Soil pH (1:5 Water)
Australian collation of soil pH (1:5 soil water) data interrogated and mapped (compliant with SLGA specifications) across Australia for the first time.
Spatial modelling of soil pH was done via Random Forest machine learning coupled with an integrative approach to combine both laboratory and field measurements.
Mapping outputs generated for both 90m and 30m grid cell resolution.
Differences in model goodness of fit for 90m and 30m modelling are near indistinguishable.
Spatial pattern of soil pH variability similar for both mapping resolutions, but obviously mapping at 30m provides more granular characterisation.
Distribution of sites across Australia with lab measured pH (4A1).
Distribution of sites across Australia with field measured pH.
% of site measurements (field or lab) with data available for specified depth interval
Integrating field measurement with lab measurements
xy-density plot of the ~85 000 cases of paired field and lab measured data for soil pH across Australia.
Spatial modelling of pH using both field and lab values
Evaluated on the completely withheld 10000 site data, the table below provides the summary model goodness of fit measures in terms of: coefficient of determination, concordance, root mean square error and bias. This is show for both modelling at 90m and 30m resolution and with and without spatial modelling of the Random Forest model residuals. The figures below and to the right provide show xy-plots of the 90m models with and without the additional variogram modelling of Random Forest model residuals. PICP plots (RF + residual model) show a acceptable quantification on the basis of correspondence between established confidence level and associated prediction interval coverage. Overall, fitted models do not show substantial bias, but there is some indication that the variogram modelling corrects this a bit further. It is difficult to really distinguish the model evaluations for the 90m and 30m modelling. The ultimate benefit of using the finer resolution modelling would lay in the fact that the mapping would be more granular and therefore more suitable for use cases with relatively smaller mapping extents such as farm and even field scales.
Model goodness of fit evaluations for both model resolutions at each depth interval and with and without variogram modelling
xy- and PICP plots illustrating model evaluating both the models and their uncertainties against a test dataset of measured soil pH data.
At the national extent there is very little apparent differences in the 90m and 30m mapping for the 0-5cm layer shown below. Zooming right down to the spatial extent of the CSIRO Boorowa farm we can compare the mapping against digital mapping specific to the farm which was produced at 5m resolution following a detailed reconnaissance soil survey Shown below there are shown clear correspondence between the maps, but noting that for the 30m mapping, it is produced without the kriged residuals of the Random Forest model which might explain perhaps systematic differences when compared against the on-farm mapping. In any case, the variability of surface soil pH is relatively small regardless on the mapping product.
National extent soil pH mapping (4A1 method) for 0-5cm depth interval for 90m (TOP) and 30m (BOTTOM) modelling.
Soil pH (0-5cm) mapping focused on the CSIRO Boorowa farm (~220ha) in southern NSW. TOP: 30m modelling output. MIDDLE: 90m modelling output. BOTTOM: 5m modelling output as described in Malone et al. 2022.