ABSTRACT The conversion between the two different systems, ISSS and FAO/USDA, of particle size distribution and soil texture classification is useful to characterize soil physical properties and usage of each published. The objective of this study is to test some functions that have been published for conversion from ISSS to FAO/USDA system for Japanese paddy soils and to select the best method. We tested the topsoils of 267 Japanese paddy fields using the log-linear method, log-normal method, multiple regression method, Skaggs et al.’s method, and Minasny and McBratney’s method. The least AIC was obtained using multiple regression method, and the equation derived was given as follows: siFAO/USDA = 1.305siISSS + 0.396fsISSS−0.100csISSS − 12.323 where si, fs, and cs are the percentage of silt, fine sand, and coarse sand respectively; ISSS and FAO/USDA is the fractionation system; and its RMSE was 3.1%. For the case that only the total sand content (s) is available instead of fine sand and coarse sand, the following equation was obtained: siFAO/USDA = 1.533siISSS + 0.314sISSS − 20.903 (RMSE = 3.7%) Among the non-empirical methods, the best estimation method was Skaggs et al.’s method, and its RMSE was 3.3%. The soil texture classification by FAO/USDA system using estimated particle size fractions by the above equation can be classified to correct categories. The accuracy ratio of the classification was 93-97%.

中文翻译：

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日本稻田土壤粒度分布和质地分类从ISSS系统到FAO/USDA系统的转换

摘要 ISSS 和FAO/USDA 两种不同系统之间的粒度分布和土壤质地分类之间的转换对于表征土壤物理特性和每个已发表的用途是有用的。本研究的目的是测试一些已发布的用于日本稻田从 ISSS 转换为 FAO/USDA 系统的函数，并选择最佳方法。我们使用对数线性方法、对数正态方法、多元回归方法、Skaggs 等人的方法以及 Minasny 和 McBratney 的方法测试了 267 个日本稻田的表土。最小 AIC 是使用多元回归方法获得的，得出的方程如下： siFAO/USDA = 1.305siISSS + 0.396fsISSS−0.100csISSS − 12.323 其中 si、fs 和 cs 是淤泥、细砂和细砂的百分比粗砂分别；ISSS和FAO/USDA是分馏系统；其均方根误差为 3.1%。对于只有总砂含量（s）而不是细砂和粗砂的情况，得到以下等式： siFAO/USDA = 1.533siISSS + 0.314sISSS − 20.903 (RMSE = 3.7%)方法中，最好的估计方法是 Skaggs 等人的方法，其均方根误差为 3.3%。粮农组织/美国农业部系统根据上述方程使用估计的粒度分数进行的土壤质地分类可以归类为正确的类别。分类准确率为93-97%。7%) 在非经验方法中，最好的估计方法是 Skaggs 等人的方法，其均方根误差为 3.3%。粮农组织/美国农业部系统根据上述方程使用估计的粒度分数进行的土壤质地分类可以归类为正确的类别。分类准确率为93-97%。7%) 在非经验方法中，最好的估计方法是 Skaggs 等人的方法，其均方根误差为 3.3%。粮农组织/美国农业部系统根据上述方程使用估计的粒度分数进行的土壤质地分类可以归类为正确的类别。分类准确率为93-97%。