# Future Trends of Global Agricultural Emissions of Ammonia in a Changing Climate

M. Beaudor, N. Vuichard, J. Lathière, D. A. Hauglustaine

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M. Beaudor, N. Vuichard, J. Lathière, D. A. Hauglustaine. Future Trends of Global Agricultural Emissions of Ammonia in a Changing Climate. Journal of Advances in Modeling Earth Systems, 2025, 17 (4), 10.1029/2023ms004186. hal-05059171

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RESEARCH ARTICLE

10.1029/2023MS004186

**Key Points:**

* Development of downscaling method to project global gridded livestock densities and ammonia emissions from agriculture until 2100

* Global future ammonia emissions in 2100 range up to 70 TgN.yr⁻¹ depending on the scenario representing an increase of 30%–50% compared to present-day

* Climate change is estimated to contribute up to 20% of the increase in total agricultural ammonia emissions

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

M. Beaudor,
mb0142@princeton.edu

**Citation:**

Beaudor, M., Vuichard, N., Lathière, J., & Hauglustaine, D. A. (2025). Future trends of global agricultural emissions of ammonia in a changing climate. *Journal of Advances in Modeling Earth Systems, 17*, e2023MS004186. https://doi.org/10.1029/2023MS004186

Received 20 DEC 2023

Accepted 21 JAN 2025

**Author Contributions:**

**Conceptualization:** M. Beaudor,
N. Vuichard

**Formal analysis:** M. Beaudor

**Funding acquisition:** N. Vuichard

**Investigation:** M. Beaudor

**Methodology:** M. Beaudor, N. Vuichard

**Supervision:** N. Vuichard, J. Lathière,
D. A. Hauglustaine

**Visualization:** M. Beaudor

**Writing – original draft:** M. Beaudor,
N. Vuichard

**Writing – review & editing:** N. Vuichard,
J. Lathière, D. A. Hauglustaine

© 2025 The Author(s). Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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# Future Trends of Global Agricultural Emissions of Ammonia in a Changing Climate

M. Beaudor¹,² ID, N. Vuichard¹ ID, J. Lathière¹, and D. A. Hauglustaine¹

¹Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA-CNRS-UVSQ, Gif-sur-Yvette, France, ²Now at The High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA

**Abstract** Because of human population growth and changes in diet, global livestock and associated ammonia (NH₃), emissions are projected to increase through the end of the century, with possible impacts on atmospheric chemistry and climate. In this study, we propose a methodology to project global gridded livestock densities and NH₃ emissions from agriculture until 2100. Based on a downscaling method, future livestock distribution has been estimated until 2100 for three Shared Socio-economic Pathways (SSP2-4.5, SSP4-3.4, and SSP5-8.5) and used in a global process-based model (Calculation of AMmonia Emissions in ORCHIDEE, CAMEO) to estimate agricultural ammonia emissions during the 21st century. Emissions under SSP4-3.4 and SSP5-8.5 calculated by CAMEO compare well with the range estimated by the Integrated Assessment Models (IAM; 50 to 66 TgN.yr⁻¹) in the framework of the Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Some opposite trends arise under SSP2.4-5 where CAMEO emissions increase consistently in response to the increasing trends in synthetic fertilizer use under this scenario. Africa is identified as the most emitting region worldwide, with NH₃ emissions ranging from 10 to 16 TgN.yr⁻¹ in 2100. Through a set of simulations, we estimated climate change as responsible for 20 % of future increase in NH₃ emissions. The produced data sets of future NH₃ emissions is an alternative option to IAM-based emissions for studies aiming at projecting the evolution of atmospheric chemistry and its impact on climate. Further model developments involving the bi-directional property of NH₃ and refinement in the future changes of agricultural practices constitute interesting perspectives.

**Plain Language Summary** Due to the growing global population and increased livestock farming, emissions of ammonia (NH₃) are expected to rise until the end of the century with possible impacts on air quality and climate. This study introduces a method to predict livestock densities and NH₃ emissions worldwide until 2100. We estimate future livestock distribution based on different socio-economic scenarios and use a modeling approach to quantify resulting NH₃ emissions. The predicted global NH₃ emissions align well with estimates from a major climate modeling project, but there are variations within the scenarios studied. By 2100, global ammonia emissions may increase by 30%–50%, reaching 50 to 70 TgN.yr⁻¹, with Africa becoming one of the most important emitter regions. Due to their sensitivity to environmental conditions, NH₃ emissions are expected to be enhanced by climate change whose contribution can reach 20%. The data generated in this study provides an alternative to traditional emission projections which usually overlook climate sensitivity. This aims to help for a better understanding of future air pollution and its interactions with climate.

## 1. Introduction

Global NH₃ emissions rose from 55 to 65 TgN.yr⁻¹ between 2000 and 2015, mainly due to the increasing livestock production and fertilizer use (Hoesly et al., 2018; van Marle et al., 2017). Livestock production is inextricably linked to land-use and land-management to support animal feed needs. Land being suitable for livestock production represents the most significant land-use system present-day, occupying up to 45% of the global land area (Reid et al., 2008). The global consumption of meat proteins over the next decade is projected to increase by 14% by 2030 compared to the base period average of 2018–2020, driven largely by income and population growth (OECD-FAO Agricultural Outlook, 2021). This upward trend in meat consumption mirrors a historical pattern observed over recent decades, characterized by the expansion of livestock production systems worldwide, with profound implications for land use. Notably, the expansion of cattle ranching has led to deforestation for new pastures, while the demand for crops like soybeans has driven arable land expansion, particularly marked in countries like Brazil (Barona et al., 2010). In the future, the African agricultural sector
will most likely also experience a crucial evolution with an estimated 10-fold increase in livestock production by the end of the century under a high development rate scenario (Riahi et al., 2017).

While emissions of some species such as sulfur dioxide ($SO_2$) and nitrogen oxides ($NO_x$) are expected to be down-regulated in the future, $NH_3$ emissions, which mainly originate from the agricultural sector, are projected to increase under all the Shared Socio-economic Pathways (SSPs) for the 21st century (https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about). Recent atmospheric modeling studies have shown the key role of future $NH_3$ emissions in the formation of ammonium nitrate aerosols and their effect on the radiative forcing (Hauglustaine et al., 2014; Pai et al., 2021; Paulot et al., 2016). As a result of these impacts on atmospheric chemistry and climate, oceanic and terrestrial ecosystems are subject to adverse effects from N deposition processes. More specifically, Liu et al. (2023) estimated an overuse of fertilizers corresponding to 15% of the ammonia deposition into the oceans contributing to eutrophication. Sensitive biomes on land such as lichens, cropland and grassland can also suffer from the excess of N deposition (in its reduced chemical form) caused by acidification (Guo et al., 2010; Johansson et al., 2012; Yang et al., 2012).

Because of the impact of $NH_3$ on air quality, climate and ecosystems, it is of high interest to understand how the evolution of the agricultural sector could drive future emissions under different SSPs and climate scenarios. In the framework of Phase 6 of the Coupled Model Intercomparison Project (CMIP6), ScenarioMIP (O'Neill et al., 2016; Riahi et al., 2017) provides scenarios of future evolution for the main drivers impacting the climate system and under the different socio-economic pathways. In this context, $NH_3$ emission projections have been produced by Integrated Assessment Models (IAMs) which consist of simplified but consistent representations of the socio-economy, land systems, and their interactions. These emission projections are the data that have been used for the atmospheric chemistry component of Earth System Models (ESM) involved in AerChemMip (Collins et al., 2017). While this effort constitutes so far the only existing emission projections for the future, it is worth noting that it has several limitations. A harmonization and downscaling of IAM's future emissions have been developed (Gidden et al., 2018) to be consistent with historical emissions and to move from the IAM original regional scale (around 10 regions at global scale depending on the IAM) to gridded data. The harmonization methodology applied assumes that the spatial heterogeneity within each large region is kept constant over time using the information from the end of the CMIP6 historical period (i.e., 2014). In addition, this harmonization and downscaling procedure has only been applied to one IAM for each SSP. The approaches used for modeling emissions in the IAMs are significantly different, which makes the set of projected emissions for the different SSPs inconsistent. Lastly, future ammonia emissions projected by IAMs do not account for the impact of climate and environmental change, despite being important drivers of emissions.

In this paper, we propose a new approach to estimate future agricultural ammonia emissions based on a single global process-based model called Calculation of AMmonia Emissions in ORCHIDEE (CAMEO, Beaudor, Vuichard, Lathière, Evangeliou, et al., 2023). By using a land surface model which responds to environmental conditions and livestock evolution, we aim to better project ammonia emissions over the 21st century. To simulate emissions with CAMEO, future livestock distributions are required but no data set has been so far released. To fill this gap, we present a downscaling method to construct projections of livestock using regional livestock trends and future grassland areas (Section 2.3). Section 3 presents the future livestock densities along with agricultural $NH_3$ emissions and a comparison of the trends with $NH_3$ emission projections performed by IAMs in the CMIP6 context. We also include an assessment of the key drivers which may impact future emissions and, in particular, the contribution of climate change ($CO_2$ concentration, temperature and precipitation). Finally, a global and regional analysis of the emissions in 2100 is presented.

