Search

We present a comprehensive, high-quality dataset characterizing soil–vegetation and land surface processes from continuous measurements conducted in two climatically contrasting study regions in southwestern 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 Alb with mean temperature 7.5 ∘C and annual precipitation of 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 (EC) stations 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 dataset 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 1200 soil samples were taken to study changes of carbon and nitrogen contents. The dataset is available at https://doi.org/10.20387/bonares-a0qc-46jc (Weber et al., 2021). One field in each region is still fully set up as continuous observatories for state variables and fluxes in intensively managed agricultural fields.

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.

Thermogravimetry (TG) is a simple method that enables rapid analysis of soil properties such as the content of total organic C, nitrogen, clay and C fractions with different stability. However, the possible link between TG data and microbiological soil properties has not been systematically tested yet and limits TG application for soil and soil organic matter assessment. This work aimed to search and to validate relationships of thermal mass losses (TML) to total C and N contents, microbial biomass C and N, basal and substrate-induced respiration, extractable organic carbon content, anaerobic ammonification, urease activity, short-term nitrification activity, specific growth rate, and time to reach the maximum respiration rate for two sample sets of arable and grassland soils. Analyses of the training soil set revealed significant correlations of TML with basic soil properties such as carbon and nitrogen content with distinguishing linear regression parameters and temperatures of correlating mass losses for arable and grassland soils. In a second stage the equations of significant correlations were used for validation with an independent second sample set. This confirmed applicability of developed equations for prediction of microbiological properties mainly for arable soils. For grassland soils was the applicability lower, which was explained as the influence of rhizosphere processes. Nevertheless, the application of TG can facilitate the understanding of changes in soil caused by microorganism’s activity and the different regression equations between TG and soil parameters reflect changes in proportions between soil components caused by land use management.

Analysis of soil properties and soil organic matter dynamics requires the use of reliable and rapid analytical techniques. Included in such applicable techniques is thermogravimetry (TG) which, typically, measures mass losses of soil heated during a temperature ramping. Previous work revealed relationships between mass losses of 10°C intervals (TG indicators) and total organic carbon (SOC), total nitrogen (TN) and clay content, and relationships between mass losses of larger temperature intervals (TG fractions) and interactions between SOC, TN and clay. However, widespread application of these relationships is hampered by the requirement to equilibrate soils at 76% relative humidity (RH) prior to and during TG measurements. In this study we tested whether the relationships between mass losses and soil properties can be observed at 43% RH by analyzing 37 mostly arable and grassland soils. It was found that at 43% RH the same TG indicators correlated significantly with soil properties as at 76% RH, but the correlations with TN and clay contents were observed at lower temperatures. New equations were developed for rapid analysis of soil properties using TG indicators and for determination of TG fractions at 43% RH. The results indicated that for routine application of these TG approaches in soil science, it is necessary to include an RH parameter that accounts for potential shifts in diagnostic temperatures when modelling the relationships between TG data and soil properties. HIGHLIGHTS: Thermogravimetry data correlate with soil organic carbon, nitrogen and clay at 43% RH. Clay and nitrogen correlated at lower temperature compared to 76% RH. New equations applicable at laboratory conditions were developed. Wider use of TG requires including information on ambient moisture.

