Evaluation of Different Predictor Models for Detailed Soil Particle-Size Distribution
References (19)
- et al.
Improved estimation of the soil particle-size distribution from textural data
Biosyst Eng
(2006) - et al.
Evaluation of soil water retention curve with the pore-solid fractal model
Geoderma
(2005) Effect of texture on the performance of soil particle-size distribution models
Geoderma
(2004)- et al.
Estimating relative hydraulic conductivity from lognormally distributed particle-size data
Geoderma
(2006) - et al.
Predicting the film and lens water volume between soil particles using particle size distribution data
J Hudrol
(2012) Information theory and an extension of the maximum likelihood principle
- et al.
Relationship between the hydraulic conductivity function and the particle size distribution
Soil Sci Soc Am J
(1999) - et al.
Characterization of particle-size distribution in soils with a fragmentation model
Soil Sci Soc Am J
(1999) - et al.
Particle size distribution models for soils of the humid tropics
J Soils Sediments
(2013)
Cited by (13)
Scaling properties of particle-size distributions of purple soils in a small agricultural watershed: A multifractal analysis
2022, CatenaCitation Excerpt :Several statistical variables, including median particle size, coefficients of sorting, uniformity and curvature, and skewness and kurtosis have also been applied to characterise soil PSD, but these variables depend strongly on probability distributions and fail to fully express the complexity of soil PSD (Li et al., 2017). In addition, some studies have attempted to describe soil PSD by employing parametric models, such as lognormal, Weibull, Gompertz, Morgan, Fredlund, and Gray and Skaggs distributions (Zhao et al., 2008; Bayat et al., 2015; Meskini-Vishkaee and Davatgar, 2018; Vaz et al., 2020). However, as these models are suitable only for unimodal distribution patterns, prior knowledge is required when selecting an adequate parameter model to characterise soil PSD (Li et al., 2017; Vaz et al., 2020).
Soil erosion progression under rill and gully erosion processes and its effect on variations of mechanisms controlling C mineralization ratio
2021, Ecohydrology and HydrobiologyCitation Excerpt :The variations in the relationship patterns between the C mineralization ratio and the associated edaphic variables within the rill and gully soils can explain the impacts of the relationship between the soil stability indicators and DOC on the control of the C mineralization ratio under the different erosion stages (Table 4 and Fig. 3). The soil aggregate size distribution and its characteristics, including the MWD and GMD, are the most fundamental physical characteristics that significantly affect the vulnerability rate of DOC contained in eroded soils to microbial mineralization (Meskini-vishkaee & Davatgar, 2018). On the other hand, labile components of OC are the determinant factors of soil degradation status (Marchetti et al., 2012) due to their impacts on the soil structural stability characteristics.
Evaluation of models for fitting soil particle-size distribution using UNSODA and a Brazilian dataset
2020, Geoderma RegionalCitation Excerpt :Therefore, fitting accuracy of the FRED-7p equation was far superior to the other evaluated equations, for all soil texture classes together, with a RMSE about two times lower than the second best fitted equation (ANDE-4p). Previous works (Meskini-Vishkaee and Davatgar, 2018; Bayat et al., 2017; Esmaeelnejad et al., 2016; Bayat et al., 2015; Weipeng et al. 2015) have reported the best average RMSE from 1 to 4% (100*g g−1) with various equations as FRED-4p, BEST-3p, Weibull and Hyperbolic, for their dataset. Thus, the resulting (fitting) accuracy for FRED-7p eq. (0.53 average RMSE) exceed considerably the best fittings accuracies previously reported with equations other than it, although RMSE may depend also on the quality of data, number of bins and number of textural classes on the dataset used in each work.
On evaluating the hypothesis of shape similarity between soil particle-size distribution and water retention function
2023, Journal of Agricultural EngineeringFitting models for a grain size distribution: a review
2023, Bulletin of Engineering Geology and the Environment