Predictive mapping of soil organic carbon stocks in South Australia’s agricultural zone
Craig Liddicoat A B E , David Maschmedt A , David Clifford C , Ross Searle C , Tim Herrmann A , Lynne M. Macdonald D and Jeff Baldock D
+ Author Affiliations
- Author Affiliations
A Department of Environment, Water and Natural Resources, GPO Box 1047, Adelaide, SA 5001, Australia.
B The University of Adelaide, North Terrace, Adelaide, SA 5005, Australia.
C CSIRO, EcoSciences Precinct, 41 Boggo Road, Dutton Park, Qld 4102, Australia.
D CSIRO, Waite Campus, Waite Road, Glen Osmond, SA 5064, Australia.
E Corresponding author. Email: craig.liddicoat@sa.gov.au
Soil Research 53(8) 956-973 https://doi.org/10.1071/SR15100
Submitted: 13 August 2014 Accepted: 21 August 2015 Published: 12 October 2015
Abstract
Better understanding the spatial distribution of soil organic carbon (SOC) stocks is important for the management and enhancement of soils for production and environmental outcomes. We have applied digital soil mapping (DSM) techniques to combine soil-site datasets from legacy and recent sources, environmental covariates and expert pedological knowledge to predict and map SOC stocks in the top 0.3 m, and their uncertainty, across South Australia’s agricultural zone. In achieving this, we aimed to maximise the use of locally sourced datasets not previously considered in national soil C assessments. Practical considerations for operationalising DSM are also discussed in the context of working with problematic legacy datasets, handling large numbers of potentially correlated covariates, and meeting end-user needs for readily interpretable results and accurate maps. Spatial modelling was undertaken using open-source R statistical software over a study area of ~160 000 km2. Legacy-site SOC stock estimates were derived with inputs from an expert-derived bulk-density pedotransfer function to overcome critical gaps in the data. Site estimates of SOC were evaluated over a consistent depth range and then used in spatial predictions through an environmental-correlation regression-kriging DSM approach. This used the contemporary Least Absolute Shrinkage and Selection Operator penalised-regression method, which catered for a large number (63 numeric, four categorical, four legacy-soil mapping themes) of potentially correlated covariates. For efficient use of the available data, this was performed within a k-fold cross-validation (k = 10) modelling framework. Through this, we generated multiple predictions and variance information at every node of our prediction grid, which was used to evaluate and map the expected value (mean) of SOC stocks and their uncertainty. For the South Australian agricultural zone, expected value SOC stocks in the top 0.3 m summed to 0.589 Gt with a 90% prediction interval of 0.266–1.086 Gt.
Additional keywords: digital soil mapping, geostatistics, LASSO, soil organic carbon, pedotransfer function, spatial analysis.
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