Cradle to farm gate life cycle assessment of oilseed crops production in Iran

Dekamin, Majid; Barmaki, Morteza; Kanooni, Amin; Meshkini, Seyed Reza Mosavi

doi:10.1016/j.eaef.2018.04.003

Cited by 12 publications

(4 citation statements)

References 29 publications

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“…In a similar study on the LCA of oilseeds production in Iran, Ardabil Province, the amount of CO 2 -eq emitted per ton of product for rapeseed, sunflower, and soybean was 2132, 2283, and 1549 kg, respectively. Moreover, the study reported that more than 70% of these emissions (GW) were due to electricity production, manure, and chemical fertilizers [45]. By comparing these results, it is clear that the GWP index for soybean production in Ardabil province is much higher than in Mazandaran, which is due to the higher consumption of these inputs and more intensive management to produce this product.…”

Section: Discussionmentioning

confidence: 99%

Sustainable Systems Engineering Using Life Cycle Assessment: Application of Artificial Intelligence for Predicting Agro-Environmental Footprint

et al. 2023

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The increase in population has increased the need for agricultural and food products, and thus agricultural production should be increased. This goal may cause increases in emissions and environmental impacts by increasing the consumption of agricultural inputs. The prediction of environmental impacts plays an important role in evaluating pollutant emissions in crop production. This study employed two artificial intelligence (AI) methods: the adaptive neuro-fuzzy inference system–fuzzy c-means (ANFIS–FCM) algorithm as a novel computational method, and an artificial neural network (ANN) as a conventional computational method to predict the environmental impacts of soybean production in different scenarios (i.e., soybean cultivation after rapeseed (R-S), wheat (W-S), and fallow (F-S)). The life cycle of soybean production was assessed in terms of environmental impacts through the IMPACT2002+ method in SimaPro. In the present study, the production of one ton of soybeans was considered the functional unit, and the boundary of the system was considered the gate of the field. According to the results, the production of each ton of soybean in the defined scenarios resulted in 0.0009 to 0.0016 DALY, 5476.18 to 8799.80 MJ primary, 1033.68 to 1840.70 PDF × m2 × yr, and 563.55 to 880.61 kg CO2-eq damage to human health, resources, ecosystem quality, and climate change, respectively. Moreover, the weighted analysis indicated that various soybean production scenarios led to 293.87–503.73 mPt damage to the environment, in which the R-S scenario had the best environmental performance. According to the results, the ANFIS–FCM algorithm acted as the best prediction model of environmental indicators for soybean cultivation in all cases related to the ANN. The range of calculated R2 for the ANFIS-FCM and ANN models were between 0.9967 to 0.9989 and 0.9269 to 0.9870, respectively. It can be concluded that the proposed ANFIS–FCM model is an efficient technique for obtaining accurate environmental prediction parameters of soybean cultivation.

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Section: Discussionmentioning

confidence: 99%

Sustainable Systems Engineering Using Life Cycle Assessment: Application of Artificial Intelligence for Predicting Agro-Environmental Footprint

et al. 2023

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“…In a similar study on the LCA for oilseeds production in Ardabil Province (Iran), the amounts of CO2-eq emitted per ton of products for rapeseed, sunflower, and soybean were reported 2132, 2283, and 1549 kg, respectively. Moreover, the study reported that more than 70% of these emissions (GW) were caused by electricity production, manure, and chemical fertilizers (Dekamin et al 2018). The comparison of results indicated clearly that the GWP index for soybean production was much higher in Ardabil Province than in Mazandaran, something which is due to the higher consumption of these inputs and more intensive management to produce this product.…”

Section: Climate Changementioning

confidence: 99%

Predicting of Agro-environmental Footprint with Artificial Intelligence (Soybean cultivation in various scenarios)