Sustainable agricultural land use requires an assessment of degradable soil organic matter (SOM) because of its key function for soil fertility and plant nutrition. Such an assessment for practical land use should consider transformation processes of SOM and its sources of different origin. In this study, we combined a 120‐day incubation experiment with thermal decay dynamics of agricultural soils altered by added organic amendments. The aim was to determine the abilities and limits of thermal analysis as a rapid approach revealing differences in the degradability of SOM. The carried out experiments based on two independent sampling sets. The first sample set consisted of soil samples taken from non‐fertilized plots of three German long‐term agricultural field experiments (LTAEs), then artificially mixed with straw, farmyard manure, sheep faeces, and charcoal equal to 60 Mg ha⁻¹ under laboratory conditions. The second sample set based on soil samples of different treatments (e.g., crop type, fertilization, cultivation) in LTAEs at Bad Lauchstadt and Muncheberg, Germany. Before and after the incubation experiment, thermal mass losses (TML) at selected temperatures were determined by thermogravimetry indicating the degradability of organic amendments mixed in soils. The results confirmed different microbial degradability of organic amendments and SOM under laboratory conditions. Thermal decay dynamics revealed incubation‐induced changes in the artificial soil mixtures primarily at TML around 300°C in the case of applied straw and sheep faeces, whereas farmyard manure showed mainly changes in TML around 450°C. Charcoal did not show significant degradation during incubation, which was confirmed by TML. Detailed analyses of the artificial soil mixtures revealed close correlations between CO₂‐C evolution during incubation and changes in TML at 300°C with R² > 0.96. Results of the soils from LTAEs showed similar incubation‐induced changes in thermal decay dynamics for fresh plant residues and farmyard manure. We conclude that the practical assessment of SOM could be facilitated by thermal decay dynamics if modified sample preparation and evaluation algorithms are used beyond traditional peak analysis.

Methods for analysis of microplastic in soils are still being developed. In this study, we evaluated the potential of a soil universal model method (SUMM) based on thermogravimetry (TGA) for the identification and quantification of microplastics in standard loamy sand. Blank and spiked soils (with amounts of one of four microplastic types) were analyzed by TGA. For each sample, thermal mass losses (TML) in 10 °C intervals were extracted and used for further analysis. To explain and demonstrate the principles of SUMM, two scenarios were discussed. The first refers to a rare situation in which an uncontaminated blank of investigated soil is available and TML of spiked and blank soils are subtracted. The results showed that the investigated microplastics degraded in characteristic temperature areas and differences between spiked and blank soils were proportional to the microplastics concentrations. The second scenario reflects the more common situation where the blank is not available and needs to be replaced by the previously developed interrelationships representing soil universal models. The models were consequently subtracted from measured TML. Sparse principal component analysis (sPCA) identified 8 of 14 modeled differences between measured TMLs and the universal model as meaningful for microplastics discrimination. Calibrating various microplastics concentrations with the first principal component extracted from sPCA resulted in linear fits and limits of detection in between environmentally relevant microplastics concentrations. Even if such an approach using calculated standards still has limitations, the SUMM shows a certain potential for a fast pre-screening method for analysis of microplastics in soils.

A field study was conducted during 2015–2017 in order to evaluate effects of the foliar application of different sources and concentrations of zinc oxide (ZnO) on the yield and morpho-physiological characteristics of saffron. Experimental treatment included the foliar application of nano ZnO at a concentration of (3, 6, and 9 g L−1), foliar application of normal ZnO at a concentration of (3, 6, and 9 g L−1) and the control. Both types of ZnO had positive effects on the saffron yield and number of flowers, but the highest yield and flower number were obtained by the foliar application of 9 g L−1 normal ZnO followed by 6 g L−1 nano ZnO. Increasing concentrations of both fertilizers up to 3 g L−1 had no significant effect on crocin, but the crocin content significantly decreased at higher concentrations. Effects of experimental treatments were significant on all morpho-physiological traits. The maximum chlorophyll a (1.74 mg.g−1) was observed in normal ZnO at a concentration of 9 g L−1 followed by na...

