Energy 36 (2011) 6714e6719
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Energy use efficiency in greenhouse tomato production in Iran Reza Pahlavan*, Mahmoud Omid, Asadollah Akram Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, School of Agriculture & Natural Resources, 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 23 July 2011 Received in revised form 21 October 2011 Accepted 23 October 2011 Available online 10 November 2011
Efficient use of energy in agriculture is one of the conditions for sustainable production. In the present study energy use pattern for tomato production in Iran was investigated and a non-parametric data envelopment analysis (DEA) technique was applied to analyze the technical and scale efficiencies of farmers with respect to energy use for crop production. The energy use pattern indicated that diesel, electricity and chemical fertilizers are the major energy consuming inputs for tomato production in the region. Moreover, the results of DEA application revealed that of the average pure technical, technical and scale efficiencies of farmers were 0.94, 0.82 and 0.86, respectively. Also the results revealed that by adopting the recommendations based on the present study, on an average, about 25.15% of the total input energy could be saved without reducing the tomato yield. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Data envelopment analysis Optimization Energy productivity Technical efficiency Yield
1. Introduction Tomato is one of the major greenhouse vegetables products worldwide. In Iran, tomato production was 4.83 million tonnes in 2008. From 2002 to 2008, greenhouse areas of Iran increased from 3380 ha to 7000 ha [1]. The share of greenhouse production was as follows: vegetables 59.3%, flowers 39.81%, fruits 0.54% and mushroom 0.35% [2]. Agriculture itself is an energy user and energy supplier in the form of bio-energy [3]. Energy is used in every form of inputs such as human, fertilizers, pesticides, machinery and electricity, to perform various operations for crop production. Energy use in agriculture has developed in response to increasing populations, limited supply of arable land and desire for an increasing standard of living. In all societies, these factors have encouraged an increase in energy inputs to maximize yields, minimize labor-intensive practices, or both [4]. Effective energy use in agriculture is one of the conditions for sustainable agricultural production, since it provides financial savings, fossil resources preservation and air pollution reduction [5]. There are several studies on the energy use pattern and benchmarking of crops production. Energy use for greenhouse vegetables (tomato, cucumber, eggplant and pepper) production were investigated [6e8]. Hatirli et al. and Mohammadi and Omid investigated energy inputs and crop yield relationship to develop * Corresponding author. Tel.: þ98 261 2801038, þ98 936 6466720(mobile); fax: þ98 261 2808138. E-mail address:
[email protected] (R. Pahlavan). 0360-5442/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2011.10.038
and estimate an econometric model for greenhouse tomato and cucumber productions, respectively [9,10]. Also Omid et al. investigated energy use pattern and benchmarking of greenhouse cucumber producers in Tehran city of Iran using data envelopment analysis (DEA) [8]. This paper presents an application of DEA to discriminate efficient tomato producers from inefficient ones, recognize wasteful uses of energy inputs by inefficient farmers and suggest necessary quantities of different inputs to be used by each inefficient farmer from every energy source.
2. Material and methods 2.1. Data collection and processing Initial data used in the DEA analysis comprised information on greenhouse tomato producers in the region. They were collected from 31 producers by using a face to face questionnaire method. Before carrying detailed analysis, we attempted to secure homogeneity by selecting only greenhouses in a specific area. The main drawbacks of deterministic frontier modelsdboth non-parametric and parametric modelsdis that they are very sensitive to outliers and extreme values, and that noisy data are not allowed. Simar [11] pointed out the need for identifying and eliminating outliers when using deterministic models. Outliers are observations that "do not fit in with the pattern of the remaining data points and are not at all typical of the rest of the data". By applying sample means, standard deviations, maximum and
