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Rhizodeposition fuels carbon (C) and nutrient cycling in soil. However, changes in the dynamics of microbial growth on rhizodeposits with increasing distance from the root is not well studied. This study investigates microbial growth on individual organic components of rhizodeposits and maize root‐derived exudates and mucilage from agricultural soil. By creating a gradient of substrate concentrations, we simulated reduced microbial access to rhizosphere C with increasing distance to the root surface. We identified distinct C‐thresholds for the activation of microbial growth, and these were significantly higher for rhizodeposits than singular, simple sugars. In addition, testing for stoichiometric constraints of microbial growth by supplementing nitrogen (N) and phosphorus (P) showed accelerated and increased microbial growth by activating a larger proportion of the microbial biomass. Early and late season exudates triggered significantly different microbial growth responses. The mineralization of early‐season exudates was induced at a high C‐threshold. In contrast, the mineralization of late‐season exudates showed 'sugar‐like' properties, with a low C‐threshold, high substrate affinity, and a reduced maximum respiration rate of microorganisms growing on the added substrate. Mucilage exhibited the highest C‐threshold for the activation of microbial growth, although with a short lag‐period and with an efficient mucilage degradation comparable to that of sugars. By determining kinetic parameters and turnover times for different root‐derived substrates, our data enable the upscaling of micro‐scale processes to the whole root system, allowing more accurate predictions of how rhizodeposition drives microbial C and nutrient dynamics in the soil. [ABSTRACT FROM AUTHOR]

Maintaining soil organic matter (SOM) is crucial for healthy and productive agricultural soils and requires understanding at the process level, including the role of SOM protection by soil aggregates and the connection between microbial growth and aggregate formation. We developed the Soil Aggregation through Microbial Mediation (SAMM) model, to represent this important connection. The pools of SAMM are fully measurable, and we calibrated and evaluated it against data from a long-term bare fallow experiment in a tropical sandy soil. This experiment received additions of plant litter of different compositions, which resulted in twice the soil carbon stocks in the best treatment compared to the control (about 8 vs. 4 t C ha -1 in 0–15 cm soil depth) after 25 years. As hypothesized, the SAMM model effectively represented the microbial growth response after the addition of litter and the subsequent formation and later destabilization of aggregates. The low correlations between different calibrated model parameters (r<0.5 for all parameters; r>0.4 for only 4 of 22) showed that SAMM is parsimonious. SAMM was able to capture differences between treatments in soil organic carbon (Nash–Sutcliffe modeling efficiency (EF) of 0.68), microbial nitrogen (EF of 0.24), and litter carbon (EF of 0.80). The amount of carbon within the aggregates (EF of 0.60) and in the free silt and clay fraction (EF of 0.24) was also simulated very well to satisfactorily. Our model results suggested that in spite of the sandy soil, up to 50 % of carbon stocks were stabilized through aggregate protection mechanisms; and that microbial and physical aggregate formation coexists. A version of the SAMM model without aggregate protection (SAMMnoAgg) initially failed to stabilize soil organic carbon (EF decreased to -3.68) and the simulation of microbial nitrogen worsened (EF of 0.13). By recalibrating SAMMnoAgg, it was possible to partially correct for the lack of aggregate protection by reducing the rate of mineral-attached carbon decomposition by about 85 % (EF of 0.68, 0.75, and 0.18 for SOC, litter carbon, and microbial nitrogen, respectively). However, the slightly better evaluation statistics of SAMM (e.g., Akaike information criterion of 5351 vs. 5554) suggest that representing aggregate dynamics in SOM models can be beneficial and necessary to understand the mechanism behind SOM dynamics. Our results indicate that current models without aggregate formation partly compensate for the absence of aggregate protection by lowering the turnover rates of other pools. Thus, they remain suitable options where data on aggregate associated carbon are not available. [ABSTRACT FROM AUTHOR]

