The global livestock sector emitted 65 TgN yr−1 in 2010, accounting for one-third of total anthropogenic reactive nitrogen (Nr) emissions1. The whole livestock production chain generated approximately 15% of global anthropogenic greenhouse gas (GHG) emissions, with ruminants and monogastric livestock contributing 5.7 and 1.4 PgCO2e GHG emissions per annum, respectively2. Livestock feed production uses approximately two-thirds of global total cropland area3 and increases in livestock-derived protein demand could accelerate food–feed competition4.

Feed use efficiency is lower in ruminants than in monogastric livestock1, leading to relatively higher Nr and GHG emissions per unit of protein production for ruminants. Reducing ruminant product consumption can help limit the environmental impacts of meat production5, yet fulfilling animal protein requirements from monogastric livestock, especially poultry, comes with trade-offs. Grain accounts for ~95% of the feed in intensive poultry farms, and poultry consume comparatively more human-edible grains than ruminants6. In contrast, about 60% of ruminant feed is human-inedible cellulose, for example, grass, crop residues and leaves7. Thus, ruminants can contribute to maximizing the usage of otherwise unusable plant biomass, benefiting food security and reducing the environmental impact of farming8,9.

We argue that maximizing cellulose use as livestock feed may reduce pressure on grain feed production, which is associated with high environmental costs and food security risks. Here we perform a counterfactual analysis, replacing ruminants with monogastric livestock, to quantify changes in Nr and GHG emissions in 166 countries, taking a full life cycle perspective. We then calculated the efficiency aspects of producing equivalent amounts of protein and the resulting Nr and GHG emissions from ruminants and monogastric livestock across different countries, considering local constraints. Based on these global analyses, we developed an optimized livestock protein production scenario by maximizing the proportion of ruminants to reduce Nr and GHG emissions, thus increasing the availability of croplands for grain-based human food production. Managing ruminants for low-emission food production could safeguard food security, reduce environmental impacts and mitigate climate change.


Cropland area and total emissions

Ruminants mainly feed on human-inedible cellulose (Extended Data Fig. 1), although they have larger feed requirements (14.8 kgNfeed per kgNruminant) than monogastric livestock (6.3 kgNfeed per kgNmonogastric). Monogastric livestock production requires around four times more cropland than ruminant production to produce an equivalent per unit protein (Fig. 1a,c and Extended Data Figs. 2 and 3) (8.0 ha per t protein for monogastric livestock versus 1.9 ha per t protein for ruminants). Global ruminants produced approximately 7 Tg protein-N in 2019. Producing the same level of proteins solely by monogastric livestock would result in 15% (7 Tg) more nitrogen losses to the environment over the whole production chain. Ammonia (NH3) emissions to the air and nitrate (NO3) released to water bodies would increase under such a scenario by 13% (3 Tg) and 18% (5 Tg), respectively, while N2O and NOx emissions would decrease by 14% (−0.3 Tg) and 17% (−0.04 Tg), respectively (Fig. 2a,b). The substantive increases are associated with grain feed production for monogastric livestock, which requires more cropland area and synthetic fertilizer input.

Fig. 1: Cropland area for producing feed required by per unit livestock protein and feed ratio for ruminant and monogastric livestock.
figure 1

a, Cropland area required to produce per unit of ruminant protein. b, Feed ratio of ruminant livestock; this value represents the percentage of nitrogen content. Ruminant feed comprises grass (61%), crop residues (23%) and crop products (16%). c, Cropland area required to produce per unit of monogastric protein. d, Feed ratio of monogastric livestock. ‘Others’ represents synthetic amino acids, fishmeal and limestone. Monogastric livestock mainly feed on crop products (81%), followed by swill (9%) and other feed (9%). The base map was applied without endorsement using data from the Database of Global Administrative Areas (GADM;

Source data

Fig. 2: Nitrogen budget and carbon emissions in global ruminants and monogastric livestock systems with the same amount of protein produced.
figure 2

a, Global nitrogen flows in monogastric livestock. ‘Manure for other’ represents the manure applied to croplands for human nutrition or crop feed for other animals. b, Global nitrogen flows in ruminant livestock. ‘Manure for other’ here comprises two parts: (1) manure deposited on other grassland (12 Tg); (2) manure applied to croplands for human nutrition or feed for other animals (37 Tg). c, Global carbon emissions from monogastric livestock (comprising emissions of CO2 and CH4). d, Global carbon emissions from ruminant livestock. The dark green flow represents green inputs, such as recycling of straw, returning manure to the field, natural nitrogen fixation and utilization of swill, etc. All numbers are in Tg yr−1.

