# Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes

Lorenzo Brilli, Luca Bechini, Marco Bindi, Marco Carozzi, Daniele Cavalli, Richard Conant, Cristopher Dorich, Luca Doro, Fiona Ehrhardt, Roberta Farina, et al.

► To cite this version:

Lorenzo Brilli, Luca Bechini, Marco Bindi, Marco Carozzi, Daniele Cavalli, et al. Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes. Science of the Total Environment, 2017, 598, pp.445-470. 10.1016/j.scitotenv.2017.03.208. hal-01537755

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Manuscript Number: STOTEN-D-16-06478R2

Title: Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes

Article Type: Review Article

Keywords: Biogeochemical models, C cycle, N cycle, management, pedo-climate

Corresponding Author: Dr. Lorenzo Brilli, Ph.D

Corresponding Author's Institution: CNR

First Author: Lorenzo Brilli, Ph.D

Order of Authors: Lorenzo Brilli, Ph.D; Luca Bechini; Marco Bindi; Marco Carozzi; Daniele Cavalli; Richard Conant; Christopher D Dorich; Luca Doro; Fiona Ehrhardt; Roberta Farina; Roberto Ferrise; Nuala Fitton; Rosa Francaviglia; Peter Grace; Ileana Iocola; Katja Klumpp; Joël Léonard; Raphael Martin; Raia Silvia Massad; Sylvie Recous; Giovanna Seddaiu; Joanna Sharp; Pete Smith; Ward N Smith; Jean-François Soussana; Gianni Bellocchi

**Abstract:** Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (45.9%), followed by limitations in the algorithms simulating the effects of management practices (33.8%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.3%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to
represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

### Response to Reviewers: Revision 2

Reviewer #3: The authors provide a thorough review of models for modeling C and N cycling in agronomic systems. The review is interest and informative, especially with regards to highlighted discrepancies in modeling capacity and accuracy. This research is important for modeling GHG, and impacts of management practices on agricultural output of GHG. Especially useful is the identification of shortcoming and potential sources of error in the models tested. The manuscript should be published after (very) minor revision. Minor suggested changes:

1. Line 58: and an adequate simulation of gas
Response to Reviewer comment No. 1: Modified as suggested.

2. line 58: Given these conditions, the assessment
Response to Reviewer comment No. 2: Modified as suggested.

3. Line 83-84: that C and nitrogen (N) cycling strongly depend on
Response to Reviewer comment No. 3: Modified as suggested.

4. Line 102: even when models are run under the same conditions of
Response to Reviewer comment No. 4: Modified as suggested.

5. Line 150: carbon dioxide (CO2), and nitrate (NO3))
Response to Reviewer comment No. 5: Modified as suggested.

6. Line 191: adequate options and parameters values allows to simulate
simulation of a wide
Response to Reviewer comment No. 6: Modified as suggested.

7. Line 193: It allows considering consideration of the effect
Response to Reviewer comment No. 7: Modified as suggested.

8. Line 211: Then, in that level 2
Response to Reviewer comment No. 8: Modified as suggested.

9. Line 218: knowledge on of the
Response to Reviewer comment No. 9: Modified as suggested.

10. line 275: of N in the soil profile
Response to Reviewer comment No. 10: Modified as suggested.

11. line 293-4: Reference evapotranspiration is accounted by calculated using the Penman-Monteith (56%), or Penman and Priestley-Taylor (44%) equations.
Response to Reviewer comment No. 11: Modified as suggested.

12. Line 311-13: In general from our analysis indicated emerged that the three main processes belonging to the general class of GHG emissions and other fluxes are almost fully simulated by the considered models. Merge single-sentence paragraphs into larger paragraphs throughout results.
Response to Reviewer comment No. 12: Modified in the text: (see L:315:318: "For better assessing how C and N cycles are involved in the simulation of GHG emissions and other fluxes within several models, three
main processes were identified (Table S4, see supplementary material).
Overall, our analysis indicates that these three main processes are
almost fully simulated by the considered models

13. Line 314: In the main process called CO2 the The most important C-fluxes from the ecosystems were considered in the main process called "CO2".

Response to Reviewer comment No. 13: Modified as suggested.

14. Line 364: e.g. STICS accounts for burial through tillage
Response to Reviewer comment No. 14: Modified as suggested.

15. Line 393: such as patterns of air temperature, precipitation, solar radiation, also and including
Response to Reviewer comment No. 15: Modified as suggested.

16. Line 427: i.e. under- or over-estimation
Response to Reviewer comment No. 16: Modified as suggested.

17. Line 432: anaerobic conditions, e.g. Bollmann
Response to Reviewer comment No. 17: Modified as suggested.

18. Line 461: The Amount of bound enzymes increases with the increasing layer charge of
Response to Reviewer comment No. 18: Modified as suggested.

19. Line 466: affects the amount of soil enzymes
Response to Reviewer comment No. 19: Modified as suggested.

20. Line 467: At least And finally, the increase. Throughout, change "fine texture soil" to "fine textured soil"
Response to Reviewer comment No. 20: Modified as suggested.

21. Line 502: due to different types of
Response to Reviewer comment No. 21: Modified as suggested.

22. Line 529: the models subroutines
Response to Reviewer comment No. 22: Modified as suggested.

23. Line 531: the fact that the model
Response to Reviewer comment No. 23: Modified as suggested.

24. Line 605: underestimation of particulate organic C
Response to Reviewer comment No. 24: Modified as suggested.

25. Line 607: fertilization and tillage, which were probably the most commonly simulated
Response to Reviewer comment No. 25: Modified as suggested.

26. Lines 619-20: related to the ecosystem and climate, which makes it difficult to define the parameter which most strongly
Response to Reviewer comment No. 26: Modified as suggested.

27. Lines 627-8: rice cultivation being too low (i.e. effect of waterlogged soil not included in RothC) being too low
Response to Reviewer comment No. 27: Modified as suggested.

28. Line 643: ones where they have been previously
Response to Reviewer comment No. 28: Modified as suggested.

29. Line 656: with the results of Li et al. (2005),
Response to Reviewer comment No. 29: Modified as suggested.

30. Line 680: agriculture fits
Response to Reviewer comment No. 30: Modified as suggested.

31. Line 693: physics, and the interface between the two that
Response to Reviewer comment No. 31: Modified as suggested.

32. Line 749: to optimize resources
Response to Reviewer comment No. 32: Modified as suggested.

33. Several places: correct spelling of vermiculite
Response to Reviewer comment No. 33: Corrected

34. Line 834: should take into account for ions interactions
Response to Reviewer comment No. 34: Modified as suggested.

35. Lines 858-861: For the N cycle, the main limitations inherent in model structure were found under different pedo-climatic conditions (51.7%), whilst for the scale of application the major weaknesses were due to different pedo-climatic conditions (20.4%). Consider rewrite - it's a bit cumbersome as written.
Response to Reviewer comment No. 35: Modified in the text: (see L:865:867: "For both the N-cycle modelling and scale of application, the main limitations were found in the response to different pedo-climatic conditions (51.1% and 20.2%, respectively).

Reviewer #4: This manuscript is trying to present a comprehensive analysis of the strengths and weaknesses of existing state-of-the-art agro-ecosystems models in terms of simulating C and N fluxes. Such an effort is timely and is expected to contribute to further activities intended to improve agro-ecosystem models to address climate change challenges. I understand that the authors have to review numerous literatures. Here I would like to point out several minor inaccuracies that I hope the authors will fix before acceptance for publication. My specific comments are as follows:

1. Page 1 line 46, instead of citing "www.faccejpi.com", better to cite other literature that focuses on describing the CN-MIP.
Response to Reviewer comment No. 1: We thank the reviewer for the comment. We changed the website address, which now specifically targets to the CN-MIP project. The project being ongoing, peer-review literature has not yet been published from CN-MIP. The current paper is the first main contribution.

2. Page 2 line 60: "appears an essential step for future research".
"appears to be ..." is better.
Response to Reviewer comment No. 2: Modified as suggested.

3. page 18 line 575: "... soil capacity to transform crop residue in SOC" is confusing.
Response to Reviewer comment No. 3: The sentence has been rewritten (see: L593:594).
4. Page 6 lines 181-185, the description of EPIC does not reflect the state-of-the-art of its development in C-N cycling. Please consider changes according to the following information.

a) The latest public version of EPIC is 1102. This version is not available on the website at epicapex.tamu.edu, but is available by contacting Jimmy Williams and has already been widely used in the USDA CEAP projects and numerous papers (e.g. Izaurralde et al. 2006; Zhang et al. 2015).

b) EPIC can simulate more than 100 crops and grasses.

c) The development of EPIC CN algorithms is closely tied to the ongoing soil and water assessment tool (SWAT; Arnold et al. 1998) development efforts as described in Zhang et al. (2013). The agro-ecosystem module within the SWAT model is based on EPIC and provides updates back to EPIC. Therefore, I suggest using EPIC/SWAT as one model, instead of only mentioning EPIC.

d) Recent development of EPIC/SWAT (Yang et al. 2017) enables simulation of "N2O losses from nitrification" and "Denitrification: N2/N2O ratio". So please change this in table 4.

e) relevant references are as follows:

Arnold, J. G., R. Srinivasan, R. S. Muttiah, and J. R. Willianms. 1998. Large area hydrologic modeling and assessment part 1: model development. Journal of the American Water Resources Association 34:73-89.

Yang, Q., X. Zhang, M. Abraha, S. Del Grosso, G. P. Robertson, and J. Chen. 2017. Enhancing the soil and water assessment tool model for simulating N2O emissions of three agricultural systems. Ecosystem Health and Sustainability 3(2):e01259. doi: 10.1002/ehs2.1259

Zhang, X., Izaurralde, R.C., Arnold, J.G., Williams, J.R. and Srinivasan, R., 2013. Modifying the Soil and Water Assessment Tool to simulate cropland carbon flux: Model development and initial evaluation. Science of the Total Environment, 463, pp.810-822.

Zhang, X., Izaurralde, R.C., Manowitz, D.H., Sahajpal, R., West, T.O., Thomson, A.M., Xu, M., Zhao, K., LeDuc, S.D. and Williams, J.R., 2015. Regional scale cropland carbon budgets: Evaluating a geospatial agricultural modeling system using inventory data. Environmental Modelling & Software, 63, pp.199-216.

Izaurralde, R.C., Williams, J.R., McGill, W.B., Rosenberg, N.J. and Jakas, M.Q., 2006. Simulating soil C dynamics with EPIC: Model description and testing against long-term data. Ecological Modelling, 192(3), pp.362-384.

Response to Reviewer comment No. 4: We thank the reviewer for these comments. We modified the text (see L176-183: "EPIC (Environmental Policy Integrated Climate) (Williams, 1995, Izaurralde et al., 2012) can simulate about 130 crop and grass species through its plant growth model, which uses unique parameter values for each species. It can predict changes in soil, water, nutrient, pesticide movements, and yields as a consequence of management decisions. It also assesses water quality, N and C cycling, climate change impacts, and the effects of atmospheric CO2. Moreover, novel algorithms were recently implemented (Izaurralde et al., 2012) to improve the simulation of C and N transformations, gas (O2, CO2, and N2O) and solute (NO3-, NO2-) movement, and ecosystem C balance and fluxes (Izaurralde et al., 2012)" and the tables (see Table 4, 5, 6 and 7) according to the information received by the EPIC development team.

For the comment at point C, we consider the use of the notation EPIC/SWAT not appropriate in this case for several reasons. As stated in lines 108-116 of the manuscript, we examined I this study the nine models used within the research project CN-MIP. Only EPIC, and not SWAT, was used within this research project. It is certainly true that EPIC and SWAT
share several algorithms and subroutines. However, the two models cannot
be unambiguously associated because EPIC is a field scale model, while
SWAT is a watershed model. In particular, EPIC simulates with a higher
level of detail the crop growth and some soil processes and dynamics.
Because of these differences, the two models produce different results
with the same inputs. EPIC and SWAT developing people at Blackland
Research Station in Temple, TX (USA), with whom we have interacted prior
to revising the manuscript, agree on dealing with EPIC and SWAT as
distinct models. This means that the use of EPIC/SWAT is not appropriate.
For the comment at point E, SWAT not being part of this exercise, and
based on our previous comment, we consider the first three references in
list suggested by the reviewer as not applicable to this study. We have
included the fourth reference suggested because it supports our analysis,
which has also implied to modify the results. The last suggested citation
was already included in the previously submitted manuscript.

5. For DOC simulation, I think DayCent can do it. Please double check and revise this information in Table 4.
Response to Reviewer comment No. 5: We thank the reviewer for the comment. The information reported in table 4 has been modified accordingly.
November 29th, 2016

Dear Editor,

We would like to submit the enclosed manuscript entitled "Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes", which we wish to be considered for publication in Science of the Total Environment.