## 2. Methods

### 2.1. The CAMEO Model

Future emissions are calculated by the process-based model CAMEO whose complete description and evaluation can be found in Beaudor, Vuichard, Lathière, Evangeliou, et al. (2023). Figure 1 represents how CAMEO simulates agricultural ammonia emissions. CAMEO is fully integrated as a module in the global Land Surface Model ORCHIDEE and is constrained by climate, land-use, synthetic N fertilizers, and livestock density (Beaudor, Vuichard, Lathière, Evangeliou, et al., 2023; Krinner et al., 2005; Vuichard et al., 2019). CAMEO computes two types of ammonia emissions: the “indoor” emissions, which come from the livestock
<figure><img src="image_3.png" /><figcaption>**Figure 1.** General description of the CAMEO model (green box) highlighting its main interactions with ORCHIDEE (yellow box) for vegetation dynamics and its external drivers (meteorological fields, N input, land use maps and livestock densities). Feeding biomass is used to estimate manure and related "indoor emissions" produced through the livestock and management part. Soil $NH_3/NH_4^+$ pools are driven by N input, mineralization, nitrification, denitrification, plant root uptakes and losses. Future livestock densities used to project future ammonia emissions are derived from the Livestock Downscaling Method combining historical livestock and biomass production distributions with future livestock trends and grassland maps (purple box).</figcaption></figure>

waste management chain (manure handled during housing, yard and storage), and emissions from soil in response to N input (synthetic fertilizer and manure applications, atmospheric deposition), fertilizer timings and meteorological factors, directly linked to the nitrogen cycle of the model. Indoor emissions are calculated based on livestock-specific activity data (how manure is handled, i.e. during housing, storage, and remained at the yard) and specific emission factors taken from EMEP/EEA (2019) and Sommer et al. (2019) for each livestock and activity data. Manure is also classified as solid or liquid form depending on the region and livestock type, as described in the Global Livestock Environmental Assessment Model (FAO, 2018) (the classification is presented in Table S3 in the Supporting Information of Beaudor, Vuichard, Lathière, Evangeliou, et al., 2023; Beaudor, Vuichard, Lathière, & Hauglustaine, 2023). In CAMEO, contrary to indoor emissions, soil emissions are estimated with a process-based approach using the nitrogen cycle of the ORCHIDEE model and accounting for gas-liquid phases equilibrium and ammonium ion dissociation in soil. ORCHIDEE includes the main microbial (implicitly represented) N processes that drive the changes in soil ammonium and nitrate pools as the mineralization, nitrification, denitrification, and also soil physical processes as nitrate, and ammonium root uptakes, leaching following the OCN model formalism (Zaehle et al., 2010), mostly based on the DNDC model (C. Li et al., 1992; C. S. Li, 2000; Y. Zhang et al., 2002). ORCHIDEE's vegetation includes 15 plant functional types (PFTs), among which two crop types (C3 and C4; i.e: two main photosynthetic strategies distinguishing plants worldwide) and four grass types (temperate, boreal, and tropical C3 grasses as well as a single C4 class).

Ammonia emissions are mostly driven by N input incorporated into the soil (mainly manure and synthetic fer-
tilizers) and environmental variables. The more N is applied to soil, the more N is available for vegetation to grow
but also for volatilization. CAMEO allows the consideration of dynamic meteorological variables (such as surface
temperature and precipitation) through physical soil processes to calculate nitrogen fluxes, while also accounting
for key agricultural drivers and vegetation dynamics. Meteorological conditions, particularly temperature and soil
humidity, directly influence ammonia emissions and the nitrogen cycle. High temperatures and low humidity tend
to increase volatilization by affecting the ammonia-ammonium equilibrium and the production of ammonia gas. These environmental factors also impact vegetation, which in turn feeds back into nitrogen availability in the soil, further influencing emissions. Agricultural drivers such as fertilization use, livestock production distributions, animal diet, and manure management also play a critical role in CAMEO. Livestock densities are a crucial input for estimating feed needs and manure production, which control both indoor and soil emissions. Under specific conditions (i.e., when the grass biomass simulated by ORCHIDEE is limited by climate for instance), the effective livestock density can be lower than the one prescribed as input. In cases where local biomass resources are limited, CAMEO adjusts livestock densities, assuming that grass feed requirements must be satisfied locally without external imports. To model this grass-feed supply, CAMEO computes a grazing Index (GI) which corresponds to the yearly fraction of above grass NPP (Net Primary Productivity) which is exported and used for ruminant needs. CAMEO sets a GI maximum value of 0.7, reflecting the portion of above-ground biomass available for grazing or cutting.

It is worth noting that the model, in terms of assumptions and chosen parameters, is identical to the one described in the reference study (Beaudor, Vuichard, Lathière, Evangeliou, et al., 2023; Beaudor, Vuichard, Lathière, & Hauglustaine, 2023), with all parameters set to their default values. For instance, manure pH which has been demonstrated to be a sensitive parameter for NH₃ emissions, is kept to its original and single fixed value of 7 (Beaudor, Vuichard, Lathière, Evangeliou, et al., 2023). The only difference in the modelling set-up between our study and (Beaudor, Vuichard, Lathière, Evangeliou, et al., 2023) is about forcings. To ensure consistency with the CMIP6 framework, we use CMIP6-related forcings for synthetic fertilizers and climate variables. Note that in the CMIP6 synthetic fertilizer forcing (LUH2v2, Hurtt et al., 2020), application of synthetic fertilizer is only assumed over croplands, not grasslands. Considering synthetic fertilizer application over croplands only leads to a total fertilization input of 97 TgNyr⁻¹ (with LUH2v2 in this study) against 118 TgNyr⁻¹ in the reference study of Beaudor, Vuichard, Lathière, Evangeliou, et al. (2023) with the Lu and Tian (2017) data set. About climate data, in this study, CAMEO is forced by 3-hourly near-surface meteorological fields simulated by the Institut Pierre Simon Laplace (IPSL) Earth System Model: IPSL-CM6A-LR ESM (Boucher et al., 2020), in the context of CMIP6 for near-surface air temperature, specific humidity, wind speed, pressure, short- and longwave incoming radiation, rainfall, and snowfall. On the other hand, in the reference paper (Beaudor, Vuichard, Lathière, Evangeliou, et al., 2023) CAMEO is forced by the Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data set (CRU-JRA V2.1) (Harris et al., 2014) provided at 6h time steps.

CAMEO has been extensively evaluated for the present-day in Beaudor, Vuichard, Lathière, Evangeliou, et al. (2023) showing a good agreement of intermediate variables with recent literature results (i.e., global crop and grass production, biomass dedicated to livestock, manure production, fertilization application, and agricultural ammonia emissions). Within this last study, multiple sensitivity tests have also been conducted to evaluate the response of CAMEO to internal parameters (i.e., soil pH, indoor emission factors, the timing of fertilization, and atmospheric concentration). To complete this evaluation, the authors have compared the seasonality of CAMEO emissions to satellite-derived emissions from the Infrared Atmospheric Sounding Interferometer (IASI) space-borne instrument ("IASI-derived emissions") and other modeling/inventory data sets highlighting very satisfying correlation scores. The so-called "IASI-derived emissions" are retrieved from the ammonia columns observed from the IASI instrument and combined with simulated ammonia lifetime according to a methodology developed by Evangeliou et al. (2020).

As the forcing files used in this present-study are not exactly the same as used in the reference study from Beaudor, Vuichard, Lathière, Evangeliou, et al. (2023), an additional analysis is provided in the Supporting Information S1 (Figure S1) to ensure that the seasonal patterns of the model are conserved against IASI-derived emissions. In fact, using the CMIP6 forcing files (described hereafter) improves the seasonal variability in the US and China where the original emissions were likely too high during July and might be explained by higher synthetic fertilizer input or enhancement from meteorology conditions.

