EVALUATING THE POTENTIAL OF AGRICULTURAL RESIDUES FOR SUSTAINABLE AVIATION FUEL PRODUCTION: A MACHINE LEARNING APPROACH

Abstract

The aviation sector faces increasing pressure to reduce greenhouse gas emissions while maintaining rapid growth in global air transport demand. Sustainable Aviation Fuel (SAF) derived from agricultural residues represents a promising pathway to decarbonize aviation without competing with food production. This study evaluates the potential for SAF production based on agricultural residues from eleven major cereal and crop groups, including wheat, barley, maize, cotton, sunflower, rice, rye, oats, spelt, millet, and canary grass. Agricultural productivity parameters, including cultivated area, total production, crop output category parameter, and yield per decare, were analyzed to determine their relationship with SAF generation potential. A comprehensive statistical assessment was conducted using paired sample statistics, correlation analysis, and paired sample t-tests based on 385 observations. The results indicate that the average SAF potential was estimated at 244.37 million liters per year, with significant variability across crop groups and years. Strong positive correlations were observed between SAF potential and total agricultural production (r = 0.990) as well as cultivated area (r = 0.964), demonstrating that biomass availability is the primary driver of residue-derived fuel production. In contrast, crop output category parameters showed a strong negative correlation with SAF potential (r = −0.751), while yield per decare exhibited no statistically significant relationship. The statistical tests confirmed significant differences between most agricultural productivity indicators and SAF potential. These findings highlight that large-scale agricultural production systems provide substantial opportunities for SAF feedstock generation. The study contributes to the understanding of biomass-based aviation fuel pathways and provides quantitative insights for policymakers and energy planners seeking to expand SAF production through agricultural residue utilization.

Keywords

• Sustainable Aviation Fuel
• Machine Learning
• Agricultural Residues
• Biomass Potential
• Aviation Decarbonization

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