the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Feb 2024
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Abstract. Biological nitrogen fixation (BNF) by symbiotic and free living bacteria is an important source of plant-available nitrogen (N) in terrestrial ecosystems supporting carbon (C) sequestration and food production worldwide. Dynamic global vegetation models (DGVMs) are frequently used to assess the N and C cycle under dynamic land use and climate. BNF plays an important role for the components of both these cycles making a robust representation of the processes and variables that BNF depends on important to reduce uncertainty within the C and N cycles and improve the ability of DGVMs to project future ecosystem productivity, vegetation patterns or the land carbon sink. Still, BNF is often modelled as a function of net primary productivity or evapotranspiration neglecting the actual drivers. We implemented plant functional type-specific limitations for BNF dependent on soil temperature and soil water content as well as a cost of BNF in the Lund Potsdam Jena managed Land (LPJmL) DGVM and compare the new (C-costly) against the previous (Original) approach and data from the scientific literature. For our comparison we simulated a potential natural vegetation scenario and one including anthropogenic land use for the period from 1901 to 2016 for which we evaluate BNF and legume crop yields. Our results show stronger agreement with BNF observations for the C-costly than the Original approach for natural vegetation and agricultural areas. The C-costly approach reduced the overestimation of BNF especially in hot spots of legume crop production. Despite the reduced BNF in the C-costly approach, yields of legume crops were similar to the Original approach. While the net C and N balances were similar between the two approaches, the reduced BNF in the C-costly approach results in a slight underestimation of N losses from leaching, emissions and harvest compared to literature values, supporting further investigation of underlying reasons, such as processes represented in DGVMs and scenario assumptions. While we see potential for further model development, for example to separate symbiotic and free living BNF, the C-costly approach is a major improvement over the simple Original approach because of the separate representation of important drivers and limiting factors of BNF and improves the ability of LPJmL to project future C and N cycle dynamics.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-2946', Anonymous Referee #1, 09 Apr 2024
This manuscript describes an effort to make the representation of BNF more realistic in LPJmL. The main results are that changing the representation of BNF – from a less-mechanistic function of AET to a more-mechanistic dependence on temperature, moisture, and N limitation – decreases overall estimates of BNF and modifies the spatial distribution, resulting in a better overall fit to data. This is a worthwhile effort, and from what I can tell the work is solid. My hope is that my feedback below will improve the work.
Major/overall suggestions:
My main areas of feedback are (1) a greater focus on relevant empirical work, (2) greater discussion of how the implementation of BNF compares to other models, and (3) more explanation of the methods.
(1) I understand that this is a modeling study, but there is a lot of relevant empirical literature that is not referenced. For example, the parameters describing responses to moisture and temperature are taken from Yu & Zhuang, which is another modeling study. That’s fine, but I would like to see more explanation of how those values compare to actual measurements of these quantities. As another example, your BNFfrac (see below as well) is a commonly measured quantity in N fixation work at the plant scale. Particularly for agricultural systems, there are large amounts of data. How do your results compare to empirical data? There are a few papers cited in the discussion about how N fixation varies as a function of N limitation, succession, etc., but there is a lot of work in these areas, and the discussion reads as if these were the first few that came up in a search rather than a synthesis of deep reading on the subject.
(2) The discussion of other model implementations of more mechanistic BNF could also be improved. Ma et al. (another version of LPJ) and Yu & Zhuang (TEM) are referenced most heavily, and Fisher et al. and Davies-Barnard & Friedlingstein are also mentioned, but there are many other implementations out there ranging from land models to ecosystem models. Kou-Giesbrecht et al. 2023 (cited in the ms) has a nice table that lists a few of the TRENDY models that incorporate mechanistic representations of BNF: CLASSIC, CLM, DLEM, OCN. There are other non-TRENDY models that have been developed that have been applied at large spatial scales – LM3/LM4 and QUINCY come to mind – and there are tons of ecosystem models (ED, MEL, CENTURY, etc.) that do something similar. Readers will want to know how your implementation compares.
(3) Explanation of the methods: I’d like to see a clearer description in the methods of how the versions were evaluated. I’d also like to see more detail about how N limitation is calculated, given that this is the key aspect of the paper. In particular, how is Ndeficit calculated?
Minor suggestions:
Given that you’ve stated that you’re modeling all BNF, not just legume-associated BNF, I suggest changing the name of flegume to ffixer or something like that.
Fig. 3 caption needs to specify what “DBF” is. I assume Davies-Barnard & Friedlingstein, but it would be nice to see in the caption, particularly given that there are other meanings of DBF (e.g., deciduous broadleaf forest).
The color scales on the global figures overemphasize the high range, making it hard to see variation in the lower range. For example, Fig. 3 looks largely like a map of agricultural BNF.
189: In the empirical literature, what you describe as BNFfrac is called %Ndfa (percent of N derived from fixation activity or derived from the atmosphere, depending on who you ask). It might help your paper to make the connection.
