Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-27T05:32:16.607Z Has data issue: false hasContentIssue false
  • English
  • Français

Multi-criteria sustainability performance assessment of horticultural crops using DEA and ELECTRE IV methods

Published online by Cambridge University Press:  13 October 2022

Narges Banaeian Open the ORCID record for Narges Banaeian [Opens in a new window] ,
Morteza Zangeneh Open the ORCID record for Morteza Zangeneh [Opens in a new window]  and
Paulina Golinska-Dawson Open the ORCID record for Paulina Golinska-Dawson [Opens in a new window]
Narges Banaeian*
Affiliation:
Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
Morteza Zangeneh
Affiliation:
Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
Paulina Golinska-Dawson
Affiliation:
Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland
*
Author for correspondence: Narges Banaeian, E-mail: banaeian@guilan.ac.ir
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents a novel approach to multi-criteria sustainability performance assessment of horticultural crops. The crops are ranked by the decision-making method ELECTRE IV with environmental, energy and technological criteria. In total eight indicators are taken into consideration and calculated based on primary data collected from over 260 farms in northern Iran. Additionally, Data Envelopment Analysis is used to calculate the technical efficiency and potential for energy saving by different management of the production units. The novel contribution of this study is the comparison of several horticultural products (oranges, kiwis, persimmons and tangerines), when most of the previous studies have focused on one product. Moreover, novel calculations of the carbon footprint are presented for oranges, tangerines and persimmons. This paper also includes the first study on the environmental impact of persimmon fruit's production. The obtained results show that energy efficiency for orange, tangerine, kiwi and persimmon products: 1.1, 0.84, 0.53 and 1.22, respectively. In each hectare of kiwi orchards, the amount of CO2 emissions of 1219 kg and the ecological footprint of 3.21 hectares have been calculated, which is statistically significant compared to orange, tangerine and persimmon. The chemical and fuel inputs have the greatest potential for reducing energy consumption in the studied products. Results of ELECTRE IV showed that kiwi is the most sustainable selection for the studied region followed by orange, persimmon and tangerine, respectively. Kiwi has also relatively low technical efficiency. This means that this product has the greatest potential for a reduction of energy consumption, while maintaining the same amount of crop. It is recommended to include the development of kiwi orchards in the policies of Guilan, but with more careful management of the production inputs.