The carbon (C) sequestration potential of croplands has recently become a subject of debate because it may contribute significantly to global climate change mitigation. By contrast, carbon dioxide (CO2) emissions from German croplands have continuously increased over the past decades as a result of soil organic carbon (SOC) losses. Contrasting results, however, have been obtained on the C sink or source function of European croplands based on long-term experiments and rather recent eddy covariance (EC) measurements. Over a period of eight years (2010–2017), we measured the net ecosystem exchange (NEE) of CO2 on six intensively managed cropland sites in two climatically different regions of southwest Germany (Kraichgau (KR) and Swabian Jura (SJ)) using the EC technique. Additionally, we measured aboveground crop biomass at three development stages and estimated management-related C inputs and exports. The inter-annual on-site variability of cumulated annual NEE was large, and neither the region nor the different sites significantly affected NEE budgets. Winter rapeseed showed the lowest CO2 uptake capacity among the observed crops, and the mean annual NEE in the years with winter rapeseed harvest was significantly lower compared to winter wheat, silage maize and winter barley. On average over 46 site-years, annual NEE showed a distinct CO2 uptake of −2580 kg CO2-C ha−1 yr−1. Considering management-related C fluxes, the resulting net biome productivity (NBP) indicated a C source function of the study sites with mean annual losses of 1190 kg C ha−1 yr−1. Due to high C removals after whole plant harvests, silage maize cropping resulted in significantly higher C losses of 4280 kg C ha−1 yr−1 compared to winter rapeseed, winter wheat and winter barley, with mean annual NBPs of 1430, −188 and −1340 kg C ha−1 yr−1, respectively. Consequently, a higher share of exported C in annual NBP resulted in higher C losses. We conclude that the recently increased importance of silage maize in crop rotations destabilizes SOC stocks, threatening the efforts in enhancing soil C sequestration. This calls for further investigations on the C sequestration potentials of more diverse crop rotations including perennial phases.

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.

Thermogravimetry is a technique measuring mass change during programmed heating. In soil analysis, it is used for determination of content of volatile fractions, thermally labile and stable fractions of soil organic matter and minerals. One method of data analysis uses the determination of mass losses in 10 °C temperature areas. In the past, their mutual correlation revealed several larger temperature areas of mass losses, which appeared to be universal for all types of soils equilibrated at the same relative humidity. However, it is unclear if mass losses in these temperature areas are connected with biogeochemical functions or processes in soil. In this work, using data from > 300 soils of different types, geographical origin, and land uses we demonstrate their linear correlation with content of organic C, total N and clay content and biological activity or their combinations. In particular, the results showed that mass losses between 200 and 300 °C, which is related to thermally labile fraction, correlates best with total organic C and less with total organic N. From 300 to 450 °C represents a more stabilized soil organic matter pool and can be best described by either by C or N contents. Mass loss 450–550 °C correlates strongly with clay content, which suggests a connection to organo-clay complexes. The low temperature interval of 30–200 °C, which corresponded to weakly and strongly bound water showed a strong connection with clay content, but a weaker connection to microbial activity. The developed equations were corrected and verified using additional soil sample sets. The correlations along with their universal applicability lead to conclusion about the possible connection of mass losses in these temperature areas to, still unknown, biogeochemical soil functions. The obtained equations may represent a new approach of rapid and universally applicable “mathematical” fractionation requiring only contents of soil organic carbon, total nitrogen, clay and water in soil equilibrated at 76% relative humidity.

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.

Digital soil mapping (DSM) relies on statistical relationships between soil properties and covariates (e.g. terrain attributes, land use class, geology) which may not explain a large proportion of measured soil properties variability. This uncertainty combined with low spatial resolution of existing maps makes it difficult to monitor soils. Diffuse reflectance infrared Fourier transform mid-infrared spectroscopy (midDRIFTS) with partial least square regression (PLSR) may offer an alternative source of high quality data for improved DSM. Previously validated midDRIFTS-PLSR models were used to predict soil total carbon (TC), total inorganic carbon (TIC), total organic carbon (TOC) and texture of 1170 samples from contrasting agroecological regions (~ 3600 km2), Kraichgau (K) and Swabian Alb (SA), Southwest Germany. MidDRIFTS-PLSR predictions were integrated with geostatistics for soil property maps (200 m resolution). An average of 93% of the total soil samples were predicted within the confidence intervals of the midDRIFTS-PLSR models for the respective properties. Ordinary kriging (OK) resulted in maps for TC, TIC, TOC and texture with a root mean square standardized error (RMSSE) ~ 1. Soil organic matter (SOM) (DRIFTS_SOM) and texture (DRIFTS_texture) maps developed in the current study were of higher spatial resolution than previously existing maps. The DRIFTS_SOM and DRIFTS_texture in the both regions showed considerable differences when compared to existing maps. The DRIFTS_SOM in K and SA regions had overlaps of 45 and 69% with the 1:200,000 existing map. While the DRIFTS_texture in K had an overlap of 92% with the 1:1,000,000 existing map, the overlap in SA region was only 11%. We conclude that traditional DSM with covariates can be improved via higher sampling density which is made possible using midDRIFTS-PLSR. Incorporation of mid-infrared spectral data with both remote sensing and other environmental data would be a further application to cope with uncertainty associated to both spectroscopic and spatial modeling.

