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| Titel |
Wet-season spatial variability in N2O emissions from a tea field in subtropical central China |
| VerfasserIn |
X. Fu, X. Liu, Y. Li, J. Shen, Y. Wang, G. Zou, H. Li, L. Song, J. Wu |
| 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 ; 12, no. 12 ; Nr. 12, no. 12 (2015-06-26), S.3899-3911 |
| Datensatznummer |
250118002
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| Publikation (Nr.) |
 copernicus.org/bg-12-3899-2015.pdf |
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| Zusammenfassung |
| Tea fields emit large amounts of nitrous oxide (N2O) to the atmosphere.
Obtaining accurate estimations of N2O emissions from tea-planted soils
is challenging due to strong spatial variability. We examined the spatial
variability in N2O emissions from a red-soil tea field in Hunan
Province, China, on 22 April 2012 (in a wet season) using 147 static mini
chambers approximately regular gridded in a 4.0 ha tea field. The N2O
fluxes for a 30 min snapshot (10:00–10:30 a.m.) ranged from −1.73 to
1659.11 g N ha−1 d−1 and were positively skewed with an average flux of
102.24 g N ha−1 d−1. The N2O flux data were transformed to
a normal distribution by using a logit function. The geostatistical analyses
of our data indicated that the logit-transformed N2O fluxes (FLUX30t)
exhibited strong spatial autocorrelation, which was characterized by an
exponential semivariogram model with an effective range of 25.2 m. As
observed in the wet season, the logit-transformed soil ammonium-N (NH4Nt),
soil nitrate-N (NO3Nt), soil organic carbon (SOCt) and total soil nitrogen
(TSNt) were all found to be significantly correlated with FLUX30t (r = 0.57–0.71,
p < 0.001). Three spatial interpolation methods (ordinary
kriging, regression kriging and cokriging) were applied to estimate the
spatial distribution of N2O emissions over the study area. Cokriging
with NH4Nt and NO3Nt as covariables (r = 0.74 and RMSE = 1.18)
outperformed ordinary kriging (r = 0.18 and RMSE = 1.74), regression
kriging with the sample position as a predictor (r = 0.49 and RMSE = 1.55)
and cokriging with SOCt as a covariable (r = 0.58 and RMSE = 1.44). The
predictions of the three kriging interpolation methods for the total
N2O emissions of 4.0 ha tea field ranged from 148.2 to 208.1 g N d−1,
based on the 30 min snapshots obtained during the wet season. Our
findings suggested that to accurately estimate the total N2O emissions
over a region, the environmental variables (e.g., soil properties) and the
current land use pattern (e.g., tea row transects in the present study) must
be included in spatial interpolation. Additionally, compared with other
kriging approaches, the cokriging prediction approach showed great
advantages in being easily deployed and, more importantly, providing accurate
regional estimation of N2O emissions from tea-planted soils. |
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