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Reduction of CO2 emission by improving energy use efficiency of greenhouse cucumber production using DEA approach
Energy 55 (2013) 676e682
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Reduction of CO2 emission by improving energy use efficiency of greenhouse cucumber production using DEA approach Benyamin Khoshnevisan, Shahin Rafiee*, Mahmoud Omid, Hossein Mousazadeh Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 November 2012 Received in revised form 31 March 2013 Accepted 16 April 2013 Available online 21 May 2013
The main purpose of this study was to determine energy use efficiency in greenhouse cucumber production using a non-parametric production function. Energy use efficiency of greenhouse cucumber producers was studied and degrees of technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) were determined using data envelopment analysis (DEA). Additionally, wasteful uses of energy inputs were assessed and energy saving from different sources was computed. Furthermore, the effect of energy optimization on CO2 emission as one of the major greenhouse gases (GHG) was investigated and total amount of CO2 emission was calculated. Average of total input and output energies were calculated as 1667164.8 MJ ha1 and 151846.2 MJ ha1. Energy use pattern indicated that natural gas and electricity were the main energy inputs. Based on the results 24.46% (407916.3 MJ ha1) of overall energy resources can be saved if the performance of inefficient farms is enhanced. Additionally, the total CO2 emission was calculated as 45177.3 kg CO2eq ha1. Finally it was concluded that, by energy optimization the total energy consumption can be reduced to the value of 1,259,248 MJ ha1 and correspondingly the total CO2 emission is reduced to value of 34995.9 kg CO2eq ha1. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Greenhouse cucumber DEA GHG emission
1. Introduction Cucumber is one of the most popular greenhouse vegetable products in Iran. Greenhouse plant production is one of the most intensive parts of the agricultural production. It is intensive not only in the sense of yield (production) and in whole year production, but also in sense of energy consumption, investments and costs [1]. From 2002 to 2007, greenhouse areas of Iran had increased from 3380 ha to 6630 ha with an increasing rate of 96%. The shares of greenhouse crops production were as follows: vegetables 59.3%, flowers 39.81%, fruits 0.54% and mushroom 0.35% [2]. Energy is used in all facets of living and in all countries, and makes possible the existence of ecosystems, human civilizations and life itself [3]. Given the growing population’s food requirements, the finite supply of fossil fuels and the environmental impacts of using this non-renewable resources, the existing relationship between agriculture and energy must be dramatically altered [4,5]. Sustainable agriculture production is closely connected with effective energy use due to financial savings, fossil resources preservation and air pollution reduction [6]. Increasing in
* Corresponding author. Tel.: þ98 2632801011; fax: þ98 2632808138. E-mail addresses: [email protected] (B. Khoshnevisan), shahinrafiee@ ut.ac.ir (S. Rafiee). 0360-5442/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.energy.2013.04.021
use of energy inputs in agriculture led to numerous environmental problems like high consumption of non-renewable energy resources, loss of biodiversity, pollution of the aquatic environment by the nutrients nitrogen and phosphorus as well as by pesticides [7]. Global warming is one of the most important issues in recent century. Agricultural greenhouse gas (GHG) emissions account 10e 12% of all manmade GHG emissions [8]. Efficiency is defined as the ability to produce the outputs with a minimum resource level required [9]. Data envelopment analysis (DEA) is a well-established technique for measuring efficiencies of a set of comparable entities by some specific mathematical programming models. These entities, which often called decisionmaking units (DMUs), perform the same function by transforming multiple-inputs into multiple outputs. Given a sample of the DMUs, the purpose of the DEA is to establish the relative efficiency of each DMU within a sample [2]. A big advantage of DEA is that it does not need any prior assumptions on the underlying functional relationships between inputs and outputs [10]. However several studies have been conducted on energy consumption in greenhouse production [1,2,11,12], none of them investigated the amount of GHG emission. Accordingly, the objectives of this study were: (a) to determine the efficiency of greenhouse cucumber producers; (b) to identify target energy requirement and wasteful uses of energy and (c) to assess the effect of energy optimization on GHG emissions.
B. Khoshnevisan et al. / Energy 55 (2013) 676e682
GHG IRS n N Nh NIRS PTE SE S2h TE VRS z
Abbreviations BCC CCR CRS d D2 DEA DMU DRS EMS FYM
BankereeCharneseeCooper (DEA model) CharneseeCoopereeRhodes (DEA model) constant returns to scale precision ðx XÞ d2/z2 data envelopment analysis decision making unit decreasing returns to scale Efficiency Measurement Systems farmyard manure
2. Materials and methods 2.1. Selection of case study region and data collection Data for the present study were collected from greenhouse cucumber producers in Esfahan province (a province located in central Iran), during SeptembereDecember 2012 period. The sample size was calculated using Neyman technique [13]:
P ð Nh Sh Þ n ¼ 2 2 P N D þ Nh S2h
(1)
where n is the required sample size; N is the number of farmers in the target population; Nh is the number of the farmers in the h stratification; S2h is the variance of the h stratification; d permitted error ratio deviated from average of population ðx XÞ, z is the reliability coefficient (1.96 which represents 95% confidence); D2 ¼ d2/z2; the permissible error in the sample population was defined to be 5% within 95% confidence interval [14]. Accordingly, it was determined to be 26, so data were randomly collected from 26 greenhouse cucumber producers using face to face questioner method. The questionnaires were designed to gather information about utilized inputs for greenhouse cucumber production such as total working hours of labors, chemical fertilizers, diesel fuel, natural gas, total electricity consumption, etc. A brief summary of sample questionnaire is provided in Table 1. Input energy recourses encompassed human labor, chemical fertilizers, farmyard manure (FYM), biocides, machinery, diesel fuel, water for irrigation, electricity, natural gas and seeds while
Table 1 A brief summary of sample questionnaire. Questionnaire No: .... Date: 2012.... Total area of greenhouse (m2): .... Duration of the production: .... Number of cucumber plants per area: . Number of fixed labors: .... Number of daily labors: .... Average number of daily irrigation: . Amount of water consumed in each irrigation: . Machinery operation used: . Types of Machinery used: . Total diesel consumption (L): .... Total electricity consumption (kW h): .... Total natural gas consumption (kW h): .... Total weight of manure (kg): .... Types of chemical fertilizers: . Total weight of chemical fertilizers from each type (kg): .... Types of chemicals: .. Total weight of chemicals from each type (kg): .... Total weight of output cucumber (kg): ....
677
greenhouse gas increasing returns to scale required sample size number of farmers in target population number of the population in the h stratification non-increasing returns of scale pure technical efficiency scale efficiency variance of h stratification technical efficiency variable returns to scale reliability coefficient (1.96 in the case of 95% reliability)
cucumber was considered as output energy. In order to convert inputs and output into the energy equivalents, energy equivalent coefficients were applied (Table 2). Water for irrigation was pumped from local agricultural well by electric pumps. Energy for pumping water was calculated as Eq. (2) [15]:
DE ¼
ggHQ
(2)
εp εq
where ‘DE’ presents direct energy (J/ha), ‘g’ is acceleration due to gravity (ms2), ‘H’ is total dynamic head (m), ‘Q’ is volume of required water for one cultivating season (m3 ha-1), ‘g’ is density of water (kg m-3), ‘εp ’ is pump efficiency (70e90%) and ‘εq ’ is total power conversion efficiency (18e20%) [16]. 2.2. Data envelopment analysis DEA is a non-parametric technique whose domain of inquiry is a set of entities which receives multiple inputs and produce multiple outputs [11]. In this study, DEA methodology was applied to determine efficiency of greenhouse cucumber producers in order to calculate the amount of energy saving and greenhouse gas (GHG) emission reduction. Input variables were considered based on
Table 2 Energy coefficients of different inputs and output used. Inputs A. Inputs 1. Machinery Tractor and self-propelled Stationary equipment Implement and machinery 2. Human labor 3. Natural gas 4. Diesel fuel 5. Biocide Herbicide Fungicide Insecticide 6. Fertilizers Nitrogen (N) Phosphate (P2O5) Potassium (K2O) 7. Sulfur (S) 8. FYM 9. Water for irrigation 10. Electricity 11. Seeds B. Output 1. Cucumber a
Unit
Energy coefficients (MJ unit1)
Reference
kg yra kg yra kg yra h m3 L
9e10 8e10 6e8 1.96 49.5 47.8
[15] [15] [15] [11] [15] [15]
kg kg kg
85 295 115
[15] [15] [15]
kg kg kg kg kg m3 kWh kg
66.14 12.44 11.15 1.2 0.3 1.02 11.93 1
[2] [2] [2] [11] [12] [2] [5] [11]
kg
0.8
[11]
The economic life of machine (year).
