American Journal of Experimental Agriculture 2(3): 485-501, 2012 SCIENCEDOMAIN international www.sciencedomain.org Energy Consumption, Input–Output Relationship and Cost Analysis for Greenhouse Productions in Esfahan Province of Iran Morteza Taki1, Yahya Ajabshirchi2, Hassan Ghasemi Mobtaker3* and Reza Abdi2 1 Young Researches Club Shahreza Branch, Islamic Azad University, Shahreza, Iran. 2 Department of Agricultural Machinery Engineering, University of Tabriz, Iran. 3 Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran. Authors’ contributions This work was carried out in collaboration with all authors. MT designed the study, collected data, wrote the protocol, and wrote the first draft of the manuscript. YA managed the analyses of the study. HGM performed the statistical analysis and interpreted the results. RA managed the literature searches. All authors read and approved the final manuscript. th Research Article Received 7 April 2012 th Accepted 6 June 2012 st Online Ready 21 June 2012 ABSTRACT The objectives of this study were to determine the energy consumption and evaluation of inputs sensitivity for greenhouse vegetable production in the Esfahan province of Iran. Data were collected from 60 farmers using a face–to–face questionnaire method. The majority of farmers in the surveyed region were growing cucumber and tomato. The results revealed that cucumber production was the most energy intensive rather than –1 tomato production. Cucumber production consumed a total of 124.44 G J ha followed by –1 tomato with 116.76 G J ha . The energy ratio (energy use efficiency) for greenhouse tomato and cucumber were estimated to be 0.92 and 0.56 respectively. This indicated an intensive use of inputs in greenhouse vegetable production not accompanied by increase in the final product. Econometric model evaluation showed the impact of human power for both tomato and cucumber production was significant at 1% levels and had the highest impact among the other inputs in greenhouse tomato and cucumber production. Economic analysis indicated that the total costs of production for one hectare of tomato ____________________________________________________________________________________________ *Corresponding author: Email: mr.mobtaker@yahoo.com; American Journal of Experimental Agriculture, 2(3): 485-501, 2012 and cucumber production were around 34939 and 31956$, respectively. Accordingly, the benefit–cost ratio for these productions was 2.74 and 1.79, respectively. The total amounts of CO2 for tomato and cucumber production were calculated as 4.622 and 4.930 –1 tons ha respectively, which indicated the high CO2 output in both cultivations. The use of diesel fuel and pesticide is in excess for tomato and cucumber production, causing an environmental risk problem in the region. Keywords: Cobb–douglas function; energy use; energy efficiency; greenhouse gas. 1. INTRODUCTION Greenhouse production is one of the most intensive parts of the world agricultural production. It is intensive not only in the sense of yield and annual production, but also in the sense of the energy consumption, investments and costs (Singh et al., 2007; Heidari and Omid, 2011). Greenhouses use large quantities of locally available non–commercial energies, such as manure, animate and seed energies and commercial energies directly and indirectly in the form of diesel, electricity, fertilizer, pesticides, irrigation water, machinery, etc. (Mandal et al., 2003). Efficient use of these energies helps to achieve increased productivity and contributes to the economy, profitability and competitiveness of agricultural sustainability of rural communities (Manes and Singh, 2005; Hatirli et al., 2006; Omid et al., 2011). Future agricultural sustainability will be achieved from an equilibrated solution of many productive, environmental, and economic issues (Park and Seaton, 1996; Helander and Delin, 2004; Fresco, 2009). Among these, improved energy efficiency and reduced greenhouse gas (GHG) emissions are fundamental (Dyer and Desjardins, 2003; Alluvione et al., 2001). While the energy requirements of agriculture are low compared to other production sectors (Tol et al., 2009; Pinstrup–Andersen, 1999), realizing efficient use