the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 2: livestock farming
Abstract. Agricultural ammonia (NH3) emissions are a major pathway of nitrogen loss, which can have significant environmental consequences, such as air and water pollution, ecosystem damage and biodiversity loss. Ammonia emissions related to livestock farming are major sources in the agricultural sector, resulting from animal housing, manure management and land application. This paper is the second part of the description of the AMmonia–CLIMate (AMCLIM) model, presenting the development and application of all three main modules to estimate NH3 emissions from livestock, including pigs, poultry (chicken), cattle, sheep and goats. The AMCLIM model simulates the flows of N species at different stages comprised in livestock agriculture. It incorporates the effects of environmental factors and also provides an adequate level of detail for the representations of human management practices. According to simulations by AMCLIM, it is estimated that NH3 emissions from global livestock farming are about 29.9 Tg N yr-1, accounting for around 30 % of total excreted nitrogen. Cattle and buffaloes systems are estimated to be the largest sources of NH3 emissions, contributing over 60 % of total livestock emissions. Both pigs and poultry systems result in more than 15 % of estimated total emissions, while sheep and goats are responsible for the remaining 7 %. High volatilization rates frequently occur in hot regions, indicating the climate-dependence of NH3 volatilization. It is also shown how AMCLIM can simulate the influence of management practices on NH3 volatilization, e.g., illustrating how fully-enclosed animal houses with heating and forced ventilation can result in higher emissions than naturally ventilated barns, while poorly managed manure leads to much more NH3 emissions.
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Status: open (until 04 Feb 2025)
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RC1: 'Comment on egusphere-2024-3803', Anonymous Referee #1, 07 Jan 2025
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The paper represents the second part of the description of the AMmonia-CLIMate (AMCLIM) model for quantifying ammonia emissions specifically from livestock production responding to environmental conditions and agricultural practices. The model which incorporates a high level of detail is well described in general and seems to perform well compared to observations and also to previous global-scale estimates. A serious and appropriate sensitivity analysis is also conducted confirming the important role of pH in the emissions.
The strength of the approach lies in the diversity of agricultural practices data and a detailed representation of ventilation scheme in the housing that are considered for ammonia emissions.The study will likely provide a major advancement for the community especially for designing precise mitigation strategies for NH3 losses but I have some comments before it can be considered for publication.
- I believe it is crucial in the introduction to put the AMCLIM approach in the state-of-the-art modeling context for ammonia emissions from agriculture. From l.51, this part can be more developed/precise to highlight the strength of AMCLIM. For instance, global processed-based models included in Land Surface Models (LSM) such as FAN v2 in CESM (Vira et al., 2020) and CAMEO in the IPSL ESM (Beaudor et al., 2023) in which detailed soil C/N cycles are implemented with multiple interactions (vegetation, soil BGC, water, and energy budget) also incorporate agricultural practices (by livestock type) and run with dynamic environmental conditions at the global scale. However one of their weaknesses is to rely on Emission Factors (EF) for the manure management part. I think it would be helpful to mention it in the introduction because AMCLIM is an interesting trade-off between process-based models/ Earth System Models and more socio-economic models such as the Integrated Assessment Models (IAMs).
- It is hard to get the global picture of the results of the different NH3 volatilizations; the "per grid" unit for the emission flux distributions (Fig 7-11) is not common and it is hard to compare with other literature results (which are usually given by /m2). Since the spatial resolution of the model is also not clearly stated, it is almost impossible to compare it with any other results. I know that the Part 1 paper also presented the ammonia flux in that unit and maybe for consistency reasons, the authors would like to stick to that unit but a clear justification should be included.
- On the same topic, to ease the comparison with other global results, one suggestion would be to include a global map of the combined synthetic fertilizer and livestock emissions (i.e. total agricultural sources) in the common units: gN/m2/yr. This map could be shown as a final result?
- The 2-year (2010 vs. 2018) comparison needs to be justified earlier in the manuscript and I am missing a description of the input that has been used for 2018. The setup for this experiment is not properly described in the Methods. Is the livestock distribution dataset for 2010 vs. 2018 the same? How the 2010 year inputs have been extrapolated?
If the meteorological impact is the major reason behind the 2-year comparison maybe the title of section 4.4 could be more explicit.
Please consider that 2018 results already appear in the previous sections when describing the maps of the volatilization rates.
The maps related to 2018 are in the Appendix, this makes the manuscript somehow very heavy for the reader to do this back and forth (especially for 6 maps x 3 animal types). Is there any way to combine these results maybe in a histoplot by region instead of multiple maps?