678
B. Khoshnevisan et al. / Energy 55 (2013) 676e682
energy per hectare (MJ ha1) and cucumber yield (kg ha1) was chosen as output variable. The DEA model has been described in details by several authors [17,18], thus a detailed description is not provided by the present study. CCR (is an abbreviation for Charnes, Cooper and Rhodes who suggested this model) and BCC (is an abbreviation for Banker, Cooper and Rhodes who suggested this model) which are regarded as two kinds of DEA models, were employed in the present study. CCR model which was built on the assumption of constant returns to scale (CRS), was suggested by Charnes et al. [18]. Later, Banker et al. [17] introduced the BCC model based on variable returns to scale (VRS) and it was also called as local efficiency model. DEA models are broadly divided into two categories on the basis of orientation: input-oriented and output-oriented. Input-oriented models have the objective of minimizing inputs while maintaining the same level of outputs, whereas output-oriented models focus on increasing outputs with the same level of inputs [19]. In this study an input oriented DEA model was used to assess efficient and inefficient DMUs. Three different forms of efficiency are defined by DEA; technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE). TE which is defined as the DMU’s ability to achieve maximum output from given inputs, can be calculated by the ratio of sum of the weighted outputs to sum of the weighted inputs [11]:
Ps u y max hk ¼ Prm¼ 1 rk rk i ¼ 1 vik xik
(3)
subject to
Ps
Prm¼ 1
urk yrj
i ¼ 1 vik xij
1; j ¼ 1; :::; n
(4)
urk ; vik 0; r ¼ 1; :::; s; i ¼ 1; :::; m where ‘k’ is the DMU being evaluated in the set of j ¼ 1,2,.,n DMUs; ‘hk ’ the measure of efficiency of DMU ‘k’, the (DMU) in the set of j ¼ 1,2,.,n (DMU)s rated relative to the others; ‘yrk ’ the amount of output ‘r’ produced by DMU ‘k’ during the period of observation; ‘xik ’ the amount of resource input ‘i’ used by DMU ‘k’ during the period of observation; ‘yrj ’ the amount of service output ‘r’ produced by DMU ‘j’ during the period of observation; ‘xij ’ the amount of resource input ‘i’ used by DMU ‘j’ during the period of observation; ‘urk ’ the weight assigned to service output ‘r’ computed in the solution to the DEA model; ‘vik ’ the weight assigned to resource of input ‘i’ computed in the solution to the DEA model; ‘m’ the number of inputs used by the DMUs; and ‘s’ the number of outputs produced by the DMUs [20]. PTE is the TE of BCC model. On the other hand BCC model decomposes the technical efficiency into pure technical efficiency for management factors and scale efficiency for scale factors. Thus, pure technical efficiency is the technical efficiency that has the effect of scale efficiency removed [21]. The following BCC model provides efficiency measure of the ith DMUs:
Minq;l qi subject to
Y l Yi X l qi Xi N0 l ¼ 1 l0
and ‘X’ are the output and input matrices for all the N DMUs. ‘N0 ’ represents the unit vector and ‘l’ is an N 1 vector of constants. A DMU is considered technically efficient and will lies on the efficiency frontier if and only if the optimal value of ‘qi ’ is equal to one. Any value less than one indicates relatively inefficient firm lying below the frontier [22]. Inappropriate operation and inadequate scale of a farm are two main reasons for inefficiency of a DMU. CCR model includes both TE and SE while BCC model calculates only PTE of DMUs. In order to obtain SE in the present study, both CCR and BCC models were calculated and SE was defined as follows [23]:
SE ¼
qCCR qBCC
(6)
where ‘qCCR ’ and ‘qBCC ’ are the CCR and BCC scores of a DMU, respectively. SE ¼ 1 shows scale efficiency (or CRS) and SE < 1 indicates scale inefficiency. 2.3. CO2 emission Production, transportation, formulation, storage, distribution and application of agricultural inputs with agricultural machinery lead to combustion of fossil fuel and use of energy from alternate sources, which also emits CO2 and other greenhouse gases into the atmosphere [24]. Nitrous oxide (N2O) is regarded as another GHG which is emitted to the atmosphere due to different agricultural practices. For instance it is produced as an intermediate product in the dinitrification process (conversion of NO 3 into N2) by soil micro-organisms. It can also be produced as a by-product in the nitrification process in which NHþ 4 is converted into NO3 . N losses in the form of N2O are closely linked to the nitrogen cycle in agriculture; intensive agriculture with a high input of nitrogen fertilizer contributes to the increase in N2O-emissions [25]. Changes in concentration of GHG and aerosols will cause great changes in global and regional scale of air temperature, rainfall and other parameters which ultimately will lead to changes in soil moisture, floods and droughts in some regions of the world. However abovementioned emissions as well as methane are all considered as GHGs, and they would have environmental impacts such as climate change and global warming on the whole of human society, but the present study merely focused on CO2 emission and the abbreviation of GHG will be used from here onwards to mention CO2 emission. We emphasis that it does not mean other emissions are not important or they can be ignored in agricultural production systems. CO2 emission coefficients of agricultural inputs were used for quantifying the GHG emissions of greenhouse cucumber production in the studied region. Table 3 summarizes GHG emission equivalents. GHG emission was calculated by multiplying the input application rate (diesel fuel, chemical fertilizers, pesticides, electricity, Machinery, and natural gas) by its corresponding emission coefficient. The excel spreadsheet was used to analyze energy use pattern. Also, in order to assess the efficiency indices of greenhouses, the DEA software Efficiency Measurement Systems (EMS), Version 1.3, was applied. 3. Results and discussions
(5)
where ‘qi ’ provides the efficiency score for ith DMU. ‘Yi ’ and ‘Xi ’ represent the output and input vectors of ith DMU, respectively. ‘Y’
3.1. Energy use pattern The quantity of input and output energies in cucumber production and their energy equivalents are summarized in Table 4. The average cucumber yield in the studied region was 189,808 kg ha1, so the total output energy was calculated as
B. Khoshnevisan et al. / Energy 55 (2013) 676e682 Table 3 Greenhouse gas (GHG) emission coefficients of agricultural inputs. Inputs
Unit
GHG coefficienta
Reference
Machinery Diesel fuel Chemical fertilizers (a) Nitrogen (N) (b) Phosphate (P2O5) (c) Potassium (K2O) Sulfur Biocide (a) Herbicide (b) Insecticide (c) Fungicide Natural gas Electricityb
MJ L
0.071 2.76
[28] [26]
kg kg kg kg
1.3 0.2 0.2 7.3
[24] [24] [24] [24]
kg kg kg m3 kWh
6.3 5.1 3.9 0.85 0.608
[24] [24] [24] [24] [27]
a b
kg CO2eq. unit1. The power plant burns LNG.
