Incorporation of spatial autocorrelation improves soil-landforming at A and B horizons

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00025798%3A_____%2F19%3A00000286" target="_blank" >RIV/00025798:_____/19:00000286 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0341816219303686?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0341816219303686?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.catena.2019.104226" target="_blank" >10.1016/j.catena.2019.104226</a>

Result language

angličtina

Original language name

Incorporation of spatial autocorrelation improves soil-landforming at A and B horizons

Original language description

Research has shown that the performance of soil–landform models would improve if the effects of spatial autocorrelationwere properly accounted for; however, it remains elusive whether the level of improvement wouldbe predictable, based on the degree of spatial autocorrelation in the model variables. We evaluated this problemusing 11 soil variables acquired from the A and B horizons along a hillslope of Žofínský Prales in the CzechRepublic. The results showed that, with no exception, there were increases in R2 and decreases in the Akaikeinformation criterion (AIC), residual autocorrelation, and root-mean-square errors (RMSEs), after incorporatingthe spatial filters extracted by spatial eigenvector mapping into non-spatial regression models. Furthermore, theimprovement of the model was positively proportional to the degree of spatial autocorrelation, inherent in thesoil variables. That is, there were strikingly linear and significant relationships, in which strongly autocorrelatedsoil variables (i.e., having a high Moran's I value) exhibited greater increases in R2 and decreases in AIC, residualautocorrelation, and RMSEs than their more weakly autocorrelated counterparts. These findings indicate that thedegree of spatial autocorrelation present in soil properties can serve as a direct indicator for how much theperformance of a traditional non-spatial soil–landform model would be enhanced, by explicitly taking intoconsideration the presence of spatial autocorrelation. More generally, our results potentially imply that the needfor and benefit from incorporating spatial effects in geopedological modeling proportionally increases as the soilproperty of interest is more spatially structured (i.e., landform variables alone cannot capture soil spatialvariability).

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

40104 - Soil science

Project

<a href="/en/project/GA18-17295S" target="_blank" >GA18-17295S: Climate and air pollution effects on forest productivity</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Catena

ISSN

0341-8162

e-ISSN

—

Volume of the periodical

183

Issue of the periodical within the volume

December :104226

Country of publishing house

DE - GERMANY

Number of pages

14

Pages from-to

nestránkováno

UT code for WoS article

000488417700043

EID of the result in the Scopus database

2-s2.0-85071738525