This is a review paper analysing strengths and weaknesses of simulation models commonly used to simulate C and N fluxes in crop and grassland systems. The content of the paper mostly reflects the experience of the consortium (bringing together 10 organizations from six countries) and the evaluation conducted in the ongoing project "C and N models intercomparison and improvement to assess management options for GHG mitigation in agro-systems worldwide" (CN-MIP, 2014-2017), established within the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI, http://www.faccejpi.com). The study assesses several processes linking soil, vegetation and atmosphere compartments (in interaction with farming practices), and provides an insight on some recent research progresses in the field of biogeochemistry that could inform further model developments

The authors believe the paper fits into the journal's scope and aim, and would be of interest for journal's readership. They would thus value and feel privileged to receive feedback from your journal.

The authors declare that they have no conflict of interest. The authors also acknowledge that the content of the manuscript has not been published previously nor is being considered for publication elsewhere.

Sincerely,
The authors
Revision 1

Reviewer #1: This review study performed by Brilli et al. is comprehensive and systematic. In the manuscript, the authors compared nine agricultural models that can simulate C and N cycling, the underlying processes, abilities, and limitations of different models were analyzed and discussed, and also some perspectives on model development are given. The manuscript is written well and informative, and is acceptable by the journal of Science of the Total Environment. However, some minor revisions are needed, especially the format, as listed below.

1. Abstract, the full name of each abbreviation should be given, such as "C", "GHG", "SOM", etc.
Modified as suggested.

2. The section 2.1 is some information on background, so I suggest integrate these two paragraphs into the introduction section. While, the section 3.1 is the method you used to analyze different models, so it would be better to move this part to approaches section.
We agree with these suggestions. Parts of sub-section 2.1. have been integrated into the Introduction section. Sub-section 3.1. has been moved to the Modelling approach section (Section 2), and has become Sub-section 2.2.

3. Line 148, change "Tab." to "Table", and the same for all the text.
Modified as suggested.

4. Line 157, add the full name to each abbreviation when it used first, and then use the abbreviation in the follow text. For example, line 163 "NPP" and "NEE"; line 427, "WFPS" should be given in line 394; line 439 "BD" should be given in line 393; line 469, the abbreviation of "SOM" has been given in line 88. Line 624 "GHG" has been given in line65. Please check the whole text carefully.
Modified as suggested.

5. Line 248, the supplementary tables should be named as Table S1, S2, ... S5.
Modified as suggested.

6. Line 316-317, why use capital letters for "Gross Primary Production" and others?
Modified as suggested (with lower-case letters).

7. Line 325-330, I suggest use "CO₂" rather than "CO₂-GHG", and use "Non CO₂-Gas" instead of "Non CO₂-GHG" because N₂ and NH₃ are not GHG.
Modified as suggested.

8. Line 448, set "2" as subscript.
Modified as suggested.

9. Line 477, not only archaea but also bacteria and fungi can carry out nitrification.
Added to the text.

10. Line 477-487, heterotrophic nitrification has been demonstrated to widely occur in terrestrial ecosystems, including cropland and grassland (Chen et al., 2015). Heterotrophic nitrification is a different process from autotrophic nitrification. Has this process been included in these nine models?
Chen, Z.M., Ding, W.X., Xu, Y.H., Müller, C., Rütting, T., Yu, H.Y., Fan, J.L., Zhang, J.B., Zhu, T.B., 2015. Importance of heterotrophic nitrification and dissimilatory nitrate reduction to ammonium in a cropland soil: Evidences from a 15N tracing study to literature synthesis. Soil Biology and Biochemistry 91, 65-75.

We thank the reviewer for this comment. In our analysis we considered the process of nitrification without discerning if it was from autotrophs or heterotrophs, since none of the models reported in the paper is able to make it.

11. Line 484, moisture is an important factor influencing nitrification rate. The authors agree. Moisture has also been mentioned in the text

12. Line 508-509, C/N is one of the major factors, so add largely before "depends on". Change "plant residues" to "organic materials".
Modified as suggested (L:502-507 in the revised text).

13. Line 509-513, it has been found that the composition of organic materials rather than a simple indicator of C/N (Bonanomi et al., 2013). Is there any model considering the composition or structure of SOM?
Bonanomi, G., Incerti, G., Giannino, F., Mingo, A., Lanzotti, V., Mazzoleni, S., 2013. Litter quality assessed by solid state 13C NMR spectroscopy predicts decay rate better than C/N and lignin/N ratios. Soil Biology and Biochemistry 56, 40-48.

We thank the reviewer for this comment. Biogeochemical processes are hard to be reproduced and their representation is often simpler than reality. As far as nitrification, we did not consider the composition of organic materials since the nine models used are mainly based on C/N ratios without distinct differences between organic components. However, this could be another point which could be treated by future modelling works.

14. Line 579-582, this sentence is confusing. Do you mean "compared with conventional tillage, no/reduced tillage may lead to ..."? Generally, we compared conservation tillage with conventional tillage.
The authors agree. The sentence has been rewritten accordingly.

15. Section 5, this section mainly focuses on SOM decomposition, how about model development in N transformation and management which are also major aspects in this review?
We thank the reviewer for the suggestion. We acknowledge in the text the importance of N transformation and management, as well as plant-soil interactions (several references have been added. See L:690-692) without developing them, which would have excessively expanded the text. As specified in the text, in this section we made a choice to focus soil biology and soil physics for the reasons explained in L:693-702

16. Line 673-678, some other important areas also generally not have been considered in the current models, such as the microbial traits, including the abundance, community structure and function, the plant-soil interaction and the feedback of ecosystem process to climate change. Particularly, the ignored microbial characteristic is an important cause of the discrepancies of model results.
We thank the reviewer for the suggestion. We have added a short paragraph concerning the inclusion of microbial traits in models at the end of the section relative to soil microbial biomass representation in models (L:729-736 in the revised text). As above, we acknowledge that other suggested areas are important without developing them, which would have made the text too heavy.
17. Table 7 is too large and there are too many references, move Table 7 and the involved references to supplementary material.

The authors believe Table 7 should be in the main text as it provides accurate information, which is essential to the rationale of the paper.

18. The references need to be carefully formatted. The issue number is presented in some reference but not all. Delete the issue number. List all the author names of each reference.

The initials of the titles should be in low case except the first word (such as line 959-961, 1341-1343, etc.). Carefully check all the superscripts and subscripts (such as line 903, 906, 1033, 1037, 1041, 1136, 1273, 1364, 1496, 1499, etc.).

The format of references was corrected.

Reviewer #2:

1. Page 4, Line 112: The word "... improvement ..." should be changed to "... improving ..."
Modified as suggested.

2. Page 4, line 126. (Graux et al., 2013). In the "References" section the "et al" should be replaced with names of other contributors.
Done.

3. Page 4, Lines 127-18: In other places such as: (Palosuo et al., 2011, Rotter et al., 2012, Asseng et al., 2013, Sándor et al., 2016) references are not fully cited in the "References" section. Apparently, these authors have used this format throughout this manuscript. This approach makes it somewhat more time consuming to keep track of works of other contributors when searching databases available on the Internet.

The reference section has been improved by adding the names of all co-authors to multi-author papers in the list.

4. Page 5, line 134: "... understanding grounded in state-of-the art knowledge." "When the phrase (state of the art) is used as a noun it is not hyphenated. It is hyphenated when it is used as an adjective. Adjective Example: I like your state-of-the-art technology. Noun Example: The technology is state of the art."
Modified as suggested.

5. Page 5, line 147: "... (Tab. 1)..." Is this abbreviation for the word "Table" permitted? The same format follows later in this manuscript. Even in the captions of table the word "Table" is abbreviated to "Tab."

The word "Tab." has been changed to "Table" in the whole text and caption.

6. Page 5, line 156: "... agriculture activity..." should read, "agricultural activity..."
Modified as suggested.

7. Page 6, line 163: "... model allows to simulate also..." Revise this please...

Modified in the text (i.e. Also, the model can simulate...).

8. Page 6, line 173: "... integrates..." should be changed to, "... integrate..."

Modified as suggested.
9. Page 5, line 181: "... model which..." should be changed to, "... model, which..."
Modified as suggested.

10. Page 5, line 189: "viii) RothC..." should be changed to, "viii) RothC..."
The formatting does not allow this change.

11. Page 6, line 194: "... model which..." should be changed to, "... model, which..."
Modified as suggested.

12. Page 7, line 197: "It allows to consider the..." should be changed to, "It allows considering the..."
Modified as suggested.

13. Page 7, line 215. Reference has been made to Table 2 but in Table 2, the parameter (*NA) has been defined as "Not information available." The proper word is "No," i.e.: "No information is available." This applies to other tables. By the way, "NA" should be defined clearly within the text of the manuscript and/or in the caption for all tables.
Modified as suggested.

14. In Table 3, column 2: The number in the parentheses should be defined.
We thank the reviewer for this comment. Numbers in brackets have been detailed in the figure caption of the text.

15. Page 7, line 25: "... (Tab. 2a-e in supplementary material)." I did not find this information.
Please clarify.
We referred to the five tables in supplementary material. Sentence has been rewritten as follows: (Tables S1-5 in supplementary material).

16. Page 8, line 245: "... longer term..." could be changed to "... longer-term..."
Modified as suggested.

17. Page 8, line 247. I could not find Table 3a.
"Table 3a" has been modified to "Table S1". It can be found in supplementary material.

18. Page 9, line 268. I could not find Table 3b.
"Table 3b" has been modified to "Table S2". It can be found in supplementary material.

19. Page 9, line 273: "The water transport calculation scheme in soil is mainly described by the capacity (or tipping bucket) approach (78%)." This needs to be rewritten. Perhaps: The soil water balance is primarily described by estimation of soil water availability through adding daily rainfall and subtracting transpiration, evaporation and runoff from an estimated maximum soil water holding capacity. The curious readers perhaps find the works by Paul et al. (2003) and Weiskittel et al. (2010)...
We thank the reviewer for this comment. We gave now a better description of the soil water balance (L:269-274 in the revised text).

20. Page 9, line 276: "... or/and..." should be changed to "... and/or..."
Modified as suggested.

21. Page 9, line 283. I could not find (Tab. 3c).
"Table 3c" has been modified to "Table S3". It can be found in supplementary material.
22. Page 10, line 309. Could not find Table 3d.
"Table 3d" has been modified to "Table S4". It can be found in supplementary material.

23. Page 11, line 358. Could not find Table 3e.
"Table 3e" has been modified to "Table S5". It can be found in supplementary material.

24. Page 13, lines 401 to 410: The "scale of application" should better defined. Authors' definition is not clear.
We added in the text (L:396-400 in the revised text) "*The scale of application refers to the influence on the model performances of the data types used. They may go from high-frequency measurements specific to the study site, which have been collected experimentally within carefully designed plans, to low-frequency data which have been administratively aggregated at a coarse spatial resolution (e.g. regional or national summaries)*".

25. Page 13, line 417: "... features..." could be changed to properties.
Modified as suggested

26. Page 14, line 447. Make sure N2O is correctly typed.
Done

27. Page 14, line 456: It is true that in soils rich in expanding pedogenic 2:1 phyllosilicates some organic molecules penetrate between the layers are as such fixed and are less susceptible to decomposition through soil enzyme activities. Additionally, however, soil enzyme activities are reduced by association of enzymes' active sites with such phyllosilicates resulting in reduced decomposition of organic matter. Lack of oxygen in clayey soils also negatively affects amount of soil enzymes through reduction in the number of enzyme-producing microorganisms. Note that a soil can have a clayey texture but different types of clay have different effect on SOC decomposition. In other words, soil enzyme activities are reduced more significantly in a soil that is montmorillonitic than a soil that is kaolinitic. The increase in temperature expected to increase the rate of microbial and biochemical reactions in the pedosphere but only at the upper part of the A horizon if soil is highly montmorillonitic. For reference, look at work by Tabatabai, Bayan and Eivazi, among others.

