Limits...
Particle-size distribution models for the conversion of Chinese data to FAO/USDA system.

Shangguan W, Dai Y, García-Gutiérrez C, Yuan H - ScientificWorldJournal (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: College of Global Change and Earth System Science, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing 100875, China.

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.

Show MeSH
Percentages predicted by one of the eleven PSD models and fractal method versus measured percentages of particles finer than 0.05 mm and 0.002 mm of 20 soils.
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fig5: Percentages predicted by one of the eleven PSD models and fractal method versus measured percentages of particles finer than 0.05 mm and 0.002 mm of 20 soils.

Mentions: PSD models to interpolate and fractal method to extrapolate have different statistical assumptions. We need to know whether the combined use of two distinct models results in a real representing soil sample. For this, 20 soil samples were used to validate the combined use, which has the observed cumulative particle-size percentages at 2 mm, 0.05 mm, and 0.002 mm of USDA standard and at the limits of T2 scheme, respectively. These samples were only available with the given specifics. We used each PSD model combined with the fractal method to predict PSD with data points of T2 scheme. Figure 5 illustrates the test of suitability of the combined method to predict percentages of particles finer than 0.05 and 0.002 mm. The r2 value was 0.960, which was slightly lower than those of PSD models. Thus, the combined method with interpolation and extrapolation is suitable for transferring data from Katschinski's to USDA scheme. However, it cannot be determined whether this method is suitable for a specific soil textural class, because the sample size is too small.


Particle-size distribution models for the conversion of Chinese data to FAO/USDA system.

Shangguan W, Dai Y, García-Gutiérrez C, Yuan H - ScientificWorldJournal (2014)

Percentages predicted by one of the eleven PSD models and fractal method versus measured percentages of particles finer than 0.05 mm and 0.002 mm of 20 soils.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4121012&req=5

fig5: Percentages predicted by one of the eleven PSD models and fractal method versus measured percentages of particles finer than 0.05 mm and 0.002 mm of 20 soils.
Mentions: PSD models to interpolate and fractal method to extrapolate have different statistical assumptions. We need to know whether the combined use of two distinct models results in a real representing soil sample. For this, 20 soil samples were used to validate the combined use, which has the observed cumulative particle-size percentages at 2 mm, 0.05 mm, and 0.002 mm of USDA standard and at the limits of T2 scheme, respectively. These samples were only available with the given specifics. We used each PSD model combined with the fractal method to predict PSD with data points of T2 scheme. Figure 5 illustrates the test of suitability of the combined method to predict percentages of particles finer than 0.05 and 0.002 mm. The r2 value was 0.960, which was slightly lower than those of PSD models. Thus, the combined method with interpolation and extrapolation is suitable for transferring data from Katschinski's to USDA scheme. However, it cannot be determined whether this method is suitable for a specific soil textural class, because the sample size is too small.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: College of Global Change and Earth System Science, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing 100875, China.

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.

Show MeSH