![]() Contrasting results have been obtained for the fitting of some models. Fredlund pointed out that the suitability of their own model improved as the proportion of clay in soils increased. The results showed that model performance was affected by soil texture, but that these functions were a good fit for the PSD of both fine-and coarse-textured soils. Rousseva proposed and investigated the suitability of exponential and power-law distribution models to fit PSDs with diverse shapes and varying numbers of measured points. Buchan proposed that lognormal models are adequate to describe only half the United States Department of Agriculture (USDA)’s soil texture triangle, and more complex models are needed to depict other soils such as sandy clay loam, sandy clay, and most clays. Many researchers have relied upon a single PSD model at one time to represent a wide range of soil texture classes, although several studies have suggested that the performance of PSD models can be affected by soil texture. The latter included models based on water-retention curves, the fragmentation model, power-law functions based on fractal geometry, exponential functions, Gompertz equations, and a model estimating PSD from limited soil texture data. Their results showed that the bimodal model gave a marginally better fit, but incorporates a sub-clay mode, and the Jaky model gave a good fit to data for many soils, better than the standard lognormal model for 23 soils. Buchan and Hwang compared five lognormal models and certain other types of models in terms of their fit to experimental PSD data. Soil PSD is frequently assumed to follow a lognormal distribution, although some soils do have a bimodal PSD. ![]() Therefore, to be able to use these discrete experimental PSD data to estimate other soil properties, many researchers have used parametric functions to extend the limited scope of PSD data. However, conventional PSD analysis captures only a limited number of particle mass fractions. Modeling the size distribution of soil particles to obtain a continuous particle size distribution (PSD) curves is useful for understanding essential soil properties such as pore distribution, water retention, hydraulic conductivity, and thermal and adsorption properties. ![]() The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. 2011CB100506)-LJL National Natural Science Foundation of China (Contract No. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedĭata Availability: All relevant data are within the paper and its Supporting Information files.įunding: This study was funded by the National Basic Research Program of China (973 Program No. Received: DecemAccepted: MaPublished: April 30, 2015Ĭopyright: © 2015 Weipeng et al. PLoS ONE 10(4):Īcademic Editor: Wenju Liang, Chinese Academy of Sciences, CHINA ![]() ![]() The FRED4 model best described the PSD of clay, silty clay, clay loam, silty clay loam, silty loam, loam, and sandy loam, whereas FRED3, MLOG3, ONLG3, ORLG3, and SHCA3 showed better performance for most soils studied.Ĭitation: Weipeng W, Jianli L, Bingzi Z, Jiabao Z, Xiaopeng L, Yifan Y (2015) Critical Evaluation of Particle Size Distribution Models Using Soil Data Obtained with a Laser Diffraction Method. The performance of most PSD models was better for soils with higher silt content and poorer for soils with higher clay and sand content. The results indicated that the Fredlund models (FRED3 and FRED4) had the best performance for most of the soils studied, followed by one logistic growth function extension model (MLOG3) and three lognormal models (ONLG3, ORLG3, and SHCA3). The fits were evaluated using Akaike’s information criterion ( AIC), adjusted R 2, and root-mean-square error ( RMSE). These models include five lognormal models, five logistic models, four van Genuchten models, two Fredlund models, a logarithmic model, and an Andersson model. The aim of this study was to compare the performance of eighteen PSD functions for fitting LDM data sets from a wide range of soil textures. Laser diffraction methods (LDM) now provide more detailed PSD measurements, but deriving a function to characterize the entire range of particle sizes is a major challenge. Mathematical descriptions of classical particle size distribution (PSD) data are often used to estimate soil hydraulic properties. ![]()
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