A novel approach to estimate soil penetrometer resistance from water content, bulk density, and shear wave velocity: A laboratory study on a loamy sand soil

Zhang, Wencan; Gao, Weiyin; Ren, Tusheng; Whalley, W. R.

doi:10.1016/j.geoderma.2020.114276

Cited by 8 publications

(1 citation statement)

References 29 publications

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“…However, the model of Dexter et al (2007) was very complicated, involving the measurement of many soil properties (e.g., bulk density, clay content and several water retention characteristics) [17]. Recently, Zhang et al ( 2020) used a mathematical model to estimate SPR from water content, bulk density and shear wave velocity [30]. Whereas the model showed good accuracy (R 2 = 0.96), the SPR was measured using a small cone penetrometer in a universal test machine laboratory and the model was not validated using data from the in situ measurements, which are necessary to verify the model performance.…”

Section: Introductionmentioning

confidence: 99%

Estimating Soil Penetration Resistance of Paddy Soils in the Plastic State Using Physical Properties

et al. 2020

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Soil penetration resistance (SPR) is an important indicator for soil strength which not only affects the growth of crop roots and crop yield but also is crucial in the design and selection of agricultural machinery. The determination of SPR in the laboratory is complex and time-consuming, while measuring SPR on-site shows high uncertainty at different times and locations due to soil heterogeneity. Therefore, this paper investigated the impact of soil parameters on SPR for paddy soils in the plastic state and then established a simple regression model to predict SPR using easy-to-obtain soil physical properties, including clay content, water content and density. Using the combined approaches of central composition rotatable design (CCRD) with response surface methodology (RSM), SPR of 20 soil samples from five paddy fields were measured in the laboratory. The results from the experiments showed that the contribution rate of each single factor to SPR from high to low was soil density, clay content and water content. Statistical analysis for the established equation suggested that the p-value for goodness of fit was significant (p < 0.001) and the p-value for lack of fit was insignificant (p > 0.05); meanwhile, the coefficient of determination (R2) was 0.95, indicating that the model was effective in predicting the SPR. Subsequently, the performance of the regression model was validated by comparing the estimated SPR with in situ field measurements, which showed high accuracy, with percent errors within 10%. Our study successfully proposed a method to estimate SPR using easy-to-measure soil properties that could be obtained from sensors in the soil or field investigations, including soil clay content, water content and wet bulk density.

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Section: Introductionmentioning

confidence: 99%