197: It’s true that 4 g N/m2/yr is a lot lower than 15, but 4 g N/m2/yr is still a huge difference.
Citation: https://doi.org/10.5194/egusphere-2023-2946-RC1 -
AC1: 'Reply on RC1', Stephen Wirth, 16 May 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2946/egusphere-2023-2946-AC1-supplement.pdf
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AC1: 'Reply on RC1', Stephen Wirth, 16 May 2024
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RC2: 'Comment on egusphere-2023-2946', Anonymous Referee #2, 10 Apr 2024
The paper present a new parameterization of biological nitrogen fixation in the LPJmL model. This new parameterization, compared to the original one, takes into account the nitrogen limitation and a carbon cost for acquisition of the BNF. This is a very important improvement as it means that nitrogen fixation is directly linked to the biological activity and nitrogen limitation, which was not the case before. Hence, the total BNF fixation is reduced compared to the original formulation, which is more in agreement with observations. So it is an important improvement for LPJmL. The paper is sound and well written. So I have only few remarks, only some suggestions to improve the paper:
Even if the original approach of simulation of BNF fixation has already been published, it would help the reader to present the original equations and then to show in detail what are the difference between "original" and "C-costly" parameterization. For instance, we understand only in the discussion about BNF fraction that the 2 parameterization are different not only on the calculation of N but also on the way this N is taken by the plant, directly in the new parameterization and mixed with soil mineral N in original which is also an important difference. Then it is important to give more details about the parameterization and how they differ. More generally, it would be also interesting to compare the new parameterization to parameterization used in others DGVMS that implements BNF.
The results focus only on BNF, but it would be interesting to see also at global scale what is the impact of the new parameterization on the carbon cycle (for instance impact on NPP, NBP). Only the impact on legumes yield is shown if figure B1.
Also on figure 4 we see the relatively large impact of the new BNF parameterization on N emissions. It would then be interesting to show a comparison of these simulated fluxes to observations, as it is done for BNF. Especially for N2O emissions. It is obviously an important component of the GHG budget. So, with the new BNF parameterization, does it improve the simulated fluxes of N2O ?Minor remarks:
l 268: The authors seem surprised that the new approach does not limit the crop yield. But if I understood well the model, it is not so surprising for me. Since in condition when NPP is not a limiting factor for BNF (that should be the case for crops) and, as the model try to fulfill the N limitation, then the simulated BNF should be sufficient to fill the N demand of the plant and then should not produce N limitation ? Then it could explain why even if the different approaches give different BNF there is no impact of yield. This is exactly what we expect from the new formulation compared to original: define the BNF to avoid N limitation but without N excess... This is also the reason why it would be interesting to show the global impact on NPP: We should expect a decrease in NPP on carbon and N limited ecosystems, as the C-cost or N stress could be too high to be fulfilled by the BNF fixation. On the contrary, we should have no change in ecosystem with few limitations even is the BNF is reduced.
Figure 2: what are the percentage indicated in blue and red in a) ?
Figure 3: the DBf term is not defined. I guess it is the observation, but it should be described
Citation: https://doi.org/10.5194/egusphere-2023-2946-RC2 -
AC2: 'Reply on RC2', Stephen Wirth, 16 May 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2946/egusphere-2023-2946-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Stephen Wirth, 16 May 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-2946', Anonymous Referee #1, 09 Apr 2024
This manuscript describes an effort to make the representation of BNF more realistic in LPJmL. The main results are that changing the representation of BNF – from a less-mechanistic function of AET to a more-mechanistic dependence on temperature, moisture, and N limitation – decreases overall estimates of BNF and modifies the spatial distribution, resulting in a better overall fit to data. This is a worthwhile effort, and from what I can tell the work is solid. My hope is that my feedback below will improve the work.
Major/overall suggestions:
My main areas of feedback are (1) a greater focus on relevant empirical work, (2) greater discussion of how the implementation of BNF compares to other models, and (3) more explanation of the methods.
(1) I understand that this is a modeling study, but there is a lot of relevant empirical literature that is not referenced. For example, the parameters describing responses to moisture and temperature are taken from Yu & Zhuang, which is another modeling study. That’s fine, but I would like to see more explanation of how those values compare to actual measurements of these quantities. As another example, your BNFfrac (see below as well) is a commonly measured quantity in N fixation work at the plant scale. Particularly for agricultural systems, there are large amounts of data. How do your results compare to empirical data? There are a few papers cited in the discussion about how N fixation varies as a function of N limitation, succession, etc., but there is a lot of work in these areas, and the discussion reads as if these were the first few that came up in a search rather than a synthesis of deep reading on the subject.
(2) The discussion of other model implementations of more mechanistic BNF could also be improved. Ma et al. (another version of LPJ) and Yu & Zhuang (TEM) are referenced most heavily, and Fisher et al. and Davies-Barnard & Friedlingstein are also mentioned, but there are many other implementations out there ranging from land models to ecosystem models. Kou-Giesbrecht et al. 2023 (cited in the ms) has a nice table that lists a few of the TRENDY models that incorporate mechanistic representations of BNF: CLASSIC, CLM, DLEM, OCN. There are other non-TRENDY models that have been developed that have been applied at large spatial scales – LM3/LM4 and QUINCY come to mind – and there are tons of ecosystem models (ED, MEL, CENTURY, etc.) that do something similar. Readers will want to know how your implementation compares.