of its own energy needs is pivotal to achieving economic sustainability and GHG emission reductions (Alluvione et al., 2011; Philibert et al., 2002). Usually, energy input–output analysis is used to evaluate the efficiency and environmental impacts of the production systems. Therefore, there was an immediate need to carry out such an analysis for future steps to be taken for any improvement in greenhouse production systems regarding the energy values of the inputs and the output. By reaching beyond agricultural boundaries and including all the steps of crop input production, energy analysis is a useful indicator of environmental and long–term sustainability (Alluvione et al., 2011). Many experimental works have been conducted on energy use in agriculture. Pashaii et al. (2011) reported the –1 energy intensity of 0.8 MJkg for production of greenhouse tomatoes in Kermanshah, Iran. Alam et al. (2005) studied the energy flow in agriculture of Bangladesh for a period of 20 years. Satori et al. (2005) studied the comparison of energy consumption on two farming system of conservation and organic in Italy. Damirjan et al. (2006) studied the energy and economic analysis of sweet cherry production. Mohammdian Sabour (2007) assessed net –1 energy gain and energy efficiency for canola in Mashhad, Iran to be 1812 MJha , and 1.03 respectively. Erdal et al. (2007) studied on energy consumption and economical analysis of sugar beet production. Faraji (2007) reported the energy intensity of mechanized wheat –1 production in Dasht–Abbas of Iran plain to be 0.206 MJkg . Nguyen et al. (2007) studied energy balance of cassava and found the positive energy balance for the production of 486 American Journal of Experimental Agriculture, 2(3): 485-501, 2012 ethanol from cassava. They illustrated GHG emissions of cassava in Thailand are low (about 0.96 kg per liter of cassava–based ethanol used versus 2.6 kg CO2). Cetin and vardar (2007) studied on differentiation of direct and indirect energy inputs in agro industrial production of tomatoes. Dyer et al. (2011) Compared fossil CO2 emissions from vegetable greenhouses in Canada with CO2 emissions from importing vegetables from the southern USA. Results showed that CO2 emissions from Canadian vegetables greenhouses were 0.35 Tg and air transport CO2 emission intensity was 1.9 times that of greenhouses. A further comparative review of studies on agricultural products can be found in (Singh et al., 2003; Singh et al., 2004; Ozkan et al., 2004; Chauhan et al., 2006; Mohammadi et al., 2008; Banaeian et al., 2010; Houshyar et al., 2010; Mobtaker et al., 2010; Mobtaker et al., 2011; Banaeian et al., 2011; Mousavi–Avval et al., 2011a; Mousavi–Avval et al., 2011b). On this basis, the main objective of this study is to examine energy use pattern and specification of GHG emission for tomato and cucumber greenhouses in Esfahan province of Iran. Furthermore, this study aims to explore the relationship between output and energy inputs using Cobb–Douglas function form. In addition, the relationship is also examined for different energy sources in the form of renewable and non–renewable, direct and indirect energy. Once estimated, the models yield elasticity of energy inputs and energy sources for Iranian agriculture as well as a set of results that can be used by policy makers or other relevant agents in order to ensure sustainability and more efficient energy use. 2. MATERIALS AND METHODS 2.1 Data Collection and Energy Equivalent Data were collected from growers in Esfahan province producing greenhouse vegetables, by using a face–to–face questionnaire in the production year 2010–2011. The survey was carried out in 10 villages where important undercover production exists. A total of 60 growers were randomly selected from the villages using the stratified random sampling method. 