Minor comments:
- Fig 1: I think representing the NH3 losses on that schematic would be useful
- L. 174 : Is there any difference between FNH3 volatilization and FNH3 throughout the equations?
- L.190 : I think the pH variation with the different processes would be interesting to describe. Perhaps a little paragraph explaining its drivers would be beneficial since it is a critical parameter admitted by the authors and known by the community.
- L.343 : I am not sure this reference is well placed in the context
- Section 2.52 : I think a table gathering information on the different inputs used for the global scale (for both 2010 and 2018) and site simulations would be useful
- L. 408: What are the spatial and temporal resolutions of the model?
- L. 486: What could be a reasonable explanation for these discrepancies?
- Section 3.2: Is there any logic behind the order of the animal types that are presented? I wonder why cattle are not presented first since they are responsible for the highest emissions.
- Section 3.2.2: I would add a reference to the section where the new poultry emissions are compared to the older version. This would help the reader to understand that there is a dedicated section for that comparison.
- The title of Section 3.3.2 could be a bit more precise: “Comparison of grazing NH3 emissions with observations” ?
- Section 3.4: For the N budget, I wonder if other N species were also taken into account. N2O, N2 and NO2 emissions can also originate from manure. Even though these EFs are relatively small compared to NH3, I think it is important to mention them for a consistent N budget. I invite the authors to have a look at studies from Sommer et al., 2019 and EMEP/EEA 2019 for more information.
- L. 806: Please explain what is the GUANO model.
- Section 4.2: The GLEAM model and estimated EF from Yang et al would benefit from a little description. I wonder how the AMCLIM EF also compares with EFs from EMEP/EEA or Sommer et al., 2019 both specific to Europe and sometimes applied to the whole globe in several process-based models (Vira et al., Beaudor et al.,). In the study from Sommer et al., 2019, they also analyzed the EFs of the ALFAM2 process-based model (given as % of TAN content in the manure applied) by season and livestock type which could be interesting to compare.
- L.833: For the animal EFs taken from Yang et al., study, would you have any references for the data presented? I don’t see any precise reference in their paper but instead this description: “NH3 EFs from different animal types were collected by keyword searches of above several databases, including “animal ammonia/NH3 emission”, “cattle”, “buffaloes”, “chickens”, “ducks”, “goats”, “sheep”, “livestock production” and “animal husbandry operations”. “
- L.918: Please define FAN v2. In addition, a little description would be beneficial for the reader maybe in the introduction as suggested earlier.
- L. 978: Please correct for "database".
- L.982: I believe the sentence in parenthesis should not be there.
References :Vira, J., Hess, P., Melkonian, J., and Wieder, W. R.: An improved mechanistic model for ammonia volatilization in Earth system models: Flow of Agricultural Nitrogen version 2 (FANv2), Geosci. Model Dev., 13, 4459–4490, https://doi.org/10.5194/gmd-13-4459-2020, 2020.
Beaudor, M., Vuichard, N., Lathière, J., Evangeliou, N., Van Damme, M., Clarisse, L., and Hauglustaine, D.: Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model, Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, 2023.
Sommer SG, Webb J and Hutchings ND (2019) New Emission Factors for Calculation of Ammonia Volatilization From European Livestock Manure Management Systems. Front. Sustain. Food Syst. 3:101. doi: 10.3389/fsufs.2019.00101
Citation: https://doi.org/10.5194/egusphere-2024-3803-RC1 - I believe it is crucial in the introduction to put the AMCLIM approach in the state-of-the-art modeling context for ammonia emissions from agriculture. From l.51, this part can be more developed/precise to highlight the strength of AMCLIM. For instance, global processed-based models included in Land Surface Models (LSM) such as FAN v2 in CESM (Vira et al., 2020) and CAMEO in the IPSL ESM (Beaudor et al., 2023) in which detailed soil C/N cycles are implemented with multiple interactions (vegetation, soil BGC, water, and energy budget) also incorporate agricultural practices (by livestock type) and run with dynamic environmental conditions at the global scale. However one of their weaknesses is to rely on Emission Factors (EF) for the manure management part. I think it would be helpful to mention it in the introduction because AMCLIM is an interesting trade-off between process-based models/ Earth System Models and more socio-economic models such as the Integrated Assessment Models (IAMs).
Data sets
Data supporting the manuscript "A dynamical process-based model AMmonia–CLIMate (AMCLIM) for quantifying global agricultural ammonia emissions – Part 1: Land module for simulating emissions from synthetic fertilizer use" Jize Jiang et al. https://doi.org/10.7488/ds/7710
Model code and software
AMmonia-CLIMate (AMCLIM) v1.0 Jize Jiang https://doi.org/10.5281/zenodo.10911886
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