## 2.2. CAMEO Forcings for 2015–2100

The following list presents the reference input data used for each simulation. The data used is available for the 3 SSPs exploited in this study and Table 1 provides an overview of how the different forcing files are used in each simulation.
<table><caption>**Table 1**
Summary of the Simulations Performed With the Corresponding Forcing Files</caption><thead><tr><th>Run name</th><th>Run period</th><th>Meteo<sup>a</sup></th><th>[CO<sub>2</sub>]<sup>b</sup></th><th>Land cover, Fertilizer<sup>c</sup></th><th>Nitrogen deposition<sup>d</sup></th><th>Livestock<sup>e</sup></th></tr></thead><tbody><tr><td>CAMEO</td><td>2002–2014</td><td>HIST<sub>y</sub></td><td>373–398</td><td>UofMD-landState</td><td>HIST</td><td>REF</td></tr><tr><td>HIST</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CAMEO</td><td>2015–2100</td><td>ssp2-4.5</td><td>400–603</td><td>MESSAGE-ssp2-4.5</td><td>ssp2-4.5</td><td>ssp2-4.5</td></tr><tr><td>SSP2-4.5</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CAMEO</td><td>2015–2100</td><td>ssp4-3.4</td><td>400–473</td><td>GCCAM4-ssp4-3.4</td><td>ssp4-3.4</td><td>ssp4-3.4</td></tr><tr><td>SSP4-3.4</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CAMEO</td><td>2015–2100</td><td>ssp5-8.5</td><td>400–1135</td><td>MAGPIE-ssp5-8.5</td><td>ssp5-8.5</td><td>ssp5-8.5</td></tr><tr><td>SSP5-8.5</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CAMEO<sub>ClimHIST</sub></td><td>2015–2100</td><td>HIST<sub>clim</sub></td><td>398</td><td>MESSAGE-ssp2-4.5</td><td>ssp2-4.5</td><td>ssp2-4.5</td></tr><tr><td>SSP2-4.5</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CAMEO<sub>ClimHIST</sub></td><td>2015–2100</td><td>HIST<sub>clim</sub></td><td>398</td><td>GCCAM4-ssp4-3.4</td><td>ssp4-3.4</td><td>ssp4-3.4</td></tr><tr><td>SSP4-3.4</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CAMEO<sub>ClimHIST</sub></td><td>2015–2100</td><td>HIST<sub>clim</sub></td><td>398</td><td>MAGPIE-ssp5-8.5</td><td>ssp5-8.5</td><td>ssp5-8.5</td></tr><tr><td>SSP5-8.5</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Note. A unique [CO₂] value is shown in the table to provide a comparison point between the simulations. <sup>a</sup>Taken from IPSL-CM6A-LR Earth System Model (see details in Section 2.2); HIST<sub>y</sub> corresponds to a historical yearly meteorological field while HIST<sub>clim</sub> is a cycling over 2011–2014. <sup>b</sup>Units in ppm. the [CO₂] range corresponds to the value at the beginning and the end of the run period; in the “ClimHIST” simulations, a fixed value corresponding to year 2014 is taken ([CO₂]₂₀₁₄). <sup>c</sup>LUH2 version 2.1 hr for CAMEO HIST and version 2.1f for all of the other simulations. <sup>d</sup>Input4MIPs.CMIP6.CMIP.NCAR. NCAR-CCMI-v1-0 for “CAMEO HIST” and v2-0 for all of the other simulations. <sup>e</sup>Livestock distributions for SSPᵢ correspond to the reconstructed projected livestock distribution data set (Dᵢ,SSPᵢ) described in Section 2.3.

### 2.2.1. Meteorology

To drive CAMEO/ORCHIDEE, we used 3-hourly near-surface meteorological fields simulated by the Institut Pierre Simon Laplace (IPSL) Earth System Model: IPSL-CM6A-LR ESM (Boucher et al., 2020) in the context of CMIP6 for near-surface air temperature, specific humidity, wind speed, pressure, short- and longwave incoming radiation, rainfall, and snowfall. We used the historical experiment outputs for the present-day conditions (2000–2014) and those produced within ScenarioMIP (O’Neill et al., 2016) for the future climate (2015–2100). For both historical and future simulations, we used the r1i1p1f1 member of each experiment.

### 2.2.2. Land-Use and Soil Data

Data used in this study originate from the Land Use Harmonization –2 data set developed in the framework of CMIP6 (LUH2, Hurtt et al., 2020). It provides land-use reconstruction over the period 1850–2014 for key aggregated land categories (primary lands, secondary lands, pasture, croplands, etc). and land-use projections over 2015–2100 for the different SSPs used in CMIP6. The SSPs allow the consideration of a wide range of mitigation efforts on emissions. Each pathway corresponds to a specific scenario designed by an IAM where the emissions are a function of a complex interaction between socio-economic factors (Riahi et al., 2017). The procedure to translate LUH2 land categories in ORCHIDEE plant functional types is described in Lurton et al. (2020). The soil-related data used to drive ORCHIDEE are texture class, pH and bulk density. Texture class is from Zobler (1988), bulk density from the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012) and soil pH from the International Geosphere-Biosphere Programme Data Information System (GLOBAL SOIL DATA TASK, 2000).

### 2.2.3. N Input

Information on the mineral fertilizer applied on C3 and C4 type cropland is part of the LUH2 data set (Hurtt et al., 2020). NHₓ and NOᵧ depositions fields have been produced by CAM-Chem model in the framework of CMIP6 and are available on input4MIP from 2015 to 2100 (Hegglin et al., n.d.).
### 2.2.4. Livestock Density

The present-day livestock density is defined by the Gridded Livestock of the World (GLW 2, Robinson et al., 2014). It provides livestock information at a quarter degree for the main livestock categories (cattle, sheep, goat, pig, and poultry). Data is available for 2006 only, which is used and kept constant for every year of the historical simulation (called “CAMEO HIST” in Table 1). To our knowledge, there is no gridded product similar to GLW2 for future scenarios over 2015–2100. The International Institute for Applied Systems Analysis (IIASA) database (https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about) provides livestock production projections ($L_{SSP,dec,reg}$, million tDM/yr, see Figure S2 in Supporting Information S1) over the period 2010–2100 per decade (dec) for all the SSPs described in Riahi et al. (2017), but only for five large regions (reg) over the globe (Asia, Latin America, Africa, OECD countries, Reforming Economies of Eastern Europe countries). The following section describes the methodology developed to reconstruct the future gridded livestock densities.

### 2.3. Downscaling Methodology for Future Livestock Densities

For each livestock category, we constructed the future gridded livestock density for a given SSP and grid-cell “c” ($D_{SSP,c}$, Heads.m⁻²) based on the historical livestock density from GLW2 ($D_{GLW2}$), and the livestock production evolution assessed by the SSP-related IAM for 2010–2100 for the five large regions defined by IIASA ($L_{SSP,dec,reg}$). The IIASA database provides only information for total livestock and not for specific livestock categories. As a consequence, we assumed that the relative distribution of the livestock categories is kept constant over time, at the regional scale but also within the grid cells, using the GLW2 information for the present-day.

The general objective is to reflect the future livestock production at the regional scale by varying their local spatial pattern within each region according to the future evolution of grassland areas. To first respect the future livestock production change at the regional scale projected by the IAMs, we need to satisfy the following equation:

$$ \frac{D_{SSP,dec,reg}}{D_{GLW2,reg}} = \frac{L_{SSP,dec,reg}}{L_{SSP,2010,reg}} \qquad (1) $$

where $D_{GLW2,reg}$ and $D_{SSP,dec,reg}$ are the regional-mean values for the region reg of respectively $D_{GLW2}$ and $D_{l,SSP}$ for the decade dec and $L_{SSP,2010,reg}$, the value of $L_{SSP,dec,reg}$ for 2010. We note $f_{SSP,dec,reg}$ the ratio $\frac{L_{SSP,dec,reg}}{L_{SSP,2010,reg}}$, representing our regional theoretical target. The regional target refers to the livestock production change at the regional level that must be respected to match the projected future livestock production in each region, as provided by IIASA. This regional target compares future livestock production in a given region during a future decade to the livestock production in that region in 2010.

In our modeling framework, we make the assumption that grass-feed livestock needs ($BM_{grass}$, gC.m⁻².yr⁻¹) were locally produced (within the grid cell) (Beaudor, Vuichard, Lathière, Evangelou, et al., 2023). $BM_{grass}$ is computed as:

$$ BM_{grass} = aNPP_{grass} \times f_{grass} \times GI \qquad (2) $$

where $aNPP_{grass}$ is the above-ground Net Primary Productivity of grassland ($gC.m⁻².[grass]·yr⁻¹$), $f_{grass}$, the fraction of grassland in the grid cell and GI a parameter named Grazing Index (unitless) which corresponds to the proportion of NPP “exported” for animal feeding (see Beaudor, Vuichard, Lathière, Evangelou, et al., 2023). The grass feed produced locally in a grid cell may increase or decrease for the different SSPs, as does $f_{grass}$ in LUH2, which enables to sustain a variable livestock production over time. Because we want to account for this “extensification” term, we do not apply directly the $f_{SSP,dec,reg}$ factor to the livestock density in a given grid cell “c,” $D_{l,GLW2,c}$, to get $D_{SSP,dec,c}$. Instead, we computed a variable $f_{SSP,dec,c}$ for each grid cell based on the ratio between the grass feed produced in the grid cell c in future decade “dec” ($BM_{grass,SSP,dec,c}$) and in the year 2010 ($BM_{grass,2010,c}$):

$$ f_{SSP,dec,c} = \frac{BM_{grass,SSP,dec,c}}{BM_{grass,2010,c}} \qquad (3) $$
As a simplification, we assumed that grassland productivity will not be impacted by climate change and will remain constant at its 2010 value for any SSP. In addition, we assume that the Grazing Index will be a proportion of its 2010 value, fixed at the regional level (x_SPP,dec,reg). In other words, the relative change in Grazing Index is assumed to be uniform within each region. The Grazing Index may increase or decrease depending on the targeted change in livestock production and grassland area. However, as done in Beaudor, Vuichard, Lathière, Evangelou, et al. (2023), we limit the GI to 70 % of the above-ground NPP (0.7). As a consequence, f_SSP,dec,c may be written as:

