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Sustainable intensification practices, such as integrated soil fertility management (ISFM), form a strategy to close yield gaps while maintaining soil fertility and, typically, are locally tested in field trials. However, to estimate the potential impact of ISFM on a regional scale, field trials are insufficient and biogeochemical models are required. These models need to be calibrated and evaluated when applied to new environments. Here, we present a robust calibration of the DayCent agroecosystem model to simulate the impact of ISFM practices on maize productivity in Kenya, using a probabilistic Bayesian calibration technique with data from long-term field trials at four sites in central and western Kenya. We assessed the efficiency of DayCent in simulating: 1) maize grain yield, 2) changes in soil organic carbon (SOC), and 3) nutrient use efficiency of applied nitrogen (N) fertilizer under different ISFM treatments, which consisted of different organic resources combined with the addition or absence of mineral N fertilizer. After model calibration, both the simulations of maize yield (Nash Sutcliffe Efficiency, NSE 0.51) and change in SOC (NSE 0.54) improved significantly compared to runs using the standard DayCent parameters (NSE of 0.33 and -1.3 for yield and SOC change, respectively). A leave-one-site-out cross evaluation indicated the robustness of the approach for spatial extrapolation, i.e., the significant improvement of model simulations was achieved by calibrating the model with data from three sites and then evaluating it with data from the remaining site. The values of model parameters related to SOC decomposition were most altered by the calibration, i.e., they were an order of magnitude higher compared to the default parameter values (derived for temperate climates). This suggests that the DayCent temperature function is not suitable to capture SOC decomposition across climates with a single set of parameter values. Further, similar maize yields were simulated for all treatments that received mineral N fertilizer and DayCent underestimated the yield increase observed in the field trials of the combined application of organic resources and mineral N compared sole mineral N application. In contrast, at low levels of nutrient inputs DayCent proved sufficiently sensitive to capture differences in maize yield levels. Finally, while mean yields by treatment were simulated well, year-to-year yield variation was not captured well by DayCent. In summary, our results indicate that DayCent is capable to estimate the mean impact that ISFM practices at typical rates of mineral fertilizer and organic resource applications have on yield and SOC, but may not be capable to estimate the differences in yield potential at very high inputs. While the cross evaluation indicated a robustness for upscaling, the suboptimal representation of year-to-year yield variabilities shows that future projections under a changing climate may be biased by the DayCent model. Consequently, improved model structures, such as improved soil moisture representation, are needed to reduce uncertainty.

Context: Crop productivity in sub-Saharan Africa cannot be substantially improved without simultaneously addressing short-term crop nutrient demand and long-term soil fertility. Integrated soil fertility management tackles both by the combined application of mineral fertilizers and organic resource inputs but few studies examined its‘ long-term effectiveness. Objective: To address this knowledge gap, this study analysed maize yield trends in four long-term (31–37 cropping seasons) field experiments in Kenya with contrasting soil textures and under different climates. Methods: All sites had two maize cropping seasons per year, received a base P and K fertilization and tested combinations of organic resource addition (1.2 and 4 t C ha-1 yr-1 ranging from farmyard manure, to high-quality Tithonia diversifolia and Calliandra calothyrsus material to low-quality saw dust), combined with (+N) and without (-N) mineral N fertilizer (120 kg N ha-1 season-1). General maize yield trends across sites and site specific trends were analyzed. Results: Across sites, the no-input control experienced significant average maize yield reductions of 50 kg ha-1 yr-1 over the study period. In contrast, the treatment with farmyard manure +N maintained yields at both 1.2 and 4 t C ha-1 yr-1. High initial yields following additions of Tithonia and Calliandra, reduced over time. Assessment by site showed site specificity of maize yields and yield trends. For example, the two climatically favorable sites in western Kenya experienced yield gains with high quality organic resources at 4 t C ha-1 yr-1, leading to yields of up to 8 t ha-1 per season, while sites in central Kenya experienced yield losses, leading to 3.5 t ha-1 per season. Yield site specificity for ± mineral N treatments was stonger than for organic resource treatments, e.g. the clayey site in central Kenya in the end showed no yield differences between ± N, except for the 1.2 t C ha-1 yr-1 farmyard manure treatment. Yet, farmyard manure plus mineral N consistently achieved highest yields of all organic resource treatments at all sites and farmyard manure addition at 1.2 t C ha-1 yr-1 (about 5 t dry matter) was the most N-efficient treatment. Conclusions: At realistic application rates, maize yield in integrated soil fertility management is best sustained by a combined application of farmyard manure and mineral N. Implications: Mixed crop-livestock systems and a combined manure and mineral N application are key ingredients for sustained productivity of smallholder systems in sub-Saharan Africa., Field Crops Research, 291, ISSN:0378-4290, ISSN:1872-6852

