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Integration of life cycle assessment, artificial neural networks, and metaheuristic optimization algorithms for optimization of tomato-based cropping systems in Iran
Department of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, University of Tehran, P.O. Box 4111, Karaj, Iran.
Department of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, University of Tehran, P.O. Box 4111, Karaj, Iran.
Department of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, University of Tehran, P.O. Box 4111, Karaj, Iran.
Department of Bioeconomy and Systems Analysis, Institute of Soil Science and Plant Cultivation, State Research Institute, Pulawy, Poland.
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2019 (English)In: International Journal of Life Cycle AssessmentArticle in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2019.
National Category
Environmental Sciences
Identifiers
URN: urn:nbn:se:kth:diva-268298DOI: 10.1007/s11367-019-01707-6Scopus ID: 2-s2.0-85075193976OAI: oai:DiVA.org:kth-268298DiVA, id: diva2:1414485
Note

QC 20200313

Available from: 2020-03-13 Created: 2020-03-13 Last updated: 2020-03-13Bibliographically approved

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Publisher's full textScopushttps://link.springer.com/article/10.1007%2Fs11367-019-01707-6

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Brandao, Miguel

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