The initialization of soil organic matter (SOM) turnover models has been a challenge for decades. Instead of using laborious and error prone size-density fractionation SOM pool partitioning, we propose the inexpensive, rapid, and non destructive Diffuse reflectance mid infrared Fourier transform spectroscopy (DRIFTS) technique on bulk soil samples to gain information on SOM pool partitioning from the spectra. Specifically, the DRIFTS stability index, defined as the ratio of aliphatic C-H (2930 cm−1) to aromatic C=C (1620 cm−1) stretching vibrations, was used to divide SOM into fast and slow cycling pools in the soil organic module of the DAISY model. Long-term bare fallow plots from Bad Lauchstädt (Chernozem, 25 years) and the Ultuna frame trial in Sweden (Cambisol, 50 years) were combined with bare fallow plots of 7 years duration in the Kraichgau and Swabian Jura region in Southwest Germany (Luvisols). All fields had been in agricultural use for centuries before fallow establishment, so classical theory would suggest an initial steady state of SOM, which was hence used to compare the performance of DAISY initializations against the newly established DRIFTS stability index. The test was done using two different published parameter sets (2.7 × 10−6 d−1, 1.4 × 10−4 d−1, 0.1 compared to 4.3 × 10−5 d−1, 1.4 × 10−4 d−1, 0.3 for the turnover rates of slow pool, fast pool and humification efficiency, respectively). The DRIFTS initialization of SOM pools significantly reduced model errors of poor performing model runs assuming steady state, irrespective of the turnover rates used, but the faster turnover parameter set fit better to all sites except Bad Lauchstädt. This suggests that soils under long-term agricultural use were not necessarily at steady state. A Bayesian calibration was applied in a next step to identify the best-fitting turnover rates for the DRIFTS stability index in DAISY, both for each site individually and for a combination of all sites. The two approaches which significantly reduced parameter uncertainty and equifinality were: (1) the addition of the physico-chemically based DRIFTS stability index, and (2) combining several sites into one Bayesian calibration, as derived turnover rates can be strongly site specific. The combination of all four sites showed that SOM is likely lost at relatively fast turnover rates with the 95 % credibility intervals of the slow SOM pools half life ranging from 278 to 1095 years, with 426 years as a value of highest probability density. The credibility intervals of this study were consistent with several recently published Bayesian calibrations of similar SOM models, all turnover rates were considerably faster than earlier models suggested. It is therefore likely that published turnover rates understimate the potential loss of SOM.

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.

As urbanization expands, interest in understanding soil properties and functions in urban environments has also increased. Urban soils play an important role in the global carbon (C) cycle. Due to natural and human influences on various forms of C, quantifying soil inorganic carbon (SIC) becomes critical in urban areas. In this study, the suitability of diffuse reflectance mid-infrared Fourier-transform spectroscopy (mid-DRIFTS) to rapidly estimate calcite and dolomite concentrations was tested. Also, the effectiveness of HCl acid pre-treatment to remove carbonates to enable proper estimation of soil organic carbon (SOC) in urban soils was evaluated. Specific mid-infrared frequencies known to be associated with carbonates, i.e. dolomite and calcite, were identified, and specific peak areas were measured and calibrated with results obtained with the volumetric calcimeter method. Multiple linear regression was used to estimate total carbonates with peaks areas at 2930 cm−1, 2515 cm−1 and 730 cm−1 resulting in a 3.72% root mean squared error of an independent validation set (RMSEV). Greater prediction accuracy of calcite (875 cm−1) and dolomite (875 cm−1 and 730 cm−1) were achieved using single peaks in bivariate relationships resulting in RMSEV of 1.27% and 4.22%, respectively. Soil samples were treated with 1 M HCl for 24 h to remove carbonates, and total carbonate, dolomite, and calcite contents were estimated again using both volumetric calcimeter and mid-DRIFTS. The reduction in carbonate concentrations post-acid treatment was considerable (97% for dolomite, and 96% for calcite and total carbonates), but was not complete. Thus, this could compromise quantification of other soil carbon fractions such as total SOC. Using mid-DRIFTS peak areas of CO3−2 functional groups represents a robust alternative for rapid, fast, and low-cost laboratory measurements of soil carbonates, and this high-throughput approach may be used in assays aiming to minimize carbonate interference in SOC estimations.

