Assessment of agricultural vulnerability and risk to climate change in sugarcane production through life cycle analysis and a multi-agent system
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Climate change has had profound impacts on agricultural systems, altering crop productivity, changing precipitation patterns, spreading pests and diseases, reducing soil quality, displacing agricultural areas, and increasing the use of inputs such as fertilizers and pesticides, which in turn leads to an increase in atmospheric emissions. To address this issue, this research proposes the use of a multi-agent system-based model to analyze the vulnerability of sugarcane production, representing complex systems and adapting to changing conditions by integrating dynamic and uncertain variables. The main advantage of the model is that it enables the quantification and analysis of critical variables, including the use of fuel, fertilizers, and nitrogen oxide (N₂O) emissions. The results demonstrate how the increase in operating trend negatively impacts environmental performance, highlighting the fragility of the system. Meanwhile, the validation of the model through structural tests and extreme conditions confirmed its reliability in supporting decision-making processes. Likewise, the average vulnerability value of the system (0.54) indicates a moderately unstable condition, susceptible to climatic and economic changes. Complementarily, the IMPACT 2002+ methodology was applied to conduct a life cycle assessment (LCA) of sugarcane, encompassing its cultivation and industrial processing. It was found that the resources used in sugar mills have the most significant environmental impact in the categories of climate change, human health, ecosystem quality, and resource consumption. This impact is caused by CO₂ emissions, the use of toxic pesticides and heavy metals, and high dependence on fossil fuels such as coal, natural gas, and oil, mainly. These findings underscore the need to enhance environmental management in Mexico's sugar sector by adopting cleaner technologies, establishing reliable ecological databases, and implementing assessment tools such as multi-agent modeling and life cycle analysis.
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