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Optimization of energy, economic, and environmental indices in sunflower cultivation: A comparative analysis
Author(s) -
Aghili Nategh Nahid,
Banaeian Narges,
Gholamshahi Alireza,
Nosrati Mohammad
Publication year - 2020
Publication title -
environmental progress and sustainable energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 66
eISSN - 1944-7450
pISSN - 1944-7442
DOI - 10.1002/ep.13505
Subject(s) - greenhouse gas , data envelopment analysis , diesel fuel , production (economics) , agricultural engineering , environmental science , energy consumption , agriculture , agricultural science , sunflower , environmental engineering , mathematics , agricultural economics , economics , engineering , statistics , waste management , biology , ecology , macroeconomics , electrical engineering , combinatorics
Abstract This study examined the input energy, economic indices, and Greenhouse Gas (GHG) emissions in sunflower farm enterprises of Kermanshah province of Iran. Different mechanization production systems involving traditional, semi‐mechanized, and mechanized ones were statistically compared. Results revealed that mechanized farms consumed more total inputs energy, while possessed significantly higher yield and better economic indices. In which, the human labor, diesel fuel, and fertilizer were the most predominant inputs in GHG emissions. In particular, traditional, semi‐mechanized and mechanized farms emitted 358, 386, and 438 kg CO 2 /ha, respectively. Also, technical efficiencies were reported as 0.88, 0.86, and 0.96, for traditional, semi‐mechanized, and mechanized farms, respectively. The relationship among different variables including energy inputs, GHG emissions, output energy, and benefit to cost ratio was studied using econometric modeling. Data envelopment analysis (DEA) and multi‐objective genetic algorithm (MOGA) were also applied to detect a set of Pareto frontiers in the combination of energy, environmental, and economic indices (energy consumption, GHG emissions, and benefit to cost ratio as three selected output parameters) for sunflower production. It has been observed that the capability of MOGA for energy saving was higher than DEA. Application results of DEA and MOGA combined algorithms showed that diesel fuel and water had the highest and lowest potential for total energy savings, respectively.