Evaluation and Optimization of Energy and Environmental Indicators Using Life Cycle Assessment and Data Envelopment Analysis (Case Study: Industrial Cattle Farms in Northern Khuzestan)

Document Type : Research Article

Authors

• R. Fathi
• M. Ghasemi-Nejad Raeini
• R. Hesampour

Department of Agricultural Machinery and Mechanization Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran

https://doi.org/10.22067/jam.2022.76155.1100

Abstract

Introduction: Environmental crises and resource depletion have adversely affected environmental resources and food security in the world. Therefore, with the global population growth in the coming years and the rising need to produce more food, attention must be given to environmental issues, energy consumption, and sustainable production. The purpose of this study is to evaluate the pattern of energy consumption, environmental impacts, and optimization of the studied energy indicators in dairy cattle breeding industrial units in Khuzestan province, Iran.
Materials and Methods: This research was conducted in Khuzestan province, located in the southwest of Iran. Energy indicators including energy ratio, energy efficiency, specific energy, and net energy were used to determine and analyze the relationships between the output and input energy. Additionally, the life cycle assessment methodology was used to assess the environmental impact. Life cycle assessment includes a goal statement, identification of inputs and outputs, and a system for assessing and interpreting environmental impacts, and can be a good indicator for assessing environmental issues related to production. The life cycle assessment method used in this study was CML-IA baseline V3.05, which includes the four steps of (1) selecting and classifying impact categories, (2) characterizing effects, (3) normalizing, and (4) weighting. Overall, 11 impact groups were studied. The Data Envelopment Analysis (DEA) method with the Anderson-Peterson model was used for optimization. This method identifies the most efficient production unit and makes it possible to rank all of the farms in the region. In this study, each production unit (farm) was considered a decision-making unit (DMU), and its production efficiency was determined based on two models. Namely, the Charnes, Cooper, and Rhodes (CCR) model also known as Constant Return to Scale (CRS), and the Banker, Charnes, and Cooper (BCC) model also known as Variable Return to Scale (VRS).
Results and Discussion: The results showed that the input and output energies per cow per day were 173.34 and 166 MJ, respectively. Livestock feed and electricity accounted for 65.47% and 27.2% of the input energy, respectively, while the oil used for tiller-scraper lubrication of fertilizer collection accounted for only 0.01%, making it the lowest input energy. Energy efficiency, specific energy, and net energy were calculated as 0.95, 0.13 kg MJ^-1, 7.51 MJ kg^-1, and -7.20 MJ per cow, respectively. In the abiotic depletion impact group, animal feed, machinery, and livestock equipment had the highest environmental impacts. The results showed that animal feed had the highest environmental emissions in all impact groups except for abiotic depletion of fossil fuels where electricity had the greatest effect. CRS model determined that 7 units were efficient; with an average efficiency of 0.78. In the BCC model, 20 production units were calculated as highly efficient, and the average efficiency was computed to be 0.78.
Conclusion: In dairy farms in Khuzestan province, animal feed and electricity were found to have the highest energy consumption. In most impact groups, animal feed had the highest environmental effects. Specifically, in the abiotic depletion impact group, animal feed, livestock machinery, and equipment had the highest environmental effects. Considering the length of the heat period and the intensity of the solar flux, the installation of solar panels on the farm's roof to generate electricity can help reduce the consumption of non-renewable energy and mitigate radiation intensity under the roof.

Keywords

• Data envelopment analysis
• Environmental impacts
• Industrial dairy farm
• Life cycle assessment

20.1001.1.22286829.1402.13.4.1.4

Main Subjects

• Agricultural systems engineering (greenhouse, fish farming, mushroom production)

©2023 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

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