(3) Explanation of the methods: I’d like to see a clearer description in the methods of how the versions were evaluated. I’d also like to see more detail about how N limitation is calculated, given that this is the key aspect of the paper. In particular, how is Ndeficit calculated?
Minor suggestions:
Given that you’ve stated that you’re modeling all BNF, not just legume-associated BNF, I suggest changing the name of flegume to ffixer or something like that.
Fig. 3 caption needs to specify what “DBF” is. I assume Davies-Barnard & Friedlingstein, but it would be nice to see in the caption, particularly given that there are other meanings of DBF (e.g., deciduous broadleaf forest).
The color scales on the global figures overemphasize the high range, making it hard to see variation in the lower range. For example, Fig. 3 looks largely like a map of agricultural BNF.
189: In the empirical literature, what you describe as BNFfrac is called %Ndfa (percent of N derived from fixation activity or derived from the atmosphere, depending on who you ask). It might help your paper to make the connection.
197: It’s true that 4 g N/m2/yr is a lot lower than 15, but 4 g N/m2/yr is still a huge difference.
Citation: https://doi.org/10.5194/egusphere-2023-2946-RC1 -
AC1: 'Reply on RC1', Stephen Wirth, 16 May 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2946/egusphere-2023-2946-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Stephen Wirth, 16 May 2024
-
RC2: 'Comment on egusphere-2023-2946', Anonymous Referee #2, 10 Apr 2024
The paper present a new parameterization of biological nitrogen fixation in the LPJmL model. This new parameterization, compared to the original one, takes into account the nitrogen limitation and a carbon cost for acquisition of the BNF. This is a very important improvement as it means that nitrogen fixation is directly linked to the biological activity and nitrogen limitation, which was not the case before. Hence, the total BNF fixation is reduced compared to the original formulation, which is more in agreement with observations. So it is an important improvement for LPJmL. The paper is sound and well written. So I have only few remarks, only some suggestions to improve the paper:
Even if the original approach of simulation of BNF fixation has already been published, it would help the reader to present the original equations and then to show in detail what are the difference between "original" and "C-costly" parameterization. For instance, we understand only in the discussion about BNF fraction that the 2 parameterization are different not only on the calculation of N but also on the way this N is taken by the plant, directly in the new parameterization and mixed with soil mineral N in original which is also an important difference. Then it is important to give more details about the parameterization and how they differ. More generally, it would be also interesting to compare the new parameterization to parameterization used in others DGVMS that implements BNF.
The results focus only on BNF, but it would be interesting to see also at global scale what is the impact of the new parameterization on the carbon cycle (for instance impact on NPP, NBP). Only the impact on legumes yield is shown if figure B1.
Also on figure 4 we see the relatively large impact of the new BNF parameterization on N emissions. It would then be interesting to show a comparison of these simulated fluxes to observations, as it is done for BNF. Especially for N2O emissions. It is obviously an important component of the GHG budget. So, with the new BNF parameterization, does it improve the simulated fluxes of N2O ?Minor remarks:
l 268: The authors seem surprised that the new approach does not limit the crop yield. But if I understood well the model, it is not so surprising for me. Since in condition when NPP is not a limiting factor for BNF (that should be the case for crops) and, as the model try to fulfill the N limitation, then the simulated BNF should be sufficient to fill the N demand of the plant and then should not produce N limitation ? Then it could explain why even if the different approaches give different BNF there is no impact of yield. This is exactly what we expect from the new formulation compared to original: define the BNF to avoid N limitation but without N excess... This is also the reason why it would be interesting to show the global impact on NPP: We should expect a decrease in NPP on carbon and N limited ecosystems, as the C-cost or N stress could be too high to be fulfilled by the BNF fixation. On the contrary, we should have no change in ecosystem with few limitations even is the BNF is reduced.
Figure 2: what are the percentage indicated in blue and red in a) ?
Figure 3: the DBf term is not defined. I guess it is the observation, but it should be described
Citation: https://doi.org/10.5194/egusphere-2023-2946-RC2 -
AC2: 'Reply on RC2', Stephen Wirth, 16 May 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2946/egusphere-2023-2946-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Stephen Wirth, 16 May 2024
Peer review completion
Journal article(s) based on this preprint
Model code and software
Model code for LPJmL5.7.9-ccostly-bnf Stephen Björn Wirth, Susanne Rolinski, Sibyll Schaphoff, Werner von Bloh, and Christoph Müller https://zenodo.org/records/10257030
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Johanna Braun
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(12451 KB) - Metadata XML
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Supplement
(2147 KB) - BibTeX
- EndNote
- Final revised paper