$$f_{SSP,dec,c} = \frac{aNPP_{grass,2010,c} \times f_{grass,SSP,dec,c} \times \min(0.7, GI_{2010,c} \times x_{SSP,dec,reg})}{aNPP_{grass,2010,c} \times f_{grass,2010,c} \times GI_{2010,c}} \quad (4)$$

f_grass,2010,c and f_grass,SSP,dec,c are the fractions of grassland in the grid-cell c for respectively 2010 and decade “dec” taken from LUH2 (Hurtt et al., 2020) (see more details in the “Land-use data” in Section 2.2). x_SPP,dec,reg is the single unknown of Equation 4 which is set by satisfying the following equation:

$$f_{SSP,dec,reg} = \frac{\sum_{c=1}^{n_{reg}} BM_{grass,SSP,dec,c} \times \text{Areas}_c}{\sum_{c=1}^{n_{reg}} BM_{grass,2010,c} \times \text{Areas}_c} \quad (5)$$

where n_reg is the number of grid cells within the region reg and Areas_c the area of the grid cell c.

The trends of the regional f_SSP,dec,c are compared to the ones from the theoretical target f_SSP,dec,reg in Section 3.1 to highlight how much future livestock production is constrained by sub-regional drivers such as the GI and future grassland projections.

The final step consists in multiplying the resulting f_SSP,dec,c (depicted in Figure S3 in Supporting Information S1) to the historical total livestock density (D_GLW2) and retrieving the future livestock density per animal category based on the historical proportion of animal category at each grid-cell (D_SSP,dec,c). Once each decade is reconstructed, a linear interpolation is applied to retrieve the intermediate year. From 2011 to 2019, we use as initial and final interpolation points the reference distribution for 2010 and the corresponding SSP distribution of the decade 2020.

## 2.4. CAMEO Simulations Set-Up

The ORCHIDEE model, including CAMEO, was run at the spatial resolution of the IPSL-CM6A-LR Earth System model (2.5° lon x 1.27° lat). We first performed a 15-year historical simulation over the 2000–2014 period (called “CAMEO HIST”) using the meteorological near-surface fields from the CMIP6 historical experiment of IPSL-CM6A-LR (Boucher et al., 2019). In the “CAMEO HIST” simulation, all forcing data (i.e., meteorology, land-use, N input) are updated yearly, except those related to livestock density, which is kept constant over time (GLW, Robinson et al., 2014).

A set of 3 future scenarios was conducted to evaluate the impact of future changes in agricultural practices (livestock densities and use of fertilizers) on agricultural ammonia emissions.

Among the SSPs developed within ScenarioMip, we used the three SSPs that correspond to the most divergent trajectories of livestock production: SSP2-4.5 (Middle of the Road, Fricko et al., 2017), SSP4-3.4 (A world of deepening inequality, Calvin et al., 2017) and SSP5-8.5 (Fossil-fueled Development—Taking the Highway, Kriegler et al., 2017). SSP4-3.4 is the scenario with the weakest livestock evolution, while SSP5-8.5 is the one with the biggest increase. SSP2-45 shows an intermediate evolution between SSP4-3.4 and SSP5-8.5 (Figure S2 in Supporting Information S1). These three scenarios are also divergent in terms of agricultural productivity and human population evolution, food demands and dietary preferences. For instance, SSP5-8.5 presents a world characterized by meat-rich diets and important food waste while SSP2-4.5 reflects medium animal demand and SSP4-3.4 presents important regional differences with high consumption lifestyles in elite socio-economic categories and low consumption for the rest (Popp et al., 2017).

In order to assess the sensitivity of the emissions to future climate change, the three scenarios were repeated under two types of climate (historical and future). The SSP simulations under a future climate are called
<table><caption>**Table 2**

Method and Input Tables Used Within the Three IAMs to Develop Agricultural NH₃ Emission Estimates in the Framework of the SSPs (Based on Rao et al., 2017)</caption><thead><tr><th>IAM</th><th>SSP</th><th>Livestock input (EF sources)</th><th>Crop input (EF sources)</th><th>Grass input (EF sources)</th></tr></thead><tbody><tr><td>MESSAGE-GLOBIOM</td><td>2-4.5</td><td>Livestock production (GLOBIOM)</td><td>Crop production (GLOBIOM)</td><td>—</td></tr><tr><td>GCCAM</td><td>4-3.4</td><td>Livestock production (Edgar 4.2)</td><td>Crop production (Edgar 4.2)</td><td>—</td></tr><tr><td>REMIND-MAgPIE</td><td>5-8.5</td><td>Nr. of animals, feed (MAgPIE/IPCC 1996, 2006)</td><td>Cropland soil: Fertilizer, manure, other N inputs (MAgPIE/IPCC 2006)</td><td>N manure input (MAgPIE/IPCC 2006)</td></tr></tbody></table>

Note. EF account for regional emission factor applied to the specified activity level (livestock, crop or grass input). Grass input corresponds to managed grassland.

"CAMEO SSPᵢ" (with i: 2-4.5, 4-3.4 or 5-8.5). They account for all SSP-related forcings and are driven by the climate data simulated by the IPSL-CM6 model for each SSP scenario. These simulations are considered as reference simulations. The future simulations in which climate is kept historical are driven by cyclic historical climatology (2011-2014) for the meteorology and a fixed value for [CO₂] corresponding to the year 2014 and are labeled "CAMEO_ClimHIST SSPᵢ." The "CAMEO SSPᵢ" and "CAMEO_ClimHIST SSPᵢ" simulations were run for 86 years over the 2015-2100 period starting from the last year of the "CAMEO HIST" simulation. All forcing data were updated every year, including the livestock distributions in this set of simulations. The different simulations and their corresponding forcing files are summarized in Table 1 and described in the following section.

## 2.5. Comparison Against Future IAM-Based Emissions

We compared the simulated agricultural NH₃ emissions, to those produced by the Integrated Assessment Models (IAMs) in the context of input4MIPs (Gidden et al., 2018) for the three SSPs considered in this study. Note that different IAMs are used to estimate agricultural NH₃ emissions for each of the three SSPs considered in this study (see Table 2). Emission calculation in any of the IAMs is based on regional emission factors (EFs) applied to specific activity input levels (livestock, crop, or managed grassland input). While MESSAGE-GLOBIOM model uses its own EFs, GCAM and REMIND-MAgPIE models are based on reference EFs from the EDGAR inventory (Janssens-Maenhout, 2011), and the IPCC methodology (Paustian et al., 2006), respectively. Emission estimation from REMIND-MAgPIE IAM is the most complex and realistic approach considering manure application over managed grasslands.

# 3. Results

## 3.1. Evolution of Livestock Distribution Until 2100

As a first result, we show the evolution of the theoretical target ($f_{SSP,dec,reg}$, showed in dotted lines) from 2020 to 2100 which is the factor by which the present-day livestock would need to be multiplied at the regional scale to strictly respect the livestock trends projected by IAMs for 5 big regions without considering any sub-regional drivers (Figure 2). The actual factor resulting from the downscaling approach presented in Section 2.3 and used to downscale livestock distribution is also shown ($f_{SSP,dec,c}$ integrated over region 'reg' presented in plain lines). As a result, the theoretical target in our approach can potentially not be fully satisfied as these trends are constrained by a spatial indicator driven by a Grazing Index and future grassland biomass productivity.

In all regions (except Africa and Latin America), the target is nearly reached for all three scenarios, meaning that the projected livestock can be satisfied by the regional modeled biomass. In Africa and Latin America, the target is far from being reached, especially under SSP5-8.5 in Africa from 2030. In 2100, the target is three times higher than what is possible to sustain with the future grassland area and the maximal use of grass NPP.

The resulting reconstructed maps for decades 2020, 2030, 2050, and 2100 of the livestock distributions simulated by CAMEO are depicted in Figure S4 in Supporting Information S1. The regions with the most significant increase in total livestock in 2100 are central and South Africa and eastern Asia under SSP4-3.4 and SSP5-8.5. In Africa, where densities were rather around 200 Heads.km⁻².yr⁻¹ in 2000, the livestock can reach 8000 Heads.km⁻².yr⁻¹ in 2100. Even earlier (in 2030), Africa is the region where livestock experience the most
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Regional theoretical targets projected by the IAMs for the three considered SSPs ($f_{SSP,dec,reg}$, dotted lines). Actual factors from the downscaling approach by which present-day livestock is multiplied to respect the livestock trends projected by IAMs and sub-regional biomass constraints ($f_{SSP,dec,c}$, integrated over the reference IIASA regions to be consistent with the regional theoretical target, plain lines). The regional abbreviation “REF” accounts for the Reforming Economies of Eastern Europe and the Former Soviet Union. Please note the different y-axis ranges for the different regions.</figcaption></figure>

significant increase (historical density × 40, see Figure S3 in Supporting Information S1). It is worth noting that some critical differences in the gridded factors $f_{SSP,dec,c}$ can exist spatially within one region depending on the present-day value of GI and the evolution of the pasture lands (see Figure S3 in Supporting Information S1).

