Digital soil mapping of an Argentinian pampa region using structural equation modelling

M.E. Angelini, G.B.M. Heuvelink, B. Kempen, D.M. Morras, D.M. Rodriguez

Research output: Chapter in Book/Report/Conference proceedingAbstract

Abstract

The most productive soils of the Argentinian Pampas are Phaeozems formed over loesslike sediments. Soil maps of this region were developed through conventional soil mapping, but the poor updateability of these maps, the lack of uncertainty information and the demand of high spatial resolution incite the application of Digital Soil Mapping (DSM) as an alternative approach. However, current DSM methods are highly empirical and have difficulty to predict many soil properties simultaneously, while preserving relationships between properties and including pedological knowledge. Therefore, we investigated the use of structural equation modelling (SEM), which has not yet been applied in DSM. SEM integrates empirical information with mechanistic knowledge by deriving 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 (e.g. climate, relief, organisms). We applied SEM to a 22,900 km2 region in the Argentinian Pampas. First, we identified the main soil forming processes and main soil properties involved. Next, we incorporated these processes and properties in a conceptual model and converted this to a SEM graphical model. Finally, we derived the SEM equations and implemented these in R code. The model was calibrated using a dataset of 350 soil profiles and environmental covariates. After calibration spatial predictions were made of over 12 soil properties, among others base saturation, thickness and organic matter content of the A horizon, and presence of natric and E horizons. We compared maps obtained with SEM and regression-kriging DSM using a validation dataset of 100 soil profiles collected through stratified simple random sampling. This allowed to quantify the accuracy of both prediction methods and test.
Original languageEnglish
Title of host publicationBook of Abstracts of the Wageningen Soil Conference 2015: Soil Science in a Changing World
EditorsS. Keesstra, G. Mol, A. Zaal, J. Wallinga, B. Jansen
Pages134-134
Publication statusPublished - 2015
Event2nd Wageningen Soil Conference 2015 - Wageningen, Netherlands
Duration: 23 Aug 201527 Aug 2015

Conference

Conference2nd Wageningen Soil Conference 2015
CountryNetherlands
CityWageningen
Period23/08/1527/08/15

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modeling
soil property
soil
soil profile
mapping method
prediction
kriging
spatial resolution
relief
saturation
calibration
organic matter
sampling
climate
sediment

Cite this

Angelini, M. E., Heuvelink, G. B. M., Kempen, B., Morras, D. M., & Rodriguez, D. M. (2015). Digital soil mapping of an Argentinian pampa region using structural equation modelling. In S. Keesstra, G. Mol, A. Zaal, J. Wallinga, & B. Jansen (Eds.), Book of Abstracts of the Wageningen Soil Conference 2015: Soil Science in a Changing World (pp. 134-134)
Angelini, M.E. ; Heuvelink, G.B.M. ; Kempen, B. ; Morras, D.M. ; Rodriguez, D.M. / Digital soil mapping of an Argentinian pampa region using structural equation modelling. Book of Abstracts of the Wageningen Soil Conference 2015: Soil Science in a Changing World. editor / S. Keesstra ; G. Mol ; A. Zaal ; J. Wallinga ; B. Jansen. 2015. pp. 134-134
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abstract = "The most productive soils of the Argentinian Pampas are Phaeozems formed over loesslike sediments. Soil maps of this region were developed through conventional soil mapping, but the poor updateability of these maps, the lack of uncertainty information and the demand of high spatial resolution incite the application of Digital Soil Mapping (DSM) as an alternative approach. However, current DSM methods are highly empirical and have difficulty to predict many soil properties simultaneously, while preserving relationships between properties and including pedological knowledge. Therefore, we investigated the use of structural equation modelling (SEM), which has not yet been applied in DSM. SEM integrates empirical information with mechanistic knowledge by deriving 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 (e.g. climate, relief, organisms). We applied SEM to a 22,900 km2 region in the Argentinian Pampas. First, we identified the main soil forming processes and main soil properties involved. Next, we incorporated these processes and properties in a conceptual model and converted this to a SEM graphical model. Finally, we derived the SEM equations and implemented these in R code. The model was calibrated using a dataset of 350 soil profiles and environmental covariates. After calibration spatial predictions were made of over 12 soil properties, among others base saturation, thickness and organic matter content of the A horizon, and presence of natric and E horizons. We compared maps obtained with SEM and regression-kriging DSM using a validation dataset of 100 soil profiles collected through stratified simple random sampling. This allowed to quantify the accuracy of both prediction methods and test.",
author = "M.E. Angelini and G.B.M. Heuvelink and B. Kempen and D.M. Morras and D.M. Rodriguez",
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Angelini, ME, Heuvelink, GBM, Kempen, B, Morras, DM & Rodriguez, DM 2015, Digital soil mapping of an Argentinian pampa region using structural equation modelling. in S Keesstra, G Mol, A Zaal, J Wallinga & B Jansen (eds), Book of Abstracts of the Wageningen Soil Conference 2015: Soil Science in a Changing World. pp. 134-134, 2nd Wageningen Soil Conference 2015, Wageningen, Netherlands, 23/08/15.