Keywords

Carbon footprintecological footprintkiwiorangepersimmontangerine
Type
Research Article
Information
Renewable Agriculture and Food Systems , Volume 37 , Issue 6 , December 2022 , pp. 649 - 659
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anielski, M and Wilson, J (2010) Environmental Footprinting for Agriculture in Alberta: Literature Review and Analysis. Alberta, Canada: Environmental Stewardship Division of Alberta Agriculture and Rural Development.Google Scholar
Anonymous (2017) The Results of the Survey of the Country's Horticulture. Tehran: Statistics Center of Iran.Google Scholar
Avval, SHM, Rafiee, S, Jafari, A and Mohammadi, A (2011) Improving energy productivity of sunflower production using data envelopment analysis (DEA) approach. Journal of the Science of Food and Agriculture 91, 1885–1892.CrossRefGoogle ScholarPubMed
Banaeian, N and Namdari, M (2011) Effect of ownership on energy use efficiency in watermelon farms - A data envelopment analysis approach. International Journal of Renewable Energy Research 1, 75–82.Google Scholar
Banaeian, N, Omid, M and Ahmadi, H (2011) Energy and economic analysis of greenhouse strawberry production in Tehran province of Iran. Energy Conversion and Management 52, 1020–1025.CrossRefGoogle Scholar
Banaeian, N, Omid, M and Ahmadi, H (2012) Greenhouse strawberry production in Iran, efficient or inefficient in energy. Energy Efficiency 5, 201–209.CrossRefGoogle Scholar
Bhunia, S, Karmakar, S, Bhattacharjee, S, Roy, K, Kanthal, S, Pramanick, M, Baishya, A and Mandal, B (2021) Optimization of energy consumption using data envelopment analysis (DEA) in rice-wheat-green gram cropping system under conservation tillage practices. Energy 236, 121499.CrossRefGoogle Scholar
Ewing, B, Moore, D, Goldfinger, S, Oursler, A, Reed, A and Wackernagel, M (2010) The Ecological Footprint Atlas 2010. Okland: Global Footprint Network.Google Scholar
Gharakhlou, M, Abdi, N and Shahraki, Z (2009) Analysis of the level of urban sustainability in informal settlements of Sanandaj (Persian). Human Geography Research 42, 1–16.Google Scholar
Hosseinzadeh-Bandbafha, H, Nabavi-Pelesaraei, A, Khanali, M, Ghahderijani, M and Chau, KW (2018) Application of data envelopment analysis approach for optimization of energy use and reduction of greenhouse gas emission in peanut production of Iran. Journal of Cleaner Production 172(Suppl. C), 1327–1335.CrossRefGoogle Scholar
Iriarte, A, Rieradevall, J and Gabarrell, X (2011) Environmental impacts and energy demand of rapeseed as an energy crop in Chile under different fertilization and tillage practices. Biomass and Bioenergy 35, 4305–4315.CrossRefGoogle Scholar
Khoshnevisan, B, Rafiee, S, Omid, M, Yousefi, M and Movahedi, M (2013) Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks. Energy 52(Suppl. C), 333–338.CrossRefGoogle Scholar
Kissinger, M and Gottlieb, D (2012) From global to place oriented hectares – The case of Israel's wheat ecological footprint and its implications for sustainable resource supply. Ecological Indicators 16, 5.CrossRefGoogle Scholar
Litskas, VD, Platis, DP, Anagnostopoulos, CD, Tsaboula, AC, Menexes, GC, Kalburtji, KL, Stavrinides, MC and Mamolos, AP (2020) Climate change and agriculture: carbon footprint estimation for agricultural products and labeling for emissions mitigation. In Betoret N and Betoret E (eds), Sustainability of the Food System. Cambridge, MA: Academic Press, pp. 33–49.CrossRefGoogle Scholar
Mohammadi, A, Rafiee, S, Mohtasebi, SS, Mousavi Avval, SH and Rafiee, H (2010a) Energy efficiency improvement and input cost saving in kiwifruit production using Data Envelopment Analysis approach. Renewable Energy 36, 2573–2579.CrossRefGoogle Scholar
Mohammadi, A, Rafiee, S, Mohtasebi, SS and Rafiee, H (2010b) Energy inputs – yield relationship and cost analysis of kiwifruit production in Iran. Renewable Energy 35, 1071–1075.CrossRefGoogle Scholar
Mohammadi, A, Rafiee, S, Jafari, A, Keyhani, A, Mousavi-Avval, SH and Nonhebel, S (2014) Energy use efficiency and greenhouse gas emissions of farming systems in north Iran. Renewable and Sustainable Energy Reviews 30(Suppl. C), 724–733.CrossRefGoogle Scholar
Mostashari-Rad, F, Nabavi-Pelesaraei, A, Soheilifard, F, Hosseini-Fashami, F and Chau, K (2019) Energy optimization and greenhouse gas emissions mitigation for agricultural and horticultural systems in Northern Iran. Energy 186, 115845.Google Scholar
Nabavi-Pelesaraei, A, Abdi, R, Rafiee, S and Mobtaker, HG (2014) Optimization of energy required and greenhouse gas emissions analysis for orange producers using data envelopment analysis approach. Journal of Cleaner Production 65(Suppl. C), 311–317.Google Scholar
Nabavi-Pelesaraei, A, Hosseinzadeh-Bandbafha, H, Qasemi-Kordkheili, P, Kouchaki-Penchah, H and Riahi-Dorcheh, F (2016a) Applying optimization techniques to improve of energy efficiency and GHG (greenhouse gas) emissions of wheat production. Energy 103, 672–678.CrossRefGoogle Scholar
Nabavi-Pelesaraei, A, Rafiee, S, Hosseinzadeh-Bandbafha, H and Shamshirband, S (2016b) Modeling energy consumption and greenhouse gas emissions for kiwifruit production using artificial neural networks. Journal of Cleaner Production 133(Suppl. C), 924–931.CrossRefGoogle Scholar
Namdari, M, Asadi Kangarshahi, A and Akhlaghi Amiri, N (2011a) Input-output energy analysis of citrus production in Mazandaran province of Iran. African Journal of Agricultural Research 6, 2558–2564.Google Scholar
Namdari, M, Kangarshahi, AA and Amiri, NA (2011b) Econometric model estimation and sensitivity analysis of input for mandarin production in Mazandaran province of Iran. Research Journal of Applied Sciences, Engineering and Technology 3, 464–470.Google Scholar
Nikkhah, A, Emadi, B and Firouzi, S (2015) Greenhouse gas emissions footprint of agricultural production in Guilan province of Iran. Sustainable Energy Technologies and Assessments 12(Suppl. C), 10–14.CrossRefGoogle Scholar
Ozkan, B, Akcaoz, H and Karadeniz, F (2004) Energy requirement and economic analysis of citrus production in Turkey. Energy Conversion and Management 45, 1821–1830.CrossRefGoogle Scholar
Salehi, M, Maleki, A, Mobtaker, HG, Rostami, S and Shakeri, H (2016) Investigation of energy inputs and CO2 emission for almond production using sensitivity analysis in Iran. Agricultural Engineering International: CIGR Journal 18, 158–166.Google Scholar
Solís-Guzmán, J, Marrero, M and Ramírez-de-Arellano, A (2013) Methodology for determining the ecological footprint of the construction of residential buildings in Andalusia (Spain). Ecological Indicators 25, 239–249.CrossRefGoogle Scholar
Soltanali, H, Nikkhah, A and Rohani, A (2017) Energy audit of Iranian kiwifruit production using intelligent systems. Energy 139(Suppl. C), 646–654.CrossRefGoogle Scholar
Uhlin, HE (1998) Why energy productivity is increasing: an I-O analysis of Swedish agriculture. Agricultural Systems 56, 443–465.CrossRefGoogle Scholar
Wackernagel, M, Schulz, NB, Deumling, D, Linares, AC, Jenkins, M, Kapos, V, Monfreda, C, Loh, J, Myers, N, Norgaard, R and Randers, J (2002) Tracking the ecological overshoot of the human economy. Proceedings of the National Academy of Sciences of the United States of America 99, 9266–9271.CrossRefGoogle ScholarPubMed
Yarali, N, Mafi Gholami, D, Jafari, A, Soltani, A and Mahmoudi, M (2010a) Environmental impact assessment (EIA) of Oshtrankooh protected area using degradation model. Environmental researches 1, 13–22.Google Scholar
Yarali, N, Soltani, A, Jafari, A, Mafi Gholami, D and Mahmoudi, M (2010b) Evaluation of environmental impact of development (EIA) on Oshtrankoh protected area using damage model. Environmental Research 1, 13–22.Google Scholar
Żak, J and Kruszyński, M (2015) Application of AHP and ELECTRE III/IV methods to multiple level, multiple criteria evaluation of urban transportation projects. Transportation Research Procedia 10, 820–830.CrossRefGoogle Scholar
Zangeneh, M, Omid, M and Akram, A (2010) A comparative study on energy use and cost analysis of potato production under different farming technologies in Hamadan province of Iran. Energy 35, 2927–2933.CrossRefGoogle Scholar