Prediction of some soil properties in volcanic soils using random forest modeling: A case study at chinyero special nature reserve (Tenerife, canary islands)

Abstract

Soil organic carbon (organic C) and pH are key soil properties and valuable indicators of soil quality, whereas phosphate retention capacity (P retention) is a diagnostic property to define andic soil properties and andic soils, with all of them typically interrelated in volcanic ash (i.e., andic) soils. In this paper, we examined the potential of a random forest (RF) regression model to predict field-measured soil pH, organic C and P retention capacity from several biophysical (type and fraction of the plant cover), bioclimatic (maximum temperature of the warmest month, precipitation and temperature seasonality, and precipitation of the driest quarter), and topographic (ruggedness and curvature of the slope) predictors in a protected forest area in Tenerife, Canary Islands. Piecewise structural equation modeling (pSEM) was subsequently used to unravel the complex, direct and indirect relationships between the biophysical, bioclimatic and topographic variables, and the selected soil properties. The RF regression model accounted for the properties of interest with varying degrees of accuracy, from organic C (R² = 0.67; RMSE = 29.86), to P retention capacity (R² = 0.44; RMSE = 18.84) and soil pH (R² = 0.31; RMSE = 0.43). The pSEM model revealed that P retention capacity is strongly linked to organic C in volcanic ash soils, and thus indirectly to the environmental variables shaping organic C variability, namely fractional vegetation cover and precipitation seasonality.