Particle-Size Distribution Models for the Conversion of Chinese Data to FAO/USDA System

Abstract

We investigated eleven particle-size distribution (PSD) models to determine the appropriate models for describing the PSDs of 16349 Chinese soil samples. These data are based on three soil texture classification schemes, including one ISSS (International Society of Soil Science) scheme with four data points and two Katschinski's schemes with five and six data points, respectively. The adjusted coefficient of determination r 2, Akaike's information criterion (AIC), and geometric mean error ratio (GMER) were used to evaluate the model performance. The soil data were converted to the USDA (United States Department of Agriculture) standard using PSD models and the fractal concept. The performance of PSD models was affected by soil texture and classification of fraction schemes. The performance of PSD models also varied with clay content of soils. The Anderson, Fredlund, modified logistic growth, Skaggs, and Weilbull models were the best.

Comparison of alternative soil particle-size distribution models and their correlation with soil physical attributes

“…In terms of their AIC, the Fred-3p, MLG, Wei and Fred-4p models ranked first to fourth. This result differs from that of Shangguan et al (2014), who found that AD and Wei had the lowest mean AIC values and hence were considered the best PSD models among eleven models that they compared. Considering the average AIC values of all of the soils they studied, Botula et al (2013) suggested that the number of fitting parameters does not always explain the differences in the quality of the fittings.…”

“…These and several other efficiency criteria are partly in agreement with published results. For example, in a study evaluating the accuracy of 11 PSD models for the conversion of Chinese data to the FAO/USDA System, Shangguan et al (2014) reported much better performance of the AD, Fred-4p, MLG, and Wei models compared to the other models. Botula et al (2013), Zhao et al, (2011) and Hwang (2004) found the fractal models as well as the Exp and L-E models to be less accurate in their study.…”

Different Soil Particle-Size Classification Systems for Calculating Volume Fractal Dimension—A Case Study of Pinus sylvestris var. Mongolica in Mu Us Sandy Land, China

“…The PSD prediction has been used for comparing and converting texture measurements from diverse PSC systems [2,21,27]. For instance, in the Second National Soil Surveys of China, soil textures were measured by ISSS (1929) and Katschinski (1957) PSC systems, in which the conversion from ISSS and Katschinski's to the widely used USDA (1993) PSC system [48,49]. In this study, PSD characteristics which are described by fractal method were investigated because of its simplicity and effectiveness to compare the different PSC systems.…”

Estimating the very fine sand fraction for calculating the soil erodibility K‐factor

“…As noted in Section 1, there are no published works evaluating procedures to estimate the VFS fraction of fine earth of soils on the basis of the broad particle‐size classes, neither are there any antecedents for the statistical analysis of texture data drawn from several hundreds of thousands of soil samples. In quantitative terms, the most notable studies to date are Nemes, Wösten, Lilly, and Oude Voshaar (), drawing on 14,584 samples, and Shangguan, Dai, García‐Gutiérrez, and Yuan (), with 16,349 samples, both aimed at evaluating the performance of several procedures and models for describing cumulative particle‐size distributions; the work of Auerswald et al (), with 19,055 samples, along with Panagos et al (), 19,969 samples, both focusing on calculating the K ‐factor; that of Levi (), which included 75,736 samples and tested pedotransfer functions; and that of Padarian, Minasny, and McBratney (), with 160,904 samples, aiming the conversion from Australian to USDA soil particle‐size classification system.…”