4.6 Article

Spatially Related Sampling Uncertainty in the Assessment of Labile Soil Carbon and Nitrogen in an Irish Forest Plantation

期刊

APPLIED SCIENCES-BASEL
卷 11, 期 5, 页码 -

出版社

MDPI
DOI: 10.3390/app11052139

关键词

dissolved organic matter; geostatistics; kriging; Sitka spruce; spatial analysis

资金

  1. China Scholarship Council (CSC)
  2. University College Dublin (UCD) [201206300050]

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The study in a Sitka spruce forest in central Ireland found moderate spatial variability in dissolved organic carbon (DOC) and dissolved total nitrogen (DTN), with concentrations of both decreasing from the southeast in the study area. Variability of both DOC and DTN increased as sampling area expanded, and cokriging technique outperformed ordinary kriging for predictions due to their high correlation.
The importance of labile soil carbon (C) and nitrogen (N) in soil biogeochemical processes is now well recognized. However, the quantification of labile soil C and N in soils and the assessment of their contribution to ecosystem C and N budgets is often constrained by limited information on spatial variability. To address this, we examined spatial variability in dissolved organic carbon (DOC) and dissolved total nitrogen (DTN) in a Sitka spruce forest in central Ireland. The results showed moderate variations in the concentrations of DOC and DTN based on the mean, minimum, and maximum, as well as the coefficients of variation. Residual values of DOC and DTN were shown to have moderate spatial autocorrelations, and the nugget sill ratios were 0.09% and 0.10%, respectively. Distribution maps revealed that both DOC and DTN concentrations in the study area decreased from the southeast. The variability of both DOC and DTN increased as the sampling area expanded and could be well parameterized as a power function of the sampling area. The cokriging technique performed better than the ordinary kriging for predictions of DOC and DTN, which are highly correlated. This study provides a statistically based assessment of spatial variations in DOC and DTN and identifies the sampling effort required for their accurate quantification, leading to improved assessments of forest ecosystem C and N budgets.

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