Modelling CO2 and CH4 emissions from drained peatlands with grass cultivation by the BASGRA-BGC model.

Cultivated peatlands under drainage practices contribute significant carbon losses from agricultural sector in the Nordic countries. In this research, we developed the BASGRA-BGC model coupled with hydrological, soil carbon decomposition and methane modules to simulate the dynamic of water table level (WTL), carbon dioxide (CO2) and methane (CH4) emissions for cultivated peatlands. The field measurements from four experimental sites in Finland, Denmark and Norway were used to validate the predictive skills of this novel model under different WTL management practices, climatic conditions and soil properties. Compared with daily observations, the model performed well in terms of RMSE (Root Mean Square Error; 0.06-0.11 m, 1.22-2.43 gC/m2/day, and 0.002-0.330 kgC/ha/day for WTL, CO2 and CH4, respectively), NRMSE (Normalized Root Mean Square Error; 10.3-18.3%, 13.0-18.6%, 15.3-21.9%) and Pearson's r (Pearson correlation coefficient; 0.60-0.91, 0.76-0.88, 0.33-0.80). The daily/seasonal variabilities were therefore captured and the aggregated results corresponded well with annual estimations. We further provided an example on the model's potential use in improving the WTL management to mitigate CO2 and CH4 emissions while maintaining grass production. At all study sites, the simulated WTLs and carbon decomposition rates showed a significant negative correlation. Therefore, controlling WTL could effectively reduce carbon losses. However, given the highly diverse carbon decomposition rates within individual WTLs, adding indicators (e.g. soil moisture and peat quality) would improve our capacity to assess the effectiveness of specific mitigation practices such as WTL control and rewetting.

Methane fluxes from a rewetted agricultural fen during two initial years of paludiculture.

Rewetting agricultural peatland abates carbon dioxide (CO2) emission, but the resulting waterlogged anaerobic soil condition may create hotspots of methane (CH4) emissions. In this study, we measured CH4 emissions from side-by-side replicated plots in an agricultural fen cultivated with reed canary grass under a control and two experimental rewetting (i.e., paludiculture) conditions as either continuously flooded to soil surface or semi-flooded where water from the flooded plots intruded from sub-surface. Fluxes were measured for two successive years at 1-2 week intervals (total 59 measurement dates) using static chambers. Annual emissions were estimated by trapezoidal linear interpolation of the measured fluxes between the measurement dates. Two-year time-weighted average ground water tables (GWT) in the flooded, semi-flooded and control plots were 1, 3 and 9 cm below soil surface, respectively. The annual average emissions from flooded plots were 82 and 116 g CH4 m-2 yr-1 in Year 1 and 2, respectively, which were significantly higher than the emissions from semi-flooded plots (35 and 69 g CH4 m-2 yr-1 in Year 1 and 2, respectively) and from control plots (3 and 9 g CH4 m-2 yr-1 in Year 1 and 2, respectively). Overall, the results showed that the GWT in paludiculture should be maintained few cm below soil surface during high temperature periods to prevent risks of high CH4 emissions.

Full GHG balance of a drained fen peatland cropped to spring barley and reed canary grass using comparative assessment of CO2 fluxes.

Empirical greenhouse gas (GHG) flux estimates from diverse peatlands are required in order to derive emission factors for managed peatlands. This study on a drained fen peatland quantified the annual GHG balance (Carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), and C exported in crop yield) from spring barley (SB) and reed canary grass (RCG) using static opaque chambers for GHG flux measurements and biomass yield for indirectly estimating gross primary production (GPP). Estimates of ecosystem respiration (ER) and GPP were compared with more advanced but costly and labor-intensive dynamic chamber studies. Annual GHG balance for the two cropping systems was 4.0 ± 0.7 and 8.1 ± 0.2 Mg CO2-Ceq ha(-1) from SB and RCG, respectively (mean ± standard error, n = 3). Annual CH4 emissions were negligible (<0.006 Mg CO2-Ceq ha(-1)), and N2O emissions contributed only 4-13 % of the full GHG balance (0.5 and 0.3 Mg CO2-Ceq ha(-1) for SB and RCG, respectively). The statistical significance of low CH4 and N2O fluxes was evaluated by a simulation procedure which showed that most of CH4 fluxes were within the range that could arise from random variation associated with actual zero-flux situations. ER measured by static chamber and dynamic chamber methods was similar, particularly when using nonlinear regression techniques for flux calculations. A comparison of GPP derived from aboveground biomass and from measuring net ecosystem exchange (NEE) showed that GPP estimation from biomass might be useful, or serve as validation, for more advanced flux measurement methods. In conclusion, combining static opaque chambers for measuring ER of CO2 and CH4 and N2O fluxes with biomass yield for GPP estimation worked well in the drained fen peatland cropped to SB and RCG and presented a valid alternative to estimating the full GHG balance by dynamic chambers.

Prediction of biogas yield and its kinetics in reed canary grass using near infrared reflectance spectroscopy and chemometrics.

A rapid method is needed to assess biogas and methane yield potential of various kinds of substrate prior to anaerobic digestion. This study reports near infrared reflectance spectroscopy (NIRS) as a rapid alternative method to the conventional batch methods for prediction of specific biogas yield (SBY), specific methane yield (SMY) and kinetics of biogas yield (k-SBY) of reed canary grass (RCG) biomass. Dried and powdered RCG biomass with different level of maturity was used for biochemical composition analysis, batch assays and NIRS analysis. Calibration models were developed using partial least square (PLS) regression from NIRS spectra. The calibration models for SBY (R(2)=0.68, RPD=1.83) and k-SBY (R(2)=0.71, RPD=1.75) were better than the model for SMY (R(2)=0.53, RPD=1.49). Although the PLS model for SMY was less successful, the model performance was better compared to the models based on chemical composition.

Chemical composition and methane yield of reed canary grass as influenced by harvesting time and harvest frequency.

This study examined the influence of harvest time on biomass yield, dry matter partitioning, biochemical composition and biological methane potential of reed canary grass harvested twice a month in one-cut (OC) management. The regrowth of biomass harvested in summer was also harvested in autumn as a two-cut management with (TC-F) or without (TC-U) fertilization after summer harvest. The specific methane yields decreased significantly with crop maturity that ranged from 384 to 315 and from 412 to 283 NL (normal litre) (kgVS)(-1) for leaf and stem, respectively. Approximately 45% more methane was produced by the TC-F management (5430Nm(3)ha(-1)) as by the OC management (3735Nm(3)ha(-1)). Specific methane yield was moderately correlated with the concentrations of fibre components in the biomass. Larger quantity of biogas produced at the beginning of the biogas assay from early harvested biomass was to some extent off-set by lower concentration of methane.