Soil aggregates store most soil organic carbon (SOC), but how does litter quality influence their formation? We hypothesized varying litter quality to facilitate differences in aggregate formation by altering the seasonal development of microbial biomass (MB) C and N, with MB driving aggregate development in a tropical sandy soil in Thailand. Aggregate development was studied in a long-term fallow experiment, receiving 10 Mg ha−1 annual applications of rice (Oryza sativa) straw (low N and polyphenols (PP)), groundnut (Arachis hypogaea) stover (high N, low PP), tamarind (Tamarindus indica) litter (medium N and PP), or dipterocarp (Dipterocarpus tuberculatus) leaf litter (low N, high PP) compared to a control. N-rich litter from groundnut and tamarind led to significantly higher MB, bulk soil C and aggregate C than dipterocarp, rice straw, and the control. Bulk soil C and small macroaggregates C of N-rich litter treatments increased about 7% in 30 weeks. Increasing MB N explained increasing small macroaggregate C and both, MB C or N were important covariates explaining temporal variations of C stored in themicroaggregates, in silt and clay. MB also explained temporal variations of aggregate fraction weights. With time, SMA C only increased in the N-rich groundnut and tamarind treatments, but decreased in other treatments. Connections of MB to aggregate C and weight substantiated the importance of microbial activity for aggregate formation and carbon sequestration. By promoting MB for longest time spans, medium-quality tamarind could best facilitateaggregate formation, and increase silt and clay C.

This chapter focuses on the effects of biotic and abiotic factors controlling soil organic carbon dynamics at continental to global scales. On the side of natural effects, it highlights processes that can control carbon inputs, turnover and stabilization in soils. On the side of anthropogenic effects, the chapter focuses on the role of climate change as well as historic and modern land conversion. The chapter also divides anthropogenic effects into direct and indirect disturbances done by humans. Both overarching sections close with a short synthesis.

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.

Despite the large body of research surrounding crop growth parameters, there is still a lack of systematic assessments on how harvestable yields of different crop types respond to varying levels of shading. However, with the advent of agrivoltaic systems, a technology that combines energy and food production, shade tolerance of cropping systems is becoming increasingly important. To address this research gap, a meta-analysis with data from two experimental approaches (intercropping and artificial shading with cloths, nets or solar panels) was performed. The aim was to quantitatively assess the susceptibility of different temperate crop types to increasing levels of shading. Crop type specific yield response curves were developed as a function of reduction in solar radiation, estimating relative crop yields compared to the unshaded controls. Only studies that reported reduction in solar radiation and crop yield per area in temperate and subtropical areas were included. The results suggested a nonlinear relationship between achieved crop yields and reduction in solar radiation for all crop types. Most crops tolerate reduced solar radiation up to 15%, showing a less than proportional yield decline. However, significant differences between the response curves of the following crop types existed: Berries, fruits and fruity vegetables benefited from reduction in solar radiation up to 30%. Forages, leafy vegetables, tubers/root crops, and C3 cereals initially showed less than proportional crop yield loss. In contrast, maize and grain legumes experienced strong crop yield losses even at low shade levels. The results provide a set of initial indicators that may be used in assessing the suitability of crop types for shade systems, and thus for agrivoltaic or other dual land-use systems. Detailed yield response curves, as provided by this study, are valuable tools in optimizing the output of annual crop components in these systems.

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.

