This study examined energy, greenhouse gas emission and ecological footprint analysis (EFA) of chickpea and lentil cultivation with different mechanization production systems. In lentil production, except for tillage operations, other operations are performed manually and the remaining straw is burned in the field; while in chickpea production, most of the agricultural operations are mechanized and residues are collected, baled and transferred to the warehouse for animal feed. In this paper, for the first time, some of the sustainability indicators are investigated and compared in two different legume production systems. Energy productivity and net energy for chickpea and lentil production were calculated at 0.036, 0.161 and 2373 and 5900 MJ per hectare, respectively. The CO2 emission and ecological carbon footprint were 173 kg CO2−eq and 0.15 global hectare for lentil and 484 and 0.87 for chickpea production. Totally, due to excessive consumption of diesel fuel and lack of proper management, the social cost of emission from straw baling in chickpea production (27.65 dollars per hectare) was higher than burning straw in lentil production (8.77). Multi-objective genetic algorithm results showed the potential of minimizing diesel fuel and fertilizer consumption and no chemical for chickpea production. Overall audition results of two different production systems revealed that traditional lentil production is more sustainable. Therefore, implementations of modern agricultural practices alone are not enough to achieve sustainability in agricultural production systems.
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 CO2/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.
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