1222 ISSN 1064-2293, Eurasian Soil Science, 2020, Vol. 53, No. 9, pp. 1222–1233. © Pleiades Publishing, Ltd., 2020. Prediction of Soil Properties Using Random Forest with Sparse Data in a Semi-Active Volcanic Mountain H. Piri Sahragard a, * and M. R. Pahlavan-Rad b, ** a Rangeland and Watershed Department, Water and Soil Faculty, University of Zabol, Zabol, Iran b Soil and Water Research Department, Golestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Gorgan, Iran *e-mail: hopiry@uoz.ac.ir **e-mail: pahlavanrad@gmail.com Received December 30, 2019; revised February 26, 2020; accepted April 1, 2020 Abstract—Understanding spatial variations of soil properties is necessary for the management of rangelands vegetation ecosystem. The present study aimed to assess the spatial variations of soil properties in the hillslope of the Taftan semi-active volcanic mountain, Sistan and Baluchestan Province, south-eastern Iran. The loca- tions of 30 sampling points were determined using random - systematic method and soil samples were taken from two depths: 0–30 and 30–60 cm. Spatial distribution of soil properties and relationships between soil properties and covariates were investigated using Random forest method. Model validation was done through 10-fold cross-validation approach. Based on results elevation, channel network base level and vertical dis- tance to channel network, were the most importance environmental variables in predicting of the some soil characteristics such as soil clay, silt, sand, SOC, and EC in two studied depths. The maps produced indicated higher clay at 30–60 depth in the higher elevations. EC amounts were increased in the lower parts of the moun- tain because of leaching. Furthermore, the highest map accuracy was related to EC map at both depths and clay at 30–60 depth. The prediction maps of other properties of soil had low accuracy. Keywords: DEM, environmental variables, Taftan, Random forest DOI: 10.1134/S1064229320090136 INTRODUCTION Assessing the spatial variation of organic carbon (OC), electrical conductivity (EC), and soil texture is important in rangeland ecosystems of arid environ- ments because of their effects on soil fertility, hydrau- lic conductivity, infiltration rate, and erosion. These characteristics can also influence plant species distri- bution [31]. Furthermore, different plant species can affect various soil properties significantly through evacuating moisture, soil nutrient uptake, and carbon stabilization [27–30]. Thus, knowledge on regularities of soil spatial variation is necessary for sustainable veg- etation management in the rangeland ecosystem, especially mountainous landscape. Determination of soil properties distribution in the mountainous area is difficult because of sampling lim- itations and the complex processes of soil formation. The mountain areas have heterogeneous environ- ments and shallow soils [18]. Here, topography and local climate are important factors in controlling soil properties such as organic matter (OM) [15]. Due to soil carbon turnover and geomorphology relationship, different pattern of spatial SOC distribution has been reported across landscapes [12]. Besides, altitude had a negative effect on SOC contents in different geo- graphical aspect. In other words, with increasing alti- tude, under different aspects, the SOC content will be reduced. [4]. Thereby, altitude variation can justify change in soil SOC stocks [32]. Due to soil significant susceptibility to erosion, SOC amount is differ from each other in different land uses in a Mediterranean cultivated field [35]. Digital soil mapping (DSM), as a powerful tool, can determine soil and environmental variables rela- tionships, thereby, spatial variations of soil properties in the desired area [23]. Different statistical models are used to make this relationship such as regression trees [39], artificial neural networks [22], generalized additive models [11], geographically weighted regression kriging [19], random forest [16–29] and Cubist [45]. The random forest (RF) has a high prediction per- formance and a random selection of variable to gener- ate each decision tree is appropriate for soil spatial dis- tribution modeling [6]. The comparison of prediction accuracy of RF and other models to predict soil char- acteristics has shown that RF has good performance in many studies. Accordingly, comparison study in Africa implies on the superiority of RF over the linear regres- GENESIS AND GEOGRAPHY OF SOILS