## 3.2. Future Trends of Ammonia Emissions Under Climate Change and Comparison With the IAM's Estimates

The evolution of the emissions under the three SSPs simulated by CAMEO ranges between 35 and 70 TgN.yr⁻¹ (Figure 3). This estimate is close to the one estimated by IAMs (Riahi et al., 2017) in 2100 (38–65 TgN.yr⁻¹, Figure 3). The global evolution of the agricultural emissions simulated by CAMEO shows an increase of around 50% by 2100 compared to 2014 under the SSP4-3.4 and SSP2-4.5, which have similar trends. Under the SSP5-8.5, the evolution is more steady and reaches its maximum value in 2040 (32%). CAMEO emission trends are close to IAM projections for SSP4-3.4 and SSP5-8.5 even though CAMEO emissions do not decrease after 2080 as in IAMs.

Moreover, there is an important difference between CAMEO and IAMs under the SSP2-4.5, where CAMEO emissions surpass the IAMs estimations by 25 TgN.yr⁻¹, with opposite trends. In our approach, SSP2-4.5 highlights the most crucial increase, while for IAMs, NH₃ emissions are the weakest under SSP2-4.5 (i.e., in 2100, emissions reach the same value as in the present day). In SSP2-4.5, efforts to mitigate climate change through dietary changes offset the rise in food and feed crop production, despite a higher population growth
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Evolution of the global agricultural NH₃ emissions for the considered SSPs from CAMEO under future climate (solid lines) and from the CMIP6 inventory (dotted lines from IAMs, Gidden et al., 2018), in TgN.yr⁻¹.</figcaption></figure>

compared to SSP5-8.5. However, in SSP2-4.5, the production of biofuel crops driven by climate mitigation surpasses the demand for food and feed. The increasing demands for food, feed, and biofuel in SSP2-4.5 lead to the most significant increase in crop production among the scenarios. Coupled with slower agricultural advancements compared to SSP5-8.5, this results in the highest fertilizer requirement among the scenarios (Fricko et al., 2017; Popp et al., 2017). The increase in fertilizer use and livestock production explains the trend we observe in the CAMEO emissions. On the other hand, when analyzing the correlation between livestock production and ammonia emissions simulated by all the IAMs under SSP2-4.5 (Figure S5 in Supporting Information S1), we observe that MESSAGE-GLOBIOM (the model selected as the reference for CMIP6) is the only IAM which reflects a negative relationship. In addition, MESSAGE-GLOBIOM is the model that simulates the lowest emission rate over 2080–2100 (34% lower than the IAM average) not only for SSP2-4.5 but also for other SSPs (not shown here). This means that other major drivers are involved in the CMIP6 ammonia emissions simulated by MESSAGE-GLOBIOM. One explanation for this contradictory evolution is that CMIP6 emissions (from multiple reference IAMs) are harmonized in order to match the historical emissions as a common starting point for all IAMs. This procedure can lead to inconsistencies between emissions and the main drivers.

Regional trends of NH₃ emissions and N input for the biggest emission regions are shown in Figures 4 and 5. It is important to note that the information about fertilizer inputs used in CAMEO is the one provided by IAMs (through the LUH2 data set), while other N inputs but also the way ammonia emissions are computed, are different between CAMEO and IAMs.

CAMEO and CMIP6 emissions are in agreement in the SSP5-8.5 at the global scale but we find regional differences between both approaches which can be underlined by compensation responses from one region to another especially in Africa, China and India, three important emitter regions. In the fossil fuel-intensive scenario SSP5-8.5, the demand and subsequent production of food and feed crops increase the most among the three scenarios considered. This increase occurs despite low population growth, driven by the predominance of diets high in animal products (Fricko et al., 2017; Kriegler et al., 2017). Interestingly, despite the highest levels of food and feed crop production, the projected fertilizer applications in SSP5-8.5 only increase slightly. This is due to the very low production of biofuel crops, a result of the absence of climate mitigation policies and rapid advancements in agricultural productivity. The diet rich in animal products also leads to a sharp rise in manure applications, which peak in the 2040s. However, after the 2060s, these applications decline due to limited grassland resources, particularly in Africa, as indicated by CAMEO. On the contrary, in the IAM approach, emissions from livestock are not locally reduced and continue increasing in response to the meat-rich diet.

The differences in total emissions under SSP2-4.5 are also significant at the regional scale. In all the regions, the CAMEO emissions exceed the emissions estimated by the IAM, except in India (Figure 4). In Europe, Latin America, and Africa, the fertilizer input under SSP2-4.5 is at its highest with at least 10 TgN.yr⁻¹ of differences
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Evolution of the regional NH₃ emissions for the considered SSPs from the agricultural sector (in TgN.yr⁻¹) and the fraction of indoor manure management simulated by CAMEO (no unit, solid lines) and total agricultural emissions from the CMIP6 inventory (in TgN.yr⁻¹, dotted lines from IAMs, Gidden et al., 2018). China-K-J refers to China-Korea-Japan.</figcaption></figure>

compared to the other SSPs over the 2030–2100 period (Figure 5). Combined with a similar pattern in manure production, mainly due to a constant increase in livestock production, it partly explains why SSP2-4.5 is distinguishable from the other SSPs in our approach. In the IAMs, even though the emissions are also the highest under SSP2-4.5 in Europe and Latin America, we mainly observe steady or negative trends in China-Japan-Korea, Latin America, Africa, and the US, which explains the global decreasing trend. These results in emissions in Latin America, Africa, and the US (Figure 4) are contrary to the fertilizer input trends shown in Figure 5. Contrary to the

<figure><img src="image_7.png" /><figcaption>**Figure 5.** Evolution of the regional N input for the considered SSPs including both manure production simulated by CAMEO (upper subplot) and mineral fertilizer use from CMIP6 as described in Section 2.2 (bottom subplot) in TgN.yr⁻¹. Both nitrogen inputs are crucial data for the calculation of ammonia emissions in CAMEO. China-K-J refers to China-Korea-Japan.</figcaption></figure>
<table><caption>**Table 3**
Global Agricultural NH₃ Emissions (TgN.yr⁻¹) for the Historical Period (HIST Line) and Under Different SSPs (Lines SSP2-4.5, SSP4-3.4, and SSP5-8.5) Simulated by CAMEO Under Future and Historical Climate (Columns CAMEO and CAMEO_climHIST Respectively) and Estimated by the IAMs (CMIP6 Column)</caption><thead><tr><th></th><th>CAMEO</th><th>CAMEO_climHIST</th><th>CMIP6</th></tr></thead><tbody><tr><td>HIST (present-day)</td><td>34</td><td>34</td><td>36</td></tr><tr><td>SSP2-4.5 (future)</td><td>70</td><td>64</td><td>38</td></tr><tr><td>SSP4-3.4 (future)</td><td>68</td><td>63</td><td>66</td></tr><tr><td>SSP5-8.5 (future)</td><td>50</td><td>39</td><td>53</td></tr></tbody></table>

IAMs, while the fertilizer input appears to play a minor role in the temporal evolution, our approach seems to better capture the trends. In China-Japan-Korea region, for instance, the total agricultural input is particularly high under SSP4-3.4 with 30 TgN.yr⁻¹ more than under other SSPs in 2100 (explained mainly by the fertilizer application). Despite this critical difference, the resulting total emissions do not specifically highlight significantly greater emissions than the other SSPs.

Not considering climate change as a driver of ammonia emissions is another limitation of the IAM methodology. Indeed, with CAMEO, we estimated a non-negligible contribution of climate change in the future emissions which reaches 7%–22% by 2100, for SSP2-4.5 and SSP5-8.5, respectively (Table 3). Change in emissions with temperature, precipitation, and CO₂ under SSP5-

8.5 differs significantly from the two other scenarios at the end of the century (Figure S6 in Supporting Information S1) where the meteorological conditions are extreme (temperature, rain, and CO₂ range up to +7.5 K, +0.5 mm/day, and +370 ppm respectively over 2080–2100). The sensitivity of the emissions to these meteorological variables depends on the scenario. For instance, under SSP5-8.5, agricultural emissions are simulated to increase by 3%/K and by 14%/mm/day. As a result, in almost all regions, we observe the biggest changes in the emissions due to climate under SSP5-8.5, especially during the last decades of the century (Figure 6). As expected, the evolution of the total agricultural emissions under climate change is well correlated to the change in soil ammonium content, an important proxy for soil emissions (Figure S6 in Supporting Information S1). Conversely, indoor emissions in CAMEO are only indirectly dependent on the climate mainly through the managed NPP (as feed for livestock), a variable much less sensitive to climate (by 0.22%/K and by 3.6%/mm/day, Figure S6 in Supporting Information S1). The impact of CO₂ change alone on emissions has not been studied here under the different SSPs as it is not possible to disentangle it from the impact of temperature and precipitation changes. However, we can observe a probable temperature threshold effect on NPP under SSP5-8.5 (Figure S6 in Supporting Information S1) indicating that vegetation cannot grow under high temperatures even though CO₂ increases in this scenario.

