Prediction of Subsoiling Draft Requirements of Silt Loam Soils From Empirical Data: Artificial Neural Network Versus Multiple Linear Regression

Abich, S.O.; Gitau, Ayub Njoroge; Nyaanga, D. M.

doi:10.37017/jeae-volume7-no2.2021-6

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…Analysis was done in the laboratory by tri-axial testing as outlined by Gitau et al (2008). Soil cohesion and angle of internal friction of the soils were determined graphically by construction of Mohr circles and applying the Mohr-Coulomb equation (Abich et al, 2021).…”

Section: Shear Strength and Shear Strength Parametersmentioning

confidence: 99%

Modeling Subsoiling Draft Requirements Using Dimensional Analysis

Abich¹,

Gitau²,

Nyaanga³

2022

JEAE

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Increased conventional tillage of farming operations has increased soil compaction. The objective of this study was to model the effects of soil compaction on draft force of sub soiling silt loam soils using dimensional analysis.A factorial experiment in a Completely Randomized Block design was used in the study. Experiments were conducted on 18 plots to investigate the effect of five-wheel pass (1, 2, 3, 4. 5) and control treatments on soil mechanico-physical properties at 0 -20, 20 -20 and 30 -40 cm depths with three replications. The data obtained from the experiments were subjected to analysis of variance and the student t-test. Dimensional analysis was investigated for its draft prediction accuracy. It was concluded that the prediction ability of the model was satisfactory since its R 2 value and RMSE were 0.8722 and 2.256 kN repectively.

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Section: Shear Strength and Shear Strength Parametersmentioning

confidence: 99%

Modeling Subsoiling Draft Requirements Using Dimensional Analysis

Abich¹,

Gitau²,

Nyaanga³

2022

JEAE

Self Cite

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“…The optimal correlation is similar (0.038) in the experiments described in [6], but the exponent = 1.659, is significantly higher. The authors [25,31] do not take into account the working speed in establishing the regressions that give the soil tillage draft force, the experiments being made for a subsoiler, with a working speed of 2.5 km /h. Remarkable and noteworthy is that the ASABE standard, [29] and not only, includes, in addition to the term dependent on the second power of the travel speed, a term dependent on the first power of the same speed.…”

Section: Respectively the Working Speed In Relation To The Average Wo...mentioning

confidence: 99%

“…Also, linear and nonlinear regressions, as well as comparisons between them, propose the authors [24], for a plough with mouldboard, having as independent process parameters soil moisture, working depth and working speed, the dependent parameter being the resistance force traction (soil tillage draft force). A comparative study of the predictions of the soil tillage draft force, obtained by neural networks and by multiple linear regressions, is presented in [25], using experiments performed with a tractor-subsoiler agricultural aggregate.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial neural networks are also used in [35], to obtain formulas for predicting the qualitative parameters of soil tillage processes. The authors [25] use as a dependent parameter the soil tillage draft force and as independent parameters the working depth, soil density, penetration resistance and soil moisture, soil shear stress and cohesion. The authors [20] consider linear regressions formed with combinations of dimensionless parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the authors cited above, we tried, before the regression statistics stage, to somehow prioritize the various parameters in experiments or their combinations, in order to use the most favourable variants in regression studies. The authors [19, [23][24][25] do not make a study of the hierarchy of the influence of independent process parameters on the considered dependent parameter, in the sense we proposed in [2]. From this idea started the investigation whose results are described in [2].…”

Section: Introductionmentioning

confidence: 99%

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The article presents a method of investigating the intensity of the connection between the parameters measured in experiments for different dynamic processes. The method is based on a function constructed from the correlation between the rows of records, called variables. The result is addressed to all specialists who try to obtain relationships between the parameters of a process, starting from the results of experimental research. It is considered that the relationships sought between the variables of the researched process do not have a theoretical foundation or do not have a corresponding one. The tool for assessing the intensity of the connection between the main parameter and the secondary parameters of a dynamic process is tested on soil tillage processes in agriculture. A hierarchy of the intensity of the connection between the main parameter and the secondary parameters or combinations thereof is provided. Using these hierarchies, researchers can create regressions based on the priorities offered by these hierarchies.

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Prediction of Subsoiling Draft Requirements of Silt Loam Soils From Empirical Data: Artificial Neural Network Versus Multiple Linear Regression

Cited by 3 publications

References 13 publications

Modeling Subsoiling Draft Requirements Using Dimensional Analysis

Modeling Subsoiling Draft Requirements Using Dimensional Analysis

A Way of Estimating the Intensity of Connections Between the Parameters of a Dynamic System

Statistical Tool to Estimate and Optimize the Intensity of the Dependence Between the Parameters of a Dynamic System

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