, chemical fertilizer and water for irrigation energiesand output was the yield of corn silage. The weights of inputs and output are calculated duringthe solvingprocess in LP.Therefore, the technical efficiency reaches to its maximum value Pure Technical Efficiency Banker, Charnes and Cooperin order to draw out the technical efficiency of DMUs presented amodelindea in 1984,which was called BCC model (Banker et al. 1984). This technical efficiency was called pure technical efficiency to be distinguished from technical and scale efficiency. It can be expressed by Dual Linear Program (DLP) (Nassiri and Singh, 2009) as: where'z' and 'u 0 ' are scalar and free in sign. 'u' and 'v' are output and inputs weight matrices, and Y and X are corresponding output and input matrices, respectively. The letters x i and y i refer to the inputs and output of ith DMU Scale Efficiency Cooper et al. (2004) defined the technical efficiency as: This decomposition shows the sources of inefficiencies. It is caused by inefficient operation (PTE) or by disadvantageous conditions displayed by the scale efficiency (SE) or by both. If the scale efficiency is less than 1, the DMU will be operating either at Decreasing Returns to Scale (DRS) if a proportional increase of all input levels produces a less than proportional increase in output levels or Increasing Return to Scale (IRS) at the converse case. This implies that resources may be transferred from DMUs operating at DRS to scale to those operating at IRS to increase average productivity at both sets of DMUs (Boussofiane et al.,1992). 1098

6 The ANOVA test and Duncan method to compare means were utilized to analyze the differences. To find the coefficient of energy ratio and technical efficiency, pure technical efficiency and scale efficiency, the Karl Pearson s correlation coefficient (r) was applied (Nassiri and Singh, 2009).To calculate inputs and output energy, the collected data analyzed byspss17software. To calculate DEA models (CCR and BCC) with radial distances to the efficientfrontier and todetermine the amount of energy loss and energy saving of inefficient DMUs, the DEA solver software (Release 6) was used. 3. Results and discussion 3.1. Energy inputs of corn silage production Table 2 shows the average energy consumption of inputs in corn silage production. The total energy usagesummed up to68928mjha 1 while the output energy of corn silage production was found to be MJha 1. The results revealed that, machineries,with energy usage of 28944MJha 1, had the highest share (42%) among inputs followed by chemical fertilizers with average energy consumption of MJha 1 (28.36%). The shares of N, P, and K in total energy usage were calculated to be 18.02%, 9.06% and 1.28%, respectively. The results were similar to that of Phipps et al.(1976)where fertilizer and fuel were major input energies. Diesel fuel energy with consumption of 10800MJha 1 ranked in third place with 15.67% and followed by energy of pumping water for irrigation with amount of 6372MJha 1 Due to lack of studies in forage crops, the results of this study are evaluated with other crops. Yilmaz et al.(2005)found that the fertilizer and machinery energy consumption of cotton production was high. Pervanchon et al.(2002)found the machinery and fertilizer inputs as highest energy consumer in potato production with share of 48% and 33%, respectively. Human labor was found to bethe least energy consumer in total energy inputs with 86MJha 1.The research results indicated that diesel fuel, fertilizers (especially N) and machinery management are the most significant factors for improving energy efficiency of corn silage production. Table 2: Energy inputs and output for corn silage production (MJha 1 ) in different farm size Inputs Farm size groups(ha) Small Medium Large (<5) (5 10) (>10) Average (MJha 1 ) Percentage A. Inputs Machinery a b c Labour Diesel fuel Chemical fertilizers a ab b Nitrogen(N) Phosphate(P 2 O 5 ) Potassium(K 2 O) Water for irrigation 6686 a 6231 b 6209 b Seed Total energy input a b c B. outputs Corn silage Note: Different letter show significant difference of means at 5% level. 1099