151846.2 MJ ha1. As can be seen in Table 4 the total energy consumption, which was applied for cucumber production, was computed as 1667164.8 MJ ha1. Natural gas with a share of 65.7% was the most energy consumer and it was followed by electricity (26.5%). Based on the evaluations the majority of natural gas and electricity were respectively used for heaters and ventilation along with drip irrigation systems. Djevic et al. [29] on their study on greenhouse energy consumption and energy efficiency reported that 92% of total energy in greenhouse lettuce production belonged to diesel fuel. They mentioned that diesel fuel was mostly used for heating systems. In other studies which was conducted by Hatirli et al. [30], Esengun et al. [31], Çetin and Vardar [32] and Canakci and Akinci [1] the same results for greenhouse crops were obtained. Energy consumption can be classified as direct-indirect and renewable-non-renewable forms. In this study direct energy consisted of human labor, diesel fuel, electricity, natural gas and water for irrigation while indirect energy sources included seeds, biocide, FYM, machinery and chemical fertilizer. Based on the results direct and indirect energy utilized for greenhouse cucumber production were calculated as 1580625.78 (95%) and 86538.98 (5%) MJ ha1, respectively. Renewable energy which included seeds, human labor, FYM and water for irrigation, was computed as 55777.07 MJ ha1 (3%). Non-renewable energy sources consumed in the studied area consisted of machinery, chemical fertilizer, electricity and diesel
Table 4 Energy inputs/output in various operations. Inputs/output
A. Input 1. Human labor 2. Chemical fertilizer a. N b. P2O5 c. K2O 3. Sulfur 4. FYM 5. Biocides 6. Machinery 7. Water for irrigation 8. Diesel fuel 9. Electricity 10. Natural gas 11. Seed Total input energy B. Output Cucumber
Unit
h kg kg kg kg kg kg kg m3 L kWh m3 kg
kg
Quantity per ha
Total energy equivalent (MJ ha1)
7190.6
14093.8
684 685 774.6 179.6 58269.2 87.5 6371.1 23726.9 186.5 37125.2 22030.5 1.1
45229.6 8516.6 8637 201.2 17480.8 5346.0 1126.8 24201.5 8916.5 442903.9 1090510.1 1.1 1667164.8
189807.7
151846.2
Percentage
679
fuel. Accordingly, the non-renewable energy was estimated as 1611387.69 MJ ha1 (97%). The results revealed that the share of non-renewable energy in greenhouse cucumber production is so high and this amount of non-renewable energy consumption which is a threat for the environment should be modified. 3.2. Identifying efficient and inefficient farmers Using the CCR model, overall technical efficiencies of all DMUs were evaluated. Additionally, PTE and SE were determined based on BCC model. On the basis of the CCR model, from the total of 26 greenhouses, which were considered for the present study, 7 DMUs (27%) had technical efficiency score of one; while, 19 greenhouses secured their efficiency score less than one and these 19 farmers were relatively considered inefficient. As can be seen in Fig. 1 only 8% of DMUs were in the range 0.9e1. BCC model results illustrated that 21 farmers (81%) out of 26 were efficient and their efficiency score was equal to unity. On the other hand the remaining 5 cucumber producers which gained efficiency scores less than one were comparatively inefficient. The average values of the PTE, TE and SE are presented in Table 5. The average values of PTE, TE and SE were calculated as 0.99, 0.68 and 0.68, respectively. The values of PTE less than one means that the target DMU is using more energy than is required [18]. The mean value of SEs (0.68) shows that there is ample scope for improving the operating practices to enhance the energy use efficiency. Based on the literature, the technical efficiency scores of 0.82 for greenhouse cucumber [11]; 0.82 for greenhouse tomato [33]; 0.68 for greenhouse rose producers [6] were reported. Also, some researchers employed DEA to assess the efficiencies of some open-field crop production and technical efficiency scores of 0.81 for asparagus production [34] and 0.88 for paddy production [35] were reported. The BCC model includes both increasing returns to scale (IRS) and decreasing returns to scale (DRS), while a non-increasing returns to scale (NIRS) model gives DRS. To determine whether a DMU has IRS or DRS an additional test is required. The values of TE for both BCC and NIRS were calculated and their calculated values were compared. The same values of TE for NIRS and BCC models show that the DMU has DRS, while the different values imply that the farm has IRS [19]. The last column of Table 5 indicates the results of RTS for some selected DMUs. These results revealed that 7 farmers (based on CCR model) had CRS while 19 DMUs were found to be operating at IRS. Therefore a proportionate increase in all inputs leads to more proportionate increase in outputs and for considerable changes in yield, technological changes in practices are required. The information on whether a farmer operates at IRS,
0.8 2.7 0.5 0.5 0.01 1 0.3 0.06 1.4 0.5 26.5 65.7 0.01 100
Fig. 1. Efficiency score distribution of greenhouse cucumber growers in Iran.
B. Khoshnevisan et al. / Energy 55 (2013) 676e682
2967.8
0.73
45229.6 8516.6 8637 201.2 17480.8 5346 1126.8 24201.5
33150.6 6125.2 6390 159.3 15631.1 3728.4 958 17785.2
12079.0 2391.4 2246.9 41.8 1849.6 1143.5 168.8 6416.3
2.96 0.59 0.55 0.01 0.45 0.28 0.04 1.57
8916.5 442903.9 1,090,510 1.1 1,667,164
6802.3 375,622 781,769 0.9 1,259,248
2114.3 67281.5 308741.1 0.2 407916.3
0.52 16.51 75.78 0.001
19.3 19.0 39.8 22.9 54.4 14.6 6.0 22.1 1.9 22.7 46.0 31.5 20.8 17.6 46.5 6.6 16.0 29.9 27.3 7.0 5.7 9.2 9.7 4.6 7.3 8.8 16.3 21.1 11.8 28.3 27.9 10.3 29.8 30.5 44.6 14.2 11.0 14.1 1.1 0.5 0.4 0.7 0.6 0.6 1.1 0.5 0.8 0.8 0.9 0.7 1.7 0.8 0.8 1.3 0.9 0.8 1.1 0.0 0.0 3.8 2.4 0.6 0.0 0.0 4.8 8.1 0.0 0.8 5.0 0.0 3.0 4.4 8.2 4.9 5.0 5.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 562.5 459.9 280.5 456.0 339.2 468.8 495.0 600.1 495.0 518.8 891.5 846.6 796.2 699.6 4545.6 680.6 845.7 660.5 766.9 401.2 260.1 323.9 367.0 249.4 385.7 413.1 324.2 441.4 335.8 214.7 238.6 365.4 274.4 310.2 701.2 298.3 454.7 586.2 5.0 3.3 2.3 4.0 3.8 5.5 5.6 4.6 6.0 4.6 10.0 8.8 6.4 11.6 8.0 6.8 9.3 8.8 10.8 60.3 44.2 49.0 49.1 29.6 58.1 51.0 52.1 86.8 48.8 53.3 79.1 43.7 83.0 76.4 91.9 77.2 64.4 64.1 9.8 8.3 6.4 8.2 6.1 7.0 11.8 7.9 10.2 10.0 12.0 11.3 10.9 12.0 10.7 11.0 13.9 9.0 11.4
Water Mach. Bio. Seeds NG Elec. Diesel Fert.
Optimum energy use (GJ/ha)
Labor Water
8.2 7.1 9.2 9.7 10.2 9.2 10.2 25.5 24.5 22.4 68.3 51.0 11.2 45.9 56.1 56.1 15.3 17.3 21.4 1.6 0.5 0.9 0.7 0.6 0.6 1.3 0.5 0.9 1.2 1.6 0.9 2.6 0.9 0.9 1.5 0.9 0.9 1.5 0.0 0.0 8.0 5.0 3.0 0.0 0.0 8.3 9.9 0.0 2.0 10.0 0.0 5.0 9.0 9.9 8.0 10.4 9.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 660.0 589.3 280.5 528.0 825.0 495.0 495.0 825.0 495.0 660.0 1815.0 1320.0 1072.5 880.0 8800.0 693.0 1056.0 924.0 1023.0 520.5 289.7 691.9 524.9 471.2 493.1 465.6 357.9 441.4 429.5 214.7 238.6 406.0 274.4 310.2 775.5 298.3 656.2 835.1
11125.9
5.7 3.3 5.7 4.8 4.8 7.2 6.2 6.2 7.6 5.7 21.5 14.3 7.6 16.7 12.0 7.6 12.0 14.3 16.7
14093.8
87.6 63.6 112.2 75.4 61.4 80.8 56.3 65.0 99.8 69.5 95.2 127.0 44.5 114.2 114.2 99.8 97.6 96.3 85.6
Target use (MJ/ha)
13.1 12.1 13.3 15.7 14.7 7.2 14.1 9.4 11.0 15.7 23.5 16.9 13.3 15.7 14.1 11.0 17.2 11.8 15.1
1. Human labor 2. Chemical fertilizer a. N b. P2O5 c. K2O 3. Sulfur 4. FYM 5. Biocides 6. Machinery 7. Water for irrigation 8. Diesel fuel 9. Electricity 10. Natural gas 11. Seed Total input energy (MJ/ha)
Energy saving (MJ/ha)
Contribution of input to savings (%)
Present use (MJ/ha)
0.59 0.60 0.85 0.98 0.97 0.52 0.36 0.52 0.36 0.62 0.62 0.50 0.50 0.40 0.51 0.28 0.48 0.52 0.48
Input
4 5 6 7 8 9 10 13 14 15 16 17 20 21 22 23 24 25 26
Table 6 Energy saving (MJ ha1) from different sources.