We thank the reviewer for the comment. We have added a paragraph concerning the response of enzyme activities in different soil types. (L:459-471 in the revised text): "*In addition, a relevant fraction of microbial extracellular enzymes is adsorbed by external and internal surfaces of clay size particles of soil phyllosilicate minerals (Burns et al., 2013). Amount of bound enzymes increases with increasing layer charge of phyllosilicates (montmorillonite > illite > kaolinite) (Bayan and Eivazi, 1999). Sorption causes conformational changes of enzymes' active sites, and in turn reduces or even suppresses the activity of enzymes (Bayan and Eivazi, 1999, Burns et al., 2013). Moreover, anaerobic conditions, that are expected to occur mostly in finer texture soils, also negatively affects amount of soil enzymes through reduction in the number of enzyme-producing microorganisms (Inglett et al., 2005). At least, the increase of clay content affects soil aggregation, indirectly affecting SOC through the creation of macro-aggregates that can physically protect organic matter molecules from further microbial mineralization (Rice, 2002, Plante et al., 2006). Thus, an overall reduction in SOM turnover in fine texture soils is expected due to reduced substrate availability and overall microbial activity.*"

28. Page 14, lines 461 and 462: Rewrite starting with, "... thus..."
Analysis: Physical and Mineralogical Methods. ISBN-13: 978-0891180883 - ISBN-10:
0891180885

We have added a short paragraph in the revised text (L:814-837) explaining the theory of ion (ammonium) fixation by 2:1 clay minerals. However we did not address all aspects related to fixation and release, directing interested readers to the reviews by Nõmmik and Vahtras (1982) and Nieder et al. (2011).

40. Page 25, lines 825 to 827: "Although the above reported weaknesses were already known due to a wide number of published studies, in the present analysis we have tried to relate them to their causes in the view of using them as an effective basis for improving current modelling approaches.". I find some of the explanations to be limited in scope. Please see comments above.

We agree with the reviewer. Based on both reviewers' comments we provided better explanation in several parts of the (revised) text about soil physical and biological processes (see responses to above comments). These explanations would hopefully result in an improvement of the text. Highlighting the complexity of physical, chemical and biological processes, we emphasize how they are difficult to be reproduced within process-based models. The added text mostly indicate as these processes should be described in more detail into models in order to increase the reliability of outputs.
Revision 2

Reviewer #3: The authors provide a thorough review of models for modeling C and N cycling in agronomic systems. The review is interest and informative, especially with regards to highlighted discrepancies in modeling capacity and accuracy. This research is important for modeling GHG, and impacts of management practices on agricultural output of GHG. Especially useful is the identification of shortcoming and potential sources of error in the models tested. The manuscript should be published after (very) minor revision. Minor suggested changes:

1. Line 58: and an adequate simulation of gas
Modified as suggested.

2. line 58: Given these conditions, the assessment
Modified as suggested.

3. Line 83-84: that C and nitrogen (N) cycling strongly depend on
Modified as suggested.

4. Line 102: even when models are run under the same conditions of
Modified as suggested.

5. Line 150: carbon dioxide (CO2), and nitrate (NO3))
Modified as suggested.

6. Line 191: adequate options and parameters values allows to simulate simulation of a wide
Modified as suggested.

7. Line 193: It allows considering consideration of the effect
Modified as suggested.

8. Line 211: Then, in theat level 2
Modified as suggested.

9. Line 218: knowledge on of the
Modified as suggested.

10. line 275: of N in the soil profile
Modified as suggested.

11. line 293-4: Reference evapotranspiration is accounted by calculated using the Penman-Monteith (56%), or Penman and Priestley-Taylor (44%) equations.
Modified as suggested.

12. Line 311-13: In general from our analysis indicated emerged that the three main processes belonging to the general class of GHG emissions and other fluxes are almost fully simulated by the considered models. Merge single-sentence paragraphs into larger paragraphs throughout results.
Modified in the text: (see L:315:318: "For better assessing how C and N cycles are involved in the simulation of GHG emissions and other fluxes within several models, three main processes were identified (Table S4, see supplementary material). Overall, our analysis
indicates that these three main processes are almost fully simulated by the considered models”).

13. Line 314: In the main process called CO2 the The most important C-fluxes from the ecosystems were considered in the main process called "CO2".
Modified as suggested.

14. Line 364: e.g. STICS accounts for burial through tillage
Modified as suggested.

15. Line 393: such as patterns of air temperature, precipitation, solar radiation, also and including
Modified as suggested.

16. Line 427: i.e. under- or over-estimation
Modified as suggested.

17. Line 432: anaerobic conditions, e.g. Bollmann
Modified as suggested.

18. Line 461: The Amount of bound enzymes increases with the increasing layer charge of
Modified as suggested.

19. Line 466: affects the amount of soil enzymes
Modified as suggested.

20. Line 467: At least And finally, the increase. Throughout, change "fine texture soil" to "fine textured soil"
Modified as suggested.

21. Line 502: due to different types of
Modified as suggested.

22. Line 529: the models subroutines
Modified as suggested.

23. Line 531: the fact that the model
Modified as suggested.

24. Line 605: underestimation of particulate organic C
Modified as suggested.

25. Line 607: fertilization and tillage, which were probably the most commonly simulated
Modified as suggested.

26. Lines 619-20: related to the ecosystem and climate, which makinges it difficult to define the parameter which most strongly
Modified as suggested.
27. Lines 627-8: rice cultivation being too low (i.e. effect of waterlogged soil not included in RothC) being too low
Modified as suggested.

28. Line 643: ones where they have been previously
Modified as suggested.

29. Line 656: with the results of Li et al. (2005),
Modified as suggested.

30. Line 680: agriculture fits
Modified as suggested.

31. Line 693: physics, and the interface between the two that
Modified as suggested.

32. Line 749: to optimize resources
Modified as suggested.

33. Several places: correct spelling of vermiculite
Corrected

34. Line 834: should take into account for ions interactions
Modified as suggested.

35. Lines 858-861: For the N cycle, the main limitations inherent in model structure were found under different pedo-climatic conditions (51.7%), whilst for the scale of application the major weaknesses were due to different pedo-climatic conditions (20.4%). Consider rewrite - it's a bit cumbersome as written.
Modified in the text: (see L:865:867: "For both the N-cycle modelling and scale of application, the main limitations were found in the response to different pedo-climatic conditions (51.1% and 20.2%, respectively).
Reviewer #4: This manuscript is trying to present a comprehensive analysis of the strengths and weaknesses of existing state-of-the-art agro-ecosystems models in terms of simulating C and N fluxes. Such an effort is timely and is expected to contribute to further activities intended to improve agro-ecosystem models to address climate change challenges. I understand that the authors have to review numerous literatures. Here I would like to point out several minor inaccuracies that I hope the authors will fix before acceptance for publication. My specific comments are as follows:

1. Page 1 line 46, instead of citing "www.faccejpi.com", better to cite other literature that focuses on describing the CN-MIP.

We thank the reviewer for the comment. We changed the website address, which now specifically targets to the CN-MIP project. The project being ongoing, peer-review literature has not yet been published from CN-MIP. The current paper is the first main contribution.

2. Page 2 line 60: "appears an essential step for future research". "appears to be ..." is better.
Modified as suggested.

3. page 18 line 575: "... soil capacity to transform crop residue in SOC" is confusing.
The sentence has been rewritten (see: L593:594).

4. Page 6 lines 181-185, the description of EPIC does not reflect the state-of-the-art of its development in C-N cycling. Please consider changes according to the following information.

a) The latest public version of EPIC is 1102. This version is not available on the website at epicapex.tamu.edu, but is available by contacting Jimmy Williams and has already been widely used in the USDA CEAP projects and numerous papers (e.g. Izaurralde et al. 2006; Zhang et al. 2015).

b) EPIC can simulate more than 100 crops and grasses.

c) The development of EPIC CN algorithms is closely tied to the ongoing soil and water assessment tool (SWAT; Arnold et al. 1998) development efforts as described in Zhang et al. (2013). The agro-ecosystem module within the SWAT model is based on EPIC and provides updates back to EPIC. Therefore, I suggest using EPIC/SWAT as one model, instead of only mentioning EPIC.

d) Recent development of EPIC/SWAT (Yang et al. 2017) enables simulation of "N2O losses from nitrification" and "Denitrification: N2/N2O ratio". So please change this in table 4.

e) relevant references are as follows:

Arnold, J. G., R. Srinivasan, R. S. Muttiah, and J. R. Willianms. 1998. Large area hydrologic modeling and assessment part 1: model development. Journal of the American Water Resources Association 34:73-89.

Yang, Q., X. Zhang, M. Abraha, S. Del Grosso, G. P. Robertson, and J. Chen. 2017. Enhancing the soil and water assessment tool model for simulating N2O emissions of three agricultural systems. Ecosystem Health and Sustainability 3(2):e01259. doi: 10.1002/ehs2.1259

Zhang, X., Izaurralde, R.C., Arnold, J.G., Williams, J.R. and Srinivasan, R., 2013. Modifying the Soil and Water Assessment Tool to simulate cropland carbon flux:
Model development and initial evaluation. Science of the Total Environment, 463, pp.810-822.

Zhang, X., Izaurralde, R.C., Manowitz, D.H., Sahajpal, R., West, T.O., Thomson, A.M., Xu, M., Zhao, K., LeDuc, S.D. and Williams, J.R., 2015. Regional scale cropland carbon budgets: Evaluating a geospatial agricultural modeling system using inventory data. Environmental Modelling & Software, 63, pp.199-216.

Izaurralde, R.C., Williams, J.R., McGill, W.B., Rosenberg, N.J. and Jakas, M.Q., 2006. Simulating soil C dynamics with EPIC: Model description and testing against long-term data. Ecological Modelling, 192(3), pp.362-384.

We thank the reviewer for these comments. We modified the text (see L176-183: "EPIC (Environmental Policy Integrated Climate) (Williams, 1995, Izaurralde et al., 2012) can simulate about 130 crop and grass species through its plant growth model, which uses unique parameter values for each species. It can predict changes in soil, water, nutrient, pesticide movements, and yields as a consequence of management decisions. It also assesses water quality, N and C cycling, climate change impacts, and the effects of atmospheric CO2. Moreover, novel algorithms were recently implemented (Izaurralde et al., 2012) to improve the simulation of C and N transformations, gas (O2, CO2, and N2O) and solute (NO3-, NO2-) movement, and ecosystem C balance and fluxes (Izaurralde et al., 2012)" and the tables (see Table 4, 5, 6 and 7) according to the information received by the EPIC development team.

For the comment at point C, we consider the use of the notation EPIC/SWAT not appropriate in this case for several reasons. As stated in lines 108-116 of the manuscript, we examined I this study the nine models used within the research project CN-MIP. Only EPIC, and not SWAT, was used within this research project. It is certainly true that EPIC and SWAT share several algorithms and subroutines. However, the two models cannot be unambiguously associated because EPIC is a field scale model, while SWAT is a watershed model. In particular, EPIC simulates with a higher level of detail the crop growth and some soil processes and dynamics. Because of these differences, the two models produce different results with the same inputs. EPIC and SWAT developing people at Blackland Research Station in Temple, TX (USA), with whom we have interacted prior to revising the manuscript, agree on dealing with EPIC and SWAT as distinct models. This means that the use of EPIC/SWAT is not appropriate.

For the comment at point E, SWAT not being part of this exercise, and based on our previous comment, we consider the first three references in list suggested by the reviewer as not applicable to this study. We have included the fourth reference suggested because it supports our analysis, which has also implied to modify the results. The last suggested citation was already included in the previously submitted manuscript.

5. For DOC simulation, I think DayCent can do it. Please double check and revise this information in Table 4.

We thank the reviewer for the comment. The information reported in table 4 has been modified accordingly.
<figure><img src="image_2.png" /></figure>

## Model weaknesses

* Limitations in simulating the effects of pedo-climatic conditions

* Limitations in the algorithms simulating the effects of management practices

* Multiplicity of scales in both time and space
**Highlights**

* We assess simulated C and N cycles in agricultural systems based on published modelling studies

* Biogeochemical models have limits in simulating pedo-climatic conditions and management effects

* We propose explicit modelling of soil microbial biomass to drive SOC turnover

* Improved approaches of gas transport in soil are required for future modelling work
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Review and analysis of strengths and weaknesses of agro-ecosystem models for
simulating C and N fluxes

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Lorenzo Brilli<sup>1,15*</sup>, Luca Bechini<sup>2</sup>, Marco Bindi<sup>1</sup>, Marco Carozzi<sup>3</sup>, Daniele Cavalli<sup>2</sup>, Richard

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Conant<sup>4</sup>, Cristopher D. Dorich<sup>4</sup>, Luca Doro<sup>5,16</sup>, Fiona Ehrhardt<sup>6</sup>, Roberta Farina<sup>7</sup>, Roberto

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Ferrise<sup>1</sup>, Nuala Fitton<sup>8</sup>, Rosa Francaviglia<sup>7</sup>, Peter Grace<sup>9</sup>, Ileana Iocola<sup>5</sup>, Katja Klumpp<sup>14</sup>, Joël

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Léonard<sup>10</sup>, Raphaël Martin<sup>14</sup>, Raia Silvia Massad<sup>3</sup>, Sylvie Recous<sup>11</sup>, Giovanna Seddaiu<sup>5</sup>,

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Joanna Sharp<sup>12</sup>, Pete Smith<sup>8</sup>, Ward N. Smith<sup>13</sup>, Jean-Francois Soussana<sup>6</sup>, Gianni Bellocchi<sup>14</sup>.