7 Comparison of input s energy consumption in three farm sizes based on the ANOVA test, showed that farms with more than 10 ha by using bigger machineries and implements use the least amount of machinery energy (13291MJha 1 ). Also, smaller farms used less chemical fertilizers (Table 2) which showed that higher levels of managements of chemical fertilizers are practiced in smaller farms. As it is shown, in large size farms, the amount of irrigation energy was least and as the farm size increases the yield of corn silage increases but not significantly. The average, maximum, minimum and standard deviation of yield, energy ratio and specific energy are shown in Table 3. The results of ANOVA test revealed significant differencesamongaverage value of energy ratio and specific energy of different sizes of corn silage while no significant differences were observed for the yield (Table 3). Increasing thefarm size leads to an increaseinenergy ratio and a decrease in specific energy.the highest energy ratio with amount of 2.8 belongs to large size farms (Table 3) DEA results Table 3: Some parameters of corn silage production in different farm size Yield (kgha 1 ) Energy Ratio Small (<5) Medium (5 10) Large (>10) Ave a a a Max Min SD Ave. 1.7 a 2.3 b 2.8 c Max Min SD Specific Energy(MJkg 1 ) Ave. 4.8 a 3.6 b 2.9 c Max Min SD Note: Different letter shows significant difference of means at 5% level. To evaluate and rank productivity performance of different groups, DEA technique was applied. Table 4 shows the frequency distribution of TE and PTE. The majority of farms in each group had technical efficiency scores of more than 90%.The TE CCR score of a farmer that is less than one indicates that the farmer is using more energy than required from different sources. The results revealed that 10%, 15% and 10% of small, medium and large size farms were efficient, respectively. Duncan compare mean test showed that there were significant differences among technical efficiencies of farmers in different farm categories and emphasizing that smaller farms had the least technical efficiency while large size of farms had the highest efficient farmers. The total median of technical efficiency was as calculated

8 The frequency distribution of TE BCC of corn silage under different farm categories in Table 4 indicated that some CCR inefficient farmers moved to BCC efficient frontier. Small farmers with growth of 13% had the highest increment followed by medium and large farms with the same growth (5%).It can be concluded that farmers on medium and large farms had more scale efficiency than others. The reason of low scale efficiency in small farms refers to mismatching between machines size and tractor horsepower, so that it increased the fuel consumption during the operations. As a result of Duncan test it was indicated that there was no significant differences between farm categories in pure technical efficiency (with average efficiency of 0.98).Chauhan et al. (2006) in a similar study calculated technical, pure technical, and scale efficiencies for rice production as 0.77, 0.92, and 0.83, respectively. Table 4: Frequency distribution of technical and pure technical efficiency of corn silage under different farm categories CCR model Small Medium Large (<5) (5 10) (>10) Total Efficient Inefficient >90% % % % 1 1 Number of farms Median of efficiency scores 0.86 a 0.95 b 0.95 b 0.92 BCC model Efficient Inefficient >90% % 1 1 Number of farms Median of efficiency scores 0.99 a 0.97 a 0.99 a 0.98 Note: Different letter shows significant difference of means at 5% level. As it can be seen in Figure 1, with increasing in farm size, the Technical Efficiency (TE) increases. Pure Technical Efficiency (PTE) was almost the same (0.97) in all groups. Iraizoz et al. (2003) calculated PTE for asparagus and tomato as 0.80 and 0.89, respectively. Scale efficiency value indicated that an increase infarm size would lead to an increase in scale efficiency. Moreover, it is clear from Fig. 1 that the average SE is about 0.93 which indicates that if inefficient farmers use their inputs efficiently, saving in energy from the different sources without any change in technological practices is possible. Identifying efficient operating practices and their dissemination can help to improve efficiency not only in the case of inefficient farmers but also for some relatively efficient ones. The efficient farmers obviously follow operating practices well. However, among the efficient farmers, some show better operating practices than others. Therefore, discrimination is required to be made among the efficient farmers while seeking the best operating practices. Table 5 shows the average present use, target use and saving energy. The last column indicates the contribution of energy that can be saved in corn silage production. The results indicate that machinery with energy saving of MJha 1 (39.41%) is the most important input followed by chemical fertilizer and diesel fuel with amount of (28.48%) and MJha 1 (16.43%), respectively. 1101

9 Figure 1: Technical, Pure Technical, and Scale Efficiency of difference farm categories Total saving in energy consumption is calculated to be MJha 1 that it is 40% of total input energy currently practiced. Chauhan et al. (2006) reported that11.6% of total input energy can be saved for rice production and the maximum contribution to the total energy savings was 33% for fertilizers. To decrease the machinery energy consumption it is recommended that the size of farms need to be matched with machines size and tractor horse power. Because of high contribution of chemical fertilizer saving (Table 5), applying precision agriculture management is needed (especially, for N fertilizer). Input Table 5: Energy use and saving (MJha 1 ) from different sources Present use (MJha 1 ) Target use (MJha 1 ) Energy saving (MJha 1 ) Diesel fuel Labor Machinery Seeds Chemical fertilizers Water for irrigation Total input energy Contribution of saving inputs (%) The Karl Pearson s correlation coefficient (r) between energy ratio and TE, PTE and SE was calculated to be 0.90, 0.89 and 0.81, respectively. It was found that 81% of farmers were efficient under BCC, 79% were efficient under CCR.A strong linear correlation was found between energy ratio and TE, PTE and SE. 4. Conclusion Data Envelopment Analysis (DEA) technique showed promising results inorder to provide suggestions and recommendations in energy consumption in crop production. Based on the present study, following conclusions are drawn: 1102

