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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43410%2F19%3A43916356" target="_blank" >RIV/62156489:43410/19:43916356 - isvavai.cz</a>

Alternative codes found

RIV/00027073:_____/19:N0000011 RIV/60460709:41210/19:79661

Result on the web

<a href="https://doi.org/10.1016/j.catena.2019.104226" target="_blank" >https://doi.org/10.1016/j.catena.2019.104226</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-landform modeling 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 autocorrelation were properly accounted for; however, it remains elusive whether the level of improvement would be predictable, based on the degree of spatial autocorrelation in the model variables. We evaluated this problem using 11 soil variables acquired from the A and B horizons along a hillslope of Žofínský Prales in the Czech Republic. The results showed that, with no exception, there were increases in R2 and decreases in the Akaike information criterion (AIC), residual autocorrelation, and root-mean-square errors (RMSEs), after incorporating the spatial filters extracted by spatial eigenvector mapping into non-spatial regression models. Furthermore, the improvement of the model was positively proportional to the degree of spatial autocorrelation, inherent in the soil variables. That is, there were strikingly linear and significant relationships, in which strongly autocorrelated soil variables (i.e., having a high Moran&apos;s I value) exhibited greater increases in R2 and decreases in AIC, residual autocorrelation, and RMSEs than their more weakly autocorrelated counterparts. These findings indicate that the degree of spatial autocorrelation present in soil properties can serve as a direct indicator for how much the performance of a traditional non-spatial soil-landform model would be enhanced, by explicitly taking into consideration the presence of spatial autocorrelation. More generally, our results potentially imply that the need for and benefit from incorporating spatial effects in geopedological modeling proportionally increases as the soil property of interest is more spatially structured (i.e., landform variables alone cannot capture soil spatial variability).

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/GA19-09427S" target="_blank" >GA19-09427S: The mystery of biogenic soil creep: the biogeomorphic role of trees in temperate and tropical forests and its ecological consequences</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

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

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

14

Pages from-to

104226

UT code for WoS article

000488417700043

EID of the result in the Scopus database

2-s2.0-85071738525