Evaluation of sustainable energy use in sugarcane production: A holistic model from planting to harvest and life cycle assessment
The study evaluates energy consumption in sugarcane production at the Salman Farsi Sugarcane Agro-Industrial Company in Khuzestan province, Iran, comparing plant cane and ratoon cycles. Plant cane show higher energy input (124,912.32 MJ ha-1) and output (107,530.44 MJ ha-1) than ratoon farms (80,317...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Environmental and Sustainability Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665972725000388 |
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| Summary: | The study evaluates energy consumption in sugarcane production at the Salman Farsi Sugarcane Agro-Industrial Company in Khuzestan province, Iran, comparing plant cane and ratoon cycles. Plant cane show higher energy input (124,912.32 MJ ha-1) and output (107,530.44 MJ ha-1) than ratoon farms (80,317.81 MJ ha-1 input and 87,586.68 MJ ha-1 output). However, ratoon cycles are more energy efficient. To lessen energy use in plant cane, the research recommends strategies like minimizing machinery use, adopting reduced and no-tillage practices, and employing efficient irrigation and spraying methods. The environmental assessment reveals that plant cane have greater negative impacts on human health, ecosystems, and resources. Specifically, human health impacts are 3.69 DALY for planted systems versus 1.54 for ratoon systems, indicating greater health risks from initial plantings. Ecosystem impacts also show more local species loss in planted systems (6.25E-04 species.yr compared to 4.11E-04 for ratoon). Moreover, resource costs are higher for planted systems at 320.12 USD2013 of sugarcane, compared to 210.46 USD2013 for ratoon production. The analysis compares Artificial Neural Network and Adaptive Neuro-Fuzzy Inference Systems models for predicting energy outputs and environmental effects. Artificial Neural Network models excel in predicting impacts for planted sugarcane, whereas Adaptive Neuro-Fuzzy Inference Systems models are more accurate for ratoon production and are computationally more efficient. The findings emphasize the need for improved sustainability and efficiency in sugarcane production through better energy management and reduced environmental impacts. |
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| ISSN: | 2665-9727 |