Multivariate Optimization of Moldboard Plow Tillage Quality Using Response Surface Methodology

ماشین های کشاورزی

Articles in Press, Accepted Manuscript, Available Online from 14 July 2025   

Document Type: Research Article

DOI: 10.22067/jam.2025.92680.1357

Authors

M. Akbari¹; I. Hazbavi^{* 1}; M. Jafarian²

¹Department of Biosystems Engineering, Faculty of Agriculture, Lorestan University, Khorramabad, Iran

²North Khorasan Agricultural and Natural Resources Research and Education Center, AREEO, Shirvan, Iran

Abstract

Introduction
Tillage of rainfed lands is performed using moldboard plows to a depth of 30 cm. Due to the influence of soil surface roughness and crop residues on moisture absorption and erosion reduction, investigation of the relationship between tillage implements’ performance and the aforementioned factors is essential. Therefore, considering the importance of preserving precipitation and preventing soil erosion, this study was conducted to investigate and optimize the effects of forward speed and tillage depth on soil surface roughness and the percentage of buried crop residue using response surface methodology.
Materials and Methods
This research was conducted in the Khomeyn region, Iran during the 2023-2024 growing season, utilizing a moldboard plow and an MF399 tractor. The objective was to investigate the effects of plowing depth and speed on soil surface roughness and the burial of plant residues. Soil surface roughness was measured using a pin meter, while the percentage of burial of plant residues was determined using image processing techniques and ImageJ software. Wheat straw residue with an initial moisture content of 8-9% was uniformly distributed at a rate of 100 g m^-2 along the designated paths. Images were captured before and after the tillage operation for subsequent processing and analysis.
To optimize the process, a central composite design (CCD) with three levels of speed (5, 7.5, and 10 km h^-1) and three levels of tillage depth (17.5, 22.5, and 27.5 cm) was employed. The objective was to determine the optimal factor levels for maximizing surface roughness and minimizing residue burial. Data were analyzed using a second-order model and Design Expert V11 software. The best model was selected based on statistical criteria.
Results and Discussion
Modeling soil surface roughness and crop residue incorporation revealed that the second-order regression model, with high coefficients of determination (R² = 0.983 and 0.96), was capable of accurately predicting these indices. The interaction effects of tillage speed and depth were significant (P < 0.01). In this study, the effect of tillage depth on soil surface roughness was greater than that of tractor speed. The regression model indicated that tillage depth plays a primary role in the amount of crop residue incorporation. Moldboard plowing demonstrated that increasing depth, particularly at high speeds, leads to increased roughness and residue incorporation, whereas increasing speed, especially at shallow depths, reduces roughness and increases incorporation. The maximum roughness was observed at the deepest tillage depth and lowest speed, while the shallowest depth and highest speed resulted in the minimum roughness.
Tillage depth and speed influence soil surface roughness and bulk density. Higher speeds decrease furrow depth and ridge height; thus, lower speeds are recommended for creating greater roughness. The highest residue incorporation (85%) was achieved at a depth of 27.5 cm and speeds of 5 and 10 km h^-1, while the lowest (70%) occurred at a depth of 17.5 cm and a speed of 5 km h^-1. Depth was more influential than speed, and nonlinear models are necessary for more accurate modeling. The developed model, with a desirability of 81%, provides the maximum roughness (10.96 cm) and minimum residue incorporation (69.34%) for a moldboard plow at a speed of 5 km h^-1 and a tillage depth of 17.5 cm.
Conclusion
This study investigated the effects of conventional tillage methods in dry land areas on soil surface roughness and the extent of crop residue burial. The results indicate that increasing tillage depth leads to an increase in both indices, while reducing tractor speed increases roughness and decreases residue burial. The optimization model revealed that at a speed of 5 km h^-1 and a depth of 17.5 cm, minimum roughness and maximum residue incorporation can be achieved. To improve regional tillage practices, it is advised to conduct further research into the long-term effects of different tillage systems. This effort will ensure a well-informed selection and implementation of the most effective methods.
Acknowledgment
The authors gratefully acknowledge the financial support provided by the University of Lorestan.

Keywords

Burial of plant residues; Forward speed; Soil surface roughness; Tillage depth

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