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Purpose: The main purpose of this study was to evaluate the use of an integrated life cycle assessment (LCA), artificial neural network, and metaheuristic optimization model to improve the sustainability of tomato-based cropping systems in Iran. The model outputs the combination of input usage in a tomato cropping system, which leads to the highest economic output and the least environmental impact. Methods: The LCA inventory was created using data from 114 open-field tomato farms in the Alborz Province of Iran during one growing period in 2015. Among all management components, the main focus was on irrigation management systems. The optimization problem was designed by integrating three indicators: carbon footprint (CF), benefit-cost ratio (BCR), and energy use efficiency (EUE) as the objective of field tomato production. The functional unit was 1 kg of tomato aligned with the system boundary of the cradle to market life cycle. Three artificial neural networks (ANNs) were applied to model relationships between the inputs and three indices (CF, BCR, and EUE) as the objective functions. Multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO) were used to minimize the CF and maximize the BCR and EUE indicators. The abovementioned aims have been pursued by developing codes in MATLAB software. Results and discussion: CF, BCR, and EUE were calculated to be 0.26 kg CO2−eq (kg tomato)−1, 1.8, and 0.5, respectively. MOGA results envisage the possibility of an increase of 86% and 50% in the EUE and BCR and a 43% reduction in the CF of tomato production systems. Moreover, EUE and BCR increased by 83% and 49%, and CF was reduced by 39% from the optimum results obtained from the MOPSO algorithm. It was revealed that in order to optimize field tomato production with the target objectives of this study, a large additional use for irrigation pipes, plastic, and machinery in comparison to current situation is required, while a large reduction of biocide, chemical fertilizer, and electricity consumption is indispensable. Conclusions: According to the results of our study, it was concluded that the optimal solutions require a modernization of irrigation systems and a decrease in the consumption of chemical fertilizers and pesticides. The implementation of management options for such solutions is discussed. [ABSTRACT FROM AUTHOR]

In order to achieve sustainable development in agriculture, it is necessary to quantify and compare the energy, economic, and environmental aspects of products. This paper studied the energy, economic, and greenhouse gas (GHG) emission patterns in broiler chicken farms in the Alborz province of Iran. We studied the effect of the broiler farm size as different production systems on the energy, economic, and environmental indices. Energy use efficiency (EUE) and benefit-cost ratio (BCR) were 0.16 and 1.11, respectively. Diesel fuel and feed contributed the most in total energy inputs, while feed and chicks were the most important inputs in economic analysis. GHG emission calculations showed that production of 1000 birds produces 19.13 t CO and feed had the highest share in total GHG emission. Total GHG emissions based on different functional units were 8.5 t CO per t of carcass and 6.83 kg CO per kg live weight. Results of farm size effect on EUE revealed that large farms had better energy management. For BCR, there was no significant difference between farms. Lower total GHG emissions were reported for large farms, caused by better management of inputs and fewer bird losses. Large farms with more investment had more efficient equipment, resulting in a decrease of the input consumption. In view of our study, it is recommended to support the small-scale broiler industry by providing subsidies to promote the use of high-efficiency equipment. To decrease the amount of energy usage and GHG emissions, replacing heaters (which use diesel fuel) with natural gas heaters can be considered. In addition to the above recommendations, the use of energy saving light bulbs may reduce broiler farm electricity consumption. [ABSTRACT FROM AUTHOR]

Corn silage and alfalfa hay are the main feedstuffs for dairy farming, and due to the drop in corn price, there is a tendency toward replacing alfalfa hay with corn silage in dairy cow diets. This study focuses on the comprehensive assessment of alfalfa hay and corn silage production systems from energy, economic and environmental perspectives. The life cycle assessment (LCA) from raw material extraction up to delivery of forage to the dairy farms was conducted to quantify the ecoprofile of forage production. The results of comparative analyses revealed that corn production was more dependent on chemical fertilizers specially nitrogen, but alfalfa production was more dependent on electricity, water and diesel fuel for farm operations. Energy use per ton of alfalfa hay dry matter was higher than that of corn silage (2.6 vs. 2.4 GJ t −1 ). On the other hand, output energy of alfalfa hay was considerably higher than that of corn silage (15.8 vs. 8.0 GJ t −1 ). Consequently, energy use efficiency of alfalfa hay was almost two times of that of corn silage (6.1 vs. 3.3). Nevertheless, corn silage had higher benefit-to-cost ratio than alfalfa hay (2.55 vs. 2.27), and higher environmental impacts in the forms of abiotic depletion, global warming potential, marine aquatic ecotoxicity, photochemical oxidation, acidification and eutrophication potential. Finally, the effects of varying input parameters of machinery and diesel fuel, electricity, seeds, fertilizers, pesticides and transportation on LCA results were assessed via sensitivity analysis. [ABSTRACT FROM AUTHOR]