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Combined application of Artificial Neural Networks and life cycle assessment in lentil farming in Iran 被引量:6
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作者 behzad elhami Majid Khanali Asadollah Akram 《Information Processing in Agriculture》 EI 2017年第1期18-32,共15页
In this study,an Artificial Neural Network(ANN)was applied to model yield and environmental emissions from lentil cultivation in Esfahan province of Iran.Data was gathered from lentil farmers using face to face questi... In this study,an Artificial Neural Network(ANN)was applied to model yield and environmental emissions from lentil cultivation in Esfahan province of Iran.Data was gathered from lentil farmers using face to face questionnaire method during 2014–2015 cropping season.Life cycle assessment(LCA)was applied to investigate the environmental impact categories associated with lentil production.Based on the results,total energy input,energy output to input ratio and energy productivity were determined to be 32,970.10 MJ ha1,0.902 and 0.06 kg MJ1,respectively.The greatest amount of energy consumption was attributed to chemical fertilizer(42.76%).Environmental analysis indicated that the acidification potential was higher than other environmental impact categories in lentil production system.Also results showed that the production of agricultural machinery was the main hotspot in abiotic depletion,eutrophication,global warming,human toxicity,fresh water aquatic ecotoxicity,marine aquatic ecotoxicity and terrestrial ecotoxicity impact categories,while direct emissions associated with lentil cultivation was the main hotspot in acidification potential and photochemical oxidation potential.In addition,diesel fuel was the main hotspot only in ozone layer depletion.The ANN model with 9-10-6-11 structure was identified as the most appropriate network for predicting yield and related environmental impact categories of lentil cultivation.Overall,the results of sensitivity analysis revealed that farmyard manure had the greatest effect on the most of the environmental impacts,while machinery was the most affecting parameter on the yield of the crop. 展开更多
关键词 Artificial Neural Network Energy balance Environmental impacts Lentil production Sensitivity analysis
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Optimization of energy consumption and environmental impacts of chickpea production using data envelopment analysis(DEA)and multi objective genetic algorithm(MOGA)approaches 被引量:7
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作者 behzad elhami Asadollah Akram Majid Khanali 《Information Processing in Agriculture》 EI 2016年第3期190-205,共16页
Energy consumption in agricultural products and its environmental damages has increased in recent centuries.Life cycle assessment(LCA)has been introduced as a suitable tool for evaluation environmental impacts related... Energy consumption in agricultural products and its environmental damages has increased in recent centuries.Life cycle assessment(LCA)has been introduced as a suitable tool for evaluation environmental impacts related to a product over its life cycle.In this study,optimization of energy consumption and environmental impacts of chickpea production was conducted using data envelopment analysis(DEA)and multi objective genetic algorithm(MOGA)techniques.Data were collected from 110 chickpea production enterprises using a face to face questionnaire in the cropping season of 2014-2015.The results of optimization revealed that,when applying MOGA,optimum energy requirement for chickpea production was significantly lower compared to application of DEA technique;so that,total energy requirement in optimum situation was found to be 31511.72 and 27570.61 MJ ha^-1 by using DEA and MOGA techniques,respectively;showing a reduction by 5.11%and 17%relative to current situation of energy consumption.Optimization of environmental impacts by application of MOGA resulted in reduction of acidification potential(ACP),eutrophication potential(EUP),global warming potential(GWP),human toxicity potential(HTP)and terrestrial ecotoxicity potential(TEP)by 29%,23%,10%,6%and 36%,respectively.MOGAwas capable of reducing the energy consumption from machinery,farmyard manure(FYM)diesel fuel and nitrogen fertilizer(the mostly contributed inputs to the environmental emissions)by 59%,28.5%,24.58%and 11.24%,respectively.Overall,the MOGA technique showed a superior performance relative to DEA approach for optimizing energy inputs and reducing environmental impacts of chickpea production system. 展开更多
关键词 Data envelopment analysis ENERGY Life cycle assessment Multi objective genetic algorithm
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Improvement of energy efficiency and environmental impacts of rainbow trout in Iran
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作者 behzad elhami Saeid Shahvarooghi Farahani Afshin Marzban 《Artificial Intelligence in Agriculture》 2019年第2期13-27,共15页
Combination of Life Cycle Assessment(LCA)and other management tools can help production units to improve economic productivity and environmental protection.In this study,a combination of LCA and Data Envelopment Analy... Combination of Life Cycle Assessment(LCA)and other management tools can help production units to improve economic productivity and environmental protection.In this study,a combination of LCA and Data Envelopment Analysis(DEA)was applied in order to improve the energy efficiency and reduce the environmental burdens of rainbow trout farm in Ardal and Lordegan regions located in Chaharmahal and Bakhtiari Province of Iran.The required data were collected from 60 rainbow trout farms in Ardal region and 38 rainbow trout farms in Lordegan region through face-to-face questionnaire method.In Ardal region,total energy inputs,rainbow trout yield and Energy Ratio(ER)were estimated as 60,483.50 MJ ton−1,281.78 ton ha−1 and 0.40,respectively,while for Lordegan region,these estimates were obtained as 77,183.63 MJ ha−1,210.50 kg ha−1 and 0.33,respectively.The results of LCA revealed that rainbow trout production in Ardal region had lower environmental burdens than Lordegan region in all impact categories.Accordingly,Environmental Emissions Final Score(EEFS)in Ardal and Lordegan regions were 1638.88 and 3484.31 ppt ton−1,respectively.The normalized results also showed that Marine Aquatic Ecotoxicity(MAE)had the highest value among all impact categories in both regions.The DEA results showed that in Ardal and Lordegan regions about 29.28%and 9.59%of the total energy can be saved without reducing the yield,respectively.The highest potential for saving energy was related to feed in both Ardal(24.74%)and Lordegan(9.12%)regions.The results of LCA coupled with DEA also revealed that there is a higher potential for reduction of environmental impacts in Ardal region compare to Lordegan region.Accordingly,the EEFS value in Ardal and Lordegan regionswere reduced by 27.34%and 8.85%,respectively.Generally,rainbow trout production in Ardal region had higher energy efficiency,lower environmental burdens and also higher potential to improve energy consumption and reduce the environmental impacts compared to Lordegan region. 展开更多
关键词 Life cycle assessment Energy ratio Data envelopment analysis Rainbow trout
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