Digital soil mapping of an Argentinian pampa region using structural equation modelling. / Angelini, M.E.; Heuvelink, G.B.M.; Kempen, B.; Morras, D.M.; Rodriguez, D.M.

Book of Abstracts of the Wageningen Soil Conference 2015: Soil Science in a Changing World. ed. / S. Keesstra; G. Mol; A. Zaal; J. Wallinga; B. Jansen. 2015. p. 134-134.

Research output: Chapter in Book/Report/Conference proceedingAbstract

TY - CHAP

T1 - Digital soil mapping of an Argentinian pampa region using structural equation modelling

AU - Angelini, M.E.

AU - Heuvelink, G.B.M.

AU - Kempen, B.

AU - Morras, D.M.

AU - Rodriguez, D.M.

PY - 2015

Y1 - 2015

N2 - The most productive soils of the Argentinian Pampas are Phaeozems formed over loesslike sediments. Soil maps of this region were developed through conventional soil mapping, but the poor updateability of these maps, the lack of uncertainty information and the demand of high spatial resolution incite the application of Digital Soil Mapping (DSM) as an alternative approach. However, current DSM methods are highly empirical and have difficulty to predict many soil properties simultaneously, while preserving relationships between properties and including pedological knowledge. Therefore, we investigated the use of structural equation modelling (SEM), which has not yet been applied in DSM. SEM integrates empirical information with mechanistic knowledge by deriving 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 (e.g. climate, relief, organisms). We applied SEM to a 22,900 km2 region in the Argentinian Pampas. First, we identified the main soil forming processes and main soil properties involved. Next, we incorporated these processes and properties in a conceptual model and converted this to a SEM graphical model. Finally, we derived the SEM equations and implemented these in R code. The model was calibrated using a dataset of 350 soil profiles and environmental covariates. After calibration spatial predictions were made of over 12 soil properties, among others base saturation, thickness and organic matter content of the A horizon, and presence of natric and E horizons. We compared maps obtained with SEM and regression-kriging DSM using a validation dataset of 100 soil profiles collected through stratified simple random sampling. This allowed to quantify the accuracy of both prediction methods and test.

AB - The most productive soils of the Argentinian Pampas are Phaeozems formed over loesslike sediments. Soil maps of this region were developed through conventional soil mapping, but the poor updateability of these maps, the lack of uncertainty information and the demand of high spatial resolution incite the application of Digital Soil Mapping (DSM) as an alternative approach. However, current DSM methods are highly empirical and have difficulty to predict many soil properties simultaneously, while preserving relationships between properties and including pedological knowledge. Therefore, we investigated the use of structural equation modelling (SEM), which has not yet been applied in DSM. SEM integrates empirical information with mechanistic knowledge by deriving 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 (e.g. climate, relief, organisms). We applied SEM to a 22,900 km2 region in the Argentinian Pampas. First, we identified the main soil forming processes and main soil properties involved. Next, we incorporated these processes and properties in a conceptual model and converted this to a SEM graphical model. Finally, we derived the SEM equations and implemented these in R code. The model was calibrated using a dataset of 350 soil profiles and environmental covariates. After calibration spatial predictions were made of over 12 soil properties, among others base saturation, thickness and organic matter content of the A horizon, and presence of natric and E horizons. We compared maps obtained with SEM and regression-kriging DSM using a validation dataset of 100 soil profiles collected through stratified simple random sampling. This allowed to quantify the accuracy of both prediction methods and test.

M3 - Abstract

SN - 9789461731685

SP - 134

EP - 134

BT - Book of Abstracts of the Wageningen Soil Conference 2015: Soil Science in a Changing World

A2 - Keesstra, S.

A2 - Mol, G.

A2 - Zaal, A.

A2 - Wallinga, J.

A2 - Jansen, B.

ER -

Angelini ME, Heuvelink GBM, Kempen B, Morras DM, Rodriguez DM. Digital soil mapping of an Argentinian pampa region using structural equation modelling. In Keesstra S, Mol G, Zaal A, Wallinga J, Jansen B, editors, Book of Abstracts of the Wageningen Soil Conference 2015: Soil Science in a Changing World. 2015. p. 134-134