Search

Soil organic carbon (SOC) losses under a changing climate are driven by the temperature sensitivity of SOC mineralization (usually expressed as Q10, the multiplier of activity with 10 °C temperature increase). The activation energy theory (AET) suggests that, due to higher activation energies, the more complex the carbon, the higher is mineralization Q10. However, studies on Q10 have been inconsistent with regard to AET. Measurements of potential soil enzymes activity Q10 even contradicted AET: Phenoloxidase (representing complex carbon) had consistently lower Q10 than the more labile xylanase and glucosidase. This study used two approaches of examining Q10 in SOC modeling: 1) Bayesian calibration (BC) and 2) using different measured enzyme Q10 as proxies for mineralization Q10 of different SOC pools. The SOC model was DAISY (S. Hansen et al., 2012). BC informed Q10 by field measured data, while the second approach tested if directly using enzyme Q10 (of phenoloxidase, glucosidase and xylanase) for DAISY pools improved simulation results. Both approaches used the temperature sensitive measurements of CO2 evolution and soil microbial biomass. The measured enzyme Q10 were from field manipulation experiments with bare fallow and vegetated plots in the two regions of Kraichgau and Swabian Jura in Southwest Germany. The enzyme-derived Q10 were used for modelling those fields and furthermore for in‑situ litterbag decomposition experiments at 20 sites in the same region. Two further laboratory experiments with temperature manipulation were included: an incubation of the field residues into soil and an incubation of bare soil from the start and year 50 of a long duration bare fallow (from Ultuna). The BC made use of CO2 and microbial data to inform about the range of Q10 of different carbon pools for the individual experiments and combined data.The BC of the residue incubation experiment constrained Q10 for metabolic (~3) and structural litter (~2). Estimated 95% credibility intervals did not overlap. The BC for Ultuna could constrain the slow and fast SOC pool with Q10 ~2.8 and ~3, respectively, but credibility intervals of both pools overlapped. The Q10 of field experiments, which had most abundant data, could not be constrained by BC, probably because their annual temparature variability was too low. However, the model errors of the field experiment could be reduced by the second approach, when the Q10 of phenoloxidase was used for to the structural litter pool as well as for the fast and slow SOC pools. Thus regional enzyme Q10 improved the model fit but only for regional simulations. Therefore, they could be useful proxies when natural temperature range is too small to inform temperature sensitivity by BC. Any trends found in this study contradicted AET, both from measured enzymes and BC of the incubation experiments. This calls for alternative Q10 hypotheses and the need for individual Q10 values for different SOC pool rather than a general one. BC approaches would benefit from a wider temperature range of field experiments and understanding what causes variable enzyme Q10 could help to improve future SOC models.

Microbial assimilation and stabilization of soil organic carbon (SOC) is an important process in global carbon cycling. For an improved understanding of climate-induced changes in ecosystem C dynamics, it is important to know the group-specific turnover of microbial C. Consequently, we wanted to answer the questions if fungi store newly assimilated C longer than bacteria and if climatic and edaphic properties of different regions affect microbial C assimilation and its subsequent release. This study presents results from a 112-day long field experiment where endogenous microorganisms from two agricultural soils were labeled with 13C labeled glucose to follow the dynamics of newly assimilated C. Both soils were representative for the respective study regions (Kraichgau and Swabian Alb). Whereas, microbial assimilation of newly added C was higher in Kraichgau than in Swabian Alb, the opposite result was obtained for the mean residence time (MRT) of microbial biomass C (76 days in Kraichgau and 93 days in Swabian Alb). The accelerated turnover rates of microbial C in the warmer soil of Kraichgau with lower clay content might be an important mechanism explaining the differences in SOC content between both regions. Gram-positive bacteria assimilated more 13C-glucose into their biomass than fungi and the MRT of C was higher in bacteria as compared to fungi in both regions. Beside these dynamic substrate utilization strategies, we observed possible cross feeding by gram-negative bacteria. Carbon MRT in fungi was region specific and was best represented by a two-pool model; the initial MRT ranged between 5 and 39 days and was, in the end, higher than 4 years. This hints toward a functional separation of the fungal community; fast growing fungi dominant in the early phase and internal redistribution of C in the second phase of decomposition. Our study identified microbial group and region specific MRT of freshly assimilated C as an important parameter, which might help to explain the commonly found variation in soil respiration and SOC stabilization.

