Pragmatic models for the prediction and digital mapping of soil properties in eastern Australia
Jonathan M. Gray A B C , Thomas F. A. Bishop B and Xihua Yang A
+ Author Affiliations
- Author Affiliations
A Office of Environment and Heritage, PO Box 3720, Parramatta, NSW 2124, Australia.
B University of Sydney, Biomedical Building C81, University of Sydney, NSW 2006, Australia.
C Corresponding author. Email: jonathan.gray@environment.nsw.gov.au
Soil Research 53(1) 24-42 https://doi.org/10.1071/SR13306
Submitted: 22 October 2013 Accepted: 15 September 2014 Published: 14 January 2015
Abstract
To help meet the increasing need for knowledge and data on the spatial distribution of soils, readily applied multiple linear regression models were developed for key soil properties over eastern Australia. Selected covariates were used to represent the key soil-forming factors of climate (annual precipitation and maximum temperature), parent material (a lithological silica index) topography (new topo-slope and aspect indices) and biota (a modified land disturbance index). The models are presented at three depth intervals (0–10, 10–30 and 30–100 cm) and are of variable but generally moderate statistical strength, with concordance correlation coefficients in the order of 0.7 for organic carbon (OC) upper depth, pHca, sum of bases, cation exchange capacity (CEC) and sand, but somewhat lower (0.4–0.6) for OC lower depths, total phosphorous, clay and silt. The pragmatic models facilitate soil property predictions at individual sites using only climate and field-collected data. They were also moderately effective for deriving digital soil maps over the state of New South Wales and a regional catchment. The models and derived maps compared well in predictive ability to those derived from more sophisticated techniques involving Cubist decision trees with remotely sensed covariates. The readily understood and interpreted nature of these products means they may provide a useful introduction to the more advanced digital soil modelling and mapping techniques. The models provide useful information and broader insights into the factors controlling soil distribution in eastern Australia and beyond, including the change in a soil property with a given unit change in a covariate.
Additional keywords: bases, digital soil maps, organic carbon, particle sizes, pH, regression models, soil formation.
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