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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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).

Název v anglickém jazyce

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

Popis výsledku anglicky

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).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40104 - Soil science

Projekt

<a href="/cs/project/GA18-17295S" target="_blank" >GA18-17295S: Vliv klimatu a znečištění ovzduší na produktivitu lesů</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Catena

ISSN

0341-8162

e-ISSN

—

Svazek periodika

183

Číslo periodika v rámci svazku

December :104226

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

14

Strana od-do

nestránkováno

Kód UT WoS článku

000488417700043

EID výsledku v databázi Scopus

2-s2.0-85071738525