Dear Juan A. Añel,

thank you very much for drawing attention to the shortcomings in the manuscript. 

Since I'm not a developer of the MONICA model, I have no rights to the intellectual property of the model. My contribution consisted only of minor modifications to the code. In consultation with my colleagues, we decided to only make the entire source code available to the reviewers to avoid any legal difficulties. Therefore, as suggested I published the model code under a restricted access with Zenodo.

https://doi.org/10.5281/zenodo.8211378

The source code without the modifications is published by the actual developers and I used the section "Software availability" to describe the original model and the project website. In the revised manuscript I will rewrite this point and include it to the "Code availiability" section. I still recommend to visit the model website https://github.com/zalf-rpm/monica for model description.

As suggested, I did the same with the data. The whole dataset is published under restricted access with Zenodo.

https://doi.org/10.5281/zenodo.8211468

I will give more information about the data used in the "Data availability" section. However, as far as I know, obtaining of the long-term monitoring data from Lower-Saxony, Germany is only possible if you work for the Federal Republic of Germany.

Data availability

The described long-term soil monitoring data is available from the State Office for Mining, Energy and Geology of the Lower Saxony state government. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from the State Office for Mining, Energy and Geology of the Lower Saxony state government from the website: https://www.lbeg.niedersachsen.de/boden_grundwasser/bodenmonitoring/bodendauerbeobachtung/das-boden-dauerbeobachtungsprogramm-von-niedersachsen-572.html and mail: bodenkundlicheberatung@lbeg.niedersachsen.de Additional data for evaluating soil temperatures and moisture contents is available from the Thünen Institute of Climate-Smart Agriculture from the website: https://www.thuenen.de/en/institutes/climate-smart-agriculture and mail: ak@thuenen.de, from the University of Hohenheim from the website: https://spurengas.uni-hohenheim.de/en/108520 and mail: ruser@uni-hohenheim.de.

Konstantin Aiteew

Thünen-Institut for Climate-Smart Agriculture

Bundesallee 65

38116 Braunschweig (Germany)

Dear Lorenzo Menichetti,

thank you very much for the assessment and the suggestions for improvement of the manuscript.

We selected the prior values ​​based on the observed upper and lower bounds of each parameter to ensure that each calibrated value is within the realistic, observed range. It is highly probable, that with a different prior probability distribution, deviating values could be achieved. However, it was particularly important to us that the parameter values ​​were based on realistic and observed values. Based on that, we could go more into detail explaining the priors in the manuscript. Aside from that, when considering the performance of the model, we would not expect that the calibration could significantly improve the model performance. For comparison, we also applied a simple hill-climbing algorithm to estimate optimal parameter values and didn’t achieve a better performance. We would also argue that complex biogeochemical models tend to perform weaker compared to simple C-turnover models and we were surprised that the MONICA model performed comparably to the simple models.  

If considering the overestimation in the soil water submodel, we didn’t evaluate the feedback between plant growth and soil water contents, but we assume that there should be at least a minor effect of the plant growth parametrisation on soil water changes. We don’t think that the parametrisation of the C-turnover submodel had a significant effect on modelled soil water. We also used the default parametrisation of the soil water submodel, so there could be a general bias in the parametrisation. However, this part needs further evaluation and we are not sure if this should be evaluated/discussed in this manuscript.

We also like the suggestion, to further clarify the difficulties of complex models, that they are inherently black boxes that make understanding the various factors/feedbacks difficult. Therefore, we used a sensitivity analysis, to estimate the effect of plant growth (if that is what you mean with fertility feedback mechanisms) and other factors on soil carbon changes. But we agree that this aspect could be developed further in the manuscript. 

Line 155-160: We used the allometric functions to estimate the C-Inputs from the harvested yields, on the one hand for the simple C-models and second for comparison with the modelled C-Inputs of the MONICA model. 