Monogastric livestock has a higher nitrogen use efficiency (NUE) at the animal-raising stage (16%) compared with ruminants (7%). However, the NUE of feed production for monogastric livestock (33%) is much lower than that of ruminants (69%), leading to the overall NUE of the livestock production chain being fairly similar for both monogastric livestock and ruminants, at around 6% globally. Furthermore, at the livestock raising stage of ruminants, more manure nitrogen is produced due to low NUE, which could be used for crop ration production for humans. More recycling opportunities and natural-based processes were available in the ruminant production chain (Fig. 2), such as straw and manure recycling and natural biological nitrogen fixation in grasslands, illustrating that ruminants can recycle more nutrients into food production, reducing overall Nr emissions.

Emissions would be higher by about 3 PgCO2e if ruminants replaced monogastric livestock at global levels (Fig. 2c,d). The increase in croplands needed for grain feed production (270 Mha) was modelled to be converted from forest, leading to an increase of 5.4 PgCO2e emission from land use change, field operations, processing and fertilizer manufacture. In contrast, about 2.5 PgCO2e emission reduction would result from avoided CH4 enteric fermentation and manure management.

Livestock species

The Nr emission intensity of ruminants (1.07 kgN per kg protein) was calculated to be lower than that of monogastric livestock (1.24 kgN per kg protein) across the whole production chain globally. Feed production accounts for 81% and 78% of total emissions in monogastric livestock and ruminants, respectively (Fig. 3a). Replacing ruminants with monogastric livestock would increase Nr emission from feed production by about one-third. Among all livestock, backyard chickens had the highest feed Nr emission intensity due to their lower feed conversion ratio (1/4−1/2 that of industrial chickens)4, followed by industrial pigs. In contrast, non-dairy small ruminants (sheep and goats) had the lowest feed Nr emission intensity since their feed typically contains a large ratio of crop residues (10–40%) and only a small ratio of grain (0–7%)6. There was no notable difference in Nr emission intensity of livestock raising between ruminants (0.23 kgN per kg protein) and monogastric livestock (0.24 kgN per kg protein) globally.

Fig. 3: Global changes in Nr and GHG emissions when replacing ruminants with monogastric livestock by region and by livestock systems.
figure 3

a, Nr emission intensity (includes NH3-N, NOx-N, NO3-N) of all livestock systems. Green (grass feed) and yellow (crop feed) represent feed Nr emissions, and blue represents Nr emissions from livestock breeding. b, GHG emission intensity (including CO2, CH4, N2O) of all livestock systems. The red bars represent the feed carbon emissions caused by land use changes, the yellow bars are the total carbon emissions from other feed production processes such as field operations, processing and blending, and the blue bars represent GHG emissions from livestock breeding. The red and black lines represent the average unit emission for ruminants and monogastric livestock, respectively. c, Total changes in Nr emissions from replacing monogastric with ruminant livestock, including NH3-N, NOx-N, NO3-N from livestock supply chains. d, Proportion of changed Nr emissions calculated by dividing total changed supply chains from a by ruminant Nr emissions. e, Total changed GHG emissions (including CO2, CH4, N2O) from replacing ruminants with monogastric livestock. f, Proportion of changed GHG emissions from replacing ruminants with monogastric livestock, calculated by dividing total changed GHG emissions from c by ruminant GHG emissions from feed production and livestock raising. The base map was applied without endorsement using data from the Database of Global Administrative Areas (GADM;

Source data

The GHG emission intensity of ruminants (93 kgCO2e per kg protein) is much lower than that of monogastric livestock (159 kgCO2e per kg protein), but variations tend to be substantial at the feed-production and livestock-raising stages (Fig. 3b). The feed GHG emission intensity of monogastric livestock systems was calculated to be far higher than for ruminants due to substantial emissions from land use change. The feed GHG emission intensity from processing, transport, blending, field operation and fertilizer manufacture was also higher for monogastric livestock (36 kgCO2e per kg protein) than for ruminants (26 kgCO2e per kg protein). The CH4 emission intensity of dairy production is lower than that of meat production, due to higher protein production efficiency and a higher share of digestible diet content. For instance, dairy cattle CH4 emission intensity (22 kgCO2e per kg protein) is much lower than that of beef cattle (173 kgCO2e per kg protein). Chickens had the lowest livestock raising GHG emission intensity due to the lack of enteric fermentation and overall CH4 emissions from manure.

