Main Article Content


The slippage is an essential criterion for evaluating the fuel consumption and the field performance of tractor. The objective of this research was to develop mathematical models using Design Expert software for modelling and predicting slippage of the CASE JX75T tractor (India manufacture) under operational field conditions. In this research, a chisel plough was used as a loading tool for the tractor under four levels of ploughing depths, with three levels of speed and two levels of cone index (CI) in silty clay soil texture. The experiments were carried out in the site of Basrah University. The results obtained from the fieldwork were analysed to evolve mathematical models and equations to predict and evaluate the performance of the tractor when the slippage occurred. According to the obtained results, the single effects of the parameters (CI, tillage depth, and forward speed) on the slippage were highly considerable (P<0.0001). Moreover, the interaction of the parameters were significant (p<0.05). The slippage of tractor increased by 187 and 116 % with increasing ploughing depth up to 25 cm and forward speed up to 1.53 m.s-1, respectively. On the other hand, tractor slippage reduced by 34% when CI increased up to 980 kPa. The data analysis showed that the developed model has passable imitation ability and excellently executed in confront of the actual data. This confirms the accuracy of the model for predicting tractor slippage under different fieldworks.


Design Expert software Modelling Cone index

Article Details

How to Cite
Almaliki, S. A. ., Himoud, M. S. ., & Muhsin, S. J. . (2021). Mathematical Model for Evaluating Slippage of Tractor Under Various Field Conditions. Basrah Journal of Agricultural Sciences, 34(1), 49–59.


  1. Aday, S. H., Muhsin, S. J. & Bander, S. A. (2011). Determination the Draft ranges at which 2WD and 4WD tractors operate at their maximum traction efficiency. Basrah Journal of Agricultural Science, 24(2), 22 - 29.
  2. Almaliki, S. (2017). Development and Evaluation of Models for MF-285 Tractor Performance Parameters Using Computational Intelligence Techniques. Ph. D. Thesis. University of Tehran, Iran, 215pp.
  3. Almaliki, S. (2018). Simulation of draft force for three types of plow using response surface method under various field conditions. Iraqi Journal of Agricultural Sciences 49, 1123-1131.
  4. Almaliki, S., Alimardani, R. & Omid, M. (2016). Fuel consumption models of MF285 tractor under various field conditions. Agricultural Engineering International: CIGR Journal, 18, 147-158.
  5. Almaliki, S., Himoud, M., & Muhsin, S. (2019). A stepwise regression algorithm for prognostication draft requirements of disk plough. Journal of Engineering and Applied Sciences, 14, 10335-10340.
  6. ASABE, Standard. (2009). ASAE D497.6 Agricultural Machinery Management Data. ASAE. St. Joseph. MI:49085,1-8.
  7. ASAE, (2003). ANSI/ASAE S296.5 DEC. 2003 (R2018) General Terminology for Traction of Agricultural Traction and Transport Devices and Vehicles. MI, USA: ASAE, St. Joseph.
  8. Barbosa, L. A. P., & Magalhaes, P. S. G. (2015). Tire tread pattern design trigger on the stress distribution over rigid surfaces and soil compaction. Journal of Terramechanics, 58, 27-38.
  9. Battiato, A., & Diserens, E. (2013). Influence of tyre inflation pressure and wheel load on the traction performance of a 65 kW MFWD tractor on a cohesive soil. Journal of Agricultural Science, 5, 197-215.
  10. Battiato, A., & Diserens, E. (2017). Tractor traction performance simulation on differently textured soils and validation: A basic study to make traction and energy requirements accessible to the practice. Soil & Tillage Research, 166, 18-32.
  11. Damanauskas, V., & Janulevicius, A. (2015). Differences in tractor performance parameters between single-wheel 4WD and dual-wheel 2WD driving systems. Journal of Terramechanics; 60, 63-73.
  12. Damanauskas, V., Janulevicius, A., & Pupinis, G. (2015). Influence of extra weight and tire pressure on fuel consumption at normal tractor slippage. Journal of Agricultural Science, Vol. 7, No. 2, 55-67.
  13. Janulevičius A., Damanauskas V., & Pupinis, G. (2018). Effect of variations in front wheels driving lead on performance of a farm tractor with mechanical front-wheel-drive. Journal of Terramechanics, 77, 23-30.
  14. Karparvarfard, S. H. & Rahmanian-Koushkaki, H. (2015). Development of a fuel consumption equation: Test case for a tractor chisel-ploughing in a clay loam soil. Biosystems Engineering; 130,23-33.
  15. Kumar, A., Tewari, V. K., Gupta, C., & Pareek, C. M. (2017). A device to measure wheel slip to improve the fuel efficiency of off road vehicles. Journal of Terramechanics, 70, 1-11.
  16. Lee, J. W., Kim, J. S., & Kim, K. U. (2016). Computer simulations to maximise fuel efficiency and work performance of agricultural tractors in rotating and ploughing operations. Biosystems engineering, 142, 1-11.
  17. Moitzi, G., Wagentristl, K., Refenner, H., Weingartmann, G., Piringer, J., Boxberger, A., & Gronauer, H. (2014). Effects of working depth and wheel slip on fuel consumption of selected tillage implements. Agricultural Engineering International: CIGR Journal 16, 182-190.
  18. Montgomery, D. C., & Runger, G. C. (2014). Applied Statistics and Probability for Engineers. John Wiley & Sons, Inc., Hoboken, NJ, xvi, 6th edition, 765pp.
  19. Muhsin, S. J. (2010). Studying the power losses of two and four wheel drive tractors (2WD and 4WD) of massy ferguson (2680). Journal of Basrah Researches (Sciences), 36, 59-66.
  20. Shafaei, S. M., Loghavi, M., & Kamgar, S. (2018). A comparative study between mathematical models and the ANN data mining technique in draft force prediction of disk plow implement in clay loam soil. Agricultural Engineering International: CIGR Journal, 20, 71-79.
  21. Shafaei, S. M., Loghavi, M., & Kamgar, S. (2019). Feasibility of implementation of intelligent simulation configurations based on data mining methodologies for prediction of tractor wheel slip. Information Processing in Agriculture, 6, 183-199.
  22. Šmerda, T., & Čupera, J. (2010). Tire inflation and its influence on drawbar characteristics and performance- energetic indicators of a tractor set. Journal of Terramechanics; 47, 395-400.
  23. Taghavifar, H., & Mardani, A. (2015). Evaluating the effect of tire parameters on required drawbar pull energy model using adaptive neuro-fuzzy inference system. Energy, 85, 586-593.
  24. Wong, J. Y. (2009). Terramechanics and off-road vehicle engineering. 2nd edition. Amsterdam, Elsevier, 488pp.
  25. Zoz, F. M., & Grisso, R. D. (2003). Traction and tractor performance. ASAE Published, USA., No. 12, 48pp.

Most read articles by the same author(s)