Mapping the soils of an Argentine Pampas region using structural equation modeling

Abstract

Current digital soil mapping (DSM) methods have limitations. For instance, it is difficult to predict a large number of soil properties simultaneously, while preserving the relationships between them. Another problem is that prevalent prediction models use pedological knowledge in a very crude way only. To tackle these problems, we investigated the use of structural equation modelling (SEM). SEM has its roots in the social sciences and is recently also being used in other scientific disciplines, such as ecology. SEM integrates empirical information with mechanistic knowledge by deriving the model equations from known causal relationships, while estimating the model parameters using the available data. It distinguishes between endogenous and exogenous variables, where, in our application, the first are soil properties and the latter are external soil forming factors (i.e. climate, relief, organisms). We introduce SEM theory and present a case study in which we applied SEM to a 22,900 km2 region in the Argentinian Pampas to map seven key soil properties. In this case study, we started with identifying the main soil forming processes in the study area and assigned for each process the main soil properties affected. Based on this analysis we defined a conceptual soil-landscape model, which was subsequently converted to a SEM graphical model. Finally, we derived the SEM equations and implemented these in the statistical software R using the latent variable analysis (lavaan) package. The model was calibrated using a soil dataset of 320 soil profile data and 12 environmental covariate layers. The outcomes of the model were maps of seven soil properties and a SEM graph that shows the strength of the relationships. Although the accuracy of the maps, based on cross-validation and independent validation, was poor, this paper demonstrates that SEM can be used to explicitly include pedological knowledge in prediction of soil properties and modelling of their interrelationships. It bridges the gap between empirical and mechanistic methods for soil-landscape modelling, and is a tool that can help produce pedologically sound soil maps.