| 14 Oct 2025
Coupled C and N turnover in a dynamic pore scale model reveal the impact of exudate quality on microbial necromass formation
Abstract. The adequate quantification of soil organic carbon (SOC) turnover is a pressing need for improving soil health and understanding climate dynamics. It is controlled by the complex interplay of microbial activity, availability of carbon (C) and nitrogen (N) sources, and the dynamic restructuring of the soil's architecture. Accurate modeling of SOC dynamics requires the representation of these processes at small spatial scales.
We present a mechanistic, spatially explicit model at the pore scale, which couples enzymatic degradation of particulate organic matter (POM), microbial necromass and root exudates with microbial growth and turnover, C respiration and N cycling depending on the C/N ratios of the different organic carbon sources. It is combined with a cellular automaton model for simulating soil structure dynamics including the stabilization of soil particles, POM or microbial necromass via organo‐mineral associations.
The virtual soil simulations use µCT data of aggregates and parameters from rhizosphere experiments without parameter fitting to explore the influence of (i) soil structural heterogeneity and connectivity, (ii) N limitation, and (iii) necromass formation on SOC storage.
Our results highlight that evolving soil architecture and pore connectivity control substrate accessibility, creating micro‐scale hot and cold spots for microbes. N availability consistently co-limits microbial growth, while a favorable C/N ratio of root exudates substantially reduces respiration and increases CUE over extended periods. Necromass emerges as long‐term SOC pool, as N from short‐term root exudation pulses promotes biomass growth and is converted into slowly degradable necromass, which can be physically protected through occlusion. The findings align with lab experiments and additionally allow us to elucidate the spatial and temporal dynamics of the drivers of carbon turnover.
Using a 2-D spatially explicit model, Rötzer et al. investigate how spatial arrangement of soil particles and quantity/quality of organic matter inputs affect carbon (C) and nitrogen (N) fluxes and storage. The topic is suitable for SOIL and for the special issue “Advances in dynamic soil modelling across scales”. While the model framework is not new, here N is added for the first time, allowing to consider organic matter quality in terms of C:N ratio, and its effects on C allocation by soil microbes (mostly via a C overflow mechanism under N limitation). The methods are appropriate, though their description should be improved (see below). Results are interesting, but my impression is that those related to quantity/quality of inputs are expected (stemming from the model assumptions) and less interesting than those related to soil structure (which I think could be expanded). Language should be improved—see some examples of common issues below. My main concerns are listed first, followed by specific comments.
Main concerns
Language. Section structure should be streamlined and harmonized, as now paragraphs are sometimes indented and sometimes not, several paragraphs contain a single sentence, and use of spaces between paragraphs is inconsistent. This makes it difficult to follow the narrative within each section. Please check citation format, as now it does not always follow standard practice (“Author (year)” for in text citations and “(Author, year)” for citations in brackets), e.g., L38, 174, 185, 286. Several sentences are hard to understand because of missing commas. To give one example, please see L199-202, where I would suggest adding four commas as follows: “Initially, POM particles with a concentration of 0.48 gCcm−3 and a C/N ratio of 100, and necromass with a concentration of 0.32 gCcm−3 and C/N ratio of 10, amounting to a volume fraction of 5% of the solid area, were randomly added to the pore space, corresponding to 60 POM and 60 necromass particles, each with a size of 6-10 μm in diameter.” In many instances word choice is not appropriate (though I am not a native speaker, so my impressions might be wrong), e.g., “Respired” instead of “Respirated” in Figure 1, L133 “outflows” instead of “sinks”, L159 “Monod” instead of “Michaelis-Menten” (kinetics don’t involve enzymes in this model), in some figure “amount” is actually a “content” (mass/mass). In other instances, the meaning of the chosen term is unclear, e.g., L44 “microbial C/N efficiency”, L71 “experimental limitation”, L401 “optimal” (what is optimized?), L558 “agitation”, L570 “exclusive” (why are some pores exclusive?). These are just some examples. A thorough proof-reading is necessary, perhaps with help from a native speaker.
Methods structure. L74-93 present the model structure, so should be moved to the Methods. L260-270 do not present any result, but rather explain how model data is analyzed, so they belong to the Methods.
Model implementation. It is not clear how the differential equations at the core of the model are solved in the hierarchical structure shown in Figure 4. Runge-Kutta method is used to solve the mass balance equations through time within a day, and then spatial fluxes are added, if I understand correctly. But if that is the case, is the solution converging numerically without successive iterations to feed back spatial flux information into the mass balance equations? Is the one-hour time step sufficiently short to ensure stability with an explicit method? Moreover, it is not clear how Eq. A2 is implemented. The inequalities compare partial derivatives on the left-hand side and contents on the right-hand side, which is not physically meaningful (units are different).