<figure><img src="image_8.png" /><figcaption>**Figure 6.** Evolution of the global agricultural NH₃ emissions for the considered SSPs from CAMEO under future climate (solid lines) and under a historical climate (dotted lines) in TgN.yr⁻¹. China-K-J refers to China-Korea-Japan.</figcaption></figure>
In Asia, climate change also has a strong positive impact on ammonia emissions under SSP4-3.4 (1–2.5 TgN.yr⁻¹).

### 3.3. Global and Regional Agricultural Emissions in 2100

Figure 7 displays the distributions of the absolute changes in the future emissions (2091–2100) compared to the historical period (2005–2014 here) over the biggest hotspot regions. While China, India, and Europe highlight the most important NH₃ fluxes (> 4 gN.m⁻².yr⁻¹) during the historical period (2014), the most important changes (reaching more than 4 gN.m⁻².yr⁻¹) are located in the Maghreb and South Africa under SSP4-3.4 and over the southeastern US under SSP2-4.5. Northern India and China also highlight important increases under both SSP2-4.5 and SSP4-3.4 scenarios. Despite a global increase under all the SSPs, there are regions where emissions slightly decrease, especially under SSP5-8.5 in India, Argentina, and Equatorial Africa, where negative anomalies can reach 2 gN.m⁻².yr⁻¹. Because of the spatial heterogeneity in the 2091–2100 simulated emissions over Africa and Asia, both regions will be further analyzed.

The evolution of agricultural emissions is contrasted over the African continent with three specific regions: Northern Maghreb, the Sahelian savanna, and Southern Africa. Northern Maghreb is characterized by a substantial increase in the emissions under SSP4-3.4 which can be directly attributed to the large increase in the mineral fertilizer use (+10 gN.m⁻².yr⁻¹, Figure S7 in Supporting Information S1). In addition to the mineral fertilizer, we observe an extension of cropland areas in the coastal region of Maghreb (at the expense of grassland) and also toward the South, where no cropland area is present in the historical period (Figure S9 in Supporting Information S1).

Regarding the Southern African pattern, a similar increase in agricultural emissions is encountered under all the SSPs in 2100. This region is associated with an enhancement of the produced and applied manure where the absolute difference between the historical and future periods can reach 10 gN.m⁻².yr⁻¹ while the present-day manure produced does not exceed 1 gN.m⁻².yr⁻¹. The important enhancement of the NPP of grass in this region (> 0.4 kgC.m⁻².yr⁻¹) suggests that the future ruminant population can be easily maintained and therefore might be the location where the regional livestock increase has been allocated in our methodology (Figure S9 in Supporting Information S1).

In Asia, the change in emissions is also contrasted spatially; India and China differ significantly, especially under the SSP5-8.5. While emissions in Northern India will slow down (−1 gN.m⁻².yr⁻¹), in central China, we observe an increase reaching more than 4 gN.m⁻².yr⁻¹. The evolution of the agricultural emissions under the SSP5-8.5 over India (Figure S8 in Supporting Information S1) can be attributed to the decrease in the N input (both fertilizer and manure). On the contrary, under SSP4-3.4, the fertilizer rate in Asia highly increases in 2100 compared to the historical period, especially in China (≥ 8 gN.m⁻².yr⁻¹). Under the SSP5-8.5 in central China, only manure N input contributes to the enhancement of the emission since almost no change in the use of synthetic fertilizer is observed (Figure S8 in Supporting Information S1). The evolution of the emissions by the IAMs in the context of CMIP6 highlights very different patterns than what is described in CAMEO (Figure S10 in Supporting Information S1). The most important changes (≥ 2 gN.m⁻².yr⁻¹) are concentrated over Africa under SSP4-3.4 and SSP5-8.5.

### 4. Discussion and Conclusions

In our study, we explored future NH₃ emissions using the process-based model CAMEO, while considering future livestock densities. We constructed future gridded livestock densities for three Shared Socioeconomic Pathways (SSPs), accounting for accurate biomass availability and projected regional livestock productions. Our estimates suggest future NH₃ emissions ranging from 50 to 70 TgN.yr⁻¹ by 2100, depending on the scenario considered. Notably, in the SSP2.4-5 scenario, characterized by high fertilizer requirements mainly for biofuels, we observed the highest increase in agricultural NH₃ emissions. Conversely, in the fossil fuel-intensive scenario (SSP5-8.5), the slowest emission trend is explained by slight increases or saturation in fertilizer and livestock manure attributed to local feeding availability, despite high meat consumption. CAMEO's strength against the IAMs approach is its ability to simulate future global emissions in response to future changes in agricultural activities and land use but also climate change. Indeed, CO₂, temperature and precipitation have direct and indirect effects on the NH₃ emissions in CAMEO. These three factors impact the growth of the vegetation which
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Agricultural emissions in the historical period (2005–2014, first column) and absolute differences between future (2091–2100) and historical emissions under the three SSPs (second, third and last columns) simulated by CAMEO under future climate. Units are in gN.m⁻².yr⁻¹.</figcaption></figure>
modifies its capacity in absorbing the nutrient and thus the nitrogen available for volatilization. Temperature and precipitation—which are also directly involved in the physical-chemical reactions at the surface-atmosphere interface -, together with CO2, contribute to 22% to the future volatilization of ammonia in SSP5-8.5 in 2100. By using a process-based model such as CAMEO, we can better understand the trends and spatial variability of future ammonia emissions and simulated emissions offer consistency between all the scenarios. The two major advances of our work are: -The construction of future ammonia emissions responding to climate change and key agricultural drivers (fertilizers, livestock production) which can be exploited for global chemistry transport models to replace the emissions produced by the IAMs. -The projection of future livestock distributions at global scale (and also our intermediate manure production data set) can be used to estimate future impact of agricultural pollution on marine or terrestrial ecosystems or atmospheric chemistry for instance. Many modeling studies are based on livestock densities for the present day to estimate manure production or N and methane emissions (Vira et al., 2019; B. Zhang et al., 2017; L. Zhang et al., 2021). In addition, manure production represents an important nitrogen input for land surface models.

Compared to the IAMs approach, CAMEO offers a more realistic representation of NH3 emissions, but strong assumptions are used and may induce some biases. A limitation in future emissions is reflected by the lack of synthetic N input over grasslands in the CMIP6 framework. In reality, the synthetic fertilization of grassland areas is non-negligible and may play a role in the future, especially with the expected land use changes and the impact on ruminant activities. In CAMEO, grasslands contribute to 30% of the total agricultural emissions in 2100 under the SSP5-8.5, mainly from the manure produced by ruminants whose population is directly regulated by the productivity of their local resource (grass).

In the future, human diet shifts may impact the distributions of the livestock categories (i.e., ruminants, pork, poultry). A recent study from the Food and Agricultural Organisation (FAO) highlighted that global protein availability from beef, pork, poultry, and sheep meat is projected to grow 5.9%, 13.1%, 17.8%, and 15.7% respectively by 2030 with differences from low to high income countries (OECD-FAO Agricultural Outlook, 2021). Specific meat production and consumption trends are driven by socio-economic factors such as population growth, income, market prices, export-import trading, consumer preferences, population aging which are not directly integrated in our approach nor the SSPs. These elements are only incorporated into CAMEO through the livestock type fractions derived from Robinson et al. (2014) for the present-day. Since no precise data is currently available regarding the future evolution of the different animal types (pork, poultry, sheep, beef) by 2100, we assume no change in our future estimates. This assumption leads to a similar constraint applied in the ruminant and non-ruminant populations when the grassland is locally limited, even though non-ruminants mainly rely on crops. In addition to the animal type, CAMEO assumes the share between solid and liquid manure to be constant over time since no data was available. However, agricultural practices including manure handling may change in the future in response to multiple factors such as political regulations (as recently implemented for instance in the Netherlands), temperature increase, extreme weather events etc. These aspects are very important and would constitute an interesting new study focusing on regional impacts. While the SSPs aim to incorporate key socio-economic drivers of emissions like population growth and dietary shifts, certain regional aspects remain overlooked, and there is a need for a consideration of agricultural system adaptation. For example, factors such as aging and rural-urban migration have been identified as potentially leading to larger, more efficient farm-scale systems, due to less human workforce capacity and higher food demand. This phenomenon could significantly reduce food-related nitrogen emissions in China (Deng et al., 2024). The representation of a dynamical soil pH is also another challenging step towards a more realistic representation of future ammonia emissions since N deposition and fertilizer application may impact soil pH. Yet this aspect is poorly implemented in land surface models due to the complexity of the acid-base chemical reactions involved.