Maize monoculture with low external inputs, as frequently practiced in sub-Saharan Africa, usually leads to the long-term loss of soil fertility. This threatens the already poor yields in the region. Practices that add organic and mineral resources to the soil therefore promise to counteract soil fertility loss by providing the potential feed-stock for microbes to build new soil organic matter. We studied the effect of organic and mineral resource addition from five organic amendment types of different quality (relative N, lignin and polyphenol contents) and quantity and from chemical nitrogen fertilizer, on soil organic carbon (SOC) and soil nitrogen in the 15 cm topsoil of four long-term trials in contrasted sites in Kenya. They had different climate and soil texture and lasted between 16 and 19 years. Treatments were identical among sites, the organic resources were Tithonia diversifolia (high quality and fast turnover) and Calliandra calothyrsus (high quality and slow turnover), stover of Zea mays (low quality and fast turnover), sawdust from Grevillea robusta trees (low quality and slow turnover) and locally available farmyard manure (undefined quality and slow turnover). The organic resources were added in the quantities of 1.2 and 4 t C ha-1 yr-1 and the experiments included a split-plot treatment of ±N addition (120 kg ha-1 in each of the two growing seasons per year).Despite site-specific differences, the general trend across sites indicated that SOC is usually lost with all treatments. Typical losses ranged from 1.9% to 0.6% loss of initial SOC yr-1 for the control and the farmyard manure (at 4t C ha-1 year-1) respectively. Adding Calliandra or Tithonia at 4t C ha-1 yr-1 also enable to slow the loss (about 1.1% of initial SOC yr-1 lost). Nevertheless, the addition of 4t C ha-1 yr-1 farmyard manure and Calliandra calothyrsus, together with mineral N addition, achieved a gain in SOC over time only in the site which had lowest initial SOC contents (about 6 g C kg-1), a sand of 31% content and a climate that was suitable for maize growth. In contrast, another site with low initial SOC content, high sand content, but a less suitable climate, with frequent failures of the maize crop, lost SOC in all treatments. In the site with initially 25 g C kg-1, the farmyard manure treatment at 4t C ha-1 yr-1 with N addition was the only treatment that could maintain SOC, while in the site with initially highest SOC (about 30 g C kg-1), all treatments lost SOC. The mineral N addition, with the exception of two treatments in the lowest fertility site, had no significant effect on the response of SOC to the different organic resource treatments. Our results indicate that farmyard manure may be the most suitable resource to reduce losses of SOC, but increases may only be possible in sites with initially low SOC contents, e.g. where, because of sufficiently long cultivation activities, a new steady state with low SOC contents has already been attained.

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.

Due to the spatial variability of soil resources in rapidly changing landscapes, such as rubber expansion areas in mountainous South East Asia, landscape based soil organic carbon (SOC) stock assessments need new approaches to obtain cost effective high-resolution soil maps. 3D modelling presents the opportunity to model changes of soil properties with soil depth and in space in one single model. While most 3D models make use of spatial autocorrelation to create soil maps, it might be feasible for upscaling to neglect the spatial autocorrelation and only model autocorrelation within the soil profiles. We propose a “mixed model over continuous depth” (MMCD), which uses a linear and quadratic term to model changes of soil properties with depth and predicts the spatial distribution of soil properties at the landscape level. As the study area of 43 km2 in South West China was subject to multiple constraints such as sparse road networks, steep terrain, and poor infrastructure, we applied the cost-constrained conditioned Latin hypercube sampling (CCLHS) scheme for soil sampling at 120 locations to a depth of 1 m. The MMCD provides information on the most important drivers of selected soil properties, and their relative importance. In this study, SOC was strongly linked to an interaction of elevation with mean horizon depth (p