10 1. The total energy input and output were calculated as and MJha 1, respectively. Machinery with energy usage of 28944MJha 1 (42%) proved to be the highest consumer followed by chemical fertilizers and diesel fuel with average energy consumption of 19550(28.36%) and 10800MJha 1 (15.67%). The shares of N, P, and K fertilizers in total energy usage were found to be 18.02%, 9.06% and 1.28%, respectively. 2. The comparison of inputs energy consumption in three farm sizes showed that farms with more than 10 ha by using wider machinery and implement use the least amount of machinery energy (13291MJha 1 ).The irrigation energy was found to be the least in this group as well. Also, less chemical fertilizers usage in smaller farms showed that higher levels of managements of chemical fertilizers are practiced in smaller farms. 3. The results of ANOVA test revealed that significant differences are exist between average value of energy ratio and specific energy. The results indicated that the highest energy ratio (2.8) and specific energy (4.8 MJkg 1 ) are attained in large and small farms, respectively. 4. The average value of technical efficiency (based on CCR model), pure technical efficiency (based on BCC model) and scale efficiency were calculated as 0.92, 0.98 and 0.93, respectively. ANOVA test results indicated that medium and large farms had higher technical and scale efficiencies value while there was no significant difference among farm categories for pure technical efficiency. 5. The contribution of saving energy for machinery was foundto be the highest (39.41%) followed by chemical fertilizer, diesel fuel and water for irrigation with shares of 28.48%, 16.43% and 10.67%, respectively. The total energy savings was calculated to be 40% of the total input energy. 6. The Karl Pearson s correlation coefficient (r) revealed that 81% of technical efficiency of farmers, 79% of pure technical efficiency and 65% of scale efficiency have been explained by energy ratio value with a strong linear correlation between energy ratio and TE, PTE and SE. 7. At the end, to improve energy ratio of corn silage production it is recommended that small farms become larger and to decrease the machinery energy consumption the size of farms need to be matched with machines size and tractor horse power. Because of high contribution of chemical fertilizer saving, precision agriculture management practices should be applied. 5. References 1. Anonymous,2008. Annual agricultural statistics. Ministry of Jihad e Agriculture of Iran. Available at URL:< 2. Banaeian, N., Zangeneh, M.,2010. Omid M.Energy use efficiency for walnut producers using Data Envelopment Analysis (DEA).AJCS.4(5): pp Banker, RD., Charnes, A., Cooper, WW.,1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci. 30, pp

11 4. Boussofiane, A., Dyson, R.G., Thanassoulis, E.,1992. Applied data envelopment analysis. European Journal of Operation Research. 52, pp Charnes, A., Cooper, W.W., Lewin, A., Seiford, L., Data envelopment analysis: theory, methodology and applications. Kluwer Academic Publishers, Boston, MA. 6. Charnes, A., Cooper, W.W., Rhodes, E.,1978. Measuring the efficiency of decision making units. Eur J Oper Res. 2, pp Chauhan, N.S., Mohapatra, P.K.J., Pandey, K.P.,2006. Improving energy productivity in paddy production through benchmarking: an application of data envelopment analysis. Energy Conversion and Management. 47, pp n.d. 8. Cooper, L.M., Seiford, L.M., Tone, K.,2006. Introduction to data envelopment analysis and its uses. Springer, New York. 9. Cooper, W.W., Seiford, L.M.,Tone, K.,2004.Data envelopment analysis: a comprehensive text with models, applications, references and DEA solver software. Massachusetts, USA: Kluwer Academic Publishers. 10. Food and Agriculture Organization(FAO), Available at URL:< 11. Franzluebbers, A.J., Francis, C.A.,1995. Energy output input ratio of maize and sorghum management systems in Eastern Nebraska. Agric, Ecosys Environ. 53(3): pp Ghasemi Mobtaker,H.,KeyhaniA.,Mohammadi,A.,Rafiee,Sh.,Akram, A.,2010. Sensitivity analysis of energy inputs for barley production in Hamedan Province of Iran.Agriculture,EcosystemsandEnvironment.137: pp Hatirli, S.A., Ozkan, B., Fert, C.,2006. Energy inputs and crop yield relationship in greenhouse tomato production. Renew Energy. 31: pp n.d. 14. Heichel, G.H.,1982, Energy Analyses of forage production systems. J. Anim Sci. 54: pp Iraizoz, B., Rapun, M., Zabaleta, I.,2003. Assessing the technical efficiency of horticultural production in Navarra, Spain. Agricultural Systems. 78: pp Kennedy, S.,2000. Energy use in American agriculture.sustainable Energy Term Paper. 17. Kitani, O., CIGR Handbook of Agricultural Engineering, Energy & Biomass Engineering, ASAE publication, St Joseph, MI,x, Vol Malana, N.M., Malano, H.M.,2006. Benchmarking productive efficiency of selected wheat areas in Pakistan and India using data envelopment analysis. Irrigation and drainage. 55, pp