Mid-infrared spectroscopy has been used with various degrees of success in quantitative and qualitative analysis of soil organic matter (SOM). This study evaluated the spectral interference caused by soil carbonates on the feasibility of using the specific peak area approach obtained by diffuse reflectance infrared Fourier transform spectroscopy in mid-range (midDRIFTS) as an indicator of SOM quality and composition. A midDRIFTS peak area corresponding to more labile SOM compounds (2930 cm−1) was related to microbial biomass carbon and nitrogen (Cmic, Nmic) and hot water extractable carbon and nitrogen (CHWE, NHWE) across two contrasting agroecological regions (Kraichgau (K) and Swabian Alb (SA), Germany) with soils containing carbonates up to 41 g kg−1 soil. Although, the 2930 cm−1 peak showed its potential use as an index for representing labile soil carbon, a poor regression performance between the relative peak area at 2930 cm−1 (rPAnt 2910 cm−1) and labile SOM fractions (R2 = 0.31–0.45) was attributed to inorganic carbon (IC) potentially interfering with this particular peak. To avoid carbonate interference, a spectral-based coefficient was developed using a multiple linear regression model consisting of TOC and TIC as predictors of the difference between peak areas at 2930 cm−1 of non-acid and acid treated spectra (ΔPA 2930 cm−1) of bulk soils. Peak areas at 2930 cm−1 of non treated spectra (PAnt 2930 cm−1) were corrected using a coefficient of 0.21 for carbonates (PAcorr 2930 cm−1), which greatly improved relationships with Cmic, Nmic, CHWE and NHWE (R2 = 0.68–0.75; P

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

An optimized spectroscopic method combining quantitative evolved gas analysis via Fourier transform infrared spectroscopy (FTIR-EGA) in combination with a qualitative in situ thermal reaction monitoring via diffuse reflectance Fourier transform infrared spectroscopy (in situT DRIFTS) is being proposed to rapidly characterize soil organic matter (SOM) to study its dynamics and stability. A thermal reaction chamber coupled with an infrared gas cell was used to study the pattern of thermal evolution of carbon dioxide (CO2) in order to relate evolved gas (i.e., CO2) to different qualities of SOM. Soil samples were taken from three different arable sites in Germany: (i) the Static Fertilization Experiment, Bad Lauchstädt (Chernozem), from treatments of farmyard manure (FYM), mineral fertilizer (NPK), their combination (FYM + NPK) and control without fertilizer inputs; (ii) Kraichgau; and (iii) Swabian Alb (Cambisols) areas, Southwest Germany. The two latter soils were further fractionated into particulate organic matter (POM), sand and stable aggregates (Sa + A), silt and clay (Si + C), and NaOCl oxidized Si + C (rSOC) to gain OM of different inferred stabilities; respiration was measured from fresh soil samples incubated at 20 °C and 50% water holding capacity for 490 days. A variable long path length gas cell was used to record the mid-infrared absorbance intensity of CO2 (2400 to 2200 cm−1) being evolved during soil heating from 25 to 700 °C with a heating rate of 68 °C min−1 and holding time of 10 min at 700 °C. Separately, the heating chamber was placed in a diffuse reflectance chamber (DRIFTS) for measuring the mid-infrared absorbance of the soil sample during heating. Thermal stability of the bulk soils and fractions was measured via the temperature of maximum CO2 evolution (CO2max). Results indicated that the FYM + NPK and FYM treatments of the Chernozem soils had a lower CO2max as compared to both NPK and CON treatments. On average, CO2max of the Chernozem was much higher (447 °C) as compared to the Cambisol sites (Kraichgau 392 °C; Swabian Alb 384 °C). The POM fraction had the highest CO2max (477 °C), while rSOC had a first peak at 265 °C at both sites and a second peak at 392 °C for the Swabian Alb and 482 °C for the Kraichgau. The CO2max increased after 490 day incubation, while the C lost during incubation was derived from the whole temperature range but a relatively higher proportion from 200 to 350 °C. In situT DRIFTS measurements indicated decreases in vibrational intensities in the order of C-OH = unknown C vibration < C-H < −COO/C =C < C = C with increasing temperature, but interpretation of vibrational changes was complicated by changes in the spectra (i.e., overall vibrational intensity increased with temperature increase) of the sample during heating. The relative quality changes and corresponding temperatures shown by the in situT DRIFTS measurements enabled the fitting of four components or peaks to the evolved CO2 thermogram from the FTIR-EGA measurements. This gave a semi-quantitative measure of the quality of evolved C during the heating experiment, lending more evidence that different qualities of SOM are being evolved at different temperatures from 200 to 700 °C. The CO2max was influenced by long-term FYM input and also after 490 days of laboratory incubation, indicating that this measurement is an indicator for the relative overall SOM stability. The combination of FTIR-EGA and in situT DRIFTS allows for a quantitative and qualitative monitoring of thermal reactions of SOM, revealing its relative stability, and provides a sound basis for a peak fitting procedure for assigning proportions of evolved CO2 to different thermal stability components.