Model setup. Some important assumptions make the simulations hard to generalize. First, the soil is assumed saturated for the duration of the simulations, but in such conditions, over 500 days anoxic conditions would develop, leading to rather different processes and controlling factors for C and N dynamics. While perhaps more difficult computationally, considering partly saturated conditions would make simulations more realistic (then one could argue that oxygen is not a limiting factor), and also allow comparing the effects of particle arrangement and pore water content on C and N dynamics. Which one dominates? Second, POM is defined as particles with a size between 6 and 10 microns, while it is operationally defined as particles larger than about 50 microns. I agree that for this kind of numerical experiments, smaller POM particles are appropriate, but I would mention that they are smaller than according to the usual definitions. More important, the C:N of POM is set to 100 g C/g N, which is reasonable for fresh conifer litter (or wood fragments), but much higher than most other plant residues. As a result, the simulated soil is almost always N limited, except at the time of root exudation, which provides inputs with lower C:N. I wonder if it should be the opposite—low organic matter C:N and high root exudate C:N?
Results presentation. Model simulations provide numerical values for fluxes and contents, but the values themselves are in general not particularly important and can be read from the figures. Now the Results section reports several numerical values that, in my view, distract from the main messages—what are the important trends and patterns we should look for in the figures? My suggestion is to streamline the Results section so that the answer to the research question (in this case a general aim rather than a question) emerges more clearly.
Analysis of results. One of the most interesting results is briefly described in L373-383. There the interaction between spatial structure and organic matter stoichiometry emerges. I would expand this analysis beyond a qualitative statement based on visual inspection of a figure, making it quantitative. What can we say in general about these interactions across model runs within a scenario, or across scenarios?
Specific comments
General: typically, mass units referring to a specific element are expressed as e.g., g C or g N, not Cg or Ng
L45: please define CUE
L73, 94: aims are repeated; I would suggest merging these sentences so all aims are in the same place
Figure 1: leaked nitrogen—is it including organic and inorganic N?
Figure 2: following the logic of the manuscript, Figure 2 might actually become Figure 1, as it illustrates the physical environment described by the model. Also, in current Figure 2, high C:N is associated to the rhizosphere, while in the model simulations POM has high C:N and root exudates have generally lower C:N; that is a bit confusing (note also that the labels “max” and “0” on the color bar for C:N are too small and can hardly be read)
L105 (and elsewhere): I wonder if it is necessary to cite a long list of articles adopting a similar approach; perhaps it would be enough to cite the last one, or the one that sets the stage for this work
L106: I would also indicate here that the model simulates a 2-dimensional domain
L110: what does “they” refer to?
L114: if POM is attached, it will become less bio-available, as it is associated to soil minerals
L116: convoluted sentence
L132: dissolved organic or inorganic N, or both?
L134: “opposed” in what sense?
L136: equations on page 8 allow for variability in microbial necromass C:N (two independent equations without constraints on the C:N ratio); with these equations, necromass is homeostatic only if microbial and necromass C:N are exactly the same and do not change through time
L157: convoluted sentence
L172: if I understand correctly, extra-cellular enzymes are assumed not limiting, not “ubiquitous” (i.e., they could be homogeneously distributed but still limiting)
L189: just a curiosity—what happens if two soil particles end up overlapping when randomly placed in the domain?
L224: are these rates expressed per unit root surface or per unit surface in the simulation domain?
L242-243: repetition of paper citations already made earlier
Section 2.4: scenario names are not very intuitive, e.g., “3Pulse” refers to a scenario with 10 pulses. Maybe I would rename them “dynamic”, “static”, “CN”, “10 pulse” without a number to keep it simple (the numbering matters less than the actual treatment you apply in each scenario)
Figure 5 (and elsewhere): panel titles are generally placed on top of their panel, line styles are too similar to be able to distinguish the lines, colors for dissolved substrate and old necromass look the same in the bottom panels
L294: what does “This” refer to?
L326-329: I am not sure why this point regarding microbial biomass nearing equilibrium is presented here when showing results for N fluxes; the same argument holds for C fluxes
L373: not clear what “event” refers to
L437: parameters were not fitted but chosen to obtain reasonable results, which is not conceptually very different
L464: plant residues in agricultural fields are likely much richer in N than assumed here
L471: this sentence states what “equilibrium” means; I am not sure I understand where the argument is going
L475-482: this result is a direct consequence of the model assumptions, so it does not seem particularly insightful
L520: exudate “favorable C:N” does not translate to “exudate diversity”—you could have residues from a single species (no diversity) with very low C:N ratio (favorable C:N)
L521: but structural changes of the magnitude experienced in agricultural fields are not included in this analysis…
L524-527: these last sentences distract from the main message of the manuscript by speculating on future work, while it would be more impactful to conclude with L523, where a key result is summarized (just my personal opinion)
L528: it would be nice if a version of the code to run a basic simulation could be provided
L543: at this point in the text the term “attractive” is not clear (it will be explained later)
L561: what do you mean by “microbial… concentration can spread”? if microbes colonize neighboring cells, they spread, while concentration per se does not spread (it might be just a terminological issue)