## 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 language | English |
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Pages (from-to) | 1688-1703 |

Number of pages | 16 |

Journal | Archives of Agronomy and Soil Science |

Volume | 65 |

Issue number | 12 |

DOIs | |

Publication status | Published - 15 Oct 2019 |

## Keywords

- data mining
- Dorudzan
- erosivity
- regression tree
- stepwise regression