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<sup>1</sup>Università degli Studi di Firenze, Department of Agri-Food Production and Environmental Sciences, 50144 Florence, Italy

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<sup>2</sup>Università degli Studi di Milano, Department of Agricultural and Environmental Sciences, Milan, Italy

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<sup>3</sup>INRA, AgroParisTech, UMR1402 EcoSys, 78850 Thiverval-Grignon, France

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<sup>4</sup>NREL, Colorado State University, Fort Collins, Colorado 80523 USA

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<sup>5</sup>Desertification Research Centre, Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy

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<sup>6</sup>INRA, 63039 Paris, France

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<sup>7</sup>CREA-RPS, Research Centre for the Soil-Plant System, Via della Navicella 2-4, 00184 Roma, Italy

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<sup>8</sup>Institute of Biological and Environmental Sciences, University of Aberdeen, St Machar Drive, AB24 3UU Aberdeen, UK

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<sup>9</sup>Queensland University of Technology, Brisbane, Australia

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<sup>10</sup>INRA, UR 1158 AgroImpact, site de Laon, F-02000 Barenton-Bugny, France

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<sup>11</sup>INRA, FARE Lab, 51100 Reims, France

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<sup>12</sup>New Zealand Institute for Plant and Food Research, 7608, Lincoln, New Zealand

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<sup>13</sup>Agriculture and Agri-Food Canada, Ottawa, Ontario K1A 0C6, Canada

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<sup>14</sup>INRA, UREP, 63039 Clermont-Ferrand, France

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<sup>15</sup>IBIMET-CNR, Via Caproni 8, 50145 Firenze, Italy

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<sup>16</sup>Texas A&M AgriLife Research, Blackland Research & Extension Center, Temple (TX), USA

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*Corresponding author. Tel.: +39 055 2755743; fax +39 055 055 2756429

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E-mail address: lorenzo.brilli@unifi.it; l.brilli@ibimet.cnr.it (L.Brilli).
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**Abstract**

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to **carbon** sequestration and **greenhouse gas** source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of **carbon** and **nitrogen** cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (https://www6.inra.fr/cnmip/Project): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (45.9%), followed by limitations in the algorithms simulating the effects of management practices (33.8%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.3%). Innovative aspects have been identified for future development of **carbon** and **nitrogen** models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of **nitrogen** shortage on **soil organic matter** decomposition, the improvements related to the production and consumption of gases and an adequate **simulation** of gas transport in soil. **Given these conditions,** the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears **to be** an essential step for future research.

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Keywords: Biogeochemical models, C cycle, N cycle, management, pedo-climate

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# 1. Introduction

The sensitivity of soil carbon (C) stocks and greenhouse gas (GHG) emissions to climate and management practices demands a comprehensive methodology for effective policy analyses (Li et al., 1994). Enhancing soil C sequestration and reducing GHG emissions from agricultural soils are key objectives for reducing the climate impact of food production and they strongly depend on agricultural practices such as crop residue return, soil tillage modalities, and enhanced nitrogen (N) fertilization management. Whether C return to soils appear as a main controlling factor, in some cases (e.g. dry climates) reduced tillage may also be an effective measure for enhancing C sequestration (e.g. Chatskikh et al., 2008; Powlson et al., 2012). To avoid pollution swapping, assessments of the potential to reduce climate impact should also include other impacts such as nitrate ($NO_3^-$) leaching into groundwater, ammonia volatilization and soil erosion, which can also be reduced, for example, by increasing the use of grazed pastures in dairy farms (Rotz et al., 2009, Peyraud, 2011). In addition, it is important to consider the interactions on the hundred-year timescale of soil C equilibration (Lardy et al., 2011) and the relatively more rapid changes induced by agricultural practices (Angers et al., 1995). It is likely that most agricultural soils are not in equilibrium with respect to C storage and have the greatest potential for short-term C losses or gains, while they may also be sensitive to the effects of long-term, climate-driven processes (Wutzler and Reichstein, 2007). It is also important to recall that C and nitrogen (N) cycling strongly depend on interactions among plant growth processes, soil water dynamics and soil N dynamics that are highly non-linear and thus difficult to predict with simple approaches.

Process-based ecosystem models take the approach of simulating underlying biogeochemical processes, such as plant photosynthesis and respiration, using mathematical equations that determine the allocation of C from atmospheric $CO_2$ into biomass down to the soil organic matter (SOM). A relatively complete suite of biogeochemical processes (e.g. plant growth, organic matter decomposition, fermentation, ammonia volatilisation, nitrification and denitrification) is generally embedded in these models, enabling computation of transport and transformations in plant-soil ecosystems. Sub-models are designed to interact with each other to describe cycles of water, C and N for target ecosystems, thus any change in the environmental factors collectively affect a group of biogeochemical reactions. Extensively tested biogeochemical models (with the coupled C-N cycling) are effective tools for examining the magnitude and spatial-temporal patterns of C and N fluxes, and play an important role in designing specific policies appropriate to the soils, climate, and agricultural conditions of a location or region. In recent decades, these tools have also been used for
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assessing the expected impacts of future climate, as represented by several climate change
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scenarios (Graux et al., 2013). However, results of state-of-the-art terrestrial biogeochemical
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models, describing the contribution of agricultural systems to C sequestration and GHG
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source/sink status, may diverge significantly even when models are run under the same
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conditions of climate, soil and management (Palosuo et al., 2011, Rotter et al., 2012, Asseng
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et al., 2013, Sándor et al., 2016; Sandor et al., 2017). Such differences between model results
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are often attributed to physical and biogeochemical processes being inadequately resolved
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and, for these models, the improvement of algorithms and structure is recommended beyond
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parameter optimization (Tian et al., 2011, Lu and Tian, 2013).

It is the goal of this paper to examine the strengths and weaknesses of nine crop and grassland models that incorporate C and N fluxes into biogeochemical frameworks and fully assess C and GHG dynamics in agricultural soils. These models are commonly applied worldwide and are used to simulate biogeochemical and related outputs by the project “C and N models intercomparison and improvement to assess management options for GHG mitigation in agro-systems worldwide” (CN-MIP, 2014-2017), established within the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI, http://www.faccejpi.com), which brings together 10 organizations from six countries. With this analysis we are not arguing against the quality of models. While highlighting weaknesses and limits of current modelling approaches as documented in several published studies, we intend to offer a general overview as a basis for new ways of improving current modelling approaches.

The following rationale has been used in the organization of this article. We first present
the conceptual basis of the models analysed and the approach used for gaining insight into
their compositional sub-systems. Section 3 presents results of the approach used. Section 4
reports on the documented performance of biogeochemical models against data, and discuss
their relative strengths and weaknesses. Section 5 presents an outlook on recent research
developments and future approaches. In Section 6, remarks are made concerning the bearing
of the findings on a wider interpretation of biogeochemical modelling.

## 2. Modelling approaches

### 2.1. *The CN-MIP models*

The nine models considered for the CN-MIP exercise are process-based models mainly
developed for crop or grassland ecosystems. They attempt to reproduce the most relevant
ecological and physiological process through a theoretical understanding grounded in state of
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the art knowledge. In this way, they reproduce specific agro-ecological dynamics under
prescribed conditions of climate, soil and management, thanks to the concepts and
relationships that interlink entities of the real world. Most models represent plant phenology
and yield-formation processes, together with functional processes at the basis of SOM (Soil
Organic Matter) turnover, gas exchange at the soil-plant-atmosphere interface and soil water
dynamics.

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The nine models analysed for this intercomparison are: APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS (Table 1). Below, a brief description of each model is provided.

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i) APSIM (The Agricultural Production Systems sIMulator) (Keating et al., 2003; Holzworth et al., 2014) simulates several systems through the interaction among plants, animals, soil, climate and management. The model allows the analysis of the whole-farm system, including crop and pasture sequences and rotations, and livestock.

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ii) CERES-EGC (Crop Environment REsource Synthesis - Environnement et Grandes Cultures) (Gabrielle et al., 1995) simulates the biogeochemical cycles of water, C and N in agro-ecosystems. The model predicts crop production and the environmental impacts related to the agricultural activity (e.g. nitrous oxide (N₂O), nitrogen oxide (NO), ammonia (NH₃), carbon dioxide (CO₂), and nitrate (NO₃)) based on management for a wide range of arable crops (e.g. wheat, barley, maize, sorghum, sunflower, pea, sugar-beet, oilseed rape and miscanthus). Crop-specific modules include approaches for plant growth and development, coupled to a generic soil sub-model.

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iii) *DayCent* (Parton et al., 1994) is a biogeochemical model able to simulate crop growth, soil C dynamics, N leaching, gaseous emissions (e.g. N₂O, NO, **nitrogen (N₂)**, NH₃, **methane (CH₄)** and CO₂) and C fluxes - e.g. **net primary production (NPP)**, **net ecosystem exchange (NEE)** - in crop fields, grassland, forest, and savanna ecosystems. Also, the model can simulate several management practices (i.e. fertilization, tillage, pruning, cutting, grazing, etc.) as well as specific external disturbances (i.e. fires).

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iv) *DNDC* (DeNitrification-DeComposition) (Li et al., 1992a) simulates C and N biogeochemistry in agro-ecosystems. The model predicts crop growth, soil regimes (i.e. temperature and moisture), soil C dynamics, N leaching, and trace gases emissions (e.g. N₂O, NO, N₂, NH₃, CH₄ and CO₂). The model was expanded in 2012 to include biophysical processes in whole-farm systems (Li et al., 2012).

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v) DSSAT (Decision Support System For Agrotechnology Transfer) (IBSNAT, 1993, Tsuji, 1998, Uehara, 1998 and Jones et al., 1998), was originally developed to facilitate

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the application of crop models in a systems approach to agronomic research. DSSAT ver. 4.6
(i.e. cropping system model, CSM) and its crop simulation models **integrate** the effects of soil,
crop phenotype, weather and management options. DSSAT includes improved application
programs for seasonal, spatial, sequence and crop rotation analyses that assess the economic
risks and environmental impacts associated with irrigation, fertilizer and nutrient
management, climate variability, climate change, soil carbon sequestration, and precision
management. The model can predict crop yield, resource dynamics such as for water, N and
C, environmental impact (i.e. N leaching), evapotranspiration and **soil organic matter (SOM)**
accumulation.

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vi) EPIC (Environmental Policy Integrated Climate) (Williams, 1995, Izaurralde et al., 2012) can simulate about 130 crop and grass species through its plant growth model, which uses unique parameter values for each species. It can predict changes in soil, water, nutrient, pesticide movements, and yields as a consequence of management decisions. It also assesses water quality, N and C cycling, climate change impacts, and the effects of atmospheric CO2. Moreover, novel algorithms were recently implemented (Izaurralde et al., 2012) to improve the simulation of C and N transformations, gas (O2, CO2, and N2O) and solute (NO3-, NO2-), movement, and ecosystem C balance and fluxes (Izaurralde et al., 2012).

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vii) PaSim (Pasture Simulation model) (Riedo et al., 1998) is a process-based, grassland-specific ecosystem model that simulates grassland and pasture productivity and GHG emissions to the atmosphere. The model consists of sub-models for grass, animals, microclimate, soil biology, soil physics and management.

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viii) *RothC* (Rothamsted Carbon model) (Coleman and Jenkinson, 1999) is a specific tool for the assessment of organic C turnover in non-waterlogged topsoil. The model allows for the effects of soil type, temperature, moisture content and plant cover on the turnover process.

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ix) STICS (Simulateur multi-disciplinaire pour les Cultures Standard) (Brisson et al., 1998) is a soil-crop model, which is built on a generic framework for plant description. Within this framework, the selection of adequate options and parameters values allows the simulation of a wide range of plants, from annual crops to perennial grasses or trees. The model simulates plant growth as well as water, C and N fluxes. It allows consideration of the effect of a large range of management options on agronomic (biomass or grain productivity and quality) and environmental (C and N storage, nitrate leaching, N2O emissions) outputs.

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Most of the models included in this review are in active development and use, and this activity can result in a temporal fluidity of model descriptions. The information provided in

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this section is based on the authors' knowledge of the state of the models at the beginning of
the project CN-MIP as well as on published material.

## 2.2. Model analysis

For reducing the uncertainty in estimating the magnitude and spatial-temporal patterns
of C and N fluxes from several agro-systems (i.e. crops, grassland and livestock), and for
improving the understanding of how these tools work, we analysed the most important
processes and approaches implemented into the models. This analysis was based on a top-
down approach focused at gaining insight into compositional sub-systems. On this basis, we
indicated three levels of information containing specific processes/approaches that were sub-
divided according to different levels of detail.

