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| Titel |
Modelling soil bulk density at the landscape scale and its contributions to C stock uncertainty |
| VerfasserIn |
K. P. Taalab, R. Corstanje, R. Creamer, M. J. Whelan |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 10, no. 7 ; Nr. 10, no. 7 (2013-07-12), S.4691-4704 |
| Datensatznummer |
250018338
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| Publikation (Nr.) |
 copernicus.org/bg-10-4691-2013.pdf |
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| Zusammenfassung |
Soil bulk density (Db) is a major contributor to uncertainties in
landscape-scale carbon and nutrient stock estimation. However, it is time
consuming to measure and is, therefore, frequently predicted using surrogate
variables, such as soil texture. Using this approach is of limited value for
estimating landscape-scale inventories, as its accuracy beyond the sampling
point at which texture is measured becomes highly uncertain. In this paper,
we explore the ability of soil landscape models to predict soil Db
using a suite of landscape attributes and derivatives for both topsoil and
subsoil. The models were constructed using random forests and artificial
neural networks.
Using these statistical methods, we have produced a spatially distributed
prediction of Db on a 100 m × 100 m grid, which was shown to
significantly improve topsoil carbon stock estimation. In comparison to
using mean values from point measurements, stratified by soil class, we
found that the gridded method predicted Db more accurately, especially
for higher and lower values within the range. Within our study area of the
Midlands, UK, we found that the gridded prediction of Db produced a
stock inventory of over 1 million tonnes of carbon greater than the
stratified mean method. Furthermore, the 95% confidence interval
associated with total C stock prediction was almost halved by using the
gridded method. The gridded approach was particularly useful in improving
organic carbon (OC) stock estimation for fine-scale landscape units at which
many landscape–atmosphere interaction models operate. |
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