Corral Pazos de Provens, EvaRapp Arrarás, ÍgorDomingo Santos, Juan Manuel2024-01-292024-01-292018Corral-Pazos-de-Provens E, Domingo-Santos JM, Rapp-Arrarás Í. Estimating the very fine sand fraction for calculating the soil erodibility K-factor. Land Degrad Dev. 2018; 29: 3595–3606. https://doi.org/10.1002/ldr.31211085-32781099-145X (electrónico)https://hdl.handle.net/10272/22983"This is the peer reviewed version of the following article: [Corral-Pazos-de-Provens E, Domingo-Santos JM, Rapp-Arrarás Í. Estimating the very fine sand fraction for calculating the soil erodibility K-factor. Land Degrad Dev. 2018; 29: 3595–3606. https://doi.org/10.1002/ldr.3121], which has been published in final form at [https://onlinelibrary.wiley.com/doi/abs/10.1002/ldr.3121]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. "Esta es la versión revisada por pares del siguiente artículo: [Corral-Pazos-de-Provens E, Domingo-Santos JM, Rapp-Arrarás Í. Estimating the very fine sand fraction for calculating the soil erodibility K-factor. Land Degrad Dev. 2018; 29: 3595–3606. https://doi.org/10.1002/ldr.3121], que se publicó en su forma final en [https://onlinelibrary.wiley.com/doi/abs/10.1002/ldr.3121]. Este artículo puede usarse con fines no comerciales de acuerdo con los Términos de Wiley. y condiciones de uso de las versiones autoarchivadas. Este artículo no puede mejorarse, enriquecerse ni transformarse de otro modo en un trabajo derivado, sin el permiso expreso de Wiley o por los derechos legales establecidos en la legislación aplicable. Los avisos de derechos de autor no deben eliminarse, oscurecerse ni modificarse. El artículo debe estar vinculado a la versión de registro de Wiley en Wiley Online Library y se debe prohibir cualquier incrustación, encuadre o puesta a disposición del artículo o sus páginas por parte de terceros desde plataformas, servicios y sitios web distintos de Wiley Online Library.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.engAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/Erosion modelingPiecewise quantile regressionPrediction intervalsRUSLETextural trianglejournal articleopen access3106.03 Control de la Erosión