Evaluation of different representations of the particle-size distribution to predict soil water retention

Nemes, Attila; Rawls, W. J.

doi:10.1016/j.geoderma.2005.04.018

Cited by 40 publications

(20 citation statements)

References 40 publications

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“…Correlation coefficients among the water content in each potential and soil physical and chemical properties were calculated and are reported in Table 2. From the values of correlation coefficients in Table 2, the soil bulk density (BD), clay and sand content have close relationships with the water content at 10 and 33 kPa at the significance level consistent with other scholars (Nemes & Rawls, 2006) and clay and sand content are only suitable for PTFs developing to predict the water content of 100, 300 and 500 kPa. The water content at 1000 and 1500 kPa were only controlled by clay conConnecting several nodes in parallel and series, a MLP network is formed (Heikki, 2008).…”

Section: Resultssupporting

confidence: 83%

“…The values of adjusted coefficients of determination (R 2 adj ) given in Table 3 indicate the range of the dependent variable that is explained by the tent. The absolute effect of BD decreased with h m in agreement with Nemes & Rawls (2006). The correlation coefficient of BD was negative for low h m and positive for high h m (i.e.…”

supporting

confidence: 80%

“…Kern (1995) found that effects of silt content and BD on θ 33 kPa and θ 1500 kPa and effect of sand content on θ 1500 kPa were not significant but that sand content significantly affected θ 33 kPa. Nemes & Rawls (2006) reported that sand and silt contents decreased and clay content increased θ FC and θ PWP . Dexter et al (2008) found a positive relation between θ PWP and clay content.…”

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confidence: 98%

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Selection of a suitable model for the prediction of soil water content in north of Iran

Esmaeelnejad

Ramezanpour

Seyedmohammadi

et al. 2015

Span. j. agric. res.

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Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and Rosetta model were employed to develop pedotransfers functions (PTFs) for soil moisture prediction using available soil properties for northern soils of Iran. The Rosetta model is based on ANN works in a hierarchical approach to predict water retention curves. For this purpose, 240 soil samples were selected from the south of Guilan province, Gilevan region, northern Iran. The data set was divided into two subsets for calibration and testing of the models. The general performance of PTFs was evaluated using coefficient of determination (R 2 ), root mean square error (RMSE) and mean biased error between the observed and predicted values. Results showed that ANN with two hidden layers, Tan-sigmoid and linear functions for hidden and output layers respectively, performed better than the others in predicting soil moisture. In the other hand, ANN can model non-linear functions and showed to perform better than MLR. After ANN, MLR had better accuracy than Rosetta. The developed PTFs resulted in more accurate estimation at matric potentials of 100, 300, 500, 1000, 1500 kPa. Whereas, Rosetta model resulted in slightly better estimation than derived PTFs at matric potentials of 33 kPa. This research can provide the scientific basis for the study of soil hydraulic properties and be helpful for the estimation of soil water retention in other places with similar conditions, too. Additional keywords: multiple linear regression; neural networks; pedotransfer function; Rosetta; soil moisture curve.

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Section: Resultssupporting

confidence: 83%

supporting

confidence: 80%

mentioning

confidence: 98%

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Selection of a suitable model for the prediction of soil water content in north of Iran

Esmaeelnejad

Ramezanpour

Seyedmohammadi

et al. 2015

Span. j. agric. res.

View full text Add to dashboard Cite

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“…The boundary of new fraction sizes can be parameters of pedotransfer functions along with the regression coefficients. Nemes and Rawls (2006) experimented with the boundary between silt and sand in the range from 20 and 63 µm and developed pedotransfer functions for water retention at −33 and −1500 kPa matric potential values. They could not point out the boundary size between silt and sand that would clearly provide better results in estimating the selected soil hydraulic properties.…”

Section: Textural Clay Sandy Sandy Clay Sandy Loamy Sandmentioning

confidence: 99%

On soil textural classifications and soil-texture-based estimations

et al. 2018

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Abstract. The soil texture representation with the standard textural fraction triplet "sand-silt-clay" is commonly used to estimate soil properties. The objective of this work was to test the hypothesis that other fraction sizes in the triplets may provide a better representation of soil texture for estimating some soil parameters. We estimated the cumulative particle size distribution and bulk density from an entropy-based representation of the textural triplet with experimental data for 6240 soil samples. The results supported the hypothesis. For example, simulated distributions were not significantly different from the original ones in 25 and 85 % of cases when the sand-silt-clay and "very coarse+coarse + medium sand − fine + very fine sand − silt+clay" were used, respectively. When the same standard and modified triplets were used to estimate the average bulk density, the coefficients of determination were 0.001 and 0.967, respectively. Overall, the textural triplet selection appears to be application and data specific.

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“…Direct measurement of saturated hydraulic conductivity can be laborious, and if destructive samples are used and soil structure is altered then the results are not very useful (Taguas et al 1999, Nemes and Rawls 2006, Sénéchal and Sénéchal 2010. The measurement of soil particle-size distribution (PSD), however, is relatively easy and accurate for methods such as laser granulometry.…”

Section: Introductionmentioning

confidence: 99%

Performance of soil particle-size distribution models for describing deposited soils adjacent to constructed dams in the China Loess Plateau

2010

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A b s t r a c t Soil particle-size distributions (PSD) have been used to estimate soil hydraulic properties. Various parametric PSD models have been proposed to describe the soil PSD from sparse experimental data. It is important to determine which PSD model best represents specific soils. Fourteen PSD models were examined in order to determine the best model for representing the deposited soils adjacent to dams in the China Loess Plateau; these were: Skaggs (S-1, S-2, and S-3), fractal (FR), Jaky (J), Lima and Silva (LS), Morgan (M), Gompertz (G), logarithm (L), exponential (E), log-exponential (LE), Weibull (W), van Genuchten type (VG) as well as Fredlund (F) models. Four-hundred and eighty samples were obtained from soils deposited in the Liudaogou catchment. The coefficient of determination (R 2 ), the Akaike's information criterion (AIC), and the modified AIC (mAIC) were used. Based upon R 2 and AIC, the three-and four-parameter models were both good at describing the PSDs of deposited soils, and the LE, FR, and E models were the poorest. However, the mAIC in conjunction with R 2 and AIC results indicated that the W model was optimum for describing PSD of the deposited soils for emphasizing the effect of parameter number. This analysis was also helpful for finding out which model is the best one. Our results are applicable to the China Loess Plateau.

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Evaluation of different representations of the particle-size distribution to predict soil water retention

Cited by 40 publications

References 40 publications

Selection of a suitable model for the prediction of soil water content in north of Iran

Selection of a suitable model for the prediction of soil water content in north of Iran

On soil textural classifications and soil-texture-based estimations

Performance of soil particle-size distribution models for describing deposited soils adjacent to constructed dams in the China Loess Plateau

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