Digital soil mapping of an Argentinian Pampa Region using structural equation modelling

15th September 2015Marcos E. Angelini, Gerard B.M. Heuvelink, Bas Kempen, Hector J.M. Morrás, Darío Rodríguez

Introduction

Interrelationships between soil properties are essential to

understand soil dynamics

Soil properties are usually predicted individually in

DSM

Our goal is to model the soil system interrelationships

Structural equation modelling (SEM)A scientific framework to analyse cause-effect connections in a system (Grace, 2006)

SEM essentials Path analysis, factor analysis & simultaneous equation

systems, starting point is a conceptual model Extension of conventional regression techniques Measurement error explicitly included Recursive paths allowed (Y-> X, X -> Y)

~23 000 km2

Phaeozems and Solonetz

320 soil profiles for model calibration

100 soil profiles for validation (stratified random sampling)

Case study: application of SEM to Argentinian Pampa

Conceptual model

1. Bases saturation2. Total Bases 3. Ratio Clay % of B/A horizons4. OC A horizon5. ESP A horizon6. ESP B horizon7. Thickness A horizon

From graphical to mathematical model

𝜂=𝛽⋅𝜂+𝛾 ⋅Χ+𝜁Structural model

Measurement model

𝛽 𝛽

𝛽

𝛾𝛾𝛾

𝛾𝛾𝛾𝛾

𝜆𝑦

𝜆𝑦

𝜆𝑦

SEM outcome (preliminary results)

lavaan & semPlot R packages

%OC A horizon (preliminary results)

R-squared 0.249

Predictors EstimateMean LST 0.375 ***SD EVI 0.296 ***Terrain Index 0.116 . SD LST 0.110 .

R-squared 0.448

Predictors EstimateMean LST -0.330 *** SD EVI 0.198 *** SD LST 0.212 ** ESP A hor. -1.374 . Terrain Index 0.171

Structural Equation Model Linear Regression Model

Final remarks

• SEM is a promising approach to model interrelationships in DSM

• SEM allows to include expert and process knowledge

• Next steps to check and validate preliminary results, as well as apply non-linear relationships and categorical variables

Thanks!