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Conversion of soil particle size distribution and texture classification from ISSS system to FAO/USDA system in Japanese paddy soils

机译:日本稻田土壤粮农组织/美国农业部系统粮农组织/美国农业部系统的土壤粒度分布及纹理分类转换

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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: si(FAO/USDA) = 1.305si(ISSS) + 0.396fs(ISSS)-0.100cs(ISSS) - 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: si(FAO/USDA) = 0.314si(ISSS) + 1.533s(ISSS) - 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%.
机译:两种不同的系统,ISS和FAO / USDA之间的转换,粒度分布和土壤纹理分类是有用的,可用于表征土壤物理性质和每次发布的使用。本研究的目的是测试已发布的一些职能,以便从ISSS转换为日本水稻土壤的ISSS,并选择最佳方法。我们使用Log-Linear方法,Log-Normal方法,多元回归方法,Skags等,Skaggs等,测试了267日日本稻田的表土。的方法和Minasny和McBratney的方法。使用多元回归方法获得最少的AIC,并且衍生的等式给出如下:SI(FAO / USDA)= 1.305SI(ISSS)+ 0.396FS(ISSS)-0.100CS(ISSS) - 12.323,其中SI,FS, CS分别是淤泥,细砂和粗砂的百分比; ISSS和FAO / USDA是分级系统;它的RMSE是3.1%。对于只有总砂含量可用而不是细砂和粗砂,获得以下等式:Si(FAO / USDA)= 0.314SI(ISSS)+ 1.533s(ISSS) - 20.903(RMSE在非经验方法中,最佳估计方法是Skaggs等人。的方法,其RMSE为3.3%。通过上述等式使用估计的粒度分数的FAO / USDA系统的土壤纹理分类可以分类为正确的类别。分类的准确率为93-97%。

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