Mach.
CRS or DRS is particularly helpful in indicating the potential redistribution of resources between the farmers, and thus, enables them to achieve to the higher yield value [36]. Table 6 summarizes the present use, target use and energy saving for each energy source. The results revealed that the total input energy can be reduced to 1259248.4 MJ ha1, while the current yield will not change. On the other hand, it means that 407916.3 MJ ha1 can be saved if all DMUs improved their conditions from energy use point of view. The last column of Table 6 shows the share of the various sources in the total input energy savings. It is evident that the
Bio.
Constant Constant Constant Increasing Increasing Increasing Increasing Increasing Increasing Increasing Constant Constant Increasing Increasing Increasing Increasing Increasing Constant Constant Increasing Increasing Increasing Increasing Increasing Increasing Increasing
Seeds
1.00 1.00 1.00 0.59 0.60 0.85 0.98 0.97 0.52 0.36 1.00 1.00 0.52 0.36 0.62 0.62 0.50 1.00 1.00 0.50 0.40 0.55 0.30 0.51 0.52 0.49 0.68 1.00 0.26 0.26
NG
1.00 1.00 1.00 0.59 0.60 0.85 0.98 0.97 0.52 0.36 1.00 1.00 0.52 0.36 0.62 0.62 0.50 1.00 1.00 0.50 0.40 0.51 0.28 0.48 0.52 0.48 0.68 1.00 0.28 0.26
Elec.
1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.99 0.93 0.94 0.94 1.00 0.98 0.99 1.00 0.93 0.02
Return to scale
Diesel
1.00 1.00 1.00 0.59 0.60 0.85 0.98 0.97 0.52 0.36 1.00 1.00 0.52 0.36 0.62 0.62 0.50 1.00 1.00 0.50 0.40 0.51 0.28 0.48 0.52 0.48 0.68 1.00 0.28 0.26
Scale efficiency (CCR/BCC)
Fert.
NIRS
Actual energy use (GJ/ha)
BCC
Labor
CCR
TE
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Average Max Min SD
Technical efficiency
Table 7 The percentages in energy savings of inefficient growers (based on CCR model).
DMU
Savings (%)
Table 5 Technical and scale efficiencies and returns to scale.
DMU
680
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681
maximum contribution to the total energy savings is 75% from natural gas, followed by electricity (16.5%), chemical fertilizers (4%) and water (1.5%). Inefficient heaters used a large quantity of natural gas in greenhouse cucumber production, so improving the efficiency of heating systems is an appropriate way which can lead to improving energy use efficiency of greenhouse cucumber production in the studied area. Moreover the nylon which were mostly used to cover greenhouses, were not appropriate for winter season and they should be changed or thicker nylon should be employed in order to minimize the waste of heat which is squandered due to conduction. These changes can lead to a large reduction in the consumption of natural gas energy used by heaters. Low level technology of ventilation, lack of thermostat controllers in suitable places of greenhouses can be added as other reasons for high energy consumption in greenhouse cucumber production. Also, using more efficient electric pumps in irrigation systems can reduce the present electricity energy consumption (16.5%). Additionally, employing alternative sources of energy such as solar energy with respect to its potential in the studied region can reduce the consumption of fossil fuels and simultaneously decrease the negative environmental impacts. Applying chemical fertilizers especially nitrogen according to the plant needs and with respect to the soil analysis is highly recommended to enhance the energy saving in the surveyed region. Technical efficiency, actual energy use and optimum energy consumption from different energy sources for inefficient greenhouse cucumber producers are summarized in Table 7. This information reveals that which energy sources are consumed inefficiently and using this information each producer understands how much energy can be decreased with respect to the optimum energy consumption which is presented in the table, without any reduction in production level.
mentioned above the energy consumption can be reduced by improving some agricultural practices and technological changes in inefficient DMUs. Subsequently, the emission of GHG can be decreased in the studied region. The results showed that target GHG emission by decreasing of 22.5% can be reduced to the value of 34995.9 kg CO2eq ha1 (Table 8). Based on the results which can be seen in Fig. 2 the most reduction was observed in natural gas by 52% of total reduced emission and followed by electricity (34%) and machinery (4%). Applying more efficient heaters, utilizing more appropriate nylon (thicker or two layer nylon) during the winter for covering the structure of greenhouses, using renewable sources of energy for producing electricity like wind and solar energy sources and applying more efficient electric pumps can lead to have cultivation with less CO2 emission.
3.3. Reduction of GHG emission
4. Conclusions
GHG emission of efficient and inefficient DMUs was investigated to determine the effect of energy optimization on environmental consequences of greenhouse production in the studied area. The total GHG emission was calculated as 45177.3 kg CO2eq ha1. With lack of more similar studies on CO2 emission of greenhouse crop production the results are compared with other agricultural production systems. The total emission of 1038 kg CO2eq ha1 was reported by Pathak and Wassmann [37] for rice cultivation. Khakbazan, Mohr [38] expressed that the total emission of wheat cultivation was computed in a range of 410e1130 kg CO2eq ha1 and it depended on fertilizer rate, location and cultivation system. The results of the present study revealed that the most amount of emission was related to the electricity energy with amount of 22572.1 kg CO2eq ha1 and followed by natural gas. As it was
In this study the input and output energies in greenhouse cucumber production were investigated. Data were collected from 26 greenhouses in Esfahan province in Iran by a face to face questionnaire method. Data envelopment analysis was used to determine the efficiency and inefficiency of farmers. GHG emission of efficient and inefficient DMUs was compared based on present and target energy consumption. The total average input and output energies were calculated as 1667164.8 MJ ha1 and 151846.2 MJ ha1, respectively. Among the input energies natural gas and electricity had the largest share with 66% and 27%, respectively. According to the CCR results 27% of producers (7 greenhouses) were technically efficient while based on the BCC model 21 producers were identified efficient (81%). Comparison between present and target energy use showed that 407916.3 MJ ha1 can be saved if all inefficient DMUs use energy according to the recommendations of this study. The maximum contribution to the total energy saving is 76% belongs to the natural gas and it is followed by electricity (16.5%). Based on the results it was observed that the total CO2 emission was 45177.3 kg CO2eq ha1 and it can be reduced to the value of 34995.9 kg CO2eq ha1.
Table 8 Present and target GHG emission of inputs. Input
Present GHG emission (kg CO2eq ha1)
Target GHG emission (kg CO2eq ha1)
GHG reduction (kg CO2eq ha1)
Diesel Fertilizers Sulfur Machinery Electricity Natural gas Biocides Total emission
514.8 1180.8 1311.2 416.4 22572.1 18725.9 455.9 45177.3
392.8 864.7 1038.5 0.4 19143.2 13424.3 132.0 34995.9
122.1 316.2 272.7 416.0 3428.9 5301.6 323.9 10181.4
Fig. 2. Total potential reduction summary of GHG emission.
Acknowledgment The financial support provided by the University of Tehran under grant number 7313285/1/11, is duly acknowledged. Also, the first author would like to widely acknowledge his dear friend Mr. Mehdi Jamali Ghahdarijani for his participation in data collection and the participating greenhouse holders.