Kashka

Sarvestani

Pirdashti

et al. 2022

Preprint

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This study employed two artificial intelligence (AI) methods called the ANFIS–FCM algorithm as a novel computational method and an artificial neural network (ANN) as a conventional computational method in order to predict the environmental impacts of soybean production in different scenarios (i.e., soybean cultivation after rapeseed (R-S), wheat (W-S), and fallow (F-S)). The 175 data of life cycle assessment (LCA) method were collected from soybean farms. The two methods called the adaptive neuro-fuzzy inference based on fuzzy C-means clustering algorithm (FCM) and the artificial neural network (ANN) were adopted to predict environmental parameters. For this purpose, the life cycle of soybean production was assessed in terms of environmental impacts through the IMPACT2002+ method in SimaPro. According to the results, the production of each ton of soybean in the defined scenarios resulted in 0.0009 to 0.0016 DALY, 5476.18 to 8799.80 MJ primary, 1033.68 to 1840.70 PDF×m2×yr, and 563.55 to 880.61 kg CO2-eq damage to human health, resources, ecosystem quality, and climate change, respectively. Moreover, the weighted analysis indicated that various soybean production scenarios led to 293.87–503.73 mPt damage to the environment in which the R-S scenario had the best environmental performance. Notably, the emissions caused by the production and application of the diesel fuel followed by chemical fertilizers, particularly N and P fertilizers, were recognized as the most important environmental hotspots in soybean production. According to the results, the ANFIS–FCM algorithm acted as the best prediction model of environmental indicators for soybean cultivation in all cases related to the ANN. The RMSE and MAPE values obtained from ANFIS–FCM were lower than the values obtained from the ANN model for all environmental indicators. For the ANFIS–FCM and ANN algorithms, R2 ranged between 0.9967 to 0.9989 and 0.9269 to 0.9870, respectively. It can be concluded that the proposed ANFIS–FCM model is an efficient technique for obtaining accurate environmental prediction parameters of soybean cultivation.

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“…The system boundaries are reported in Figure 1, a cradle-to-gate approach was herein applied. Hemp seed (as other seeds) can be the raw material for other industrial processes, therefore the system boundary was fixed at the factory gate, as reported in other studies [24]. Life cycle inventory (LCI): the second phase involves the modeling of the product system, data collection, as well as the description and verification of data [25].…”

Section: Life Cycle Assessmentmentioning

confidence: 99%

“…In this study, a cradle-to-factory gate analysis was considered, because hemp seed can be the raw material of many industries. Moreover, this is the most used approach for seed crops [24].…”

Section: Introductionmentioning

confidence: 99%

Hemp Seed Production: Environmental Impacts of Cannabis sativa L. Agronomic Practices by Life Cycle Assessment (LCA) and Carbon Footprint Methodologies

et al. 2020

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This paper evaluated the environmental impacts of different agronomic practices for a hemp seed crop grown in Mediterranean environment. The following agricultural variables have been considered: seven monoecious hemp varieties(Epsilon68 (E68), Fedora17 (F17), Felina32 (F32), Ferimon (Fe), Futura75 (F75), Santhica27 (S27), Uso31 (U31)), three plant densities (40, 80, and 120 plants m−2), and two levels of nitrogen (N) fertilization (50 and 100 kg ha−1 of N). Life cycle assessment (LCA) and carbon footprint (CF) methodologies have been applied to evaluate impacts. In all hemp genotypes, the impacts grew by decreasing both N fertilizer and plants densities. The scenario most impacting was E68/F75/S27 genotypes cultivated with 50 kg ha−1 of N fertilizer and 40 plants m−2, while the lowest one was Fe with 100 kg ha−1 of N fertilizer and 120 plants m−2. The highest CF was found for E68/F75/S27 cultivated with 50 kg ha−1 of N fertilizer and 40 plants m−2 (18.720 kg CO2 eq). This study highlighted the most environmentally sustainable agronomic practices to support farmer and decision maker in Cannabis sativa L. cultivation for seed production.

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Cradle to farm gate life cycle assessment of oilseed crops production in Iran

Cited by 12 publications

References 29 publications

Sustainable Systems Engineering Using Life Cycle Assessment: Application of Artificial Intelligence for Predicting Agro-Environmental Footprint

Sustainable Systems Engineering Using Life Cycle Assessment: Application of Artificial Intelligence for Predicting Agro-Environmental Footprint

Predicting of Agro-environmental Footprint with Artificial Intelligence (Soybean cultivation in various scenarios)

Hemp Seed Production: Environmental Impacts of Cannabis sativa L. Agronomic Practices by Life Cycle Assessment (LCA) and Carbon Footprint Methodologies

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