@article{10272/22983,
year = {2018},
url = {https://hdl.handle.net/10272/22983},
abstract = {The K‐factor of the universal soil loss equation is a core component in many erosion models, as a measure of soil erodibility. It can be estimated by a nomograph, where the summed fractions of silt and very fine sand (VFS) are basic inputs. Frequently, only the three broad particle‐size classes of sand, silt, and clay are measured in laboratories; thus, the VFS fraction must be estimated. Three models are currently available for this estimation, namely, (a) the Revised Universal Soil Loss Equation formula, (b) the European Soil Data Centre method, and (c) the Shirazi–Boersma theory, all three use just the sand fraction as explanatory variable. Nevertheless, their accuracy has
never been assessed, and this is the main purpose of this study. The data used to test the VFS estimation methods were drawn from the National Cooperative Soil Survey Soil Characterization Database, incorporating data from more than 300,000 soil horizon samples. The test results show a poor performance of the models, all of which were found to be unsuitable for 31.1% of the textural triangle, accounting for 32.3% of the soil samples. Moreover, it is demonstrated that any conceivable model based solely on the broad particle‐size classes would suffer from a high degree of uncertainty. Consequently, the number of explanatory variables should be increased in order to improve the performance of models. An alternative prediction chart is provided for the first approximation of K‐factor, based on the textural triangle.},
organization = {The authors would like to thank Ellis Benham, from USDA/NRCS Kellogg Soil Survey Laboratory, for the advice given on the use of the NCSS Soil Characterization Database and Daniel C. Yoder from the University of Tennessee-Institute of Agriculture, for his help in defining authorship and quoting of RUSLE2 documents.},
publisher = {Wiley},
title = {Estimating the very fine sand fraction for calculating the soil erodibility K‐factor},
author = {Corral Pazos de Provens, Eva and Rapp Arrarás, Ígor and Domingo Santos, Juan Manuel},
}