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Energy journal homepage: www.elsevier.com/locate/energy
Reduction of CO2 emission by improving energy use efficiency of greenhouse cucumber production using DEA approach Benyamin Khoshnevisan, Shahin Rafiee*, Mahmoud Omid, Hossein Mousazadeh Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 November 2012 Received in revised form 31 March 2013 Accepted 16 April 2013 Available online 21 May 2013
The main purpose of this study was to determine energy use efficiency in greenhouse cucumber production using a non-parametric production function. Energy use efficiency of greenhouse cucumber producers was studied and degrees of technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) were determined using data envelopment analysis (DEA). Additionally, wasteful uses of energy inputs were assessed and energy saving from different sources was computed. Furthermore, the effect of energy optimization on CO2 emission as one of the major greenhouse gases (GHG) was investigated and total amount of CO2 emission was calculated. Average of total input and output energies were calculated as 1667164.8 MJ ha1 and 151846.2 MJ ha1. Energy use pattern indicated that natural gas and electricity were the main energy inputs. Based on the results 24.46% (407916.3 MJ ha1) of overall energy resources can be saved if the performance of inefficient farms is enhanced. Additionally, the total CO2 emission was calculated as 45177.3 kg CO2eq ha1. Finally it was concluded that, by energy optimization the total energy consumption can be reduced to the value of 1,259,248 MJ ha1 and correspondingly the total CO2 emission is reduced to value of 34995.9 kg CO2eq ha1. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Greenhouse cucumber DEA GHG emission
1. Introduction Cucumber is one of the most popular greenhouse vegetable products in Iran. Greenhouse plant production is one of the most intensive parts of the agricultural production. It is intensive not only in the sense of yield (production) and in whole year production, but also in sense of energy consumption, investments and costs [1]. From 2002 to 2007, greenhouse areas of Iran had increased from 3380 ha to 6630 ha with an increasing rate of 96%. The shares of greenhouse crops production were as follows: vegetables 59.3%, flowers 39.81%, fruits 0.54% and mushroom 0.35% [2]. Energy is used in all facets of living and in all countries, and makes possible the existence of ecosystems, human civilizations and life itself [3]. Given the growing population’s food requirements, the finite supply of fossil fuels and the environmental impacts of using this non-renewable resources, the existing relationship between agriculture and energy must be dramatically altered [4,5]. Sustainable agriculture production is closely connected with effective energy use due to financial savings, fossil resources preservation and air pollution reduction [6]. Increasing in
* Corresponding author. Tel.: þ98 2632801011; fax: þ98 2632808138. E-mail addresses: [email protected] (B. Khoshnevisan), shahinrafiee@ ut.ac.ir (S. Rafiee). 0360-5442/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.energy.2013.04.021
use of energy inputs in agriculture led to numerous environmental problems like high consumption of non-renewable energy resources, loss of biodiversity, pollution of the aquatic environment by the nutrients nitrogen and phosphorus as well as by pesticides [7]. Global warming is one of the most important issues in recent century. Agricultural greenhouse gas (GHG) emissions account 10e 12% of all manmade GHG emissions [8]. Efficiency is defined as the ability to produce the outputs with a minimum resource level required [9]. Data envelopment analysis (DEA) is a well-established technique for measuring efficiencies of a set of comparable entities by some specific mathematical programming models. These entities, which often called decisionmaking units (DMUs), perform the same function by transforming multiple-inputs into multiple outputs. Given a sample of the DMUs, the purpose of the DEA is to establish the relative efficiency of each DMU within a sample [2]. A big advantage of DEA is that it does not need any prior assumptions on the underlying functional relationships between inputs and outputs [10]. However several studies have been conducted on energy consumption in greenhouse production [1,2,11,12], none of them investigated the amount of GHG emission. Accordingly, the objectives of this study were: (a) to determine the efficiency of greenhouse cucumber producers; (b) to identify target energy requirement and wasteful uses of energy and (c) to assess the effect of energy optimization on GHG emissions.
B. Khoshnevisan et al. / Energy 55 (2013) 676e682
GHG IRS n N Nh NIRS PTE SE S2h TE VRS z
Abbreviations BCC CCR CRS d D2 DEA DMU DRS EMS FYM
BankereeCharneseeCooper (DEA model) CharneseeCoopereeRhodes (DEA model) constant returns to scale precision ðx XÞ d2/z2 data envelopment analysis decision making unit decreasing returns to scale Efficiency Measurement Systems farmyard manure
2. Materials and methods 2.1. Selection of case study region and data collection Data for the present study were collected from greenhouse cucumber producers in Esfahan province (a province located in central Iran), during SeptembereDecember 2012 period. The sample size was calculated using Neyman technique [13]:
P ð Nh Sh Þ n ¼ 2 2 P N D þ Nh S2h
(1)
where n is the required sample size; N is the number of farmers in the target population; Nh is the number of the farmers in the h stratification; S2h is the variance of the h stratification; d permitted error ratio deviated from average of population ðx XÞ, z is the reliability coefficient (1.96 which represents 95% confidence); D2 ¼ d2/z2; the permissible error in the sample population was defined to be 5% within 95% confidence interval [14]. Accordingly, it was determined to be 26, so data were randomly collected from 26 greenhouse cucumber producers using face to face questioner method. The questionnaires were designed to gather information about utilized inputs for greenhouse cucumber production such as total working hours of labors, chemical fertilizers, diesel fuel, natural gas, total electricity consumption, etc. A brief summary of sample questionnaire is provided in Table 1. Input energy recourses encompassed human labor, chemical fertilizers, farmyard manure (FYM), biocides, machinery, diesel fuel, water for irrigation, electricity, natural gas and seeds while
Table 1 A brief summary of sample questionnaire. Questionnaire No: .... Date: 2012.... Total area of greenhouse (m2): .... Duration of the production: .... Number of cucumber plants per area: . Number of fixed labors: .... Number of daily labors: .... Average number of daily irrigation: . Amount of water consumed in each irrigation: . Machinery operation used: . Types of Machinery used: . Total diesel consumption (L): .... Total electricity consumption (kW h): .... Total natural gas consumption (kW h): .... Total weight of manure (kg): .... Types of chemical fertilizers: . Total weight of chemical fertilizers from each type (kg): .... Types of chemicals: .. Total weight of chemicals from each type (kg): .... Total weight of output cucumber (kg): ....
677
greenhouse gas increasing returns to scale required sample size number of farmers in target population number of the population in the h stratification non-increasing returns of scale pure technical efficiency scale efficiency variance of h stratification technical efficiency variable returns to scale reliability coefficient (1.96 in the case of 95% reliability)
cucumber was considered as output energy. In order to convert inputs and output into the energy equivalents, energy equivalent coefficients were applied (Table 2). Water for irrigation was pumped from local agricultural well by electric pumps. Energy for pumping water was calculated as Eq. (2) [15]:
DE ¼
ggHQ
(2)
εp εq
where ‘DE’ presents direct energy (J/ha), ‘g’ is acceleration due to gravity (ms2), ‘H’ is total dynamic head (m), ‘Q’ is volume of required water for one cultivating season (m3 ha-1), ‘g’ is density of water (kg m-3), ‘εp ’ is pump efficiency (70e90%) and ‘εq ’ is total power conversion efficiency (18e20%) [16]. 2.2. Data envelopment analysis DEA is a non-parametric technique whose domain of inquiry is a set of entities which receives multiple inputs and produce multiple outputs [11]. In this study, DEA methodology was applied to determine efficiency of greenhouse cucumber producers in order to calculate the amount of energy saving and greenhouse gas (GHG) emission reduction. Input variables were considered based on
Table 2 Energy coefficients of different inputs and output used. Inputs A. Inputs 1. Machinery Tractor and self-propelled Stationary equipment Implement and machinery 2. Human labor 3. Natural gas 4. Diesel fuel 5. Biocide Herbicide Fungicide Insecticide 6. Fertilizers Nitrogen (N) Phosphate (P2O5) Potassium (K2O) 7. Sulfur (S) 8. FYM 9. Water for irrigation 10. Electricity 11. Seeds B. Output 1. Cucumber a
Unit
Energy coefficients (MJ unit1)
Reference
kg yra kg yra kg yra h m3 L
9e10 8e10 6e8 1.96 49.5 47.8
[15] [15] [15] [11] [15] [15]
kg kg kg
85 295 115
[15] [15] [15]
kg kg kg kg kg m3 kWh kg
66.14 12.44 11.15 1.2 0.3 1.02 11.93 1
[2] [2] [2] [11] [12] [2] [5] [11]
kg
0.8
[11]
The economic life of machine (year).