Even small changes in microbial decomposition rates of soil organic carbon (SOC) at the regional scale have the potential to modify land-atmospheric feedbacks at the global scale. Limited understanding of the regulation of microbial driven processes has led to major uncertainty in global SOC estimates. Therefore, to better understand the large scale processes controlling SOC dynamics, we examined the influence of SOC quantity, quality, and soil physical and biochemical properties on soil basal respiration and of the temperature sensitivities (Q10) of soil respiration and enzymes (β-glucosidase and xylanase) at two scales: landscape (two individual areas, each approximately 27 km2) and regional (pooled data of both areas). Soil samples (0–30 cm soil depth) originated from 41 agricultural sites distributed over two areas in southwest Germany differing in climatic and geological conditions. We used a two-step data analysis procedure; variable selection through random Forest regression, followed by shortlisting of significant explanatory variables using linear mixed-effect models. Microbial biomass regulated soil basal respiration at both scales, whereas soil C:N ratio played an important role only at the regional scale based on mixed-effect models. Soil texture significantly explained temperature sensitivity (Q10) of soil respiration at both scales. Different SOC quality fractions characterized by midDRIFTS played a minor role, whereas extractable organic C related negatively to the respiration Q10. Soil properties controlling soil enzymes (Q10) were scale-specific. We found pH to be the main factor affecting β-glucosidase Q10 at the landscape scale. We argue that scale-specificity of variables may depend on homogeneity of study areas and should be considered when exploring SOC dynamics. Our study identified direct and indirect controlling factors affecting soil basal respiration and its temperature sensitivity, providing vital information for SOC dynamics at large scales.

The direction and magnitude of change of soil organic carbon (SOC) stocks due to global warming depend strongly on the temperature sensitivity (e.g., Q10) of carbon mineralization. To date, most multi-pool SOC models assume a general Q10 of 2 despite experimental evidence suggesting different Q10 for different carbon fractions. The aim of this study was to test if the use of experimentally derived pool specific Q10 values improves the performance of SOC models. Five contrasting data sets from three field experiments and two laboratory incubations were used to study the link between carbon pool recalcitrance and Q10 using two different approaches: a) Bayesian calibration of the Daisy SOC model parameters to infer Q10 of SOC and crop-litter pools, and b) using measured Q10 values of carbon degrading enzymes as proxies for Q10 of different Daisy pools. Namely β-glucosidase (median Q10 of 1.82) was assigned to metabolic litter and phenol/peroxidase (1.35) to structural litter and both SOC pools. To partition litter-carbon and SOC into model pools, the lignin-to-nitrogen ratio and the ratio of aliphatic/aromatic-carboxylate carbon were used, respectively. Measurements included soil microbial biomass, soil carbon dioxide (CO2) evolution and remaining carbon in soils and crop-litter. In the Bayesian calibration, strong differences in inferred Q10 values of the same pools between experiments suggested that intrinsic substrate recalcitrance was not the main driver of temperature sensitivity. For field experiment simulations, both the Q10 values derived by Bayesian calibration and measured enzyme Q10 were centered around values below 2, contrasting with high Q10 values for mineralization under laboratory incubations (close to 3). Furthermore, assigning measured phenol/peroxidase Q10 values to the slow crop-litter as well as both SOC pools and β-glucosidase to the fast crop-litter pool (approach b), could significantly improve model performance compared to using the default Q10 value of 2 for all pools. Root-mean-squared-deviation reductions were between 3 and 10% for field experiments, with no change in the laboratory experiments. Thus, site specific Q10 values of soil enzymes show potential as proxies for pool specific Q10. We present a new conceptual framework to explain the observed differences in temperature sensitivities between experiments as a result of two fundamental driving factors classified in a) state variables, that fluctuate in time, and b) soil properties, that are constant over decades. Measured enzyme Q10 values were interpreted as a proxy incorporating both factors. More than intrinsic substrate recalcitrance, the state variables such as physical protection, substrate abundance and unfavorable conditions for microorganisms control temperature sensitivity of mineralization. To reduce the uncertainty in global SOC simulations under a changing climate, their relative contributions should be disentangled and then implemented into SOC models.