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; Mohammadi and Omid, 2010): (1) Energy Output (MJ ha -1 ) Energy ratio  Energy Input (MJ ha -1 ) Energy productivity  Tomato or Cucumber (kg ha -1 ) Energy Input (MJ ha -1 ) Energy input (MJ ha -1 ) Specific energy  Tomato or Cucumber (kg ha -1 ) Net energy  Energy Output (MJ ha -1 ) - Energy Input (MJ ha -1 ) (2) (3) (4) 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 (Dalgaard et al., 2001; Unakitan et al., 2010). An increase in the ratio indicates improvement in energy efficiency and vice 487 American Journal of Experimental Agriculture, 2(3): 485-501, 2012 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., 2010). For the growth and development, energy demand in agriculture can be divided into direct (DE) and indirect (IDE) energies or alternatively as renewable and non–renewable energies (Kizilaslan, 2009). The indirect energy includes the pesticide, fertilizers, seeds and machinery. The direct energy includes human labor, fuel and electricity power. The non–renewable energy (NRE) sources include fuel, electricity, fertilizers, pesticide and machinery, whereas the renewable energy (RE) sources include human power, seeds and manure fertilizers (Yilmaz et al., 2005). The energetic efficiency of the agricultural system has been evaluated by the energy ratio between output and input. Human power, machinery, diesel, fertilizer, pesticide, water for irrigation and seed amounts, and output yield have been used to estimate the energy ratio. Energy equivalents, shown in Table 1, were used for estimation; these coefficients were adapted from several literature sources. The sources of mechanical energy used in the selected farms include tractors and diesel oil. The mechanical energy was computed –1 regarding to the total fuel consumption (l ha ) in various operations; therefore, the energy –1 consumed was calculated using conversion factors, and was expressed in MJha (Dalgaard et al., 2001; Bayramoglu and Gundogmus, 2009). The energy of a tractor and its equipment reveals the amount of energy needed for unit weights and calculates repair and care energy, transport energy, total machine weight, and average economic life (Ozkan et al., 2004). Table 1. Energy equivalents for different inputs and outputs in agricultural production Inputs and Output Inputs Human power Machinery Diesel fuel pesticide Herbicides Fungicides Insecticides Fertilizer Nitrogen Phosphate Potassium Manure Water for irrigation Electricity Seed Output Tomato and Cucumber -1 Unit Energy equivalent (MJ) Unit ) Reference h kg l kg kg kg kg kg kg kg kg tons 3 M kWh kg 1.96 64.8 47.8 Singh, 2002 Singh, 2002 Singh, 2002 238 216 101.2 Shrestha, 1993 Shrestha, 1993 Shrestha, 1993 66.14 12.44 11.15 303.10 1.02 11.93 1.0 Yaldiz et al., 1993 Nagy, 1999 Nagy, 1999 Nagy, 1999 Nagy, 1999 Pathak and Binning, 1985 Singh, 2002 kg 0.8 Yaldiz et al., 1993 2.2 Analysis of Energy with Mathematical Models Realizing that the output is a function of inputs, production function can be expressed as Y  F ( X it ) (5) where Y is output level, X i is a vector of input variables that affect output such as fertilizer, diesel fuel, electricity etc, and t is a time subscript. 488 American Journal of Experimental Agriculture, 2(3): 485-501, 2012 In order to estimate this relationship, a mathematical function needs to be specified. For this purpose, several functions were tried and the Cobb–Douglas production function was chosen since it produced better results among the others. The Cobb–Douglas production function is expressed in general form as follows (Hatirli et al., 2005): n Ln Yt  β   β Ln( X it )  ε t 0 Where ε t i 1 (6) i Y denotes the yield of the t th farmer, β 0 is a constant, β i denotes coefficients, and t is the error term, assumed normally distributed with mean 0 and constant variance . Assuming that when the energy input is zero, the crop production is also zero, Eq. (6) reduces to: n Ln Y t   β Ln( X it )  ε t i 1 (7) i Total physical energy consisted of human, electricity, diesel fuel, machinery, seed, fertilizer, water for irrigation and pesticide. Following this explanation, Eq. (7) can be given as: LnY t  β LnFR  β LnMA  β LnHU  β LnCH  β LnSE  β LnDS  β LnEL  β LnWA  ε t 1 2 3 4 5 6 7 8 (8) Where Y is the output, FR is the fertilizer, MA is the machinery, HU is the human power, CH is the total pesticide, SE is the seed, DS is the diesel fuel and EL is the electricity input and WA is the water for irrigation input. The study was also aimed at investigating the relationship between output and different energy forms. More specifically, we considered