Soil erodibility and its prediction in semi-arid regions

Yaser Ostovari*, Shoja Ghorbani-Dashtaki, Lalit Kumar, Farzin Shabani

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Pedotransfer functions (PTFs) have been used to save time and cost in predicting certain soil properties, such as soil erodibility (K-factor). The main objectives of this study were to develop appropriate PTFs to predict the K-factor, and then compare new PTFs with Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE) K-factor models. The K-factor was measured using 40 erosion plots under natural rainfall in Simakan Watershed, an area of 350 km2 in central of Iran. The Regression Tree (RT) and Multiple Linear Regression (MLR) were used to develop PTFs for predicting the K-factor. The result showed that the mean of measured K was 0.01 t h MJ−1 mm−1. The mean K value predicted by USLE and RUSLE was 2.08 and 2.84 times more than the measured K, respectively. Although calcium carbonate was not considered in the original USLE and RUSLE K-factors, it appeared in the advanced PTFs due to its strong positive significant impact on aggregate stability and soil infiltration rate, resulting in decreased K-factor. The results also showed that the RT with R2 = 0.84 had higher performance than developed MLR, USLE and RUSLE for the K estimation.

Original languageEnglish
Pages (from-to)1688-1703
Number of pages16
JournalArchives of Agronomy and Soil Science
Volume65
Issue number12
DOIs
Publication statusPublished - 15 Oct 2019

Keywords

  • data mining
  • Dorudzan
  • erosivity
  • regression tree
  • stepwise regression

Fingerprint Dive into the research topics of 'Soil erodibility and its prediction in semi-arid regions'. Together they form a unique fingerprint.

Cite this