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Multivariate mapping of soil with structural equation modelling
Author(s) -
Angelini M. E.,
Heuvelink G. B. M.,
Kempen B.
Publication year - 2017
Publication title -
european journal of soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.244
H-Index - 111
eISSN - 1365-2389
pISSN - 1351-0754
DOI - 10.1111/ejss.12446
Subject(s) - multivariate statistics , soil science , covariance , structural equation modeling , soil test , cation exchange capacity , soil carbon , linear regression , partial least squares regression , environmental science , mathematics , statistics , soil water
Summary In a previous study we introduced structural equation modelling ( SEM ) for digital soil mapping in the A rgentine P ampas. An attractive property of SEM is that it incorporates pedological knowledge explicitly through a mathematical implementation of a conceptual model. Many soil processes operate within the soil profile; therefore, SEM might be suitable for simultaneous prediction of soil properties for multiple soil layers. In this way, relations between soil properties in different horizons can be included that might result in more consistent predictions. The objectives of this study were therefore to apply SEM to multi‐layer and multivariate soil mapping, and to test SEM functionality for suggestions to improve the modelling. We applied SEM to model and predict the lateral and vertical distribution of the cation exchange capacity ( CEC ), organic carbon ( OC ) and clay content of three major soil horizons, A , B and C , for a 23 000‐km 2 region in the A rgentine P ampas. We developed a conceptual model based on pedological hypotheses. Next, we derived a mathematical model and calibrated it with environmental covariates and soil data from 320 soil profiles. Cross‐validation of predicted soil properties showed that SEM explained only marginally more of the variance than a linear regression model. However, assessment of the covariation showed that SEM reproduces the covariance between variables much more accurately than linear regression. We concluded that SEM can be used to predict several soil properties in multiple layers by considering the interrelations between soil properties and layers. Highlights We tested structural equation modelling ( SEM ) for multi‐layer and multivariate soil mapping. SEM models soil property covariation better than multiple linear regression. The SEM re‐specification step improves prediction accuracy. SEM supports learning about soil processes from data.