different energy forms as renewable or nonrenewable, as direct or indirect. As a functional form, the Cobb–Douglas production function was selected and specified in the following forms (Hatirli et al., 2005): Ln Y t  ϕ LnDE  ϕ LnIDE  ε t 1 2 1 2 Ln Y t  µ LnRE  µ LnNRE  ε t (9) (10) Where RE and NRE denote renewable and non–renewable energy forms, respectively. DE represents direct energy and IDE denotes indirect energy. Conservation farming practices, such as direct seeding and good fertilizer placement have increased soil organic carbon levels, which helps to offset GHG emissions, thereby reducing the industry’s net GHG emissions (Dyer and Desjardins, 2003). Reducing GHG emissions simply means that crops and livestock are raised more efficiently, thus reducing on wasteful losses of inputs such as nitrogen (nitrous oxide) and energy (methane). Adoption of conservation practices will help to reduce GHG emissions. In this paper the corresponding amount of greenhouse gas (GHG) emissions was calculated. The diesel fuel combustion can –1 be expressed as fossil CO2 emissions with equivalent of 2764.2 g L (Dyer and Desjardins, 2003). Also, the machinery and fertilizer supply terms can be expressed in terms of the fossil energy required to manufacture and transport them to the farm with CO2 equivalents of –1 –1 0.071 Tg PJ (Neitzert et al., 1999) and 0.058 TgPJ (Dyer and Desjardins, 2007; Vergé et al., 2007) for machinery and chemical fertilizers, respectively. 489 American Journal of Experimental Agriculture, 2(3): 485-501, 2012 The economic analysis for cucumber and tomato production was investigated. Net return, gross profit and benefit to cost ratio were calculated. The net return was calculated by subtracting the total cost of production from the gross value of production per hectare. The gross return was calculated by subtracting the variable cost of production. The benefit–cost ratio was calculated by dividing the gross value of production by the total cost of production per hectare (Zangeneh et al., 2010): Total production value  Yeild (kg ha -1 )  Tomato or Cucumber price($ kg -1 ) Gross return  Total production value ($ ha -1 ) - Variable cost of production ($ ha -1 ) Net return  Total production value ($ ha -1 ) - Total production cost ($ ha -1 ) BC  Total production value ($ ha -1 ) Total production cost ($ ha -1 ) productivi ty  (11) (12) (13) (14) Total yeild (kg ha -1 ) Total production cost ($ ha -1 ) (15) Basic information on energy inputs and greenhouse yields were entered into an Excel spreadsheet and simulated using Eviews 5 software. 3. RESULTS AND DISCUSSION 3.1 Energy Use in Greenhouse Tomato Production The inputs used in tomato production and their energy equivalents, output energy equivalent and energy ratio are illustrated in Table 2. About 10 kg pest and disease control pesticide and 971 kg chemical fertilizer were used in greenhouse tomato production on a hectare basis. The shares of nitrogen fertilizer, phosphorus and potassium were 32.5%, 38.2% and 29.3%, respectively, in the total chemical fertilizer used. The use of human power and –1 –1 machinery were 5815.2 hrha and 52.3 kgha . –1 The total energy equivalent of inputs was calculated as 116.76 GJha . Diesel fuel had the highest share, of 40%, followed by fertilizer (30%) and electricity (12%), respectively. The –1 average yield of tomatoes was found 135 tha and its energy equivalent was calculated to –1 be 108 GJha . 3.2 Energy Use in Greenhouse Cucumber Production The inputs, used in the cucumber production and their energy equivalents, together with the energy equivalent of the yield were illustrated in Table 3. As indicated in the table about 10 kg pesticide, 871 kg chemical fertilizer and 14.2 tones manure were used in greenhouse cucumber production on a hectare basis. The use of human power and machinery were –1 –1 3789and 40hha , respectively. Average cucumber yield was 88123 kg ha . The total –1 energy input was calculated 124.44 GJha . Diesel fuel was the energy input in the total with a share of 45%. This was followed by fertilizers (25%) and electricity (20%). The distributions of inputs used in the production of cucumber and tomato are given in Fig. 1. 