期刊
SOIL SCIENCE SOCIETY OF AMERICA JOURNAL
卷 79, 期 6, 页码 1556-1566出版社
WILEY
DOI: 10.2136/sssaj2015.04.0164
关键词
-
类别
资金
- National Natural Science Foundation of China [41271315, 41571296]
- Program for New Century Excellent Talents in University [NCET-130487]
- programmes of Northwest AF University [2014YQ007]
- Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources [A315021381]
The laser-diffraction method (LDM) can rapidly determine soil particlesize distributions (PSDs), but LDM-derived PSDs cannot be directly used to classify soil textures by referring to the standards of the classical sieve-pipette method (SPM). Our objectives were to explore calibration models for converting PSD data from LDM (volume, %) to SPM (mass, %), and to evaluate the precision of textural classification by using LDM data. We determined the PSDs using both methods for 235 soil samples of various textures collected from three typical land uses, on the Loess Plateau of China. The LDM generally underestimated clay fractions by an average of 45.1%, and overestimated silt fractions by an average of 18.3% compared with SPM. Differences in PSD data between the two methods, indicated by coefficient Cs, increased with increasing clay contents for the 235 samples (P < 0.05). Three calibration models could, however, convert the clay, silt, and sand contents from the volume percentage (LDM) to the mass percentage (SPM). After the conversion, the mean coefficient C between the two methods decreased from 7.9 to 4.1% for the validation samples (n = 78). The distributions of soil textures within the USDA textural triangle agreed well in 71 of the 78 samples, for measured and converted PSD data. The three types of land use did not affect the differences between measured and converted PSD data (P > 0.05). Soil textures can thus be rapidly determined by converting PSD data from the faster volume-based LDM to data equivalent to the mass-based SPM, independent of land-use type.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据