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Evaluation of models for fitting soil particle-size distribution using UNSODA and a Brazilian dataset
Geoderma Regional ( IF 4.1 ) Pub Date : 2020-03-16 , DOI: 10.1016/j.geodrs.2020.e00273
Carlos Manoel Pedro Vaz , Ednaldo José Ferreira , Aldolfo Durand Posadas

The mineral soil Particle Size Distribution (PSD) is a fundamental and very useful soil characteristic widely used to support soil classification, soil management and soil processes modeling. An important limitation of PSD is the lack of standardization for granulometric fractions obtained from different soil classification systems, making difficult to compare and combine data. To overcome this drawback several attempts have been made to mathematically fit PSD data in order to allow soil texture data conversion among soil databases. Parametric equations have been proposed and/or evaluated for particular systems, but none of them has been studied, compared and proved being effective for soils of different types, regions and textural classes. Thus, the purpose of this work was evaluating and comparing performances of several PSD equations on a more comprehensive soil database as well as providing a method capable of fitting a general model for any soil texture. Cumulative PSD data of 221 soil samples, carefully selected from UNSODA and Brazilian/Embrapa datasets to include 12 soil textural classes mapped in the texture triangle, were used in this study to evaluate the fitting performance of commonly applied PSD equations with three and four parameters for unimodal data (SKAG-3p, ANDE-4p, BEST-3p, FRED-4p) and a seven-parameters equation (FRED-7p) used for gap-graded bimodal shaped data. The FRED-7p equation showed outstanding fittings accuracies with average RMSE (100*g g−1) of 0.53 for all soils, about twice as low as the second best fitted equation (ANDE-4p). FRED-7p fitting accuracy was slightly influenced by soil texture, showing a small increase as sand content increases and a decrease as silt content increases. The FRED-7p equation, originally proposed to fit cumulative gap-graded bimodal PSD, performed properly and accurately for all textural classes, including both unimodal and bimodal shaped PSDs. As a result of that, it is strongly recommended as a general model for any kind of soil PSD.



中文翻译:

使用UNSODA和巴西数据集评估拟合土壤粒径分布的模型

矿物土壤粒度分布(PSD)是一种基本且非常有用的土壤特征,广泛用于支持土壤分类,土壤管理和土壤过程建模。PSD的一个重要局限性是从不同土壤分类系统获得的粒度级分缺乏标准化,这使得比较和合并数据变得困难。为了克服该缺点,已经进行了数次尝试以数学方式拟合PSD数据,以允许在土壤数据库之间转换土壤质地数据。已经针对特定系统提出和/或评估了参数方程,但是没有一个参数方程被研究,比较和证明对不同类型,区域和质地类别的土壤有效。从而,这项工作的目的是在更全面的土壤数据库上评估和比较几个PSD方程的性能,并提供一种能够拟合任何土壤质地的通用模型的方法。这项研究使用了221种土壤样品的累积PSD数据,这些数据是从UNSODA和巴西/巴西Embrapa数据集中精心选择的,包括12个土壤纹理类别映射到纹理三角形中,用于评估常用的PSD方程的拟合性能,该方程具有三个和四个参数,单峰数据(SKAG-3p,ANDE-4p,BEST-3p,FRED-4p)和用于间隙分级双峰形数据的七参数方程式(FRED-7p)。FRED-7p方程显示出卓越的拟合精度,平均均方误差(100 * g g)这项研究使用了221种土壤样品的累积PSD数据,这些数据是从UNSODA和巴西/巴西Embrapa数据集中精心选择的,包括12个土壤纹理类别映射到纹理三角形中,用于评估常用的PSD方程的拟合性能,该方程具有三个和四个参数,单峰数据(SKAG-3p,ANDE-4p,BEST-3p,FRED-4p)和用于间隙分级双峰形数据的七参数方程式(FRED-7p)。FRED-7p方程显示出卓越的拟合精度,平均均方误差(100 * g g)这项研究使用了221种土壤样品的累积PSD数据,这些数据是从UNSODA和巴西/巴西Embrapa数据集中精心选择的,包括12个土壤纹理类别映射到纹理三角形中,用于评估常用的PSD方程的拟合性能,该方程具有三个和四个参数,单峰数据(SKAG-3p,ANDE-4p,BEST-3p,FRED-4p)和用于间隙分级双峰形数据的七参数方程式(FRED-7p)。FRED-7p方程显示出卓越的拟合精度,平均均方误差(100 * g g)FRED-4p)和用于间隙分级双峰形数据的七参数方程式(FRED-7p)。FRED-7p方程显示出卓越的拟合精度,平均均方误差(100 * g g)FRED-4p)和用于间隙分级双峰形数据的七参数方程式(FRED-7p)。FRED-7p方程显示出卓越的拟合精度,平均均方误差(100 * g g)所有土壤的-1)为0.53,大约是第二最佳拟合方程(ANDE-4p)的两倍。FRED-7p拟合精度受土壤质地的影响很小,随含沙量的增加略有增加,而随含沙量的增加而减少。FRED-7p方程最初是为拟合累积的间隙梯度双峰PSD而提出的,它对所有纹理类别(包括单峰和双峰形状的PSD)都正确且准确地执行。结果,强烈建议将其作为任何类型的土壤PSD的通用模型。

更新日期:2020-03-16
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