In light of the large role that soil organic matter (SOM) plays in maintaining healthy and productive agricultural soils, it is crucial to understand the processes of SOM protection including the role of soil aggregate protection. Yet, few numerical process models include aggregate formation and even fewer represent the important connection between microbial growth and aggregate formation. Here, we propose a model of S oil A ggregation through M icrobial M ediation (SAMM), which consist of measurable pools and 5 couples soil aggregate formation to microbial growth. The model was evaluated against data from a long term bare-fallow experiment in a tropical sandy soil, subject to plant litter additions of different compositions. The SAMM model effectively represented the microbial growth response after litter addition and the following formation and later disruption of aggregates. Model parameter correlation was low (all r < 0.5; r > 0.4 for only 4 of 22 parameters) showing that SAMM is well parameterized. Differences between treatments resulting from different litter compositions could be captured by SAMM for soil organic carbon (Nash-Sutcliffe modelling efficiency (EF) of 0.68), microbial nitrogen (EF of 0.24) and litter carbon (EF of 0.80). Aggregate-related fractions, i.e., carbon inside aggregates (EF of 0.60) and also carbon in the free silt and clay fraction (EF of 0.24) were simulated very well to satisfactory. Analysis of model parameters led to further noteworthy insights. For example, model results suggested that up to 50 % of carbon in the soil is stabilized through aggregate protection, even in a sandy soil, and that both microbial activity and physical aggregate formation coexist. When aggregate formation was deactivated, the model failed to stabilize soil organic carbon (EF dropped to -3.68) and microbial nitrogen was represented less well (EF of 0.13). By re-calibrating the model version with deactivated aggregates, it was possible to partly correct for removing the aggregate formation, i.e., by reducing the decomposition rate of mineral attached carbon by about 85 % (EF of 0.68, 0.75 and 0.18 for SOC, litter carbon and microbial nitrogen, respectively). Yet, the overall slightly better evaluation statistics (e.g., Akaike information critereon of 5351 vs 5554) show the potential importance of representing aggregate dynamics within SOM models. Our results indicate that current models without aggregate formation partly compensate the missing protection effect by lowering turnover rates of other pools and thus may still be suitable options where data on aggregate associated carbon is not available. [ABSTRACT FROM AUTHOR]

The patterns of change in bioclimatic conditions determine the vegetation cover and soil properties along the altitudinal gradient. Together, these factors control the spatial variability of soil respiration (RS) in mountainous areas. The underlying mechanisms, which are poorly understood, shape the resulting surface CO2 flux in these ecosystems. We aimed to investigate the spatial variability of RS and its drivers on the northeastern slope of the Northwest Caucasus Mountains, Russia (1,260-2,480 m a.s.l.), in mixed, fir, and deciduous forests, as well as subalpine and alpine meadows. RS was measured simultaneously in each ecosystem at 12 randomly distributed points using the closed static chamber technique. After the measurements, topsoil samples (0-10 cm) were collected under each chamber (n = 60). Several soil physicochemical, microbial, and vegetation indices were assessed as potential drivers of RS. We tested two hypotheses: (i) the spatial variability of RS is higher in forests than in grasslands; and (ii) the spatial variability of RS in forests is mainly due to soil microbial activity, whereas in grasslands, it is mainly due to vegetation characteristics. Unexpectedly, RS variability was lower in forests than in grasslands, ranging from 1.3-6.5 versus 3.4-12.7 µmol CO2 m-1 s-1, respectively. Spatial variability of RS in forests was related to microbial functioning through chitinase activity (50% explained variance), whereas in grasslands it was related to vegetation structure, namely graminoid abundance (27% explained variance). Apparently, the chitinase dependence of RS variability in forests may be related to soil N limitation. This was confirmed by low N content and high C:N ratio compared to grassland soils. The greater sensitivity of grassland RS to vegetation structure may be related to the essential root C allocation for some grasses. Thus, the first hypothesis concerning the higher spatial variability of RS in forests than in grasslands was not confirmed, whereas the second hypothesis concerning the crucial role of soil microorganisms in forests and vegetation in grasslands as drivers of RS spatial variability was confirmed. [ABSTRACT FROM AUTHOR]

The article titled "Gibbs energy or enthalpy—What is relevant for microbial C‐turnover in soils?" discusses the relationship between microbial carbon utilization and energy transformation in soil. The authors argue that a consolidated concept for coupled carbon use efficiency (CUE) and energy use efficiency (EUE) in soil systems requires the correct application of thermodynamic principles and state variables. They criticize the oversimplifications and inconsistencies in a previous review on the topic, pointing out errors in the understanding of Gibbs energy, the use of the NOSC approach, and the comparison of activation energy and reaction enthalpy. The authors conclude that the previous review's statements and equations lack validity and are not supported by data. The article is authored by Matthias Kästner, Thomas Maskow, Anja Miltner, Marcel Lorenz, Sören Thiele‐Bruhn, Tobias Bölscher, Sergey Blagodatsky, Thilo Streck, Holger Pagel, and Evgenia Blagodatskaya. [Extracted from the article]