A novel method coupling pyrolysis with mid-infrared spectroscopy (Py-MIRS) was developed to characterize soil organic matter (SOM) chemistry in soils. The pyrolyzer was interfaced to the MIR spectrometer by means of a Brill cell™ (CDS Analytica). The set-up generates pyrolysis fingerprint spectra from which individual pyrolysis products can be related to SOM bulk chemistry. Py-MIRS development involved the testing of experimental conditions like pyrolysis temperature (550, 700, 1000 °C), heating rate (20 °C s−1 and 20 °C ms−1) and time (15, 30 and 60 s) using reference standard compounds ranging from carbohydrates to phenols varying in chemical and structural composition like levoglusogan, gluten, tannin, syringol, pectin and leucine falling within different compound categories (carbohydrates, amino acids, proteins, phenols, etc) as well as soil samples. Pyrolysis yields of prominent specific functional groups, like aliphatics (C-H stretching at 2930 cm−1) and Cdouble bond; length as m-dashC aromatics (1510 cm−1), varied with pyrolysis temperature, heating rate and time. The preferred settings for high pyrolysis yield and minimized secondary reactions were obtained at a pyrolysis temperature of 700 °C, heating rate of 20 °C ms−1 and heating time of 30 s. The suitability of Py-MIRS to detect changes in SOM composition was evaluated by comparing Py-MIRS results to Diffuse Reflectance Fourier Transform mid-Infrared Spectroscopy (DRIFTS) results. Soil samples taken from the Static Fertilization Experiment, Bad Lauchstädt, Germany (Chernozem) revealed a major SOM contribution of the peak at 1750 cm−1 (Cdouble bond; length as m-dashO), followed by peaks at 950 (C-H), 1510 (Cdouble bond; length as m-dashC), 1176 (C-H, O-H) cm−1, with smaller contributions from the 2930 (C-H) and 3015 (CH4) cm−1 peaks, apart from a dominant CO2 peak. Using the preferred pyrolysis settings, Py-MIRS as well as DRIFTS results further indicated that soils receiving organic (e.g. farmyard manure) inputs were highly enriched in aliphatic groups, while their absence favored the accumulation of carboxyl and aromatic groups as well as polysaccharides. Py-MIRS allowed via semi-quantification of pyrolysis products a rapid monitoring of SOM bulk chemistry with a high degree of reproducibility. It was concluded that Py-MIRS represents a fast, effective and reproducible technique to characterize changes in the SOM bulk chemistry as a result of management practices. It also allows to minimize acknowledged constraints of other analytical techniques used to characterize SOM bulk chemistry such as mineral interferences and associated secondary reactions.