490 American Journal of Experimental Agriculture, 2(3): 485-501, 2012 Table 2. The physical inputs used in the production of tomato and their energy equivalences Input (unit) 1. Pesticide (kg) Quantity per unit area (ha) 9.7 Total energy equivalent (MJ) 1715.6 Herbicides (kg) Fungicides (kg) Insecticides (kg) 2. Human power (hr) 3. Machinery (kg) 4. Fertilizer (kg) Nitrogen fertilizer (kg) Phosphate (kg) Potassium (kg) Manure (tones) 5. Seeds (kg) 6. Diesel fuel (l) 7. Electricity (kWh) 8. Water for irrigation 3 Total (m ) energy input (MJ) –1 Yield (kg ha ) 3.1 2.7 3.9 5815.2 52.3 – 315.0 371.0 285.0 21.2 0.1 985.5 1200.0 3716.0 – 135000.0 737.8 583.2 394.6 11397.8 3389.0 35052.7 20834.1 4615.2 3177.7 6425.7 0.1 47106.9 14316.0 3790.3 116768.4 108000.0 Percentage 2 10 3 30 0 40 12 3 100 Bold characters are main inputs Table 3. The physical inputs used in the production of cucumber and their energy equivalences Input (unit) 1. Pesticide (kg) Herbicides (kg) Fungicides (kg) Insecticides (kg) 2. Human power (h) 3. Machinery (kg) 4. Fertilizer (kg) Nitrogen fertilizer Phosphate (kg) (kg) Potassium (kg) Manure (tones) 5. Seeds (kg) 6. Diesel fuel (l) 7. Electricity (kWh) 8. Water for irrigation 3 (m ) energy input Total –1 Yield (MJ) (kg ha ) Quantity per unit area (ha) 10.1 2.5 3.4 4.2 3789.0 40.0 – 2.095 325.0 251.0 14.2 0.15 1165.0 2056.0 1769.0 – 88123.0 Total energy equivalent (MJ) 1754.1 595.0 734.0 425.1 7426.4 2592.0 30656.0 19511.0 4043.0 2798.0 4304.0 0.12 55687.0 24528.0 1804.0 124447.5 70498.0 Percentage 1 6 2 25 45 20 1 100 Bold characters are main inputs 491 American Journal of Experimental Agriculture, 2(3): 3): 485-501, 2012 60000 Cucumber Tomato GJ ha–1 50000 40000 30000 20000 10000 0 Energy inputs Fig. 1. The anthropogenic enic energy input ratios in the production of cucum mber and tomato 3.3 Energy Indices in Tom omato and Cucumber Production The energy ratio (energy use efficiency), energy productivity, specific energy, y, net energy gain and the distribution of inp inputs used in the production of tomato and cucumbe ber according to the direct, indirect, renewabl able and non–renewable energy groups, are given in Table T 4. Table 4. Energy output–in –input ratio and forms in cucumber and tomato production pro Items Un Unit Cucumber Tomato Energy ratio Energy productivity Specific energy Net energy a Energy forms b Direct energy c Indirect energy d Renewable energy Non– renewable c energy Total energy input Energy output – –1 kgMJ –1 MJ MJkg –1 MJ MJha 0.56 0.70 1.41 –53949.5 0.92 1.15 0.86 –8768.4 –1 87641.4 35002.12 11730.52 110913 124447.5 70498 76611 40157.4 21613.8 95154.5 116768.4 108000 MJ MJha –1 MJ MJha –1 MJ MJha –1 MJ MJha –1 MJ MJha –1 MJ MJha Percentage Cucumber Tomato To 71 29 19 90 100 66 34 10 81 a Energy rgy equivalent of water for irrigation is not included b inclu clude human labor, fuel and electricity power c include de the pesticide, fertilizers, seeds and machinery d includ lude human labor, seeds and manure fertilizers c include fu fuel, electricity, pesticide, fertilizers and machinery It can be seen that the ratio tio of direct and indirect energy and also the ratios of renewable and non–renewable energy ar are fairly different from each other in tomato and cuc ucumber (Fig. 2). Erdal et al. (2009) investi estigated the relationship between fruit yield and energy en inputs used in stake tomato produc uction under field conditions in Tokat province of Turkey. Tur They 492 American Journal of Experimental Agriculture, 2(3): 3): 485-501, 2012 reported that among the total otal energy used, 57.12% was in the form of direct ect energy ener and 77.54% was in the form of non non-renewable energy. The ratio of renewable energ ergy including the energies of human power and far fertilizer d farm inputs, within the total energy in both productions is very low. Renewable energy nergy resources (solar, hydroelectric, biomass ass, wind, ocean and geothermal energy) are inexha haustible and offer many environmentall benef benefits over conventional energy sources. Eac ach type of renewable energy also has its own special advantages that make it uniquely suite ited to certain applications (Miguez et al.,, 2001 2001). The use of renewable energy gy offers a range of exceptional benefits, including: d ng: a decrease in external energy dependence; nce; a boost to local and regional component manufacturing manuf industries; promotion of regio gional engineering and consultancy services special ializing in the use of renewable energy, dec decrease in impact of electricity production and trans ansformation; increase in the level of servic vices for the rural population; creation of employment, ent, etc. (Kaya, 2006). Within the enterprises es that were analyzed, 81% and 90% of input energy ergy resources used for the production of tom tomato and cucumber was non–renewable energy. 