Using model averaging to combine soil property rasters from legacy soil maps and from point data

- Model averaging is used to combine an ensemble of soil maps.
- A number of model averaging methods are used and compared.
- Granger-Ramanathan averaging is an efficient model averaging compared to others.
- A novel approach for deriving uncertainties of model averaging outputs is detailed.

The objective of this study was to determine the efficacy of model averaging (ensemble modelling) as an approach for combining digital soil property maps derived from disaggregated legacy soil class maps and scorpan kriging (using soil point data). The study is based in the Dalrymple Shire, QLD and continues on the soil pH mapping work of Odgers et al. (2014a). Equal weights averaging (EW), Bates-Granger or variance weighted averaging (VW), Granger-Ramanathan averaging (GRA), and Bayesian model averaging (BMA) were compared in this study. Model averaged predictions were estimated to 2 m depth at regular depth intervals. 90% prediction intervals of the model averaged predictions were derived numerically. Neither the disaggregated soil map nor the scorpan kriging map was particularly accurate. Predictions from model averaging however did improve upon the accuracy, where at all depths, the combined predictions were an improvement on using either of the contributing soil maps alone. We recommend the use of GRA for digital soil mapping applications because its performance is equal to or better than the generally preferred BMA approach, yet far simpler to implement, and is computationally efficient. For regional soil studies where polygon mapping and soil point data are available, ensemble modelling is a useful combinatorial approach.