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energy per hectare (MJ ha1) and cucumber yield (kg ha1) was chosen as output variable. The DEA model has been described in details by several authors [17,18], thus a detailed description is not provided by the present study. CCR (is an abbreviation for Charnes, Cooper and Rhodes who suggested this model) and BCC (is an abbreviation for Banker, Cooper and Rhodes who suggested this model) which are regarded as two kinds of DEA models, were employed in the present study. CCR model which was built on the assumption of constant returns to scale (CRS), was suggested by Charnes et al. [18]. Later, Banker et al. [17] introduced the BCC model based on variable returns to scale (VRS) and it was also called as local efficiency model. DEA models are broadly divided into two categories on the basis of orientation: input-oriented and output-oriented. Input-oriented models have the objective of minimizing inputs while maintaining the same level of outputs, whereas output-oriented models focus on increasing outputs with the same level of inputs [19]. In this study an input oriented DEA model was used to assess efficient and inefficient DMUs. Three different forms of efficiency are defined by DEA; technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE). TE which is defined as the DMU’s ability to achieve maximum output from given inputs, can be calculated by the ratio of sum of the weighted outputs to sum of the weighted inputs [11]:
Ps u y max hk ¼ Prm¼ 1 rk rk i ¼ 1 vik xik
(3)
subject to
Ps
Prm¼ 1
urk yrj
i ¼ 1 vik xij
1; j ¼ 1; :::; n
(4)
urk ; vik 0; r ¼ 1; :::; s; i ¼ 1; :::; m where ‘k’ is the DMU being evaluated in the set of j ¼ 1,2,.,n DMUs; ‘hk ’ the measure of efficiency of DMU ‘k’, the (DMU) in the set of j ¼ 1,2,.,n (DMU)s rated relative to the others; ‘yrk ’ the amount of output ‘r’ produced by DMU ‘k’ during the period of observation; ‘xik ’ the amount of resource input ‘i’ used by DMU ‘k’ during the period of observation; ‘yrj ’ the amount of service output ‘r’ produced by DMU ‘j’ during the period of observation; ‘xij ’ the amount of resource input ‘i’ used by DMU ‘j’ during the period of observation; ‘urk ’ the weight assigned to service output ‘r’ computed in the solution to the DEA model; ‘vik ’ the weight assigned to resource of input ‘i’ computed in the solution to the DEA model; ‘m’ the number of inputs used by the DMUs; and ‘s’ the number of outputs produced by the DMUs [20]. PTE is the TE of BCC model. On the other hand BCC model decomposes the technical efficiency into pure technical efficiency for management factors and scale efficiency for scale factors. Thus, pure technical efficiency is the technical efficiency that has the effect of scale efficiency removed [21]. The following BCC model provides efficiency measure of the ith DMUs:
Minq;l qi subject to
Y l Yi X l qi Xi N0 l ¼ 1 l0
and ‘X’ are the output and input matrices for all the N DMUs. ‘N0 ’ represents the unit vector and ‘l’ is an N 1 vector of constants. A DMU is considered technically efficient and will lies on the efficiency frontier if and only if the optimal value of ‘qi ’ is equal to one. Any value less than one indicates relatively inefficient firm lying below the frontier [22]. Inappropriate operation and inadequate scale of a farm are two main reasons for inefficiency of a DMU. CCR model includes both TE and SE while BCC model calculates only PTE of DMUs. In order to obtain SE in the present study, both CCR and BCC models were calculated and SE was defined as follows [23]:
SE ¼
qCCR qBCC
(6)
where ‘qCCR ’ and ‘qBCC ’ are the CCR and BCC scores of a DMU, respectively. SE ¼ 1 shows scale efficiency (or CRS) and SE < 1 indicates scale inefficiency. 2.3. CO2 emission Production, transportation, formulation, storage, distribution and application of agricultural inputs with agricultural machinery lead to combustion of fossil fuel and use of energy from alternate sources, which also emits CO2 and other greenhouse gases into the atmosphere [24]. Nitrous oxide (N2O) is regarded as another GHG which is emitted to the atmosphere due to different agricultural practices. For instance it is produced as an intermediate product in the dinitrification process (conversion of NO 3 into N2) by soil micro-organisms. It can also be produced as a by-product in the nitrification process in which NHþ 4 is converted into NO3 . N losses in the form of N2O are closely linked to the nitrogen cycle in agriculture; intensive agriculture with a high input of nitrogen fertilizer contributes to the increase in N2O-emissions [25]. Changes in concentration of GHG and aerosols will cause great changes in global and regional scale of air temperature, rainfall and other parameters which ultimately will lead to changes in soil moisture, floods and droughts in some regions of the world. However abovementioned emissions as well as methane are all considered as GHGs, and they would have environmental impacts such as climate change and global warming on the whole of human society, but the present study merely focused on CO2 emission and the abbreviation of GHG will be used from here onwards to mention CO2 emission. We emphasis that it does not mean other emissions are not important or they can be ignored in agricultural production systems. CO2 emission coefficients of agricultural inputs were used for quantifying the GHG emissions of greenhouse cucumber production in the studied region. Table 3 summarizes GHG emission equivalents. GHG emission was calculated by multiplying the input application rate (diesel fuel, chemical fertilizers, pesticides, electricity, Machinery, and natural gas) by its corresponding emission coefficient. The excel spreadsheet was used to analyze energy use pattern. Also, in order to assess the efficiency indices of greenhouses, the DEA software Efficiency Measurement Systems (EMS), Version 1.3, was applied. 3. Results and discussions
(5)
where ‘qi ’ provides the efficiency score for ith DMU. ‘Yi ’ and ‘Xi ’ represent the output and input vectors of ith DMU, respectively. ‘Y’
3.1. Energy use pattern The quantity of input and output energies in cucumber production and their energy equivalents are summarized in Table 4. The average cucumber yield in the studied region was 189,808 kg ha1, so the total output energy was calculated as
B. Khoshnevisan et al. / Energy 55 (2013) 676e682 Table 3 Greenhouse gas (GHG) emission coefficients of agricultural inputs. Inputs
Unit
GHG coefficienta
Reference
Machinery Diesel fuel Chemical fertilizers (a) Nitrogen (N) (b) Phosphate (P2O5) (c) Potassium (K2O) Sulfur Biocide (a) Herbicide (b) Insecticide (c) Fungicide Natural gas Electricityb
MJ L
0.071 2.76
[28] [26]
kg kg kg kg
1.3 0.2 0.2 7.3
[24] [24] [24] [24]
kg kg kg m3 kWh
6.3 5.1 3.9 0.85 0.608
[24] [24] [24] [24] [27]
a b
kg CO2eq. unit1. The power plant burns LNG.
