2015
DOI: 10.5194/bg-12-2737-2015
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Dynamic C and N stocks – key factors controlling the C gas exchange of maize in heterogenous peatland

Abstract: Abstract. The drainage and cultivation of fen peatlands create complex small-scale mosaics of soils with extremely variable soil organic carbon (SOC) stocks and groundwater levels (GWLs). To date, the significance of such sites as sources or sinks for greenhouse gases such as CO2 and CH4 is still unclear, especially if the sites are used for cropland. As individual control factors such as GWL fail to account for this complexity, holistic approaches combining gas fluxes with the underlying processes are require… Show more

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Cited by 22 publications
(21 citation statements)
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References 89 publications
(105 reference statements)
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“…The concept of dynamic aerated soil C and N stocks was developed in the underlying project and first tested for arable sites in one of the study areas (Pohl et al ., ). It integrates soil organic matter, soil C:N ratio, and bulk density with the daily water table dynamics.…”
Section: Methodsmentioning
confidence: 97%
“…The concept of dynamic aerated soil C and N stocks was developed in the underlying project and first tested for arable sites in one of the study areas (Pohl et al ., ). It integrates soil organic matter, soil C:N ratio, and bulk density with the daily water table dynamics.…”
Section: Methodsmentioning
confidence: 97%
“…Data noise that originated from either turbulence or pressure fluctuation caused by chamber deployment or from increasing saturation and canopy microclimate effects was excluded by the application of a death‐band of 5% to each measurement (Davidson et al ., ; Kutzbach et al ., ; Langensiepen et al ., ). Multiple data subsets based on a variable moving window with a minimum length of 4 min were generated for each measurement (Hoffmann et al ., ) and linearly fitted (ordinary least squares; Leiber‐Sauheitl et al ., ; Leifeld et al ., ; Pohl et al ., ) to calculate the concentration change with time. Resulting multiple NEE fluxes per measurement (based on the moving window data subsets per measurement) were subsequently scrutinized by the following threshold criteria: (i) range of within‐chamber air temperature less than ±1.5 K (R eco and NEE fluxes) and deviation of photosynthetic active radiation (PAR) less than ±20% of the average; (ii) significant regression slope ( P ≤ 0.1); and (iii) nonsignificant tests ( P > 0.1) for normality (Lillifor′s adaption of the Kolmogorov–Smirnov test), homoscedasticity (Breusch‐Pagan test) and linearity of CO 2 concentration data.…”
Section: Methodsmentioning
confidence: 99%
“…To calculate c/ t, data subsets based on a variable moving window with a minimum length of 4 min were used . c/ t was computed by applying a linear regression to each data subset, relating changes in chamber headspace CO 2 concentration to measurement time (Leiber-Sauheitl et al, 2013;Leifeld et al, 2014;Pohl et al, 2015). In the case of the 15 s measurement frequency, a death band of 5 % was applied prior to the moving window algorithm.…”
Section: Co 2 Flux Calculation and Gap Fillingmentioning
confidence: 99%
“…Accounting for the abovementioned methodical limitations, a number of studies investigated spatial patterns in gaseous C exchange by using manual chamber measurement systems (Eickenscheidt et al, 2014;Pohl et al, 2015). Compared to EC measurements, these systems are characterized by a low temporal resolution, where the calculated net ecosystem CO 2 exchange (NEE) is commonly based on extensive gap filling (Gomez-Casanovas et al, 2013;Savage and Davidson, 2003) conducted using empirical modeling, for example .…”
mentioning
confidence: 99%