R. Pahlavan et al. / Energy 36 (2011) 6714e6719 Table 1 Energy coefficients of different inputs and outputs used.
Nomenclature
Input & output
DEA GJ DMU FAO TE PTE CCR BCC CRS VRS SE IRS DRS RTS SD
data envelopment analysis Giga joule decision making unit food and agriculture organization technical efficiency pure technical efficiency Banker, Charnes & Cooper Charnes, Cooper & Rhodes constant returns to scale variable returns to scale scale efficiency increasing returns to scale decreasing returns to scale return to scale standard deviation
A. Input 1. Human labour 2. Diesel fuel 3. Electricity 4. Fertilizers (a) Nitrogen (b) P2O5 (c) K2O (d) Micro 5. Chemicals B. Output 1. Tomato
minimum values and plots of all the variables four outliers were identified. The remaining 27 DMUs were used in developing inputoriented DEA models. The Esfahan province is located within 30e42 and 34e30 north latitude and 49e36 and 55e32 east longitude. The average size of the studied greenhouses has been found to be 0.2 ha. The commercial greenhouses surveyed here were made from galvanized steel. Steel’s greatest value in greenhouse construction is its strength. Also they are long-lasting, low cost, and require less framework (thus less shadowing) than any other framing material thanks to steel’s natural strength. The top of the greenhouses was covered with UV stabilized plastic sheet 200 m thickness. Data were collected from the farmers in the production period of 2009-2010. The size of each sample was determined using the Neyman technique [12]. The inputs used in tomato production were in the form of human labour, chemical fertilizer, chemicals, electricity and machinery; while the tomato yield was the single output. The energy equivalents of these inputs and output were calculated using the energy equivalent coefficients as presented in Table 1. The previous study was used to determine the energy equivalents’ coefficients [4,9,13,14,15]. The total input equivalent can be calculated by adding up the energy equivalences of all inputs in Giga Joule (GJ). Based on the energy equivalents of the inputs and output, the indicators of energy use including energy ratio (energy use efficiency), energy productivity and net energy were calculated as follow [12, 16]:
. Energy Use Efficiency ¼ Energy Output GJ ha1 Energy Input GJ ha1 . Energy Productivity ¼ tomato output ton ha1 Energy Input GJ ha1 Net Energy ¼ Energy Output GJ ha1 Energy Input GJ ha1
6715
(1)
2.2. Data envelopment analysis Farrell (1957) proposed a new approach to efficiency measurement and the production frontier at the micro level [17]. He divided
Reference
hr L MW ton
1.96 * 103 5.63 * 102 11.93
[4] [13] [4]
ton
66.14 12.44 11.15 120 120
[14] [14] [14] [15] [13]
ton
0.8
[9]
2.3. Technical efficiency Technical efficiency (TE) can be calculated by the ratio of sum of weighted outputs to sum of weighted inputs [20]:
PP
up yp;j
n q¼1 q xq;j
(3)
Energy coefficient, (GJ unit1)
economic efficiency into resource use (technical) and allocative (price) components. He proposed a piecewise linear envelopment of data as the conservative estimate of the production frontier which envelopes observation points as closely as possible which was estimated by solving a system of linear equations. According to Farrell (1957), technical efficiency (TE) represents the ability of a decision-making unit (DMU) to produce maximum output given a set of inputs and technology (output-oriented) or, alternatively, to achieve maximum feasible reductions in input quantities given input prices and output (input-oriented) [17]. The choice between input and output-oriented measures is a matter of concern, and selection may vary according to the unique characteristics of the set of DMUs under study. In this study, input-oriented DEA seems more appropriate, given that it is more reasonable to argue that in the agricultural sector a farmer has more control over inputs rather than output levels. DEA allows for the measurement of relative efficiency for a group of DMUs that use various inputs to produce outputs. There are two kinds of DEA models included: CCR and BCC models. The CCR model [18] is built on the assumption of constant returns to scale (CRS) of activities, but the BCC model [19] is built on the assumption of variable returns to scale (VRS) of activities. The DEA models have been described in details by several authors [18,19], thus a detailed description is not provided here. The dual (envelopment) form of the DEA linear programming problem is simpler to solve than the ratio and multiplier forms due to fewer constraints. Efficiency by DEA is defined in three different forms: overall technical efficiency (TECCR), pure technical efficiency (TEBCC) and scale efficiency (SE).
q ¼ Pp¼1 Q
(2)
Units
(4)
where ‘x’ and ‘y’ are inputs and outputs, ‘v’ and ‘u’ are input and output weights, respectively, ‘q’ is the number of inputs (q ¼ 1,2, . , Q); ‘p’ is the number of outputs (p ¼ 1,2, ., P), and ‘j’ represents jth DMU. The first development of DEA was by Charnes, Cooper and Rhodes (CCR) to measure the efficiency of individual DMUs. Mathematically, the CCR DEA model for measuring the inputoriented technical efficiency of a DMU is written as follows [21]:
max q s:t: : Y l Yo qXo X l 0; qfree; l 0:
(5)
6716
R. Pahlavan et al. / Energy 36 (2011) 6714e6719
where q is the technical efficiency of DMU to be evaluated DMUo and l represents the intensity of the efficient DMUs in projecting inefficient DMUs onto the efficient frontier, also called the convexity constant. The optimal efficiency of a DMU, q*, will be less than or equal to 1. DMUs with q*
