Soil erodibility and its prediction in semi-arid regions

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

Research output: Contribution to journalArticlepeer-review

2 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 km 2 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 R 2  = 0.84 had higher performance than developed MLR, USLE and RUSLE for the K estimation.

Original languageEnglish
Pages (from-to)1688-1703
Number of pages17
JournalArchives of Agronomy and Soil Science
Volume65
Issue number12
Early online date15 Feb 2019
DOIs
Publication statusE-pub ahead of print - 15 Feb 2019

Keywords

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

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