The starting point (level 1) was the detection of discrete units considered in agricultural
modelling, which are essential to characterize agricultural systems. In this level, characterized
by the lowest level of detail required for the analysis, we differentiated five general classes
that should be implemented within all biophysical/biogeochemical process-based models for
crops and grasslands. These classes concern ecological and physiological processes,
management options, soil structure, and weather inputs (Table 2).

Then, at the level 2 (intermediate level of detail) specific processes were identified
within each general class (level 2). In this level 20 "main processes" were identified, which
we retained as basic to describe the most important biophysical/biogeochemical dynamics
(Table 3) of each general class indicated in the previous level.

Finally, in the level 3 (highest level of detail) almost 200 modelling approaches (i.e.
methods, options or components), identifying specific dynamics or mechanisms contained
within the previous main processes (supplementary material) were reported (level 3). These
approaches were extrapolated taking into account the current existing knowledge of the
different methods, options and components able to describe the most important
biophysical/biogeochemical dynamics (Tables S1-5 in supplementary material).

There are a number of advantages to such a “top-down” approach. An advantage is the
insight that can be gained from examining the level of detail that each model provides. This in
turn helps in identifying areas in the model structures to establish their reliability and
relevance for intended purposes. Such an approach also helps in tracing possible links with
the basic processes of each model (identification of the strengths and weaknesses) either in
the case of mismatch between model outputs and measurements, or in the case of
disagreement among model results in similar conditions.
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Results reported below were based on the highest level of detail (level 3 – see supplementary material).

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# 3. Results

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## 3.1. Meteorological variables

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Meteorological inputs strongly influence model outputs since they affect plant growth, plant development stages, and soil turnover/balances, including flux exchanges at the soil-plant-atmosphere interface. The number and type of climatic variables required by each model informs us about the relationship between model outputs and climate drivers. In principle, for the modelling of surface reactions and diffusion of volatile products (e.g. N₂O emissions, soil water content dynamics), the higher the resolution in the climate information (e.g. hourly to sub-hourly weather inputs), the more accurate the model response is for short-term processes but the higher the probability that missing data may be present in the weather series used. For **longer-term** processes such as soil organic carbon (SOC) decomposition, higher temporal resolution data may not improve the accuracy of the model response.

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From our analysis (Table S1, see supplementary material) we observed that the nine models involved in CN-MIP mostly use climate inputs at daily resolution (89%), whereas PaSim uses the hourly time scale (but with an option also available for daily inputs), and RothC uses a monthly time-step.

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The most commonly used meteorological variables are precipitation, air temperature and wind speed. Concerning air temperature, the daily maximum and minimum air temperatures are used by almost all models (89%).

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Relative humidity (daily mean) and global solar radiation are also used by 67% and 56% of the models, respectively. The atmospheric concentration of CO₂ is an optional input for many models (78%), with the exception of CERES-EGC and RothC.

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Finally, only a few models use specific meteorological variables such as cloudiness, sunshine duration, dew-point temperature and actual vapour pressure.

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## 3.2. Soil

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Similarly to climate inputs, soil characteristics also have a great influence on model outputs. These characteristics strongly influence crop growth and fluxes related to the gaseous biogeochemical cycles as water, C and N. Some soil inputs are assumed as constant values (i.e. parameters), not changing during the simulation. Different soil properties (e.g. texture, pH, bulk density, etc.) can affect plant growth and the environmental conditions for the

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microbial activity driving the formation and decomposition of SOM and mediating
biochemical processes.

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From our analysis (Table S2, see supplementary material), it emerged that soil processes
are mostly calculated based on the differentiation of the soil profile into a sequence of distinct
layers, with generation of outputs for each of these subdivisions. In PaSim model, the whole
soil profile is the basis for the modelling of C dynamics. The soil temperature is calculated
from energy balance (44%) or based on a response function of air temperature (56%).

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The soil water balance is mainly simulated by using the 'tipping bucket' approach (78%), in which the soil water availability is accounted for by adding rainfall and subtracting evapotranspiration and runoff (Weiskittel et al., 2010) from an estimated maximum soil water holding capacity (which depends on texture and the soil organic matter content). This approach is also defined "cascading", since it assumes that water can move only downward through the soil profile, filling up the layers until field capacity is reached.

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For the transport and transformation of N in the soil profile, most models estimate pools
and fluxes of NO3-N (78%) and/or NH4-N (89%).

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3.3. Plant ecophysiology and partitioning

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Crop and grassland models differ in the algorithms reflecting plant ecophysiology (growth and development) and partitioning (above and below-ground biomass and yield), which can lead to differences in simulated yield and total biomass, in turn affecting estimated C and N fluxes.

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In our analysis (Table S3, see supplementary material), almost half of the models
consider the mechanism of C allocation as a function of development stage (56%), whilst
almost all the models take into account C assimilation (89%). The latter is mainly driven by
RUE-type processes (Radiation Use Efficiency) and/or P-R = gross photosynthesis –
respiration-type processes (56%).

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Phenology is simulated by almost all models (89%) through the use of growing degree
days (GDD) (89%), whilst photoperiod and vernalization are represented by 56% of the
models.

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Leaf area is accounted for by considering the leaf area index (LAI) (89%), whilst the
simulation of the number of leaves and evolution of the specific leaf area are almost ignored.

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Reference evapotranspiration is accounted by using Penman-Monteith (56%), Penman and Priestley-Taylor (44%).

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Root distribution is simulated by 78% of the models, mainly through a linear approach (56%).

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For the most part, models consider a dynamic partitioning of assimilates among plant organs (78%), based on the age of organs (78%). Within-plant partitioning occurs across roots, grains, stems and sheaths, and leaf blades, for 89, 78, 78 and 67% of the models, respectively.

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Yield formation is mainly based on partitioning during reproductive stages (67%) and harvest index-type (44%). The yields mostly simulated are forage (89%), roots and grains (78%), tubers (67%) and fibre (56%).

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The factors limiting plant growth most strongly among the nine models were water deficit and nitrogen deficiency (88%).

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### 3.4. GHG emissions and other fluxes

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For better assessing how C and N cycles are involved in the simulation of GHG emissions and other fluxes within several models, three main processes were detected (Table S4, see supplementary material). **Overall, our analysis indicates that these three main processes are almost fully simulated by the considered models.**

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The most important C-fluxes from the ecosystems were considered **in the main process called "CO₂"**. More specifically, they include the **gross primary production (GPP)**, **NPP**, **NEE**, **the net biome production (NBP)** and several types of respiration processes, e.g. ecosystem respiration (RECO), heterotrophic respiration from both soil and grazing animals, and autotrophic respiration.

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NPP and NEE are the most commonly simulated C-fluxes (67%), followed by GPP (56%) and RECO (44%), whilst just a few models simulate the NBP. Despite only 44% of the models taking into account RECO, most of them only consider soil respiration (89%). Plant respiration is considered by 56% of the models, whilst only 33% of the models take into account respiration from grazing animals.

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Among all of the models analysed only DNDC is able to simulate all the **CO₂** fluxes considered. More than 70% of CO₂-GHG can be simulated also by APSIM, DayCent and PaSim. The **CO₂** simulated by the highest number of models (i.e. six models) are NPP, NEE and soil respiration.

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The main non-CO₂ fluxes (for simplicity called **non CO₂-gas**) include CH₄, N₂O, several N emissions (i.e. NH₃, NOₓ, N₂) and O₃.

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N₂O emissions are most commonly simulated (78%), followed by NH₃ (56%). By contrast, only a few models generate CH₄ and N₂ emission outputs (44%) and NOₓ (33%). None of the models provide **ozone (O₃)** emissions output.

N₂O emissions provided by the models are mostly generated by denitrification and nitrification (78%), mainly based (i.e. >70% of the models) on a soil N pools (e.g. nitrate pool, NH₄ pool) with soil water and temperature acting as main drivers of change on mineral N pools.

Among all models analysed DayCent and DNDC were able to simulate all **non CO₂-gas** considered in our analysis. However, more than 70% of **non CO₂-gas** can be simulated also by APSIM, PaSim and CERES-EGC. The non CO₂-GHG simulated by the highest number of models (i.e. seven models) was N₂O. The models able to simulate the highest number of variables (i.e. CH₄, N₂O and N₂) were APSIM, DayCent, DNDC and PaSim.

Ten specific N processes were considered in the models: nitrification, denitrification, volatilization, leaching, symbiotic fixation, assimilation, mineralization, immobilization, plant uptake, and clay fixation. All these processes were widely simulated (i.e. >70%) by the models considered in our analyses, with the only exception of clay fixation, that is considered only by DNDC model.

Among the models analysed, only RothC does not take into account any N process. All the remaining models are able to simulate each of the N processes considered in our analysis, with the only exceptions being APSIM, which does not consider NH₃ volatilization, and PaSim and STICS, which only take account of assimilation indirectly (C:N-driven).

### 3.5. Management

All models are able to simulate the impact of the most common farming practices (i.e. harvesting, mowing, fertilization, tillage, irrigation, etc.) on the processes described so far. By contrast, specific options for grasslands, such as plant use and nutrient returns from grazing animals (as well as animal performances such as weight growth and milk production) are simulated by a lower number of models (Table S5, see supplementary material).

Harvesting, cutting, tillage, irrigation and crop rotation are widely simulated (>70% of models). Moreover, all models simulate fertilization and residue management. Concerning fertilization, however, only application of mineral N and organic amendments are widely simulated, while only a few models simulate other types of fertilizer such as phosphorus, potassium, sulphur and calcium. Similarly, the management of crop residues is based mainly on their burning or leaving on the ground surface, whilst only 33% of the models also
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consider burial (e.g. STICS accounts for burial through tillage). Among other agricultural
practices, about half of the models consider pruning and water management (i.e. rice), but
only a few consider pesticide application.

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The practices considered in the analysis are generally set by users. Some models also
offer options to trigger management events (i.e. fertilization and irrigation) based on changing
conditions during the simulation.

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Simulation of grazing, animal performances and nutrient returns were taken into
account as specific options for grasslands.

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Concerning grazing, models are for the most part based on user-determined settings (start and end dates, animal density); some of them also include options related to evolving conditions (APSIM, EPIC and PaSim), selective grazing (APSIM and PaSim) and trampling effect (APSIM).

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Animal performance simulation is considered by 55% of the models through
simple/static methods (APSIM and EPIC) or detailed/dynamic methods (PaSim), and based
on feeding standards or fill units (APSIM, DNDC and RothC).

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Finally, nutrient return was considered by 66% of the models, based on uniform
distribution of returns across the whole field.

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CERES-EGC, DSSAT and STICS do not include very specific agricultural options for grasslands. APSIM is the most detailed model for grasslands.

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4. C and N cycles: performance, strengths and weaknesses

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In this section, we provide an overview of the C and N approaches used by the CN-MIP
models (see **Table 4** and supplementary), and their performance as documented for a broad
gradient of geographic and climatic conditions, as well as a variety of soil types and
management practices, to gain insight into their main strengths and weaknesses. To do that,
we have summarised the results of 130 published modelling studies (Table 5).

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In the analysis of the effects on C and N cycles of pedo-climatic conditions, we
considered variations of soil features such as temperature and moisture, texture, bulk density
(BD), pH, SOC, C and N dynamics and water-filled pore space (WFPS), and climate
conditions such as patterns of air temperature, precipitation, solar radiation, and including
frequency and intensity of extreme events such as floods and drought. Management practices
include changes in agricultural practices such as tillage, fertilization, irrigation, crop variety
on soil and vegetation and, in turn, on C and N cycles. The scale of application refers to the
influence on the model performances of the data types used. They may go from high-

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frequency measurements specific to the study site, which have been collected experimentally
within carefully designed plans, to low-frequency data which have been administratively
aggregated at a coarse spatial resolution (e.g. regional or national summaries).

Several types of weaknesses emerged in 95 modelling studies (Table 6), where
criticalities in assessing the impact of pedo-climatic conditions (45.9%) and management
practices (33.8%) on environmental variables are reflected in unsatisfactory model
performances. These latter were mostly related to limitations of model structure with respect
to difficulties of the algorithms in simulating the effects of different management practices on
C and N cycling. By contrast, only a few weaknesses were due to the scale of application,
strictly related to the high variability in time and space of C and N cycles (16.2% and 4.1%
for pedo-climatic conditions and management practices, respectively). For the C cycle, major
limitations of model structure were related to management practices (43.6%), whilst for the
scale of application, the major weaknesses were due to different pedo-climatic conditions
(11.5%). For the N cycle, however, limitations inherent in model structure were predominant
under different pedo-climatic conditions (51.1%), whilst for the scale of application, major
weaknesses were due to different pedo-climatic conditions (20.2%).

