Ashkan Nabavi-Pelesaraei and Sama Amid/ Elixir Energy & Environment 69 (2014) 23696-23701 23696 Introduction Energy in agriculture is important in terms of crop production and agro processing for value adding. Human, animal and machinery is extensively used for crop production in agriculture. Energy use depends on mechanization level, the quantity of active agricultural worker and cultivable land. Efficient use and study impacts of these energies on crop production help to achieve increased production and productivity and help the economy, profitability and competitiveness of agricultural sustainability of rural communities (Singh et al., 2002). Eggplant (Solanum melongena L.), also known as Aubergine, Brinjal or Guinea squash is one of the nontuberous species of the nightshade family Solanaceae (Kantharajah and Golegaonkar, 2004). One of the most important issues in recent century is the global warming, and greenhouse gas (GHG) emission is the main factor of this happening. There is scientific consensus that global warming will pose one of the major environmental challenges in the future. While the bulk of the so called GHGs originates from fossil fuel consumption (Pathak and Wassmann, 2007). Production, transportation, storage, distribution of the inputs and application with machinery lead to combustion of fossil fuel and use of energy from alternate sources, which also emits greenhouse gases into the atmosphere. Thus, an understanding of the emissions expressed in kilograms of carbon equivalent (kg CE) for different tillage operations, fertilizers and pesticides use, supplemental irrigation practices and harvesting is essential to identifying C-efficient alternatives such as biofuels and renewable energy sources for seedbed preparation, soil fertility management, pest control and other farm operations (Pishgar- Komleh et al., 2012). Data envelopment analysis (DEA) is known as a mathematical procedure that uses a linear programming technique to assess the efficiencies of decision- making units (DMU). A non-parametric piecewise frontier, which owns the optimal efficiency over the datasets, is composed of DMUs and is constructed by DEA for a comparative efficiency measurement. Those DMUs that are located at the efficiency frontier are efficient DMUs. These DMUs own the best efficiency among all DMUs and have their maximum outputs generated among all DMUs by taking the minimum level of inputs (Lee and Lee, 2009). In recent years, there have been numerous applications of DEA to measure the energy efficiency and GHG emissions reduction in agricultural production systems. Khoshnevisan et al. (2013a) applied the DEA technique to analyze the efficiency and CO 2 emissions reduction of input use in cucumber production. In another study by Khoshnevisan et al. (2013b), the DEA technique was subjected to the data on energy use and GHG reduction for wheat production from 260 farmers in Isfahan province, Iran. Nabavi-Pelesaraei et al. (2014) investigated the optimization of energy inputs for orange production and comparison between efficient and inefficient producers from the GHG emissions point of view using DEA approach. Based on the literature, there has been no study on application of DEA for assessing the impact of improving energy use efficiency on GHG emission. Accordingly, the objectives of this study were: (a) to determine the efficiencies of eggplant farmers; (b) to identify target energy requirement and wasteful uses of energy and (c) to assess the effect of energy optimization on GHG emissions. Materials and methods Sampling design Guilan province is located in the North of Iran, within 36 ◦ 34 ׳ and 38 ◦ 27 ׳ north latitude and 48 ◦ 53 ׳ and 50 ◦ 34 ׳ east longitude. Reduction of greenhouse gas emissions of eggplant production by energy optimization using DEA approach Ashkan Nabavi-Pelesaraei 1 and Sama Amid 2 1 Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran. 2 Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Mohaghegh Ardabili, Ardabil, Iran. ABSTRACT The main objective of this study was the determination of optimum energy requirement and potential of greenhouse gas (GHG) reduction of eggplant production by non-parametric method of data envelopment analysis (DEA) in Guilan province, Iran. The BCC and CCR models of DEA were applied to energy optimization. The results showed the 29, 39 and 30 units had the score equal one in technical, pure technical and scale efficiency, respectively. Also, the average of technical, pure technical and scale efficiency was calculated as 0.771, 0.956 and 0.806, respectively. The total saving energy was about 2830 MJ ha -1 and diesel fuel had the highest share of total energy saving with 48.49%. The energy use efficiency of target units was more than present units about 26%. The GHG emissions analysis indicated that total GHG emissions of present and optimum units were about 515 and 401 kgCO 2eq. ha -1 , respectively. So, the total potential of GHG emissions reduction was found about 115 kgCO 2eq. ha -1 . Moreover, the diesel fuel had the highest percentage of GHG emissions reduction by 58.58%; followed by machinery with 17.24% and nitrogen with 15.12%. Generally, it can be said the DEA approach was appropriate methods for energy optimization and reduction of GHG emissions in eggplant production. © 2014 Elixir All rights reserved. Elixir Energy & Environment 69 (2014) 23696-23701 Energy and Environment Available online at www.elixirpublishers.com (Elixir International Journal) ARTICLE INFO Article history: Received: 18 March 2014; Received in revised form: 20 April 2014; Accepted: 28 April 2014; Keywords Data envelopment analysis, Eggplant, Energy use efficiency, GHG emissions, Optimization. Tele: E-mail addresses: ashkan.nabavi@yahoo.com © 2014 Elixir All rights reserved