12 19. Mandal, K.G., Saha, K.P., Gosh, P.L.,Hati, K.M., Bandypadhyay, K.K.,2002. Bioenergy and economic analyses of soybean based crop production systems in central India. Biomass Bioenergy. 23: pp Mohammadi, A., Omid, M.,2010. Economical analysis and relation between energy inputs and yield of greenhouse cucumber production in Iran. Appl Energy. 87: pp n.d. 21. Mohammadi, A., Tabatabaeefar, A., Shahin, S., Rafiee, S., Keyhani, A.,2008. Energy use and economical analysis of potato production in Iran a casestudy: Ardabil province. Energy Convers Manage. 49: pp Nassiri, S.,M., Singh, S.,2009. Study on energy use efficiency for paddy crop using data envelopment analysis (DEA) technique. Applied Energy. 86, pp Nutrient requirements of dairy cattle, Eddition 2001, National academy press, washington D.C. 24. Omid, M., Ghojabeige, F., Delshad, M., Ahmadi, H.,2010. Energy use pattern and benchmarking of selected greenhouses in Iran using data envelopment analysis. Energy Convers Manage. 52(1). n.d. 25. Ozkan, B., Akcaoz, H., Fert, C.,2004. Energy input output analysis in Turkish agriculture. Renew Energy. 29: pp Pervanchon, F., Bockstaller, C., Girardin, P.,2002. Assessment of energy use in arable farming systems by means of an agroecological indicator: the energy indicator. Agric Syst. 72: pp Phipps, R.H., Pain, B.F., Mulvany, P.M.,1976. A comparison of the energy output/input relationship forforage maize and grass leys on the dairy farm.agriculture and Environment. 3: pp Robinson, P.H.,2001. Estimating the energy value of corn silage and other forages.available at URL;< 189.pdf>. 29. Schroede, J.W.,2004. Corn Silage Management.NDSU Extension Service publication. AS Singh, H., Mishra, D., Nahar, N.M.,2002. Energy use pattern in production agriculture of a typical village in aridzone, India part I. Energy Convers Manage. 43(16): pp Singh, J.M.,2000.On farm energy use pattern in different cropping systems in Haryana, India.Germany: Int Inst of Management University of Flensburg, Sustainable Energy Systems and Management, MasterofScience. 32. Singh, S., Pannu, C.J.S., Singh, J.,2000. Optimization of energy input for raising cotton crop in Punjab. Energy Convers Manage. 41(17): pp

13 33. Singh, S., Singh, S., Mittal, J.P., Pannu, C.J.S.,1998. Frontier energy use for the cultivation of wheat crop in Punjab. Energy Conversation Manage. 39(5/6): pp Wheaton, H., Martz, F., Meinershagen, F., Sewell, H.,1993.Corn Silage.G4590. See also:< 35. Yaldiz, O., Ozturk, H., Zeren, Y., Bascetincelik, A.,1993. Energy usage in production of field crops in Turkey. 5th Int. Cong. on Mechanization and Energy Use in Agriculture, Oct Kusadasi,Turkey. 36. Yamane, T.,1967. Elementary sampling theory. New Jersey, USA: Prentice Hall Englewood Cliffs. 37. Yilmaz, I. Akcaoz, H., Ozkan, B.,2005. An analysis of energy use and input output costs for cotton production in Turkey. Renew Energy. 30: pp Zangeneh, M., Omid, M., Akram, A.,2010. A comparative study on energy use and cost analysis of potato production under different farming technologies in Hamadan province of Iran. Energy. 35(7): pp Zhou, P., Ang, B.W., Poh, K.L.,2008. A survey of data envelopment analysis in energy and environmental studies. European Journal of Operational Research.189, pp