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Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study

Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study
Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study
In the recent centuries, one of the most important ongoing challenges is energy consumption and its environmental impacts. As far as agriculture is concerned, it has a key role in the world economics and a great amount of energy from different sources is used in this sector. Since researchers have reported a high degree of inefficiency in developing countries, it is necessary for the modern management of cropping systems to have all factors (economics, energy and environment) in the decision-making process simultaneously. Therefore, the aim of this study is to apply Multi-Objective Particle Swarm Optimization (MOPSO) to analyze management system of an agricultural production. As well as MOPSO, two other optimization algorithm were used for comparing the results. Eventually, Taguchi method with metrics analysis was used to tune the algorithms’ parameters and choose the best algorithms. Watermelon production in Kerman province was considered as a case study. On average, the three multi-objective evolutionary algorithms could reduce about 30 % of the average Greenhouse Gas (GHG) emissions in watermelon production although as well as this reduction, output energy and benefit cost ratio were increased about 20 and 30 %, respectively. Also, the metrics comparison analysis determined that MOPSO provided better modeling and optimization results.
Agricultural management, Energy, GHG emissions, MOPSO, NRGA-II, NSGA, Optimization
1436-3240
1167-1187
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Yousefi, Marziye
750c35de-8b63-48fa-9461-1d166f0d297f
Maghsoudlou, Hamidreza
31029a70-7ce3-45c8-8db1-8218d39345e0
Jahangiri, Sanaz
f4c37fb0-14ac-4b60-b0b7-d2088d37c034
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Yousefi, Marziye
750c35de-8b63-48fa-9461-1d166f0d297f
Maghsoudlou, Hamidreza
31029a70-7ce3-45c8-8db1-8218d39345e0
Jahangiri, Sanaz
f4c37fb0-14ac-4b60-b0b7-d2088d37c034

Barak, Sasan, Yousefi, Marziye, Maghsoudlou, Hamidreza and Jahangiri, Sanaz (2016) Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study. Stochastic Environmental Research and Risk Assessment, 30 (4), 1167-1187. (doi:10.1007/s00477-015-1098-1).

Record type: Article

Abstract

In the recent centuries, one of the most important ongoing challenges is energy consumption and its environmental impacts. As far as agriculture is concerned, it has a key role in the world economics and a great amount of energy from different sources is used in this sector. Since researchers have reported a high degree of inefficiency in developing countries, it is necessary for the modern management of cropping systems to have all factors (economics, energy and environment) in the decision-making process simultaneously. Therefore, the aim of this study is to apply Multi-Objective Particle Swarm Optimization (MOPSO) to analyze management system of an agricultural production. As well as MOPSO, two other optimization algorithm were used for comparing the results. Eventually, Taguchi method with metrics analysis was used to tune the algorithms’ parameters and choose the best algorithms. Watermelon production in Kerman province was considered as a case study. On average, the three multi-objective evolutionary algorithms could reduce about 30 % of the average Greenhouse Gas (GHG) emissions in watermelon production although as well as this reduction, output energy and benefit cost ratio were increased about 20 and 30 %, respectively. Also, the metrics comparison analysis determined that MOPSO provided better modeling and optimization results.

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More information

e-pub ahead of print date: 12 June 2015
Published date: 1 April 2016
Keywords: Agricultural management, Energy, GHG emissions, MOPSO, NRGA-II, NSGA, Optimization

Identifiers

Local EPrints ID: 434851
URI: https://eprints.soton.ac.uk/id/eprint/434851
ISSN: 1436-3240
PURE UUID: 5f31aab7-7abb-4d37-962f-eae63afc81fc
ORCID for Sasan Barak: ORCID iD orcid.org/0000-0001-7715-9958

Catalogue record

Date deposited: 11 Oct 2019 16:30
Last modified: 03 Dec 2019 01:20

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Contributors

Author: Sasan Barak ORCID iD
Author: Marziye Yousefi
Author: Hamidreza Maghsoudlou
Author: Sanaz Jahangiri

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