In addition to the effects of climate which have been investigated in this study (temperature, precipitation and CO2 changes), extreme weather events may also impact punctually but strongly agricultural ammonia emissions. Extreme precipitation in dry extensive livestock regions as the Sahel may cause an intensification of ammonia volatilization via instantaneous pulses as measured by satellite and ground based instruments (Delon et al., 2017; Hickman et al., 2021). Heat waves, drought and extreme moisture may also directly impact agricultural emissions by provoking the death of livestock herds as witnessed in the past few years in the Mid-US and by damaging crop production which directly impacts feed and food availability (Lesk et al., 2022). Even though we have not considered any animal or crop growth boundaries in our analysis, the impact of general extreme climate patterns on livestock activities have been indirectly integrated through the response of the biomass availability to
increased temperature and precipitation. Studying the impact of extreme weather events on land biogeochemistry cycles requires a suitable modeling framework involving the coupling between a climate model and a land surface model. Moreover, a satisfying spatial resolution and the improvement of subgrid land scale heterogeneities (Chaney et al., 2021) which guarantee a proper resolution of extreme precipitation and impacts (tropical cyclones, flooding events etc) are important. These aspects appear limited in the context of CMIP6, where daily precipitation extremes can vary from one ESM to another as highlighted over the US by Srivastava et al. (2020).

In the effort of better understanding the future effects of agricultural practices on atmospheric chemistry, climate, and subsequent nitrogen deposition on ecosystems, Beaudor et al. (2024) exploited the future ammonia emissions in the atmospheric module of the IPSL ESM (LMDZ-INCA). The proper coupling between the land surface nitrogen cycle and atmospheric chemistry and climate in the IPSL ESM constitutes the next promising level of development and is planned for further investigation.

## Data Availability Statement

The code of ORCHIDEE including the CAMEO module, used in this paper, is the ORCHIDEE-CAMEO version (r7693) and is available at https://doi.org/10.14768/db1cf5ce-6fd2-4b4c-a3d1-598e2283c19d (Beaudor, 2023). The simulated data used for figure plotting in this paper can be accessed from the Zenodo repository: https://zenodo.org/records/10100435 (Beaudor, Vuichard, Lathière, & Hauglustaine, 2023).

## Acknowledgments

We acknowledge the support of the ESM2025 project from the European Union's Horizon 2020 research and innovation program under grant agreement No 101003536. DH also acknowledges the support from the Research Council of Norway under project No. 336227 "AMMONIA: Climate and environmental impacts of green ammonia (NH3)." The simulations were performed using HPC resources from GENCI (Grand Equipement National de Calcul Intensif) under project gen2201. We thank Benjamin Bodirsky for the fruitful discussions about the IIASA products. Finally, we thank Maxwell Pike for taking the time to carefully proofread our manuscript and the anonymous reviewers who contributed to the improvement of the manuscript.

## References

Barona, E., Ramankutty, N., Hyman, G., & Coomes, O. T. (2010). The role of pasture and soybean in deforestation of the Brazilian Amazon. *Environmental Research Letters*, 5(2), 024002. https://doi.org/10.1088/1748-9326/5/2/024002

Beaudor, M. (2023). ORCHIDEE_cameo_gmd_2022. *ESPRI/IPSL*. https://doi.org/10.14768/DB1CF5CE-6FD2-4B4C-A3D1-598E2283C19D

Beaudor, M., Hauglustaine, D., Lathière, J., Van Damme, M., Clarisse, L., & Vuichard, N. (2024). Evaluating present-day and future impacts of agricultural ammonia emissions on atmospheric chemistry and climate (accepted in acp). *EGUsphere*, 2024, 1–40.

Beaudor, M., Vuichard, N., Lathière, J., Evangeliou, N., Van Damme, M., Clarisse, L., & Hauglustaine, D. (2023). Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model. *Geoscientific Model Development*, 16(3), 1053–1081. https://doi.org/10.5194/gmd-16-1053-2023

Beaudor, M., Vuichard, N., Lathière, J., & Hauglustaine, D. (2023). Global ammonia emissions from CAMEO throughout the century for 3 scenarios (2000-2100) [Dataset]. *Zenodo*. https://doi.org/10.5281/zenodo.10100435

Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., et al. (2019). IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP ssp245. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.5264

Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., et al. (2020). Presentation and evaluation of the IPSL-CM6A-LR climate model. *Journal of Advances in Modeling Earth Systems*, 12(7), e2019MS002010. https://doi.org/10.1029/2019MS002010

Calvin, K., Bond-Lamberty, B., Clarke, L., Edmonds, J., Eom, J., Hartin, C., et al. (2017). The SSP4: A world of deepening inequality. *Global Environmental Change*, 42, 284–296. https://doi.org/10.1016/j.gloenvcha.2016.06.010

Chaney, N. W., Torres-Rojas, L., Vergopolan, N., & Fisher, C. K. (2021). HydroBlocks v0.2: Enabling a field-scale two-way coupling between the land surface and river networks in Earth system models. *Geoscientific Model Development*, 14(11), 6813–6832. https://doi.org/10.5194/gmd-14-6813-2021

Collins, W. J., Lamarque, J.-F., Schulz, M., Boucher, O., Eyring, V., Hegglin, M. I., et al. (2017). AerChemMIP: Quantifying the effects of chemistry and aerosols in CMIP6. *Geoscientific Model Development*, 10(2), 585–607. https://doi.org/10.5194/gmd-10-585-2017

Delon, C., Galy-Lacaux, C., Serça, D., Loubet, B., Camara, N., Gardrat, E., et al. (2017). Soil and vegetation-atmosphere exchange of NO, NH₃, and N₂O from field measurements in a semi arid grazed ecosystem in Senegal. *Atmospheric Environment*, 156, 36–51. https://doi.org/10.1016/j.atmosenv.2017.02.024

Deng, O., Wang, S., Ran, J., Huang, S., Zhang, X., Duan, J., et al. (2024). Managing urban development could halve nitrogen pollution in China. *Nature Communications*, 15(1), 401. https://doi.org/10.1038/s41467-023-44685-y

EMEP/EEA. (2019). EMEP/EEA air pollutant emission inventory guidebook 2019 [File]. Retrieved from https://www.eea.europa.eu/publications/emep-eea-guidebook-2019/part-b-sectoral-guidance-chapters/4-agriculture/3-b-manure-management/view

Evangeliou, N., Balkanski, Y., Eckhardt, S., Cozic, A., Damme, V., Coheur, P.-F., et al. (2020). 1 10-year satellite-constrained fluxes of ammonia improve 2 performance of chemistry transport models (Vol. 41).

FAO. (2018). Global livestock environmental assessment model. Version 2. Data reference year: 2010. *Food and Agriculture Organization of the United Nations*, 121.

FAO/IIASA/ISRIC/ISSCAS/JRC, H. (2012). JRC: Harmonized world soil database (version 1.2). FAO.

Fricko, O., Havlik, P., Rogelj, J., Klimont, Z., Gusti, M., Johnson, N., et al. (2017). The marker quantification of the shared socioeconomic pathway 2: A middle-of-the-road scenario for the 21st century. *Global Environmental Change*, 42, 251–267. https://doi.org/10.1016/j.gloenvcha.2016.06.004

Gidden, M. J., Fujimori, S., van den Berg, M., Klein, D., Smith, S. J., van Vuuren, D. P., & Riahi, K. (2018). A methodology and implementation of automated emissions harmonization for use in Integrated Assessment Models. *Environmental Modelling & Software*, 105, 187–200. https://doi.org/10.1016/j.envsoft.2018.04.002

GLOBAL SOIL DATA TASK. (2000). Global gridded surfaces of selected soil characteristics (IGBP-DIS). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/569

Guo, J. H., Liu, X. J., Zhang, Y., Shen, J. L., Han, W. X., Zhang, W. F., et al. (2010). Significant acidification in major Chinese croplands. *Science*, 327(5968), 1008–1010. https://doi.org/10.1126/science.1182570
Harris, I., Jones, P., Osborn, T., & Lister, D. (2014). Updated high-resolution grids of monthly climatic observations—The CRU TS3.10 dataset. *International Journal of Climatology*, **34**(3), 623–642. https://doi.org/10.1002/joc.3711

Hauglustaine, D. A., Balkanski, Y., & Schulz, M. (2014). A global model simulation of present and future nitrate aerosols and their direct radiative forcing of climate. *Atmospheric Chemistry and Physics*, **14**(20), 11031–11063. https://doi.org/10.5194/acp-14-11031-2014

Hegglin, M., Kinnison, D., & Plummer, D. (n.d.). Historical and future ozone database (1850-2100) in support of CMIP6, in preparation. Retrieved from https://blogs.reading.ac.uk/ccmi/forcing-databases-in-support-of-cmip6/

