Cation Exchange Capacity

Maps above show estimated CEC for the 0-5cm depth interval (left) and areas where model extension is contentious (right; black areas) and applicable (coloured).

Background

Version 1 Soil and landscape Grid of Australia (Grundy et al. 2015), produced digital mapping of Effective Cation Exchange Capacity (ECEC) which is defined as the total amount of exchangeable bases which are mostly sodium, potassium, calcium and magnesium (collectively termed as bases) in non-acidic soils and bases plus aluminium and hydrogen in acidic soils.


Of the data described in Viscarra Rossel et al. (2015) there were 13836 cases with an ‘ECEC’ measurement coming from 3284 sites distributed throughout Australia. According to recent extracts (Jan 2022) from the Soil Data Federator (Searle et al. 2021) the are: 23490 sites with measured Na, 24212 (Mg), 24181 (K), 24221 (Ca), 3274 (Al) and 1624 (H). The are 6107 sites with a cation exchange capacity measurement where most have been derived through measurement of bases only.

The current product, national digital soil maps of cation exchange capacity, described here entails the use of those data pertaining to those data with CEC measurement.

This dataset is made of soil measurements using the following methods as described in Rayment and Lyons (2010): method not recorded (1096), 15A1 (161), 15A2 (365), 15B1 (553), 15B2 (34), 15C1 (3229), 15D1 (265), 15E1 (28), 15K1 (376). The distribution of these sites, colour-coded by each method is shown on Figure 1.

Site distribution across Australia which have measurements of cation exchange capacity. Dots are colour coded accordingly to measurement method. Measurement method codes correspond to those in Rayment and Lyons (2010).

There is a clear understanding of which soil characteristics drive variations of CEC (a measure of the soil’s ability to hold positively charged ions) in soils. These characteristics include clay mineralogy, soil organic matter and pH (Weil and Brady 2016).

To complement the CEC measurement data, we used data cases (12474) where there is a measured CEC together with soil texture and soil organic carbon co-located measurements. A machine learning pedotransfer function model with these data, together with spatial covariates was used to extend the geographic spread and density of CEC data in order to potentially improve digital soil mapping efforts.

By extending the model to all sites and cases with co-located measurements of soil carbon and texture and covariate information, the collated information grew from 6107 sites to more than 21000.

In terms of vertical characterisation of the more than 70000 individual cases (collection of data across all sites and depth intervals), the proportional number of cases for the 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, and 100-200cm is 27%, 26%, 18%, 13%, 20% and 6% respectively.

Effectively about half of the cases characterise the top 15cm of soils across the country, and relatively few characterise below 1m. This general pattern of skewed representation of soil information is common in soil databases such that topsoils are much more frequently characterised than subsoils.

Distribution of soil measurement site data used for national mapping of CEC. Green dots are sites with a measured CEC, while grey dots are sites where CEC was inferred based on measured soil carbon and texture data and environmental covariate information

Digital Soil Modelling Steps


  • Extensive data processing was involved post data extraction form SoilData

  • Spatial modelling is underpinned by the Cubist (Quinlan 1993) machine learning algorithm.

  • The spatial modelling integrates both measurement CEC data and CEC data derived by pedotransfer function. The derived CEC have an associated uncertainty and this is incorporated into the spatial model via a simple monte-carlo approach.

  • The spatial model included a soil depth interval term in order to exploit covariance relationships of soil information within a soil profile. Thus modelling is considered a full soil profile predictive modelling framework.

  • Prediction uncertainties in this work were done using an approach based on local-errors and clustering (UNEEC) method developed by Shrestha and Solomatine (2006).

  • Soil maps of predictions and associated uncertainties (expressed as lower and upper prediction limits for 90% confidence) were generated for the following depth intervals: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, 100-200cm.

  • Additional analysis was performed to determine the spatial extent of model extensibility, distinguishing areas of the map where model extension would be considered reliable based on the dsitirubtiuion of data and the covariate data coverage.

Results


Models were evaluated using a test set of 5000 cases. XY-plot between observed and predicted values is shown to the right together with goodness of fit measures.


The environmental data space together with soil depth interval were divided up into 93 classes, each having their own model error distribution based on out-of-bag evaluations. Evaluated on the 5000 test cases the PICP plot demonstrates the desirable fidelity to the 1:1 line indicating the uncertainties are well-defined and expressed.

Mapping outputs can be visualised with this link




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