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Soil organic matter (SOM) turnover models predict changes in SOM due to management and environmental factors. Their initialization remains challenging as partitioning of SOM into different hypothetical pools is intrinsically linked to model assumptions. Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) provides information on SOM quality and could yield a measurable pool-partitioning proxy for SOM. This study tested DRIFTS-derived SOM pool partitioning using the Daisy model. The DRIFTS stability index (DSI) of bulk soil samples was defined as the ratio of the area below the aliphatic absorption band (2930 cm−1) to the area below the aromatic–carboxylate absorption band (1620 cm−1). For pool partitioning, the DSI (2930 cm−1 ∕ 1620 cm−1) was set equal to the ratio of fast-cycling ∕ slow-cycling SOM. Performance was tested by simulating long-term bare fallow plots from the Bad Lauchstädt extreme farmyard manure experiment in Germany (Chernozem, 25 years), the Ultuna continuous soil organic matter field experiment in Sweden (Cambisol, 50 years), and 7 year duration bare fallow plots from the Kraichgau and Swabian Jura regions in southwest Germany (Luvisols). All experiments were at sites that were agricultural fields for centuries before fallow establishment, so classical theory would suggest that a steady state can be assumed for initializing SOM pools. Hence, steady-state and DSI initializations were compared, using two published parameter sets that differed in turnover rates and humification efficiency. Initialization using the DSI significantly reduced Daisy model error for total soil organic carbon and microbial carbon in cases where assuming a steady state had poor model performance. This was irrespective of the parameter set, but faster turnover performed better for all sites except for Bad Lauchstädt. These results suggest that soils, although under long-term agricultural use, were not necessarily at a steady state. In a next step, Bayesian-calibration-inferred best-fitting turnover rates for Daisy using the DSI were evaluated for each individual site or for all sites combined. Two approaches significantly reduced parameter uncertainty and equifinality in Bayesian calibrations: (1) adding physicochemical meaning with the DSI (for humification efficiency and slow SOM turnover) and (2) combining all sites (for all parameters). Individual-site-derived turnover rates were strongly site specific. The Bayesian calibration combining all sites suggested a potential for rapid SOM loss with 95 % credibility intervals for the slow SOM pools' half-life being 278 to 1095 years (highest probability density at 426 years). The credibility intervals of this study were consistent with several recently published Bayesian calibrations of similar two-pool SOM models, i.e., with turnover rates being faster than earlier model calibrations suggested; hence they likely underestimated potential SOM losses.

We present a comprehensive, high-quality dataset characterising soil-vegetation and land-surface processes from continuous measurements conducted in two climatically contrasting study regions in South West Germany: the warmer and drier Kraichgau region with a mean temperature of 9.7 °C and annual precipitation of 890 mm, and the cooler and wetter Swabian Alp with mean temperature 7.5 °C and annual precipitation 1042 mm. In each region, measurements were conducted over a time period of nine cropping seasons from 2009 to 2018. The backbone of the investigation was formed by six eddy-covariance stations (EC) which measured fluxes of water, energy and carbon dioxide between the land surface and the atmosphere at half-hourly resolution. This resulted in a dataset containing measurements from a total of 54 site*years containing observations with a multitude of crops, as well as considerable variation in local growing season climates. The presented multi-site, multi-year data set is composed of crop-related data on phenological development stages, canopy height, leaf area index, vegetative and generative biomass and their respective carbon and nitrogen content. Time series of soil temperature and soil water content were monitored with 30-min resolution at various points in the soil profile, including ground heat fluxes. Moreover, more than 1,200 soil samples were taken to study changes of carbon and nitrogen contents. The data set was uploaded to the Pangaea database and can be accessed at https://doi.org/10.20387/bonares-a0qc-46jc (for the review process, please refer to the data availability section). One station in each region has now been set up as continuous observatories of state variables and fluxes in intensively managed agricultural fields.

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.

Unregulated surface gold mining contributes to deforestation and land degradation in Ghana and Burkina Faso (West Africa). In addition, small-scale gold mining uses a technology for gold amalgamation that pollutes the environment with mercury (Hg) and adversely affects human health. In the framework of the recently started Mercury-AMF-project we aim to reduce the environmental damage caused by mercury used in gold mining in Ghana and Burkina Faso. This will be achieved by developing and implementing novel arbuscular mycorrhizal fungi (AMF) - plant systems as a strategy to reclaim mercury-contaminated sites. The cultivation of pioneer plants on contaminated soils can reduce the mercury pollution. Symbiotic mycorrhizal associations of those plants may strengthen the potential to remediate Hg-contaminated soils.The implementation of the project is based on the following specific activities:Characterization of the arbuscular mycorrhizal fungus (AMF) candidates in the soils of Ghana and Burkina Faso; Development of prototype AMF plant systems as an innovative strategy for the remediation of Hg-contaminated sites; Testing of mycophytoextraction methods to reduce the Hg soil concentration below threshold values; Examination of the return of Hg-contaminated sites to agricultural use and the promotion of sustainable land management in gold mining regions; Set-up of modelling approaches for the efficiency of mycophytoextraction methods and Hg plant uptake; Exploration and communication of institutional and socio-economic framework conditions for the introduction of AMF plant systems. During the first year of the project soil and plant sampling campaigns in Ghana and Burkina Faso were organised for screening the AMF-candidates capable for symbiosis with local plant species and tolerant to the mercury pollution. Clarification of possible mechanisms of phytoremediation is the next essential component of the research: several pathways of decontamination are possible including phytostabilization, phytovolatilization and phytoextraction. Based on the first results, field experimental trials with new AMF-plant systems will be established.