Abstract

The existing classifications of soil texture are based on the sedimentation data. The aim of this article is to consider the ways to develop soil textural classification on the basis of particle-size distribution (PSD) data obtained by the laser diffraction method. A detailed comparison of PSD data obtained by the classical pipette method and the method of laser diffractometry has been performed. We have shown the reproducibility of the laser diffraction method and the effect of the oxidation stage on the soil texture class. This study is based on eight genetic soil types (overall, 32 full-profile soil pits) forming a zonal soil sequence from Podzols (Subpolar Urals) to ferrallitic soil (southwest Oceania) and differing in their mineralogical compositions, textures, and elementary pedogenetic processes. The direct use of the Kachinskii and USDA classifications with the data of the laser diffraction method leads to mistakes in determining the soil texture class in 43 and 65% of cases, respectively. The increasing complexity of recalculation, introduction of new variables, and accounting for interlaboratory errors allow us to determine correctly the texture class according to the Kachinskii and USDA classifications in no more than 70 and 72% of soil samples, respectively. The most simple and effective approach to solve the classification problem for the laser diffraction method is to calibrate existing classifications directly on the basis of data on soil samples, for which the texture class was determined by the field method.

中文翻译：

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激光衍射法发展土壤质地分类的方法

摘要

现有的土壤质地分类基于沉积数据。本文的目的是考虑基于通过激光衍射法获得的粒度分布（PSD）数据发展土壤质地分类的方法。已对通过经典移液器法和激光衍射法获得的PSD数据进行了详细的比较。我们已经证明了激光衍射法的可重复性以及氧化阶段对土壤质地类别的影响。这项研究基于八种遗传土壤类型（总共32个完整剖面的土壤坑），形成了从Podzols（亚极乌拉尔）到铁铝土（西南大洋洲）的地带性土壤序列，并且其矿物学组成，质地和基本的成岩作用也不同。直接将Kachinskii和USDA分类与激光衍射法数据一起使用会导致分别在43％和65％的情况下确定土壤质地类别时出错。重新计算的复杂性不断增加，引入了新变量并考虑了实验室间的误差，这使我们能够分别根据不超过70％和72％的土壤样本中的Kachinskii和USDA分类正确确定质地类别。解决激光衍射法分类问题的最简单有效的方法是直接根据土壤样品的数据来校准现有的分类，而土壤样品的纹理类别是通过现场方法确定的。