151846.2 MJ ha1. As can be seen in Table 4 the total energy consumption, which was applied for cucumber production, was computed as 1667164.8 MJ ha1. Natural gas with a share of 65.7% was the most energy consumer and it was followed by electricity (26.5%). Based on the evaluations the majority of natural gas and electricity were respectively used for heaters and ventilation along with drip irrigation systems. Djevic et al. [29] on their study on greenhouse energy consumption and energy efficiency reported that 92% of total energy in greenhouse lettuce production belonged to diesel fuel. They mentioned that diesel fuel was mostly used for heating systems. In other studies which was conducted by Hatirli et al. [30], Esengun et al. [31], Çetin and Vardar [32] and Canakci and Akinci [1] the same results for greenhouse crops were obtained. Energy consumption can be classified as direct-indirect and renewable-non-renewable forms. In this study direct energy consisted of human labor, diesel fuel, electricity, natural gas and water for irrigation while indirect energy sources included seeds, biocide, FYM, machinery and chemical fertilizer. Based on the results direct and indirect energy utilized for greenhouse cucumber production were calculated as 1580625.78 (95%) and 86538.98 (5%) MJ ha1, respectively. Renewable energy which included seeds, human labor, FYM and water for irrigation, was computed as 55777.07 MJ ha1 (3%). Non-renewable energy sources consumed in the studied area consisted of machinery, chemical fertilizer, electricity and diesel
Table 4 Energy inputs/output in various operations. Inputs/output
A. Input 1. Human labor 2. Chemical fertilizer a. N b. P2O5 c. K2O 3. Sulfur 4. FYM 5. Biocides 6. Machinery 7. Water for irrigation 8. Diesel fuel 9. Electricity 10. Natural gas 11. Seed Total input energy B. Output Cucumber
Unit
h kg kg kg kg kg kg kg m3 L kWh m3 kg
kg
Quantity per ha
Total energy equivalent (MJ ha1)
7190.6
14093.8
684 685 774.6 179.6 58269.2 87.5 6371.1 23726.9 186.5 37125.2 22030.5 1.1
45229.6 8516.6 8637 201.2 17480.8 5346.0 1126.8 24201.5 8916.5 442903.9 1090510.1 1.1 1667164.8
189807.7
151846.2
Percentage
679
fuel. Accordingly, the non-renewable energy was estimated as 1611387.69 MJ ha1 (97%). The results revealed that the share of non-renewable energy in greenhouse cucumber production is so high and this amount of non-renewable energy consumption which is a threat for the environment should be modified. 3.2. Identifying efficient and inefficient farmers Using the CCR model, overall technical efficiencies of all DMUs were evaluated. Additionally, PTE and SE were determined based on BCC model. On the basis of the CCR model, from the total of 26 greenhouses, which were considered for the present study, 7 DMUs (27%) had technical efficiency score of one; while, 19 greenhouses secured their efficiency score less than one and these 19 farmers were relatively considered inefficient. As can be seen in Fig. 1 only 8% of DMUs were in the range 0.9e1. BCC model results illustrated that 21 farmers (81%) out of 26 were efficient and their efficiency score was equal to unity. On the other hand the remaining 5 cucumber producers which gained efficiency scores less than one were comparatively inefficient. The average values of the PTE, TE and SE are presented in Table 5. The average values of PTE, TE and SE were calculated as 0.99, 0.68 and 0.68, respectively. The values of PTE less than one means that the target DMU is using more energy than is required [18]. The mean value of SEs (0.68) shows that there is ample scope for improving the operating practices to enhance the energy use efficiency. Based on the literature, the technical efficiency scores of 0.82 for greenhouse cucumber [11]; 0.82 for greenhouse tomato [33]; 0.68 for greenhouse rose producers [6] were reported. Also, some researchers employed DEA to assess the efficiencies of some open-field crop production and technical efficiency scores of 0.81 for asparagus production [34] and 0.88 for paddy production [35] were reported. The BCC model includes both increasing returns to scale (IRS) and decreasing returns to scale (DRS), while a non-increasing returns to scale (NIRS) model gives DRS. To determine whether a DMU has IRS or DRS an additional test is required. The values of TE for both BCC and NIRS were calculated and their calculated values were compared. The same values of TE for NIRS and BCC models show that the DMU has DRS, while the different values imply that the farm has IRS [19]. The last column of Table 5 indicates the results of RTS for some selected DMUs. These results revealed that 7 farmers (based on CCR model) had CRS while 19 DMUs were found to be operating at IRS. Therefore a proportionate increase in all inputs leads to more proportionate increase in outputs and for considerable changes in yield, technological changes in practices are required. The information on whether a farmer operates at IRS,
0.8 2.7 0.5 0.5 0.01 1 0.3 0.06 1.4 0.5 26.5 65.7 0.01 100
Fig. 1. Efficiency score distribution of greenhouse cucumber growers in Iran.
B. Khoshnevisan et al. / Energy 55 (2013) 676e682
2967.8
0.73
45229.6 8516.6 8637 201.2 17480.8 5346 1126.8 24201.5
33150.6 6125.2 6390 159.3 15631.1 3728.4 958 17785.2
12079.0 2391.4 2246.9 41.8 1849.6 1143.5 168.8 6416.3
2.96 0.59 0.55 0.01 0.45 0.28 0.04 1.57
8916.5 442903.9 1,090,510 1.1 1,667,164
6802.3 375,622 781,769 0.9 1,259,248
2114.3 67281.5 308741.1 0.2 407916.3
0.52 16.51 75.78 0.001
19.3 19.0 39.8 22.9 54.4 14.6 6.0 22.1 1.9 22.7 46.0 31.5 20.8 17.6 46.5 6.6 16.0 29.9 27.3 7.0 5.7 9.2 9.7 4.6 7.3 8.8 16.3 21.1 11.8 28.3 27.9 10.3 29.8 30.5 44.6 14.2 11.0 14.1 1.1 0.5 0.4 0.7 0.6 0.6 1.1 0.5 0.8 0.8 0.9 0.7 1.7 0.8 0.8 1.3 0.9 0.8 1.1 0.0 0.0 3.8 2.4 0.6 0.0 0.0 4.8 8.1 0.0 0.8 5.0 0.0 3.0 4.4 8.2 4.9 5.0 5.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 562.5 459.9 280.5 456.0 339.2 468.8 495.0 600.1 495.0 518.8 891.5 846.6 796.2 699.6 4545.6 680.6 845.7 660.5 766.9 401.2 260.1 323.9 367.0 249.4 385.7 413.1 324.2 441.4 335.8 214.7 238.6 365.4 274.4 310.2 701.2 298.3 454.7 586.2 5.0 3.3 2.3 4.0 3.8 5.5 5.6 4.6 6.0 4.6 10.0 8.8 6.4 11.6 8.0 6.8 9.3 8.8 10.8 60.3 44.2 49.0 49.1 29.6 58.1 51.0 52.1 86.8 48.8 53.3 79.1 43.7 83.0 76.4 91.9 77.2 64.4 64.1 9.8 8.3 6.4 8.2 6.1 7.0 11.8 7.9 10.2 10.0 12.0 11.3 10.9 12.0 10.7 11.0 13.9 9.0 11.4
Water Mach. Bio. Seeds NG Elec. Diesel Fert.
Optimum energy use (GJ/ha)
Labor Water
8.2 7.1 9.2 9.7 10.2 9.2 10.2 25.5 24.5 22.4 68.3 51.0 11.2 45.9 56.1 56.1 15.3 17.3 21.4 1.6 0.5 0.9 0.7 0.6 0.6 1.3 0.5 0.9 1.2 1.6 0.9 2.6 0.9 0.9 1.5 0.9 0.9 1.5 0.0 0.0 8.0 5.0 3.0 0.0 0.0 8.3 9.9 0.0 2.0 10.0 0.0 5.0 9.0 9.9 8.0 10.4 9.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 660.0 589.3 280.5 528.0 825.0 495.0 495.0 825.0 495.0 660.0 1815.0 1320.0 1072.5 880.0 8800.0 693.0 1056.0 924.0 1023.0 520.5 289.7 691.9 524.9 471.2 493.1 465.6 357.9 441.4 429.5 214.7 238.6 406.0 274.4 310.2 775.5 298.3 656.2 835.1
11125.9
5.7 3.3 5.7 4.8 4.8 7.2 6.2 6.2 7.6 5.7 21.5 14.3 7.6 16.7 12.0 7.6 12.0 14.3 16.7
14093.8
87.6 63.6 112.2 75.4 61.4 80.8 56.3 65.0 99.8 69.5 95.2 127.0 44.5 114.2 114.2 99.8 97.6 96.3 85.6
Target use (MJ/ha)
13.1 12.1 13.3 15.7 14.7 7.2 14.1 9.4 11.0 15.7 23.5 16.9 13.3 15.7 14.1 11.0 17.2 11.8 15.1
1. Human labor 2. Chemical fertilizer a. N b. P2O5 c. K2O 3. Sulfur 4. FYM 5. Biocides 6. Machinery 7. Water for irrigation 8. Diesel fuel 9. Electricity 10. Natural gas 11. Seed Total input energy (MJ/ha)
Energy saving (MJ/ha)
Contribution of input to savings (%)
Present use (MJ/ha)
0.59 0.60 0.85 0.98 0.97 0.52 0.36 0.52 0.36 0.62 0.62 0.50 0.50 0.40 0.51 0.28 0.48 0.52 0.48
Input
4 5 6 7 8 9 10 13 14 15 16 17 20 21 22 23 24 25 26
Table 6 Energy saving (MJ ha1) from different sources.