120000 MJha-1 100000 Cucumber Tomato 80000 60000 40000 20000 0 Non- renewable energy Renewable energy Energy forms Fig. 2. Percentages of total tal en energy input in the form of renewable (RE) and d nonrenewable (NRE) for cucum mber and tomato production in Esfahan province o Iran ce of 3.4 Econometric Modell E Estimation and Greenhouse Emission of Cucumber C and Tomato Productio tion In order to estimate the relat relationship between energy inputs and output (cuc ucumber and tomato yield), Cobb–Douglas las production function was chosen and assessed using usi ordinary least square (OLS) estimation ation technique. Since the coefficient of variables in this function is in log form also represents nts elasticities (Mohammadi and Omid, 2010). Cob obb–Douglas production function indicatess a pr priori restriction on models of substitution among in ong inputs. For data used in this study,, aut autocorrelation was tested using Durbin–Watson method ethod (Hatirli et al., 2005). The Durbin–Wa atson values were found to be 1.75 and 1.89 for cuc ucumber and tomato respectively, which h ind indicates that there was no autocorrelation at the 5% significance s 2 level in the estimated mode odels. The R values were determined as 0.97 and 0.98 for cucumber and tomato respec ectively; implying that around 0.97 and 0.98 of the v e variability in the energy inputs was expl xplained by this model. Regression results for Eq. (8) were 493 American Journal of Experimental Agriculture, 2(3): 485-501, 2012 estimated and are shown in Table 5 and 6. It can be seen form Table 5 that the contribution of human power and pesticide energies are significant at the 1% level on cucumber production. This indicates that with an additional use of 1% for each of these inputs would lead to 0.45% and 0.33% increase in yield. The elasticities of machinery, electricity and water for irrigation energies were estimated at 0.20, 0.12 and 0.15, respectively (all significant at the 5% level). The impact of chemical fertilizers, diesel fuel and seed energies on yield were estimated statistically insignificant with a negative sign. Mohamadi and Omid (2010) estimated an econometric model for greenhouse cucumber production in Tehran province of Iran. They concluded that among the energy inputs, human energy was found as the most important input that influences yield. Singh et al. (2004) concluded that in zone 2 of Punjab, the impact of human and electrical energies were significant to the productivity at 1% level. Table 5. Economic estimation result of cucumber greenhouses Variables Ln Y t  β 1 Coefficient LnFR  β 2 LnMA  β Human power Diesel fuel Machinery Pesticide Fertilizers Electricity Water for irrigation Seed Durbin–Watson 2 R * 3 LnHU  β 4 LnCH  t–Ratio β 5 LnSE  β 6 LnDS  β 7 β LnEL  8 LnWA  ε t * 0.45 –0.17 0.20 0.33 –0.07 0.12 0.15 –0.05 1.75 0.97 ** 5.93 ns –0.13 ** 3.54* 5.12 ns –0.32 ** 2.12** 2.23 ns –0.13 Significance at 1% level; Significance at 5% level; ns Not significant The effect of energy inputs on tomato production was also investigated by estimating Eq. (8) and regression result for this model is shown in Table 6. Human power had the highest impact (0.78) among other inputs and significantly contributed on the productivity at 1% level in this cultivation. It indicates that a 1% increase in the human power input led to 0.78% increase in yield in these circumstances. Table 6. Economic estimation result of tomato greenhouses Variables Coefficient t–Ratio LnY t  β LnFR  β LnMA  β LnHU  β LnCH  β LnSE  β LnDS  β LnEL  β LnWA  ε t 1 2 Human power Diesel fuel Machinery Pesticide Fertilizer Electricity