Recent progress in analytical methodology and computer modeling has significantly increased the knowledge about soil composition, processes, and functions. However, the complexity of soil processes hampers the development of globally applicable predictive models, which limits the practical assessment of soils under different land uses. This work focused on the identification of regional specific and generic soil properties toward the understanding global soil processes and functions by analyzing samples from contrasting climatic regions, parent materials, and land uses. Thermogravimetry was used as a primary method due to its ability to analyze whole soil samples including bound water and without the need for extraction or fractionation of soil components. A newly developed method was applied for mutual comparison of thermal mass losses and to search for relationships between soil components, which were assumed to be consequences of generic regulation processes established during soil formation. The results suggest the presence of soil regional-specific characteristics mainly between water binding and soil organic and inorganic matter components. As a common feature for all soils, a relationship between thermal mass losses was found, which can be explained as the influence of clay on water binding, accumulation of proteinaceous materials, and amount of organo-clay complexes. In addition, the applied approach supports the detection of generic soil forming processes in individual samples and is promising method to distinguish between organic carbon of pedogenetic origin from carbon of other sources such as charcoal or ash. Although, the underlying factors of these interrelations are not fully understood, thermogravimetry seems to be a helpful tool not only for simple screening of soil properties, but also for modeling underlying soil forming processes.

Development of spectroscopic prediction models via partial least squares regression (PLSR) suggests that model performance is highly affected by means of calibration and nature of the dataset. This study compares the predictive performance of PLSR models obtained by cross-validation and independent validation to quantify physico-chemical soil properties from their mid-infrared diffuse reflectance Fourier transform spectra (midDRIFTS) across two contrasting regions, Kraichgau (K) and Swabian Alb (SA), in Southwest Germany. We evaluated the capability of midDRIFTS-PLSR models for predicting total carbon (TC), organic carbon (TOC), inorganic carbon (TIC), nitrogen (TN), mineral N (Nmin), C:N ratio, hot water extractable C and N (CHWE, NHWE), microbial biomass C and N (Cmic, Nmic), pH, bulk density, and clay, silt and sand contents of 126 soil samples. Based on calibrated models, most soil properties were predicted successfully using either calibration approach with residual prediction deviations ≥3 and R2 > 0.9, except for Nmin, C/N ratio, pH, bulk density and sand. However, predictive performance of generic independent validation derived models (GIC) of test set was considerably higher than generic cross-validation models. Validation using GIC models gave relatively the same predictive performance with those obtained in calibration except for Nmin. Validation of region specific cross-validated models, however, resulted in successful predictions only for TC, TIC, TOC and TN in SA and TC and TIC and TOC in K. Our results show the superiority of independent validation over both generic and region specific cross-validation as a robust tool for predicting soil properties without further laboratory measurements.

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.

Measurements of carbon dioxide (CO2)-evolution from soils are important in evaluating biomass and activity of soil microorganisms, as well as decomposition of soil organic matter. The Respi- cond VI is a fully computerized system allowing continuous measurement of CO2 evolution in short- and long-term soil incubation experiments in up to 96 incubation vessels. The measure- ment of CO2 evolution is based on the absorption of CO2 by an electrolyte (KOH solution) produc- ing a change in the cell conductance measured using two electrodes. In this study, the Respicond VI was recalibrated yielding 174.5 mg CO2 as constant A expressing the theoretical maximum amount of CO2 absorbed in 10 ml 0.5 M KOH. This value of A corresponds to 34.9 mg CO2 ml −1 1 M KOH. The constant A does neither depend on the investigated incubation temperatures (5˚C - 25˚C) nor on the concentrations of the KOH solutions (0.5, 0.1, 0.05 M KOH). To eliminate any influence of changing incubation temperatures, either induced by uncertainties in temperature control or as a part of the experimental setup, on the conductance of KOH solution, a correction procedure was developed using a factor calculated from changing conductance of KOH solutions in incubation vessels without soil.