## 4.1. *Model structures and pedo-climatic conditions*

Soil properties and climate conditions emerged as important factors for ensuring the
effective representation of outputs (Table 7). While climate issues were mainly related to
precipitation only, pedological factors concerned both the effect of changes of physical
 texture, bulk density and soil hydrologic properties) and chemical (C and N processes) soil
properties on C and N cycles.

Concerning soil physical characteristics, a primary role in modelling issues was played
by the soil water properties. Errors in the simulation of soil water content (SWC) were the
main cause of general discrepancies concerning C and N emissions in many studies (Table 7).
Discrepancies in C and N outputs were also observed under specific soil water conditions
such as the impact of soil freezing and thawing (Li et al., 2010) or soil shrinking and swelling
(Babu et al., 2006). Again, an inappropriate setting of initial state variables determined
discrepancies in N emissions (i.e. under- or over-estimation of N₂O emissions peaks,
Gabrielle et al., 2006). Considerable overestimations of N₂O emissions were found to be
closely related to overestimation of WFPS. WFPS is indeed one of the most important soil
variables influencing C and N cycles. For instance, microbially-mediated soil respiration and
N cycling processes tend to be higher or lower with increasing soil water content (e.g.
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increased nitrification under aerobic conditions, increased denitrification under anaerobic conditions, e.g. Bollmann. As WFPS reaches high values, soil respiration tends to decline and denitrification occurs, resulting in N losses via N₂O and N₂ emissions. This condition was observed especially for DayCent (Stehfest and Muller, 2004, Abdalla et al., 2010, Xing et al., 2011, Ryals et al., 2014, 2015) and DNDC (Saggar et al., 2004, Abdalla et al., 2010). Fast drainage is a particular issue for both the DayCent and DNDC models which drain water in excess of field capacity immediately. This condition makes these models unable to accurately predict N emissions at sites that consistently show soil moisture above FC (e.g. Uzoma et al., 2015).

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Soil bulk density (BD) was also a source of modelling error in simulating C and N cycles. For CERES-EGC, Gabrielle et al. (2006) found a discrepancy in N₂O emission peaks due to inappropriate parametrization of soil water retention properties and bulk density from test site to regional scales. Drouet et al. (2011) confirmed that BD was one of the most influential factors for N₂O emissions in CERES-EGC. The effect of BD increase was also reported for DayCent by De Gryze et al. (2010) and Abdalla et al. (2009), respectively, which observed an underestimation of N₂O emissions in a conservation tillage treatment due to the increase in BD, and an associated decrease in pore space over time as DayCent maintains a steady BD and simulation compaction, while the conservation tillied field site resulted in increased BD and reduced N₂O emissions (Pisante et al., 2015). In fact, most of the selected models, with the exception of EPIC, DNDC and STICS, do not simulate soil compaction or loosening, as BD remains constant over time.

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Texture was also found to be an influential soil physical characteristic. Congreves et al. (2016) found an underestimation in NH₃ emissions with the DNDC model, which is unable to simulate a heterogeneous soil profile. Similarly, Gagnon et al. (2016) confirmed that DNDC does not effectively discriminate across soil textures to simulate soil CO₂ respiration. Clay concentration affects SOC accumulation in different ways. According to some studies (Nichols, 1984, Burke et al., 1989), SOC increases with increasing clay content due to the bonds between the surface of clay particles and organic matter that retard the decomposition process. In addition, a relevant fraction of microbial extracellular enzymes is adsorbed by external and internal surfaces of clay size particles of soil phyllosilicate minerals (Burns et al., 2013). The amount of bound enzymes increases with the increasing layer charge of phyllosilicates (montmorillonite > illite > kaolinite) (Bayan and Eivazi, 1999). Sorption causes conformational changes of enzymes' active sites, and in turn reduces or even suppresses the activity of enzymes (Bayan and Eivazi, 1999, Burns et al., 2013). Moreover,

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anaerobic conditions, that are expected to occur mostly in finer texture soils, also negatively affect the amount of soil enzymes through reduction in the number of enzyme-producing microorganisms (Inglett et al., 2005). Finally, the increase of clay content affects soil aggregation, indirectly affecting SOC through the creation of macro-aggregates that can physically protect organic matter molecules from further microbial mineralization (Rice, 2002, Plante et al., 2006). Thus, an overall reduction in SOM turnover in fine textured soils is expected due to reduced substrate availability and overall microbial activity.

However, the effect of texture on SOC decomposition is controversial. For instance, for 10 sites in Canada ($^{13}$C-labelled study) Gregorich et al. (2016) found that temperature (neither soil texture nor other soil properties) was the only driver of decomposition. Furthermore, texture parametrization is another possible source of error. For instance, Gijsman et al. (2002) indicated that inaccuracies in soil texture data used as inputs may have affected soil retention characteristics, thus resulting in discrepancies in SOC and soil mineral N dynamics.

Soil chemical processes are generally similar between the models and all models considered showed difficulty in reproducing the observed C and N cycles. The processes influencing SOM in the models include nitrification, denitrification, immobilization and mineralization.

Discrepancies between modelled and observed data were often related to an inappropriate SOC content parametrization (Pathak et al., 2005, Calanca et al., 2007, Causarano et al., 2007, Smith et al., 2012, Gagnon et al., 2016). However, a considerable source of error was also due to overestimation of SOC content (Abdalla et al., 2010, Gijsman et al., 2002) or to the rate of soil C decomposition (Snow et al., 1999, De Gryze et al., 2010, Li et al., 2015).

Nitrification is a two-stage process, performed by different groups of Archaea, **bacteria** and **fungi**, consisting in the oxidation of ammonia or ammonium to nitrite (step 1) followed by the oxidation of the nitrite to nitrate (step 2). For DayCent, Li et al. (2005) and Del Grosso et al. (2008) found that overestimation in the nitrification rate was one of the main sources of error for N emissions estimation. This was also found by Drouet et al. (2011), showing that discrepancies in N₂O emissions simulated by CERES-EGC were due to the high sensitivity of the model to the maximum rate of nitrification. The nitrification rate, however, is usually associated with a number of environmental factors including the substrate and oxygen (O₂) concentration, **moisture**, temperature and pH. For instance, this was observed by Li et al. (2005), who pointed out that poor simulation of NH₄⁺ was caused by the inaccurate regulation of the effect of temperature on nitrification in DayCent.
Denitrification is a process where the reduction of soil nitrate to N-containing gases takes place. The major discrepancies between modelled and observed N emissions were due to an underestimation of the denitrification rate (Thorburn et al., 2010, Xing et al. 2011, Fitton et al., 2014a, b). The underestimation of the denitrification rate can be due to different *types* of errors. For instance, for APSIM, Thorburn et al. (2010) found the source of error in the model parametrization, with the default value of denitrification coefficient much lower than the optimized value. By contrast, Xing et al. (2011) indicated the response of denitrification rate to soil temperature and moisture (or WFPS) as the main source leading to the underestimation of denitrification. Generally, denitrification rates have been reported to be directly proportional to temperature (Seitzinger, 1988), whilst soils with high organic matter content (high dissolved organic C) and anaerobic conditions (i.e. waterlogged or poorly-drained soils) can more easily favour high denitrification rates.

Another important source of modelling error resulted from the inaccurate estimation of the immobilization-mineralization processes. **N immobilization or mineralization depends on the C:N ratio of the organic materials.** The C:N ratio generally tends to decrease as the organic matter becomes more decomposed. Inaccurate C:N parametrization can easily lead to errors in C and N cycle related outputs. For instance, Li et al. (2015) observed for the DSSAT model that differences between the modelled and measured soil C:N ratios led to SOC overestimation. In the EPIC model, He et al. (2006) observed that general discrepancies in C and N dynamics (i.e. lower net N mineralization rate, humification, etc.) were likely due to N mineralization algorithms which may have underpredicted net N mineralization (NMN) observable under field conditions. Smith et al. (2008) and Fitton et al. (2014a, b) found that the underestimation in mineralization rate led to underestimation of N₂O emissions. In the same way, Del Grosso et al. (2010) indicated that overestimation of N₂O emissions was due to N mineralization rates that were too high and too responsive to climate drivers.

Finally, climate conditions influence the C and N outputs in several studies analysed. Some issues were related to how the climate data have been used. For instance, in APSIM, Thorburn et al. (2010) found discrepancies in N emissions (i.e. underestimation of denitrification and N₂O peaks) due to the application of spatially averaged rainfall data instead of the use of specific test-site rainfall data. In other cases, the main issues were due to the sensitivity of the model subroutines. For instance, Wattenbach et al. (2010) observed overestimation in NEE peaks in southern European regions due to issues in coupling water and C-fluxes. These issues were probably caused by the fact **that** the model was originally developed to represent conditions typical of Northern regions. Again, Lawton et al. (2006)
reported overestimation of NEE because of the oversensitivity of PaSim to initial conditions/winter conditions. Most of the issues related to general discrepancies in simulated C and N cycles, however, were related to precipitation only (Stehfest and Muller, 2004, Jarecki et al., 2008, De Gyrze et al., 2010, Ludwig et al., 2011, Lehuger et al., 2014). Precipitation and the resulting soil water dynamics strongly influence N cycling in terrestrial ecosystems since it affects both physical transport and N biological transformations by soil microorganisms (Brooks et al., 1999, Corre et al., 2002, Aranibar et al., 2004).

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## 4.2. Model structure and management

Management has a great impact on C and N cycles. In biophysical and biogeochemical models, the correct representation of practices such as fertilization, irrigation and tillage in crop systems, and cutting and grazing in grassland systems, is needed to ensure the greatest suitability of outputs.

In the models, fertilization, which influences soil C and N transformations (e.g. acidification following fertilization) and trace gas emissions, was often not well represented (Table 7). For DayCent, Fitton et al. (2014a, b) indicated an underestimation of N₂O emissions due to the low sensitivity of the model at low N application rates. In DNDC, Congreves et al. (2016) found that NH₃ emissions were underestimated due to a simple modelled cascade water flow, which may have limited the ability of the model to simulate slurry infiltration rates. Also, Causarano et al. (2007) observed general discrepancies in C-dynamics (i.e. overestimation of microbial biomass C and total organic C, underestimation of particulate organic C), due to inadequate representation of the effects of tillage and manure in the EPIC model. Another issue related to fertilization was the inability of many models to replicate the effect of specific types of fertilizer. For instance, using DayCent Stehfest and Muller (2004) found overestimation of N₂O emissions under urine application, where N was concentrated in small hotspots. For the same model, Ryals et al. (2014 and 2015) underestimated CO₂ emissions since no soil water benefits were provided by adding compost. This condition was likely due to the lack of increased modelled decomposition because the model was not able to increase soil water contents when compost was added. Gu et al. (2014) overestimated N₂O emissions, soil nitrate and ammonia concentrations due to the inability of DNDC to include canopy interception and foliar N uptake when spraying liquid fertilizer.

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Finally, residue management was one of the main weaknesses related to N management (Cavero et al., 1996, Sleutel et al., 2006, Rampazzo Todorovic et al., 2010, Wang et al., 2013). The amount of N applied with residues depends on the quantity of residues and their N
concentration. These two factors affect the mineralization-immobilization turnover, whilst their net balance varies with environmental conditions (mainly soil moisture and temperature) and the characteristics of the organic matter (i.e. C:N and the decomposition rate). Since residues directly influence soil C and N processes, residue management in the models resulted in consistent modelling weaknesses. For instance, Justes et al. (2009) underestimated the N mineralization in STICS due to inappropriate parametrization of the model (i.e. default values of the decomposition module were used). In a similar way, Liu et al. (2009) overestimated the SOC content when stubble (wheat and lupine) was applied due to the use of the conventional setting of the stubble retention factor in RothC. Using DayCent and DNDC, Smith et al. (2012) underestimated SOC due to a slight overestimation of residue removal impact. However, the authors indicated that this could have been partly due to the inherent variability in SOC measurements. Smith et al. (2012) also found that DNDC tended to underestimate the rate of SOC change as affected by residue removal at some sites. Using DSSAT, Hartkamp et al. (2004) overestimated SOC in the crop rotations with N fertilization. This overestimation was due to inaccurate initial SOC (i.e. overestimated SOC values) which was related to an overestimation of the biomass incorporated into the soil. Similarly, Wang et al. (2005) underestimated the SOC content using the EPIC model due to a structural error in underestimating the return of corn residues. He et al. (2006) found general discrepancies in C and N dynamics because the EPIC model underestimated the capacity of the soil to transform crop residues into SOM.

