Predicting Soil Bulk Density Using Advanced Pedotransfer Functions in an Arid Environment

Abstract

<abstract><title><italic>Abstract.</italic></title> Soil bulk density (ρ_b) measurement is essential for many soil management and engineering applications in addition to the emerging need to estimate soil carbon reserves. Recently, pedotransfer functions (PTFs) have been gaining widespread recognition for their capability to predict soil bulk density using extractable soil available databases. However, these functions are site specific and might be hampered when used in different environments. The objective of this study is to develop and compare PTFs for estimating soil ρ_b in an arid environment from texture fractions and soil organic carbon (SOC). The examined PTFs were simple linear regression (SLR), multiple non-linear regression (NLR), stepwise multiple linear regression (SWR), partial least squares (PLS), and artificial neural networks (ANNs). A large data set of soil samples procured from the Amman-Zarqa basin, a typical arid environment in north Jordan, were used to calibrate and validate the PTFs. Soil organic carbon and sand contents had moderate correlations with soil ρ_b, and both were almost interchangeable predictors of soil ρ_b. Moreover, it has been found that increasing the number of predictor variables in the PTF models enhanced the prediction capabilities of soil ρ_b. Furthermore, ANNs had high capabilities in predicting soil ρ_b. As compared to SLR models, the ANNs were 57.5% less unbiased, 85% less precise, and 18.4% more accurate in predicting soil ρ_b in arid environments.