The response of agroecosystem carbon (C) respiration fluxes to environmental changes needs to be better understood as respiration subcomponents may respond differently to management and seasonal weather dynamics, which is important for soil organic matter (SOM) modeling. Respiration measurements at two different spatial and temporal scales (eddy covariance (EC) and soil chambers) were used to ascertain the relationship between temperature and CO2 flux of different ecosystem respiration components (ecosystem (Reco), soil and root combined, and soil). Further, different model approaches (static versus dynamic reference CO2 rate (rb) and activation energy type parameter (E0) with an Arrhenius-like function) in order to partition Reco into above- and belowground autotrophic (RA_above, RA_below) and heterotrophic respiration (RH_SOM) were tested. Canopy level CO2 fluxes in winter wheat and silage maize were measured by EC stations and soil surface CO2 flux by a handheld chamber analyzer in arable fields in Southwest Germany over a period of three growing seasons (2009, 2010, and 2012). Additionally, successive bare fallow plots were installed at the beginning of each growing season to partition soil respiration between autotrophic and heterotrophic sources (including “labile” soil C (newest bare fallow) as the difference to the oldest bare fallow). Stepwise model building was tested with keeping rb and E0 constant (static method) and then by varying rb and E0 each individually or together by time period (dynamic method) over the whole growing season (15, 10 or 7 days for Reco, measurement periods for soil chamber measurements). The dynamic models were superior as measured by Aaike Information Criteria (AIC) and coefficient of determination (average R2, 0.15 for the static model and 0.50 for the dynamic model). In the best fitting model for each crop-year (lowest AIC), rb was successfully estimated in each time period (relative standard error

Understanding in situ enzyme activities could help clarify the fate of soil organic carbon (SOC), one of the largest uncertainties in predicting future climate. Here, we explored the role of soil temperature and moisture on SOM decomposition by using, for the first time, modelled in situ enzyme activities as a proxy to explain seasonal variation in soil respiration. We measured temperature sensitivities (Q 10 ) of three enzymes (β-glucosidase, xylanase and phenoloxidase) and moisture sensitivity of β-glucosidase from agricultural soils in southwest Germany. Significant seasonal variation was found in potential activities of β-glucosidase, xylanase and phenoloxidase and in Q 10 for β-glucosidase and phenoloxidase activities but not for xylanase. We measured moisture sensitivity of β-glucosidase activity at four moisture levels (12%–32%), and fitted a saturation function reflecting increasing substrate limitation due to limited substrate diffusion at low water contents. The moisture response function of β-glucosidase activity remained stable throughout the year. Sensitivity of enzymes to temperature and moisture remains one of the greatest uncertainties in C models. We therefore used the response functions to model temperature-based and temperature and moisture-based in situ enzyme activities to characterize seasonal variation in SOC decomposition. We found temperature to be the main factor controlling in situ enzyme activities. To prove the relevance of our modelling approach, we compared the modelled in situ enzyme activities with soil respiration data measured weekly. Temperature-based in situ enzyme activities explained seasonal variability in soil respiration well, with model efficiencies between 0.35 and 0.78. Fitting an exponential response function to in situ soil temperature explained soil respiration to a lesser extent than our enzyme-based approach. Adding soil moisture as a co-factor improved model efficiencies only partly. Our results demonstrate the potential of this new approach to explain seasonal variation of enzyme related processes.