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

Author

Listed:
  • Nebojša Balać

    (Kite d.o.o., 21333 Čenej, Serbia
    Department of Agricultural Engineering, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, 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)

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.

Suggested Citation

  • Nebojša Balać & Zoran Mileusnić & Aleksandra Dragičević & Mihailo Milanović & Andrija Rajković & Rajko Miodragović & Olivera Ećim-Đurić, 2025. "Implementation of XGBoost Models for Predicting CO 2 Emission and Specific Tractor Fuel Consumption," Agriculture, MDPI, vol. 15(11), pages 1-19, May.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:11:p:1209-:d:1669707
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