Estimating the USLE Soil Erodibility Factor in Sicily, South Italy

• Published in: Applied engineering in agriculture Vol. 28; no. 2; pp. 199 - 206
• Main Authors: Bagarello, V, Di Stefano, C, Ferro, V, Giordano, G, Iovino, M, Pampalone, V
• Format: Journal Article
• Language: English
• Published: 2012
• Subjects: clay soils; erodibility; soil conservation; soil organic matter; summer; Universal Soil Loss Equation; water erosion; Sicily; United States; Italy
• ISSN: 0883-8542; 1943-7838
• DOI: 10.13031/2013.41347

The Universal Soil Loss Equation (USLE) is used by professionals and technicians to predict soil loss by water erosion and to establish soil conservation measures. One of the key elements of the USLE is the K factor, which is a measure of the soil erodibility. Given the difficulty in collecting sufficient data to adequately measure K, early in the USLE's history the soil erodibility nomograph method was developed to allow estimation of K based on standard soil properties. Since the nomograph approach was developed based on a small number of soils in the United States, it is necessary for other contexts to check the nomograph's ability to predict the soil's true erodibility. Considering that soil organic matter data are difficult to obtain, an estimation procedure of the soil erodibility factor, K, based only on soil textural data is desirable. In this investigation, the soil erodibility factor was first experimentally determined for the clay soil at the Sparacia (Sicily) experimental station. A relatively low value (0.039 t ha h ha -1 MJ -1 mm -1 ) was determined, and summer erodibility was found to be more than twice the value of winter erodibility. This measured K value was 1.85 times the nomograph K, which for many practical applications is not a large difference. Finally, using 1813 data points, a procedure for estimating K using only soil textural data was developed for Sicily. The errors of the predictions did not exceed a factor of two and three for 94.4% and 99.2% of the data points, respectively, suggesting a satisfactory ability of the developed procedure to yield an estimate of K with a reduced input dataset.