Tillage is one of the agricultural practices most commonly simulated by the models and an issue in most modelling applications. The use of tillage or reduced tillage can greatly affect soil properties, and since the models do not adjust some soil properties over time (such as bulk density) which results in inaccuracies in simulations. Compared with conventional tillage, no/reduced tillage may lead to increasing rather than decreasing emissions (e.g. due to higher density and WFPS, more SOM near the soil surface thus higher denitrification potential, tendency to acidification and thus lower reduction of N₂O to N₂, etc.). Identifying mechanisms which help understand simulate emissions with no tillage is thus a key issue. In our analysis management effects (i.e. tillage) which influences topsoil erosion emerged as a point of weakness. This is because many models do not take into account adequately C-losses due to erosion. For instance, Nieto et al. (2010, 2013) overestimated SOC content using RothC, whilst Billen et al. (2009) observed general discrepancies in SOC content with EPIC.

Another point of weakness in simulated tillage was the inadequate representation of changes in soil properties over time. For instance, Luo et al. (2011), using APSIM,
underestimated SOC decomposition. In this case, whilst tillage may have led to acceleration in soil C oxidation due to changes in soil environmental parameters (i.e. water retention, porosity, aeration, etc.), APSIM failed to simulate changes in these soil properties over time, which is a common issue amongst most models. Similarly, Causarano et al. (2007) found general discrepancies in C dynamics (i.e. overestimation of microbial biomass C and total organic C, underestimation of particulate organic C) due to an inadequate reproduction of the effects of tillage and manure on soil properties.

In addition to fertilization and tillage, which were probably the most commonly simulated agronomic practices, model weaknesses were found in relation to other practices. For instance irrigation, especially accompanied by fertilization, was observed to affect simulated C and N cycles. Jackson et al. (1994) and Cavero et al. (1999) underestimated N fluxes under irrigated experiments using EPIC. The main source of error was related to an overestimation of the soil N losses via leaching or denitrification during the irrigated crop period. Grassland management was also seen to be a possible point of weakness for the models. For instance, Lawton et al. (2006), Vuichard et al. (2007) and Ma et al. (2015) observed general discrepancies in C-fluxes (i.e. net ecosystem exchange and ecosystem respiration) under different grazing intensities using a grassland-specific model (PaSim). As suggested by Vuichard et al. (2007), a continuous defoliation by grazing is indeed difficult to account for as a permanent disturbance in the model. The grazing effect, however, is associated with other parameters related to ecosystem and climate conditions, which makes it difficult to pinpoint the parameter which most strongly influences the uncertainty of the model output (Gottschalk et al., 2007).

Finally, model weaknesses also result from management options that are not included. This type of weakness has emerged in several studies carried out using the RothC model. For instance Skjemstad et al. (2004) found general discrepancies in C dynamics due to ecosystem disturbances, which were not included in RothC (i.e. clearing and burning of pulled vegetation). Shirato and Yokazawa (2005) underestimated SOC content due to the decomposition rate of SOM under rice cultivation being too low (i.e. effect of waterlogged soil not included in RothC), and Farina et al. (2013) reported some discrepancies in C-fluxes when the model simulated rotations that included a fallow period.

### 4.3. Time-scale

Biophysical and biogeochemical models enable the estimation of C and N emissions at various temporal and spatial scales. Compared to the emission factor approaches often used
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by organizations and individuals to calculate GHG emissions for a range of activities, these tools include the influences of agricultural practices, land-use change and soil properties, and estimate the influences of weather on emissions over time.

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The ability of these models to accurately reproduce detailed dynamics of C and N emissions depends on the degrees of complexity of the model itself. Current process models, with high complexity, are able to calculate in detail both C and N emissions due to their consideration of all soil-plant-atmosphere interactions. These tools are able to provide reasonable estimates of trace gas emissions from soils, usually for a specific site and at seasonal or annual time scales. By contrast, however, they are less successful at finer time resolution (e.g. daily) and on different sites from the ones where **they** have been previously calibrated. In our analysis several studies showed weaknesses due to the time-spatial scale associated with both pedo-climatic conditions and management.

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Concerning time-scale weaknesses, Xing et al. (2011) underestimated N₂O emissions at the daily time step using APSIM, while the use of the hourly time step may have likely improved the estimate of predicted total daily emissions. This is because, in the APSIM model, as in most models, N₂O emissions were released immediately to the atmosphere without delay upon change in environmental conditions whereas the observations indicated that there was a 1-10 hour lag between peaks of soil moisture and gaseous emissions. Similarly, Lehuger et al. (2011) using CERES-EGC indicated an overestimation in N₂O emissions, probably due to a possible time lag between the production of gaseous N₂O in the soil and its emission to the atmosphere. Also, several studies carried out using DayCent (Parton et al., 2001, Del Grosso et al., 2005, 2010) observed some discrepancies in simulated N emissions due to time-lag. This was found to agree with **the results of** Li et al. (2005), which indicated that DayCent often has a **1-day** lag before emissions occur. In all these cases, the use of hourly time step may result in better predictions especially in conjunction with the addition of a description of gas diffusion into soil, which could result in a delay between N₂O production and emission.

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Concerning spatial-scale weaknesses, Gabrielle et al. (2006) found discrepancies in N₂O emission peaks using CERES-EGC. This was probably due to soil property parametrization (i.e. soil water retention properties and bulk density) which may have led to differences in N outputs from test sites to the regional scale. Using EPIC, general discrepancies in C-fluxes (i.e. overestimation of microbial biomass C and total organic C, underestimation of particulate organic C) were likely caused by spatial differences in C fraction due to differing soil landscapes (Calanca et al., 2007). Schnebelen et al. (2004) overestimated soil N absorption
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with the STICS model. This was probably due to propagation of errors for continuous
simulations compared to single-year simulations. More specifically, the underestimation of
some parameters in the previous year may have led to errors in the following years.

**5. New developments/future perspectives**

In the above analysis, an indication was given of models' predictive strength, while also
hinting at possible limitations in the underlying hypotheses from the literature in the cases
where discrepancies between model and observation occurred. Despite this extensive analysis,
knowledge of basic mechanisms driving C and N cycles in agricultural systems is still far
from complete and key questions remain, including: what exactly triggers the cascade of
events that finally lead to biological responses? How to differentiate between causes and
consequences? How does the knowledge derived from system observations relate to
mechanistic events? How does the current knowledge on C and N cycling in agriculture fit
with available mechanistic representations? Discrepancies between model outputs and
observations can be ascribed to a wide diversity of causes, without any real tendency to
associate them with one or another cause. The analysis reported in this work suggested
however three (quite large) areas of interest for possible improvements of C and N models: i)
soil biology, comprising SOM heterogeneity, decomposition kinetics, and N immobilization;
ii) soil physics, including the representation of soil physical properties and the simulation of
its effects on reaction rates; and iii) soil management, which indirectly affect soil processes by
modifying soil physical, chemical and biological properties.

Based on the main issues found in our analysis, despite recognizing the importance of
soil management (Andales et al., 2000), N transformations (Heinen, 2006, Congreves et al.,
2016), and plant-soil interactions (Kuzyakov, 2002, Roosevelt and Schnepf, 2008, Kuzyakov and
Xu, 2013), here we focus on some innovative aspects related to soil biology and soil physics,
and the interface between the two that requires attention (Blagodatsky and Smith, 2012). This
choice is justified in that development of robust predictive frameworks is critical to managing
soil biology and its essential functions and services (Thrall et al., 2011). They can help
disentangling the causal links between soil biology and structure, physical-chemical factors
and ecological processes (e.g. nutrient cycling, soil C sequestration) that contribute to plant
community development and function. In addition, how soil communities respond to and
impact on plant succession (e.g. via regulatory networks that respond to the availability of
fixed N) may be important for predicting the role of plant-soil feedbacks in determining the
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dynamics of soil microbial communities and the impact of anthropogenic disturbance on soil
diversity and function.

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Soil microbial biomass (SMB) is generally only implicitly modelled by representing it
as a C pool not affecting substrate decomposition directly (Manzoni and Porporato, 2009).
Approaches of this type mostly implement solutions that are biologically meaningful (e.g.
representing realistically SOM turnover) and computationally tractable within a simulation
(i.e. with reduced overall complexity of the full model and a limited number of free
parameters to be tuned), which make them suitable for analyses in long-term studies
(Manzoni and Porporato, 2009, Sierra et al., 2015a). In recent years, researchers have
advocated a representation of SOM turnover driven by SMB to gain insight into decomposing
SOM-SMB interactions (Schimel and Weintraub, 2003, Lawrence et al., 2009, Blagodatsky et
al., 2010, Schmidt et al., 2011). For C and N substrates, concentration constraints driven by
microbial allocation patterns could thus be represented in novel biogeochemical models based
on microbial physiology (Allison et al., 2014). In this way, models based on microbial
biomass-driven SOM decomposition are promising to provide a realistic simulation of SOM
turnover in relation to changes in environmental conditions compared to existing models that
do not explicitly simulate SMB (Lawrence et al., 2009, Allison et al., 2010, Conant et al.,
2011, Sierra et al., 2015b). It is quite common to use classical enzymatic kinetics like
Michaelis-Menten or Monod-type kinetics to implement substrate-SMB co-limitation
(Blagodatsky and Richter, 1998, Hadas et al., 1998, Wutzler and Reichstein, 2013, Cavalli et
al., 2016), even if simpler decomposition kinetics have also been proposed (Manzoni and
Porporato, 2007, Withmore, 2007, Wutzler and Reichstein, 2008). Conversely, more general
model formulations are described in Neill and Gignoux (2006) and Neill and Guenet (2010) to
simulate microbial growth in soil accounting for both positive and negative priming effects.
The priming effect is defined as any change (positive or negative) of native SOM
decomposition rate following the addition of exogenous organic matter or nutrients, compared
to no addition (Fontaine et al., 2007, Kuzyakov et al., 2000, Kuzyakov, 2010, Chen et al.,
2014, Perveen et al., 2014).

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Even in models with explicit SMB, microbial community is usually simulated with one
or few pools, each representing microorganisms belonging to a different functional group
(Moorhead and Sinsabaugh, 2006). However, further model developments could be achieved
if diversity in soil microbial traits is included in the model, allowing microorganisms with
optimal strategies to outperform other microorganisms with less favorable traits in a given
environment (Allison et al., 2012, 2014). In such models, genomic and metagenomics data

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can be integrated with other sources of information to define distributions of microbial traits that are used to characterize the microbial community (Vereeken et al., 2016).

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Another important aspect regarding SOM turnover is the effect of N shortage on SOM decomposition. Soil microorganisms are characterised by a narrow range of variation in their C to N ratio (Cleveland and Liptzin, 2007, Xu et al., 2013); thus, they can be approximately considered homeostatic (i.e. they do not change markedly their C to N ratio according to substrate C to N ratio). Mechanisms of adaptation to stoichiometric imbalances between substrates and SMB were reviewed in detail by Mooshammer et al. (2014a). One postulated mechanism of adaptation regards the variation of microbial C use efficiency (CUE, defined as the ratio between newly-formed biomass C and decomposed C) and of N use efficiency (NUE, defined similarly to CUE) to accommodate for excess or deficit of C or N (Manzoni et al., 2012, Sinsabaugh et al., 2013, Mooshammer et al., 2014b, Jeyer et al., 2016).

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Soil organic matter decomposition is operated mostly by the activity of extracellular enzymes (Burns et al., 2013), and any cost associated with the production of enzymes decreases CUE (Manzoni et al., 2012). Microorganisms evolved to optimize resource allocation for the synthesis of exoenzymes in response to environmental and physiological factors (Allison et al., 2011). According to Sinsabaugh and Follstad Shah (2012) and Sinsabaugh et al. (2016) CUE and NUE are both related to the activities of C and N acquiring exoenzymes (measured as potential activities of β-1,4-glucosidase, and β-1,4-N-acetylglucosaminidase and leucine aminopeptidase, respectively). Thus, variations in CUE and NUE arise because SMB regulates exoenzyme production (in terms of amounts and type of synthetized enzymes) to attenuate the differences between their growth requirements and available resources (Sinsabaugh et al., 2016).

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Another mechanism of CUE regulation by SMB when SOM decomposition is N-limited is overflow metabolism (Russel and Cook, 1995): excess C is excreted as extracellular C compounds (like polysaccharides) (Hadas et al., 1998, Cavalli et al., 2016), or lost as CO2 (Schimel and Weintraub, 2003, Neill and Gignoux, 2006). Conversely, when N is in excess relative to C (decomposition is limited by C availability), net N mineralisation occurs. Models usually implement N deficit effects on SOM decomposition with the N inhibition hypothesis (Manzoni and Porporato, 2009), that is, SOM turnover is reduced according to N availability, and thus CUE does not change. Alternatively, other models (Izaurralde et al., 2006, Withmore, 2007) allow SMB to vary its C to N ratio according to stoichiometric imbalances, and thus they consider SMB as non-homeostatic.

