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 required to understand the carbon (C) gas exchange of drained fens. It can be assumed that the stocks of SOC and N located above the variable GWL – defined as dynamic C and N stocks – play a key role in the regulation of the plant- and microbially mediated CO2 fluxes in these soils and, inversely, for CH4. To test this assumption, the present study analysed the C gas exchange (gross primary production – GPP; ecosystem respiration – Reco; net ecosystem exchange – NEE; CH4) of maize using manual chambers for 4 years. The study sites were located near Paulinenaue, Germany, where we selected three soil types representing the full gradient of GWL and SOC stocks (0–1 m) of the landscape: (a) Haplic Arenosol (AR; 8 kg C m−2); (b) Mollic Gleysol (GL; 38 kg C m−2); and (c) Hemic Histosol (HS; 87 kg C m−2). Daily GWL data were used to calculate dynamic SOC (SOCdyn) and N (Ndyn) stocks. Average annual NEE differed considerably among sites, ranging from 47 ± 30 g C m−2 yr−1 in AR to −305 ± 123 g C m−2 yr−1 in GL and −127 ± 212 g C m−2 yr−1 in HS. While static SOC and N stocks showed no significant effect on C fluxes, SOCdyn and Ndyn and their interaction with GWL strongly influenced the C gas exchange, particularly NEE and the GPP : Reco ratio. Moreover, based on nonlinear regression analysis, 86% of NEE variability was explained by GWL and SOCdyn. The observed high relevance of dynamic SOC and N stocks in the aerobic zone for plant and soil gas exchange likely originates from the effects of GWL-dependent N availability on C formation and transformation processes in the plant–soil system, which promote CO2 input via GPP more than CO2 emission via Reco. The process-oriented approach of dynamic C and N stocks is a promising, potentially generalisable method for system-oriented investigations of the C gas exchange of groundwater-influenced soils and could be expanded to other nutrients and soil characteristics. However, in order to assess the climate impact of arable sites on drained peatlands, it is always necessary to consider the entire range of groundwater-influenced mineral and organic soils and their respective areal extent within the soil landscape.

High emissions of greenhouse gases from grasslands on peat and other organic soils

“…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.…”

Net ecosystem fluxes and composition of biogenic volatile organic compounds over a maize field–interaction of meteorology and phenological stages

“…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.…”

Detecting small-scale spatial heterogeneity and temporal dynamics of soil organic carbon (SOC) stocks: a comparison between automatic chamber-derived C budgets and repeated soil inventories

“…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.…”

“…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 .…”