Mach.
CRS or DRS is particularly helpful in indicating the potential redistribution of resources between the farmers, and thus, enables them to achieve to the higher yield value [36]. Table 6 summarizes the present use, target use and energy saving for each energy source. The results revealed that the total input energy can be reduced to 1259248.4 MJ ha1, while the current yield will not change. On the other hand, it means that 407916.3 MJ ha1 can be saved if all DMUs improved their conditions from energy use point of view. The last column of Table 6 shows the share of the various sources in the total input energy savings. It is evident that the
Bio.
Constant Constant Constant Increasing Increasing Increasing Increasing Increasing Increasing Increasing Constant Constant Increasing Increasing Increasing Increasing Increasing Constant Constant Increasing Increasing Increasing Increasing Increasing Increasing Increasing
Seeds
1.00 1.00 1.00 0.59 0.60 0.85 0.98 0.97 0.52 0.36 1.00 1.00 0.52 0.36 0.62 0.62 0.50 1.00 1.00 0.50 0.40 0.55 0.30 0.51 0.52 0.49 0.68 1.00 0.26 0.26
NG
1.00 1.00 1.00 0.59 0.60 0.85 0.98 0.97 0.52 0.36 1.00 1.00 0.52 0.36 0.62 0.62 0.50 1.00 1.00 0.50 0.40 0.51 0.28 0.48 0.52 0.48 0.68 1.00 0.28 0.26
Elec.
1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.99 0.93 0.94 0.94 1.00 0.98 0.99 1.00 0.93 0.02
Return to scale
Diesel
1.00 1.00 1.00 0.59 0.60 0.85 0.98 0.97 0.52 0.36 1.00 1.00 0.52 0.36 0.62 0.62 0.50 1.00 1.00 0.50 0.40 0.51 0.28 0.48 0.52 0.48 0.68 1.00 0.28 0.26
Scale efficiency (CCR/BCC)
Fert.
NIRS
Actual energy use (GJ/ha)
BCC
Labor
CCR
TE
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Average Max Min SD
Technical efficiency
Table 7 The percentages in energy savings of inefficient growers (based on CCR model).
DMU
Savings (%)
Table 5 Technical and scale efficiencies and returns to scale.
DMU
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681
maximum contribution to the total energy savings is 75% from natural gas, followed by electricity (16.5%), chemical fertilizers (4%) and water (1.5%). Inefficient heaters used a large quantity of natural gas in greenhouse cucumber production, so improving the efficiency of heating systems is an appropriate way which can lead to improving energy use efficiency of greenhouse cucumber production in the studied area. Moreover the nylon which were mostly used to cover greenhouses, were not appropriate for winter season and they should be changed or thicker nylon should be employed in order to minimize the waste of heat which is squandered due to conduction. These changes can lead to a large reduction in the consumption of natural gas energy used by heaters. Low level technology of ventilation, lack of thermostat controllers in suitable places of greenhouses can be added as other reasons for high energy consumption in greenhouse cucumber production. Also, using more efficient electric pumps in irrigation systems can reduce the present electricity energy consumption (16.5%). Additionally, employing alternative sources of energy such as solar energy with respect to its potential in the studied region can reduce the consumption of fossil fuels and simultaneously decrease the negative environmental impacts. Applying chemical fertilizers especially nitrogen according to the plant needs and with respect to the soil analysis is highly recommended to enhance the energy saving in the surveyed region. Technical efficiency, actual energy use and optimum energy consumption from different energy sources for inefficient greenhouse cucumber producers are summarized in Table 7. This information reveals that which energy sources are consumed inefficiently and using this information each producer understands how much energy can be decreased with respect to the optimum energy consumption which is presented in the table, without any reduction in production level.
mentioned above the energy consumption can be reduced by improving some agricultural practices and technological changes in inefficient DMUs. Subsequently, the emission of GHG can be decreased in the studied region. The results showed that target GHG emission by decreasing of 22.5% can be reduced to the value of 34995.9 kg CO2eq ha1 (Table 8). Based on the results which can be seen in Fig. 2 the most reduction was observed in natural gas by 52% of total reduced emission and followed by electricity (34%) and machinery (4%). Applying more efficient heaters, utilizing more appropriate nylon (thicker or two layer nylon) during the winter for covering the structure of greenhouses, using renewable sources of energy for producing electricity like wind and solar energy sources and applying more efficient electric pumps can lead to have cultivation with less CO2 emission.
3.3. Reduction of GHG emission
4. Conclusions
GHG emission of efficient and inefficient DMUs was investigated to determine the effect of energy optimization on environmental consequences of greenhouse production in the studied area. The total GHG emission was calculated as 45177.3 kg CO2eq ha1. With lack of more similar studies on CO2 emission of greenhouse crop production the results are compared with other agricultural production systems. The total emission of 1038 kg CO2eq ha1 was reported by Pathak and Wassmann [37] for rice cultivation. Khakbazan, Mohr [38] expressed that the total emission of wheat cultivation was computed in a range of 410e1130 kg CO2eq ha1 and it depended on fertilizer rate, location and cultivation system. The results of the present study revealed that the most amount of emission was related to the electricity energy with amount of 22572.1 kg CO2eq ha1 and followed by natural gas. As it was
In this study the input and output energies in greenhouse cucumber production were investigated. Data were collected from 26 greenhouses in Esfahan province in Iran by a face to face questionnaire method. Data envelopment analysis was used to determine the efficiency and inefficiency of farmers. GHG emission of efficient and inefficient DMUs was compared based on present and target energy consumption. The total average input and output energies were calculated as 1667164.8 MJ ha1 and 151846.2 MJ ha1, respectively. Among the input energies natural gas and electricity had the largest share with 66% and 27%, respectively. According to the CCR results 27% of producers (7 greenhouses) were technically efficient while based on the BCC model 21 producers were identified efficient (81%). Comparison between present and target energy use showed that 407916.3 MJ ha1 can be saved if all inefficient DMUs use energy according to the recommendations of this study. The maximum contribution to the total energy saving is 76% belongs to the natural gas and it is followed by electricity (16.5%). Based on the results it was observed that the total CO2 emission was 45177.3 kg CO2eq ha1 and it can be reduced to the value of 34995.9 kg CO2eq ha1.
Table 8 Present and target GHG emission of inputs. Input
Present GHG emission (kg CO2eq ha1)
Target GHG emission (kg CO2eq ha1)
GHG reduction (kg CO2eq ha1)
Diesel Fertilizers Sulfur Machinery Electricity Natural gas Biocides Total emission
514.8 1180.8 1311.2 416.4 22572.1 18725.9 455.9 45177.3
392.8 864.7 1038.5 0.4 19143.2 13424.3 132.0 34995.9
122.1 316.2 272.7 416.0 3428.9 5301.6 323.9 10181.4
Fig. 2. Total potential reduction summary of GHG emission.
Acknowledgment The financial support provided by the University of Tehran under grant number 7313285/1/11, is duly acknowledged. Also, the first author would like to widely acknowledge his dear friend Mr. Mehdi Jamali Ghahdarijani for his participation in data collection and the participating greenhouse holders.
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