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Modeling of soil penetration resistance using multiple linear regression (MLR)

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In agricultural areas, the use of machinery leads to improved yields. Nevertheless, its inadequate implementation and excessive utilization can seriously affect the soil efficiency. In fact, latter can be generated by increasing the penetration resistance and subsequently, it results in the compaction phenomenon. This problem becomes considerable with the increasing report wheel/soil. The aim of this work was to evaluate the efficiency through the prediction of soil penetration resistance (Rp) using a statistical model based on moisture content, density, tractor weight, number of passes, and the wheel inflation pressure. Experimental works (211 measurements) were analyzed and the penetration resistance was modeled using multiple linear regressions (MLR). Besides, the developed model elucidates the variables affecting the accentuation of soil Rp and allows the investigation of equations for novel sampled soils. Our results showed that the parameters related to soil and tractors were significant to explain Rp. The adopted model in the MLR analysis emphasizes that the mechanical parameters of ground measurements are statistically significant in estimating and evaluating Rp. The statistical calculation of the R 2 expresses 83% of the variance in Rp generated by the various parameters related to soil and tractor. In view of the importance of estimating the penetration resistance (Rp), the regression equation shows that the weight of the tractor and the number of passages contributed the most to the proposed model for the soil.

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This work was supported by the Tunisian Ministry of Agriculture (Higher Institute of Agronomic Sciences of Chott Mariem) and Ministry of Higher Education and Scientific Research (Lab UR11ES76, Faculty of Sciences Sfax). We thank all the staff.

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Correspondence to Nahla Ben Salah.

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Elaoud, A., Hassen, H.B., Salah, N.B. et al. Modeling of soil penetration resistance using multiple linear regression (MLR). Arab J Geosci 10, 442 (2017).

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