Implementation of XGBoost Models for Predicting CO 2 Emission and Specific Tractor Fuel Consumption

Nebojša Balać, Zoran Mileusnić, Aleksandra Dragičević, Mihailo Milanović, Andrija Rajković, Rajko Miodragović and Olivera Ećim-Đurić ()
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Nebojša Balać: Kite d.o.o., 21333 Čenej, Serbia
Zoran Mileusnić: Department of Agricultural Engineering, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, Serbia
Aleksandra Dragičević: Department of Agricultural Engineering, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, Serbia
Mihailo Milanović: Department of Agricultural Engineering, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, Serbia
Andrija Rajković: Department of Agricultural Engineering, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, Serbia
Rajko Miodragović: Department of Agricultural Engineering, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, Serbia
Olivera Ećim-Đurić: Department of Agricultural Engineering, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, Serbia

Agriculture, 2025, vol. 15, issue 11, 1-19

Abstract: Tillage is one of the most energy-intensive operations in crop production, leading to high fuel consumption and the emission of harmful gases such as CO 2 and NO x . This study was conducted under real field conditions to explore how soil parameters influence variations in fuel use and exhaust emissions. A machine learning approach based on the XGBoost algorithm was applied to develop predictive models for CO 2 concentrations in exhaust gases and specific fuel consumption. The CO 2 prediction model achieved an accuracy exceeding 80%, while the model for fuel consumption reached over 65%. Although not optimized for high precision, these models offer a valuable basis for preliminary assessments and highlight the potential of data-driven approaches for improving energy efficiency and environmental sustainability in agricultural mechanization.

Keywords: tractor exhausts emission; predictive soil tillage; VRA soil tillage; precision agriculture; machine learning; XGBoost model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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