Water for irrigation Seed Durbin–Watson 2 R * 3 4 0.78 –0.12 0.27 0.57 –0.09 0.20 0.02 –0.13 1.89 0.98** 5 6 Significance at 1% level; Significance at 5% level; 7 8 * 5.45 ns –0.18 ** 2.09* 4.56 ns –0.28 ** 2.27ns 0.72 ns –0.09 ns Not significant 494 American Journal of Experimental Agriculture, 2(3): 3): 485-501, 2012 The second important input ut for tomato production was found as pesticide with 0.57 0.5 elasticity followed by machinery with h 0.2 0.27 elasticity. Hatirli et al. (2006) developed an n econometric e model for greenhouse tomato ato production in Antalya province of Turkey and reported repor that human power, chemical ferti ertilizers, biocides, machinery and water energy were ere important inputs significantly contributed uted to yield. The coefficient of diesel fuel, fertilizer zer and seed energy were found to be –0.12, 0.12, –0.09 and –0.13, a negative value show that additional addi units of inputs are contributing negat egatively to production, i.e. less production with more ore input. The sensitivity of energy inputss for cucumber and tomato production with partial ial regression coefficients on output level are de depicted in Fig. 3. Although, the share of diesel el fuel and fertilizer were 40% and 30% of the totall energy ener input, the use of these inputs in n tom tomato production per hectare in the research area a is equal to other estimates of Iran’s average erage. 1 Coefficient 0.8 0.6 cucumber tomato 0.4 0.2 0 0.20.4- Energy inputs Fig. 3. Sensitivity analys alysis of energy inputs in cucumber and tomato prod roduction The relationship between the direct and indirect energies, as well as renewable ble and non– renewable energies on the yi yield of each greenhouse production was investigate ated by Eqs 9 and 10, respectively. The rresults are presented in Table 7. As can be seen, se all the regression coefficients of DE and RE forms were positive and significant (p ant (p<1%). The regression coefficients of ID IDE for cucumber and NRE for tomato were also lso significant (p<1%). Other regression coef coefficients contributed on the yield (p<5%). The impac pacts of DE, IDE, RE and NRE were estim timated in the range of 0.17–1.21. The impact of IDE was more than the impact of DE on cucum ucumber yield. Similar results can be seen en in the study of Heidari and Omid (2011) for gr or greenhouse production of tomato and cucum ucumber in Tehran province of Iran. Statistical tests revealed re that DW values were 1.98–2.33 3 for Eqs. 9 and 10; indicating that there is no autoco ocorrelation at the 5% significance level in the estimated models. Results indicated that tomato ato and cucumber production are mostly depending ding on fossil energy sources. As it can be seen in Table 8, the total amounts of CO2 for cuc ucumber and tomato production were calcul alculated as 4.930 and 4.622 tons respectively. Diesel el fuel had the highest share (65.27% and 58.89 58.89%) in both of cucumber and tomato production. on. Pishgar et al. (2011) reported the amount mount of CO2 emission for corn silage production on in Tehran 495 American Journal of Experimental Agriculture, 2(3): 485-501, 2012 province of Iran to be 2792000 tons. Using ethanol and biodiesel as biofuel is essential in the 21st century to reduce the high GHG emissions. Field operations with minimum machinery use (especially tillage operation) and machinery production need to be considered to reduce the amount of CO2. Table 7. Econometric estimation of direct vs indirect and renewable vs. non– renewable energy in tomato and cucumber production Variables Cucumber yield Coefficient t–Ratio Tomato yield Coefficient t–Ratio LnY t  ϕ LnDE  ϕ LnIDE  ε t 1 2 DE( ) IDE( ) Durbin–Watson 2 R Ln Y t  µ LnRE  µ LnNRE  ε t 0.23 0.17 2.33 0.90 2.96 * 2.10 * 0.59 0.51 2.28 0.89 4.50 ** 4.90 RE( ) NRE( ) Durbin–Watson 2 R 0.78 0.32 1.98 93.0 6.23 ** 3.17 * 0.37 1.21 2.12 0.95 4.12 * 6.54 1 * 2 * ** * Significance at 1% level; Significance at 5% level Table 8. Amount of greenhouse gas emission in cucumber and tomato production Input Cucumber production Diesel fuel Machinery fertilizer Total Tomato production Diesel fuel Machinery fertilizer Total Consumption (MJ) Equivalent –1 (Tg (CO2) PJ ) Amount of CO2 (ton) Percentage 