Hickman, J. E., Andela, N., Dammers, E., Clarisse, L., Coheur, P.-F., Van Damme, M., et al. (2021). Changes in biomass burning, wetland extent, or agriculture drive atmospheric NH₃ trends in select African regions. *Atmospheric Chemistry and Physics*, **21**(21), 16277–16291. https://doi.org/10.5194/acp-21-16277-2021

Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., et al. (2018). Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). *Geoscientific Model Development*, **11**(1), 369–408. https://doi.org/10.5194/gmd-11-369-2018

Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., et al. (2020). Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. *Geoscientific Model Development*, **13**(11), 5425–5464. https://doi.org/10.5194/gmd-13-5425-2020

Janssens-Maenhout, G. (2011). *EDGARv4.2 emission maps*. Joint Research Centre (JRC). Retrieved from http://data.europa.eu/89h/jrc-edgar-emissionmapsv42

Johansson, O., Palmqvist, K., & Olofsson, J. (2012). Nitrogen deposition drives lichen community changes through differential species responses. *Global Change Biology*, **18**(8), 2626–2635. https://doi.org/10.1111/j.1365-2486.2012.02723.x

Kriegler, E., Bauer, N., Popp, A., Humpenöder, F., Leimbach, M., Strefler, J., et al. (2017). Fossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century. *Global Environmental Change*, **42**, 297–315. https://doi.org/10.1016/j.gloenvcha.2016.05.015

Krinner, G., Viovy, N., Noblet-Ducoudré, N. d., Ogée, J., Polcher, J., Friedlingstein, P., et al. (2005). A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. *Global Biogeochemical Cycles*, **19**(1). https://doi.org/10.1029/2003GB002199

Lesk, C., Anderson, W., Rigden, A., Coast, O., Jägermeyr, J., McDermid, S., et al. (2022). Compound heat and moisture extreme impacts on global crop yields under climate change. *Nature Reviews Earth & Environment*, **3**(12), 872–889. https://doi.org/10.1038/s43017-022-00368-8

Li, C., Frolking, S., & Frolking, T. A. (1992). A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity. *Journal of Geophysical Research*, **97**(D9), 9759–9776. https://doi.org/10.1029/92JD00509

Li, C. S. (2000). Modeling trace gas emissions from agricultural ecosystems. In R. Wassmann, R. S. Lantin, & H.-U. Neue (Eds.), *Methane emissions from major rice ecosystems in Asia* (pp. 259–276). Springer Netherlands. https://doi.org/10.1007/978-94-010-0898-3_20

Liu, L., Xu, W., Wen, Z., Liu, P., Xu, H., Liu, S., et al. (2023). Modeling global oceanic nitrogen deposition from food systems and its mitigation potential by reducing overuse of fertilizers. *Proceedings of the National Academy of Sciences of the United States of America*, **120**(17), e2221459120. https://doi.org/10.1073/pnas.2221459120

Lu, C., & Tian, H. (2017). Global nitrogen and phosphorus fertilizer use for agriculture production in the past half century: Shifted hot spots and nutrient imbalance. *Earth System Science Data*, **9**(1), 181–192. https://doi.org/10.5194/essd-9-181-2017

Lurton, T., Balkanski, Y., Bastrikov, V., Bekki, S., Bopp, L., Braconnot, P., et al. (2020). Implementation of the CMIP6 forcing data in the IPSL-CM6A-LR model. *Journal of Advances in Modeling Earth Systems*, **12**(4), e2019MS001940. https://doi.org/10.1029/2019MS001940

OECD-FAO Agricultural Outlook. (2021). OECD-FAO agricultural outlook 2021–2030.

O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., et al. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. *Geoscientific Model Development*, **9**(9), 3461–3482. https://doi.org/10.5194/gmd-9-3461-2016

Pai, S. J., Heald, C. L., & Murphy, J. G. (2021). Exploring the global importance of atmospheric ammonia oxidation. *ACS Earth and Space Chemistry*, **5**(7), 1674–1685. https://doi.org/10.1021/acsearthspacechem.1c00021

Paulot, F., Ginoux, P., Cooke, W. F., Donner, L. J., Fan, S., Lin, M.-Y., et al. (2016). Sensitivity of nitrate aerosols to ammonia emissions and to nitrate chemistry: Implications for present and future nitrate optical depth. *Atmospheric Chemistry and Physics*, **16**(3), 1459–1477. https://doi.org/10.5194/acp-16-1459-2016

Paustian, K., ravindranath, n., & Van Amstel, A. (2006). IPCC guidelines for national greenhouse gas inventories. Volume 4 agriculture, forestry and other land use.

Popp, A., Calvin, K., Fujimori, S., Havlik, P., Humpenöder, F., Stehfest, E., et al. (2017). Land-use futures in the shared socio-economic pathways. *Global Environmental Change*, **42**, 331–345. https://doi.org/10.1016/j.gloenvcha.2016.10.002

Rao, S., Klimont, Z., Smith, S. J., Van Dingenen, R., Dentener, F., Bouwman, L., et al. (2017). Future air pollution in the shared socio-economic pathways. *Global Environmental Change*, **42**, 346–358. https://doi.org/10.1016/j.gloenvcha.2016.05.012

Reid, R., Galvin, K., & Kruska, R. (2008). Global significance of extensive grazing lands and pastoral societies: An introduction. In *Fragmentation in semi-arid and arid landscapes: Consequences for human and natural systems* (pp. 1–24). https://doi.org/10.1007/978-1-4020-4906-4_1

Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C., Fujimori, S., et al. (2017). The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: An overview. *Global Environmental Change*, **42**, 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009

Robinson, T. P., Wint, G. R. W., Conchedda, G., Van Boeckel, T. P., Ercoli, V., Palamara, E., et al. (2014). Mapping the global distribution of livestock. *PLoS One*, **9**(5), e96084. https://doi.org/10.1371/journal.pone.0096084

Sommer, S. G., Webb, J., & Hutchings, N. D. (2019). New emission factors for calculation of ammonia volatilization from European livestock manure management systems. *Frontiers in Sustainable Food Systems, 3*, 101. https://doi.org/10.3389/fsufs.2019.00101

Srivastava, A., Grotjahn, R., & Ullrich, P. A. (2020). Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions. *Weather and Climate Extremes*, **29**, 100268. https://doi.org/10.1016/j.wace.2020.100268

van Marle, M. J. E., Kloster, S., Magi, B. I., Marlon, J. R., Daniau, A.-L., Field, R. D., et al. (2017). Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750–2015). *Geoscientific Model Development*, **10**(9), 3329–3357. https://doi.org/10.5194/gmd-10-3329-2017

Vira, J., Hess, P., Melkonian, J., & Wieder, W. R. (2019). An improved mechanistic model for ammonia volatilization in Earth system models: Flow of Agricultural Nitrogen, version 2 (FANv2). *Geoscientific Model Development Discussions*, 1–49. https://doi.org/10.5194/gmd-2019-233

Vuichard, N., Messina, P., Luyssaert, S., Guenet, B., Zaehle, S., Ghattas, J., et al. (2019). Accounting for carbon and nitrogen interactions in the global terrestrial ecosystem model ORCHIDEE (trunk version, rev 4999): Multi-scale evaluation of gross primary production. *Geoscientific Model Development*, **12**(11), 4751–4779. https://doi.org/10.5194/gmd-12-4751-2019
Yang, Y., Ji, C., Ma, W., Wang, S., Wang, S., Han, W., et al. (2012). Significant soil acidification across northern China's grasslands during 1980s–2000s. *Global Change Biology*, **18**(7), 2292–2300. https://doi.org/10.1111/j.1365-2486.2012.02694.x

Zaehle, S., Friend, A. D., Friedlingstein, P., Dentener, F., Peylin, P., & Schulz, M. (2010). Carbon and nitrogen cycle dynamics in the O-CN land surface model: 2. Role of the nitrogen cycle in the historical terrestrial carbon balance. *Global Biogeochemical Cycles*, **24**(1). https://doi.org/10.1029/2009GB003522

Zhang, B., Tian, H., Lu, C., Dangal, S. R. S., Yang, J., & Pan, S. (2017). Global manure nitrogen production and application in cropland during 1860–2014: A 5 arcmin gridded global dataset for Earth system modeling. *Earth System Science Data*, **9**(2), 667–678. https://doi.org/10.5194/essd-9-667-2017

Zhang, L., Tian, H., Shi, H., Pan, S., Qin, X., Pan, N., & Dangal, S. R. (2021). Methane emissions from livestock in East Asia during 1961–2019. *Ecosystem Health and Sustainability*, **7**(1), 1918024. https://doi.org/10.1080/20964129.2021.1918024

Zhang, Y., Li, C., Zhou, X., & Moore, B. (2002). A simulation model linking crop growth and soil biogeochemistry for sustainable agriculture. *Ecological Modelling*, **151**(1), 75–108. https://doi.org/10.1016/S0304-3800(01)00527-0

Zobler, L. (1988). A world soil hydrology file for global climate modelling. In *International Geographic Information Systems (IGIS) symposium: Overview of research needs and the research agenda* (Vol. 1, p. 12).