CONTEXT Dual land-use systems allow for a high land-use efficiency and are becoming increasingly relevant amid the rising scarcity of land. Agrivoltaics is a prominent example, yet there are farming system-specific trade-offs when simultaneously producing agricultural output and photovoltaic power. OBJECTIVE Our objective is to report a novel analytical F ramework to assess the E conomic benefits and the AD option P otential of dual L and- U se S ystems (FEADPLUS). The framework is developed with the goal of enabling a straightforward application in large farm-level datasets. METHODS FEADPLUS is grounded in neoclassical economic theory and applied to the case of agrivoltaics. An annualized profitability condition is derived and decomposed to identify the main components determining the agrivoltaic systems' economic viability, allowing for a comprehensive analysis of farm-specific synergies and trade-offs. Modifications enable calculation of the break-even electricity tariff and the relative change in agricultural contribution margin below the agrivoltaic system. The framework's functionality is demonstrated using data for cereal and vegetable farming systems on the Filder Plain, Southern Germany. RESULTS AND CONCLUSIONS We show that farm-specific characteristics explain differences in the adoption potential under equal solar radiations. Cereal and vegetable farms could adopt agrivoltaics at a tariff of 8.63 and 9.00 EUR-cents kWh−1, respectively. Yet, the agricultural contribution margins from land cultivated below the agrivoltaics system decline by 40.3% and 73.9%, respectively. The decline is due to shading effects on crop yields, higher machinery and labor costs, and the foregone agricultural contribution margins from area lost due to the agrivoltaics mounting structure. In the presence of such trade-offs, the adoption of agrivoltaics is more profitable for farms growing low-value crops, such as cereals, than high-value crops like vegetables. Our sensitivity analyses show that this may change if there are synergies, e.g., positive shading effects on yield. Moreover, they indicate that agricultural contribution margins in some scenarios, which could incentivize farmers to abandon farming below the agrivoltaics system. This highlights the need for policymakers to put adequate safeguards in place. SIGNIFICANCE Dual land-use systems are still understudied, but their high land-use efficiency becomes increasingly relevant in light of the mounting pressures on land. FEADPLUS is the first framework that allows estimation of economic benefits and adoption potential across farming systems and specific technology setups under different policy designs. To this end, it meets researchers' demands for a simple tool and allows for many extensions, e.g., incorporation of stochastics or aspects of (dynamic) optimization and economies of scale.

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.

Despite the growing importance of energy cropping systems, little is known about their soil organic carbon (SOC) stocks in topsoils and subsoils. Furthermore, information regarding the impact of N-fertilization on C-sequestration for perennial compared with annual energy cropping systems is scarce. In order to study SOC changes in the soils of different energy cropping systems, a long-term study was established in southwestern Germany with the following cropping systems: energy maize ( Zea mays ) with reduced tillage, Miscanthus ( M. x giganteus ), switchgrass ( Panicum virgatum ) and willow ( Salix schwerinii x viminalis ), as well as a crop rotation with conventional tillage (CT) and no-till (NT) consisting of oilseed rape ( Brassica napus ssp . oleifera ), winter triticale ( Triticale triticosecale ) and winter wheat ( Triticum aestivum ). The soil is a Haplic Luvisol (Siltic). For each cropping system three N-fertilization regimes adapted to the needs of each crop were applied. With the main hypothesis that perennial energy cropping systems increase SOC stocks compared with conventional annual cropping systems, the SOC stocks were analyzed to a depth of 90 cm after 11 years of continuous cropping (2002–2012). Compared with the control (CT) with 76 Mg SOC ha −1 , the perennial crops had significantly higher SOC stocks in all N-fertilization regimes, which amounted to 92–95 Mg SOC ha −1 in the N1-fertilization regime. The crop rotation with NT also had higher SOC stocks with 93 Mg SOC ha −1 in the N1-fertilization regime. N-fertilization generally led to higher SOC stocks in all cropping systems, although SOC stocks did not increase any further from reduced to highest crop-specific N-fertilization, with the exception of energy maize. The current findings also stress the importance of subsoil carbon analyses: SOC stocks at a depth from 30 to 90 cm made up 44–55% of the total stocks and differed significantly between cropping systems.

Peak area (PA) integration of diffuse reflectance mid infrared Fourier transform spectroscopy (DRIFTS) spectra is useful to study soil organic matter (SOM) quality and as a potential modelling proxy. Residual water of soil samples after drying affects DRIFTS spectra, mainly in the region >2500 cm−1. Therefore, SOM quality related PA at different wavenumbers should be influenced to varying degrees. We studied how absolute, normalized and relative PA related to SOM functional groups (at 2930, 1620, 1530 and 1159 cm−1), as well as their ratios were influenced by oven drying of soil samples at increasing temperatures (from 32 °C to 105 °C). All organic and even mineral associated PA significantly increased with drying temperature. The PA of aliphatic C H stretching (2930 cm−1), located on the shoulder of the broad O H stretching PA, was influenced most strongly. Our results indicate that using 105 °C as drying temperature and storing samples in a desiccator is the best way to minimize water interference. These findings apply to relative PA and ratios of organic compounds, while no effect of drying temperatures on PA correlations to C or N were found.