55687 2592 26352 84631 0.0578 0.071 0.058 – 3.218 0.184 1.528 4.930 65.27 3.73 31.00 100 47106.9 3389 28627 79122.9 0.0578 0.071 0.058 – 2.722 0.240 1.660 4.622 58.89 5.20 35.91 100 3.5. Economic Analysis of Tomato and Cucumber Production The total cost of tomato and cucumber production and the gross value of this production were calculated and shown in Table 9. The fixed and variable expenditures included in the cost of production were calculated separately. The total expenditure for the tomato and –1 cucumber production were 34939 and 31956$ ha , respectively, while the gross production –1 value were found to be 95850 and 57280$ ha , respectively. The share of variable costs in total costs of tomato and cucumber production was 66% and 62%, respectively. With respect to results of Table 7, the benefit–cost ratio from tomato and cucumber production in the surveyed farms was calculated to be 2.74 and 1.79, respectively. Other researchers reported similar results, such as 2.53 for sweet cherry (Demirjan et al., 2006), 2.37 for orange 496 American Journal of Experimental Agriculture, 2(3): 485-501, 2012 (Chauhan et al., 2006), 1.17 for sugar beet (Erdal et al., 2007), 1.57 for corn silage (Pishgar Komleh et al., 2001), 1.03 for stake-tomato (Esengun et al., 2007), 1.68 for cucumber and 3.28 for tomato (Heidari and Omid, 2011). Table 9. Economic analysis of tomato and cucumber production Cost and return components Yield Sale price Gross value of production Variable cost of production Fixed cost of production Total cost of production Total cost of production Gross return Net return Benefit–cost ratio Productivity Unit –1 kgha –1 $kg –1 $ha –1 $ha –1 $ha –1 $ha –1 $kg –1 $ha –1 $ha – –1 kg$ Tomato 135000 0.71 95850 23159 11780 34939 0.27 72691 60911 2.74 3.86 Cucumber 88123 0.65 57280 19986 11970 31956 0.36 37294 25324 1.79 2.76 4. CONCLUSION In this study, the level of energy consumption for input and output energies in tomato and cucumber production were investigated in Esfahan province of Iran. Data were collected from 60 greenhouses by a face to face questionnaire technique. Greenhouses were selected through a stratified random sampling technique. The following results were obtained: –1 1. Tomato production consumed a total of 116768.38 MJha , while the cucumber –1 consumed 124447.5 MJha . 2. Diesel fuel is the major energy input in both types of production. Output energy, energy ratio and energy productivity of the tomato production were higher than cucumber production. 3. The impact of human power energy input in both of cucumber and tomato production was significantly positive on yield (p <1%). The regression coefficients of fertilizer and diesel fuel inputs for both productions were found negative, indicating that power consumption of fertilizer and fuel are high in the surveyed greenhouses. 4. The benefit–cost ratio for cucumber and tomato production was found to be 1.79 and 2.74 respectively. The mean net return from cucumber and tomato production were –1 25324 and 60911 $ ha , respectively. 5. Total amounts of CO2 for cucumber and tomato production were calculated as 4.930 and 4.622 tons respectively. Diesel fuel had the highest share (65.27% and 58.89%) in both of cucumber and tomato production. It is possible to decrease greenhouse gas emission in agricultural production by reduction of non–renewable energy sources that create environmental problems. Therefore, policy makers should take the necessary measurements to ensure more environmentally friendly energy use patterns in the Persian agriculture. Finally, in the research area, greenhouse operators are still increasing the amount of inputs used in vegetable production. However, the timing of 497 American Journal of Experimental Agriculture, 2(3): 485-501, 2012 any applications and use of the inputs are not significant issues for the Iranian greenhouse producer. This inevitably leads to problems associated with energy use such as global warming, nutrient loading and pesticide pollution, as indicated above. Therefore, there is a need to develop a new policy to force producers to use all inputs on time and enough undertake more energy–efficient practices. 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