Line 232: This range seems quite arbitrary, but it was used in another publication and it worked quite well to achieve a random walk in the parameter space. Basically, each step for generating new candidate values was limited by 1 – 25 % of the parameter space. We assume the formulation we used was to complicated. We try to explain this aspect a bit better in the revised version. 

Konstantin Aiteew

Thünen-Institut for Climate-Smart Agriculture

Bundesallee 65

38116 Braunschweig (Germany)

Dear Livia Rasche,

We agree that most current process-based models were developed specifically to represent mineral soils. The MONICA model is no exception. The nutrient dynamics, mineralisation, crop types, water balances and other factors are specifically adapted and calibrated for mineral soils. However, with the advent of reliable hydraulic parameters for organic soils as published by Wallor et al. (2018) in Geoderma (Geoderma 313, 69-81 and 319, 208-218) MONICA would in principle be able to simulate water dynamics in organic soils. The carbon and nitrogen turnover routines in MONICA are also in principle capable of reproducing the C and N dynamics in organic soils; the influence of the water table on the decomposition processes is currently under revision (Khaledi et al., J. Veg. Sci, under review). At this stage, MONICA has not been tested for simulating organic soils, but also this is currently under development (ongoing PhD project).

Regarding the second question: We removed the years 1992 to 2000 from the analysis to avoid statistical biases. The initial soil organic carbon measurements were used to initialise the model (initial SOC content is a necessary model input). At the beginning of the simulation, the measured soil carbon content is not different from the modeled one. If we had left all years in the statistical analysis, this would have resulted in a better model performance due to overfitting.  Furthermore, in most cases, soil organic carbon changes very slowly. As a result, the actual model performance can only be adequately compared with a correspondingly long series of measurements, and from our perspective, removing the first years of measurements provides a more accurate representation since the measured and simulated values do not coincide as likely.

Regarding the third question: Using independent data for validation, we have shown that the calibrated MONICA model can describe soil carbon changes within a time frame of 25 years under temperate climate conditions.  SOC models that explicitly model microbial dynamics might be advantageous for representing processes like priming or SOC responses on warming. However, the microbial models reviewed in Chandel et al. 2023 vary widely in terms of model structure, processes considered and parameters required, which makes it all the clearer that there is currently no consensus on which approach is best suited to represent SOC decomposition. Some of the models are evaluated and compared in Sulman et al. (2018) in Biogeochemistry (141, 109-123) using experimental data from litter input studies and warming experiments. In evaluating these models, they find high variability in model results and conclude that first-order models already produce divergent projections due to parameter uncertainties, and that structural diversity among models would exacerbate these uncertainty. We believe that increasing differences between models due to structural diversity does not represent a degradation in predictive ability, but rather a more accurate estimate of predictive uncertainty (Bradford et al., 2016 in J. Ecol 104, 229-238; Lovenduski and Bonan, 2017 in Environ. Res. Lett. 12).  It is unclear if the adoption of one of the various microbial approaches could increase the model accuracy of MONICA. Because of the increased model complexity and number of parameters, the evaluation and calibration of an incorporated microbial model for regional conditions, would also require a more constraining data set, preferably including those events where microbial models might outperform models based on first order kinetics.   

Regarding the fourth question: As mentioned earlier, we evaluated the calibrated model using independent data (sites). Therefore. we believe MONICA provides robust estimates of SOC changes for temperate climates, a wide range of soil conditions, and management options represented in the data set (crops, crop sequences, organic amendments etc.). How MONICA performs with data outside this definitional range is unknown to us and should be investigated through further model testing. However, the model’s ability to describe plant growth, which is necessary for estimating carbon inputs from crop residues, has been extensively tested for a wide range of climate and soil conditions (Bassu et al., 2014 in Glob. Change Biol. 20, 2301-2320; Kostková et al., 2021 in Agri. Sci. 159, 69-89, …). Currently there are projects underway that investigate the validity of MONICA simulations for crop yields and soil carbon changes at different spatial scales, but for now we are confident that MONICA can provide adequate results for a variety of different cropping systems in temperate climates.

Thank you very much and with kind regards,

The authors