Mapping Soil Cation-Exchange Capacity using Bayesian Modeling and Proximal Sensors at the Field Scale

Cation exchange capacity (CEC) affects soil fertility, acidity, and structural resilience. This is particularly the case in sugarcane growing areas of Australia because the soil there is sandy (>60%), strongly acidic (pH < 5.5), and strongly sodic (exchangeable sodium percentage [ESP] > 15%). Unfortunately, obtaining information on CEC at the field extent is time-consuming and expensive. Here, we used a digital soil mapping approach to add value to limited (40) topsoil (0-0.3 m) and subsoil (0.6-0.9 m) CEC information. We first collected proximally sensed ancillary data from three sources, including a digital elevation model (DEM), gamma-ray (gamma-ray) spectrometer (RS700) and electromagnetic (EM) induction instruments. We then use a Bayesian inference approach (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA-SPDE) implemented in R software to model the CEC and ancillary data. Accuracy (RMSE), bias (ME), and concordance (Lin's) of models were also generated from the different sources of ancillary data, either in combination or alone. We concluded, overall, that the INLA-SPDE approach could provide estimations of the posterior marginal distributions of the model parameters as well as the model responses as reported by other researchers. We also concluded that using the ancillary data sources in combination was most accurate (e.g., RMSE = 0.72) to predict CEC, least biased (e.g., ME = 0.07) and had the highest concordance (e.g., Lin's = 0.69) in both the topsoil and subsoil than using the ancillary data alone. The best ancillary data, when used alone for mapping CEC in the topsoil, was gamma-ray spectrometry, followed by EM data and elevation. For subsoil CEC, it was elevation, followed by gamma-ray spectrometry and then soil electrical conductivity (ECa) data. The maps of the credibility interval (CI) indicated that better predictions were achieved in the topsoil and indicated where improvements in prediction could be achieved in the subsoil.