Biological transformation of organic matter in soil is a crucial factor affecting the global carbon cycle. In order to understand these complex processes, soils must be investigated by a combination of various methods. This study compares the dynamics of biological mineralization of soil organic matter (SOM) determined via CO2 evolution during an 80-day laboratory incubation with their thermo-oxidative stability determined by thermogravimetry (TG). Thirty-three soil samples, originating from a wide range of geological and vegetation conditions from various German national parks were studied. The results showed a correlation between the amount and rate of respired CO2 and thermal mass losses of air-dried, conditioned soils occurring around 100 °C with linear coefficients of determination up to R2 = 0.85. Further, correlation of soil respiration with thermal mass losses around 260 °C confirmed previous observations. The comparison of TG profiles from incubated and non-incubated soils underlined the importance of thermal mass losses in these two temperature intervals. Incubated soils had reduced thermal mass losses above 240 °C and conversely an increased mass loss at 100–120 °C. Furthermore, the accurate determination of soil properties by TG such as soil organic carbon content was confirmed, and it was shown that it can be applied to a wider range of carbon contents as was previously thought. It was concluded that results of thermal analysis could be a helpful starting point for estimation of soil respiration and for development of methods revealing processes in soils.

The impacts of deforestation and land cover change upon underlying soils were examined on one hillside in central Armenia. Soil characteristics in three land cover areas—forest, coppice, and pasture—were recorded and soil samples were analyzed. Deforestation and land cover change were found to increase erosion rates. From soil horizon and structural characteristics, it can be estimated that 40 cm of soil have been lost in the pasture and 20 cm have been lost in the coppice compared to the forest. Soil organic carbon was also affected by deforestation and land cover change. Compared to the forest (4.7% organic carbon), both the coppice (3.7%) and the pasture (3.4%) had lower values. Phosphorus, potassium, and nitrogen content varied and may have been affected by erosion, animal deposition, differing amounts of vegetative residues present, differing organic matter decomposition rates, and differing hydrological processes. Deforestation was also found to change the species composition of seedlings and saplin...

The study examined the interrelationships between chemical and microbiological quality indicators of soil and their ability to differentiate plots under contrasting fertilization regimes. The study was based on a long-term field experiment established on an Udic Ustocrepts in 1966. The soil was cropped with maize (Zea mays L.) and winter wheat (Triticum aestivum L.) and received no organic fertilization (control), wheat straw and maize stalk (crop residue) or cattle manure (manure) in combination with increasing levels of mineral N (N0 and N200). To asses whether seasonal fluctuations of measured properties might mask the effects of fertilization, soil samples were collected four times within a growing season. Manure amendment increased soil TOC and TN, while crop residue amendment had no significant effects. Mineral N increased TN only in April, while in September it decreased water extractable organic C (WEOC). Data of diffuse reflectance Fourier transform mid-infrared spectroscopy (DRIFTS) gave evidence for a higher relative contribution of the aliphatic peak at 2930 cm−1 and a lower relative contribution of the aromatic peaks at 1620 cm−1 and 1520 cm−1 under manure. Manure amendment stimulated enzymatic activities, increased microbial biomass carbon (Cmic) and total phospholipids (PLFAs), and reduced the metabolic quotient (qCO2). Patterns of PLFAs indicated that manure amendment increased the ratio of Gram-positive to Gram-negative bacteria. Crop residue amendment had no significant effects, while in September mineral N inhibited protease activity and reduced the Gram-positive to Gram-negative ratio. Microbial-related parameters fluctuated over time but their seasonality did not hamper the identification of fertilization-induced effects. The selected properties proved to be valuable indicators of long-term changes of soil quality and were strongly interrelated: changes in soil organic matter content and composition induced by manure amendment were accompanied by changes in abundance and function of the soil microbial community. Partial least square analysis obtained relating DRIFTS spectra to measured soil properties produced accurate predictive models for TOC and PLFAs, and moderately accurate models for Cmic, showing the potential of DRIFTS to be used as a rapid soil testing technique for soil quality monitoring.