جستجوی مقالات مرتبط با کلیدواژه "ارزیابی چرخه زندگی" در نشریات گروه "منابع طبیعی"

This research aims to explore the energy usage and environmental impact of cultivating soybean and peanut, with a specific focus on a case study. The cultivation of these crops is crucial for agricultural production, and understanding their energy use and potential damage is essential for sustainable farming. The results of this study will be valuable for farmers, policymakers, and researchers working towards sustainable agricultural production. The study reveals that soybean production in Ardabil province of total energy was 43170.20 MJ ha-1, also, output energy was 40417.28 MJ ha-1, while peanut production requires 28677.36 MJ ha-1 of total energy and produces 28677.36 MJ ha-1 of output energy. The results of the Life Cycle Assessment (LCA) show that emissions on farms are closely related to the inputs used in soybean and peanut production. Diesel fuel and chemical fertilizers are the main sources of emissions in both systems, with soybean farming showing higher levels of diesel fuel pollutants due to reduced usage compared to peanuts. The distribution of emissions from different sources in soybean and peanut production is highlighted in the study. Both methods of production have a significant impact on human health, with soybean responsible for 70% of emissions and peanuts contributing 65%. Diesel fuel and nitrogen fertilizer have the most significant environmental impact, accounting for over 50% of the overall impact. Proper management of nitrogen fertilizer is essential for maximizing crop growth and yield, making it a top priority for researchers and farmers.

In recent years, due to the limitation of water resources, the use of pressured irrigation methods has received much attention. Although the use of pressurized irrigation systems increases the water productivity, these systems have received less attention in terms of energy use and environmental aspects. So, the aim of this study was to evaluate the energy and environmental indicators of almond orchards under surface and pressured irrigation methods in Shahr-e Kord and Saman region of Chaharmahal and Bakhtiari province. The required data, including information about inputs and energy consumed in almond orchards, were collected using questionnaires and face-to-face interviews. No significant difference was observed regarding the performance of almond production in the two mentioned irrigation systems. Examining the results of water consumption in two systems showed that the pressured irrigation system consumes about 82% less water than the surface irrigation system, while the energy consumption in this system was about 12% more than the surface irrigation system. Energy ratio of almond production in surface irrigation systems was computed as 2.40, while the corresponding value in pressured system was 2.07. In other words, surface irrigation had better conditions in terms of energy indicators. The investigation of the environmental indicators of two irrigation systems showed that the use of the pressured irrigation system reduces the environmental damage

In this study, strawberry production in hydroponic greenhouses in Saujblag city was evaluated and analyzed in terms of environmental consequences caused by the consumption of agricultural inputs, exergy demand index and emergy demand. The results of the evaluation indicators were obtained using the IMPACT 2002+ method in Simapro software. The results showed that phosphate fertilizer (P2O5) with about 89% has the greatest effect on the resource extraction effect. According to the weighting calculations, the human damage category had the highest amount and the ecosystem quality category had the least amount of damage. Combustion of diesel fuel and electricity had a significant effect on human health damage with values of 43.68 mPt and 32.17 mPt, respectively. According to the exergy index, non-renewable fossil resources with a share of about 99% and a value of 10,893.13 megajoules per ton accounted for the highest cumulative exergy demand. Also, based on the results, the highest emergy in crop production is related to human labor with a value of 1.73E+16 (sej/ha) and then the highest emergy values were related to irrigation water and natural gas with a value of 1.36E+16 and 5.36E+15, respectively. Based on the results, it is suggested that by using new methods of greenhouse management based on the optimal consumption of agricultural inputs and replacing all kinds of chemical fertilizers with organic fertilizers, the environmental effects of strawberry production will be reduced and the sustainability of this product will be increased.

In this research, modeling of energy consumption and environmental emissions for different process of tea production in Gilan province (Iran) were investigated using regression and artificial neural networks. Data related to agricultural and industrial activities of tea production chain were collected through questionnaire survey from tea farms of different counties in Guilan province. Also, the datatbank of agricultural organization, tea research institute and tea factories were collected. The results showed that the total energy input and output for green tea leaf production were 34343.51 and 10996.45 MJ/ha, respectively. Also, the total energy input for dry tea was obtained 64885.23 MJ/ton. The results of the life cycle assessment in tea farms revealed that the highest effect belonged to nitrogen in abiotic depletion, dcidification, eutrophication and global warming potential impacts. The survey of life cycle assessment in the tea processing factories illustrated that diesel fuel had the highest share in abiotic depletion, ozone layer depletion, human toxicity, fresh water aquatic ecotoxicity and photochemical oxidation impact categories. In acidification, eutrophication, global warming potential impacts, green leaf input had the highest share. Moreover, the highest share of marine aquatic ecotoxicity and terrestrial ecotoxicity was belonged to coated paper. The results of energy and environmental emissions modeling for green tea leaf production showed that artificial neural networks can predict the outputs and inputs with more accuracy than regression technique.