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Decomposition of SOM in soil occurs at microsites showing varying N availability (Schimel and Bennett, 2004). This is caused by heterogeneity of both SOM and of soil physical properties (Schmidt et al., 2011). Thus, N is supposed to flow from micro-sites showing net N mineralisation to others showing net N immobilisation (Schimel and Bennett, 2004). Mathematically, the heterogeneity of SOM decomposition in a first approximation can be simulated considering that not all organic N in substrates is available to SMB, according to the parallel hypothesis (Manzoni and Porporato, 2007). The use of a simple lumped SOM model, based on the parallel approach, was shown to provide almost similar results to the same model structure that explicitly took into account the heterogeneity of soil decomposition, and in which all organic N in substrates was available to decomposers, according to a direct assimilation pathway (Manzoni et al., 2008).

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The heterogeneity of SOM is simulated with models that comprise several pools of different decomposability (Nicolardot et al., 2001, Manzoni and Porporato, 2009, Sierra et al., 2011, Sierra and Müller, 2015). In many models, decomposition constants of model pools incorporate intrinsic chemical recalcitrance of SOM, and availability of SOM to decomposers (Nicolardot et al., 2001, Sierra and Müller, 2015). However, it was recently emphasised that chemically-labile (or high-quality, and thus potentially easily-degradable) molecules can persist in soil for a long time due to constraints on their microbial decomposition not related to intrinsic chemical characteristics (Kleber, 2010, Marschner et al., 2008): biology of decomposers, abiotic reactions and desorption, environmental variables and physicochemical stabilisation processes (Ekschmitt et al., 2005, Kemmit et al., 2008, Kleber et al., 2011, Schmidt et al., 2011, Dungait et al., 2012). Regarding SOM physical and chemical stabilisation, models that explicitly represent protected and unprotected SOM pools of similar chemical characteristics (Kuka et al., 2007) allow separating intrinsic recalcitrance (substrate quality) from availability, and thus enable simulating long-term stabilisation of chemically easily-decomposable high-quality SOM (Dungait et al., 2012). In addition, more sophisticated and realistic approaches to simulate soil physicochemical heterogeneity, and thus variability of SOM decomposition, were implemented in SOM models that represent soil as 3D structure in which decomposition takes place (Garnier et al., 2008, Masse et al., 2007, Monga et al., 2009, 2014).

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Improving soil biology aspects related to the production and consumption of gases (O₂, CO₂, CH₄, N₂O, and N₂) will improve the simulation of soil gas concentrations. However, this is not sufficient to achieve proper simulations of GHG emissions, as accounting for gas transport through the soil profile is also important. As pointed out by Blagodatsky and Smith
809 (2012), it is necessary to find the right balance in complexity between biological and soil
810 physical simulations. For example, the higher soil tortuosity the higher the N₂/N₂O ratio,
811 because N₂O has more possibilities to be reduced when the escape pathway from the N₂O
812 production sites to the atmosphere (and thus its diffusion time) is longer. Adequate simulation
813 of gas transport in soil can be achieved using mechanistic models based on water, heat, and
814 gas transport equations, and gas-liquid phase exchange. A further connection among soil
815 biology and soil physics research will be to simulate SOM turnover and gas production,
816 consumption, and transport in a 3D soil structure using the concepts presented above, so as to
817 achieve a more realistic representation of environmental effects (soil temperature and
818 moisture), especially in the context of climate change.

819 One final observation is that all of the model improvements presented above require
820 adequate simulation of initial conditions of inorganic N availability. Thus, it is mandatory that
821 all processes affecting soil ammonium concentration be accurately simulated. Among these,
822 ammonium fixation in non-exchangeable form by clay minerals in fine-textured soils can play
823 a central role in determining the availability of N for microorganisms. Research on cation
824 exchange in soil demonstrated that monovalent cations with low hydration energy and ionic
825 radius that fits the ditrigonal cavities of the basal oxygen planes of 2:1 clay minerals are
826 selectively sorbed at frayed edges of illite (partially weathered micas) and vermiculite, and at
827 interlayer positions of vermiculite (Sawhney, 1972). Sorption of NH₄⁺ (like K⁺, Rb⁺, and Cs⁺)
828 in such exchange sites causes interlayer dehydration and layer collapse (Nieder et al., 2011).
829 Such ions are strongly held against replacement by other cations and are termed fixed. After
830 its application to soil with fertilisers, a relevant fraction of ammonium can be very rapidly
831 (hours or days) fixed by clay minerals (Nõmmik, 1957) and is very slowly released during the
832 following weeks or months (Steffens and Sparks, 1997). This fraction of applied N is thus not
833 immediately available for nitrification, microbial immobilisation, and plant uptake. For a
834 comprehensive survey of the factors influencing ammonium fixation / release readers can
835 refer to reviews by Nõmmik & Vahtras (1982) and Nieder et al. (2011).

836 Despite its importance, ammonium fixation / release is not commonly simulated by
837 crop/grassland system and SOM models. The rapid fixation can be simulated with well-
838 known isotherms, which represent the static adsorption of an ion onto a surface (Cameron and
839 Kowalenko, 1976, Cavalli et al., 2015) as a function of ion concentration. However,
840 ammonium exchange reactions in soil are affected by the presence of other cations (such as
841 K⁺ and Ca²⁺), and thus models should take into account for ion interactions (Bradbury and
842 Baeyens, 2000; Evangelou and Lumbanraja, 2002). Research is needed to estimate model
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parameters depending on soil characteristics (such as type of clay, potassium concentration, and soil water content) and to simulate ammonium release over time.

## 6. Summary and concluding remarks

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At present, process-based biogeochemical models represent a valuable tool for examining the magnitude and spatial-temporal patterns of C and N fluxes in terrestrial biosphere dynamics. Our analysis shows that there is still great divergence between models in the simulation of C sequestration and GHG source/sink status, in relation to a different interpretation of physical and biogeochemical processes.

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Representative works have been summarized to provide a general overview of the state-of-the-art of models, and to allow process-based models (the nine identified in this study) to be compared and selected for the simulation of C and N cycles in crop and grassland systems. We classified models into categories according to three levels of knowledge: five general classes (level 1), 20 main processes (level 2), and 196 methods/options/components (level 3), and then we assessed the tools in terms of the comprehensiveness of processes related to pedo-climatic and management options, and their accuracy in a variety of contexts.

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This review highlighted strengths and weaknesses of the models analysed. Essentially, they involve limitations in simulating the effects of pedo-climatic conditions (45.9%) and different management practices (33.8%). Other weaknesses (i.e. 20.3%) were due to the scale of application in time and space.

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The major limitations of model structure related to C-cycles were observed under management practices (43.6%), whilst for the scale of application the major weaknesses were due to different pedo-climatic conditions (11.5%). For both the N-cycle modelling and scale of application, the main limitations were found in the response to different pedo-climatic conditions (51.1% and 20.2%, respectively).

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All the models considered here showed positive and negative features and none may necessarily be ideal in any particular circumstance. If the model chosen is not able to reproduce the output required, two or more of these models may be combined to derive upper and lower values for all simulated outputs. Moreover, a decision about which model or models to use should be seen as dynamic, not static. As conditions change, or if one model proves unsuccessful, they can be adapted or replaced with other, more suitable, models.

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Although the above reported weaknesses were already known due to a wide number of published studies, in the present analysis we have tried to relate them to their causes in the view of using them as an effective basis for improving current modelling approaches.
Although different avenues could be considered to improve models (e.g. Coucheney et al., 2015), mainly depending on the purpose of modelling, to overcome the reported limitations and account for the effect of multiple disturbances (i.e. pedo-climatic conditions, management practices, scale of analysis) affecting basic processes, as well as to simplify the decision of which model to choose to understand mechanistically specific study-context and to make detailed predictions in a large diversity of situations, some innovative aspects should be considered in the modelling work. Among these, we target the representation of SOM turnover driven by SMB, the effect of N shortage on SOM decomposition, improvement related to the production and consumption of gases (O₂, CO₂, CH₄, N₂O, and N₂), adequate simulations of gas transport in soil, the use of a 3D soil structure in order to achieve a more realistic representation of environmental effects (soil temperature and moisture), especially in the context of climate change.

Model improvement thus implies extending the existing body of knowledge on ecological and biogeochemical concepts, to allow them to be incorporated using novel approaches, thus improving the representation of the dynamics of the ecosystems, and the related advantages for stakeholders.

893
894 Acknowledgements

895 This work was developed by the CN-MIP project of the Joint Programming Initiative

896 ‘FACCE’ (https://www.faccejpi.com) under the auspices of the Global Research Alliance for

897 Agricultural Greenhouse Gases – Integrative Research Group

898 (http://globalresearchalliance.org/research/integrative). The project, coordinated by the French

899 National Institute for Agricultural Research (INRA), received funding by the ‘FACCE’ Multi-

900 partner Call on Agricultural Greenhouse Gas Research through its national financing bodies.

901 The authors acknowledge Val Snow (AgResearch – Christchurch, New Zealand) for several

902 constructive comments and stimulating discussions on the subject of this paper. We are also

903 grateful to the EPIC development team, particularly Jimmy R. Willliams (Texas A&M

904 University) and R. César Izaurralde (University of Maryland), who advised about properties

905 and new developments of the model.

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1

Review and analysis of strengths and weaknesses of agro-ecosystem models for
simulating C and N fluxes

2

3

4

Lorenzo Brilli<sup>1,15*</sup>, Luca Bechini<sup>2</sup>, Marco Bindi<sup>1</sup>, Marco Carozzi<sup>3</sup>, Daniele Cavalli<sup>2</sup>, Richard

5

Conant<sup>4</sup>, Cristopher D. Dorich<sup>4</sup>, Luca Doro<sup>5,16</sup>, Fiona Ehrhardt<sup>6</sup>, Roberta Farina<sup>7</sup>, Roberto

6

Ferrise<sup>1</sup>, Nuala Fitton<sup>8</sup>, Rosa Francaviglia<sup>7</sup>, Peter Grace<sup>9</sup>, Ileana Iocola<sup>5</sup>, Katja Klumpp<sup>14</sup>, Joël

7

Léonard<sup>10</sup>, Raphaël Martin<sup>14</sup>, Raia Silvia Massad<sup>3</sup>, Sylvie Recous<sup>11</sup>, Giovanna Seddaiu<sup>5</sup>,

8

Joanna Sharp<sup>12</sup>, Pete Smith<sup>8</sup>, Ward N. Smith<sup>13</sup>, Jean-Francois Soussana<sup>6</sup>, Gianni Bellocchi<sup>14</sup>.

9

10

<sup>1</sup>Università degli Studi di Firenze, Department of Agri-Food Production and Environmental

11

Sciences, 50144 Florence, Italy

12

<sup>2</sup>Università degli Studi di Milano, Department of Agricultural and Environmental Sciences,

13

Milan, Italy

14

<sup>3</sup>INRA, AgroParisTech, UMR1402 EcoSys, 78850 Thiverval-Grignon, France

15

<sup>4</sup>NREL, Colorado State University, Fort Collins, Colorado 80523 USA

16

<sup>5</sup>Desertification Research Centre, Department of Agricultural Sciences, University of Sassari,

17

07100 Sassari, Italy

18

<sup>6</sup>INRA, 63039 Paris, France

19

<sup>7</sup>CREA-RPS, Research Centre for the Soil-Plant System, Via della Navicella 2-4, 00184

20

Roma, Italy

21

<sup>8</sup>Institute of Biological and Environmental Sciences, University of Aberdeen, St Machar

22

Drive, AB24 3UU Aberdeen, UK

23

<sup>9</sup>Queensland University of Technology, Brisbane, Australia

24

<sup>10</sup>INRA, UR 1158 AgroImpact, site de Laon, F-02000 Barenton-Bugny, France

25

<sup>11</sup>INRA, FARE Lab, 51100 Reims, France

26

<sup>12</sup>New Zealand Institute for Plant and Food Research, 7608, Lincoln, New Zealand

27

<sup>13</sup>Agriculture and Agri-Food Canada, Ottawa, Ontario K1A 0C6, Canada

28

<sup>14</sup>INRA, UREP, 63039 Clermont-Ferrand, France

29

<sup>15</sup>IBIMET-CNR, Via Caproni 8, 50145 Firenze, Italy

30

<sup>16</sup>Texas A&M AgriLife Research, Blackland Research & Extension Center, Temple (TX),

31

USA

32

33

*Corresponding author. Tel.: +39 055 2755743; fax +39 055 055 2756429

34

E-mail address: lorenzo.brilli@unifi.it; l.brilli@ibimet.cnr.it (L.Brilli).