National variations

Nitrogen and GHG emission intensity vary notably between ruminants and monogastric livestock and across countries due to differences in technology, facilities in livestock farming, farmers’ knowledge and practices and climatic conditions (Extended Data Figs. 4 and 5). Moreover, there are considerable differences in the change ratio of nitrogen and GHG emissions when replacing ruminants with monogastric livestock (Extended Data Fig. 6).

Most countries and regions showed increased Nr emissions when replacing ruminants with monogastric livestock, predominantly those in East and South Asia, Africa, Oceania and North America due to relatively low NUE in grain feedstock production for monogastric livestock (Fig. 3c,d). India was the country that showed the largest increase in Nr emissions, estimated at 2.5 Tg (+44%). On a continental scale, Africa has the largest increase ratio (+66%, 2.2 Tg), followed by South Asia (+50%, 4.3 Tg). However, in most South American and European countries, Nr emissions of monogastric livestock were lower than emissions of ruminants, especially in Brazil, which showed a reduction of about 2.7 TgN (−57%) if replacing ruminants with monogastric livestock. This is because Brazil’s grain feed production has a comparatively high NUE (69%) because it is based on soybean production with low rates of synthetic nitrogen fertilizer application resulting in an overall low Nr emission rate.

Except for most countries in South America and several countries in Africa, all remaining countries showed increased GHG emissions if no ruminant livestock production was undertaken due to substantial demand for grain feed and the resulting land use conversion of forest to croplands (Fig. 3e,f). India, Pakistan and China showed the most substantial increase in GHG emissions with 524 Tg (+99%), 289 Tg (+155%) and 273 Tg (+102%) CO2e, respectively. Oceania (+206%, 329 Tg) had the highest increase ratio due to low enteric CH4 emissions of ruminants and high land use change emissions. In contrast, in central and eastern South America, such as Brazil, Argentina, Paraguay and Uruguay, GHG emissions decreased by 255 Tg (−45%), 66 Tg (−60%), 16 Tg (−61%) and 13 Tg (−45%) CO2e, respectively. High crop yield decreased the total cropland area required for monogastric livestock feed and high gross energy intake by ruminants induced high enteric CH4 emission in these countries.

Optimized scenario setting and analysis

Four scenarios were designed to mitigate livestock Nr and GHG emissions through optimizing livestock production systems: a BAU scenario (business as usual) and three mitigation scenarios (SYS, FED, ALL) (Fig. 4), with related environmental welfare and food security benefits (Fig. 5). The SYS scenario assumed maximizing ruminant production with consideration of grassland carrying capacity and maximum available straw feed for countries where ruminant Nr emissions are lower than Nr emission from monogastric livestock (Methods). The SYS2 scenario (an extreme variant of the SYS scenario) aims at maximizing ruminant production based on the current maximum total cellulose production potential, assuming the straw is not returned to the field and ruminants make maximum use of straws. The FED scenario was set to optimize feed and manure management in line with a global average emission intensity (decreasing the higher level to global average emission intensity while maintaining the original lower emission intensity). Finally, the ALL scenario integrated the SYS and FED scenarios.

Fig. 4: Optimizing livestock system to reduce Nr and GHG emissions under different scenarios.
figure 4

a, Total Nr emissions from ruminants and monogastric livestock under various scenarios. The ALL scenario represents a combination of the SYS and the FED scenario and is not related to SYS2. b, Total GHG emissions from ruminants and monogastric livestock. c, Total Nr emission (NH3-N, NOx-N and NO3-N) reduction ratio. d, GHG emission (including CO2, CH4, N2O) reduction ratio under SYS scenario compared to BAU. White areas depict no change. e, Nr emission reduction ratio. f, GHG emission reduction ratio under the FED scenario compared to BAU. g, Nr emission reduction ratio. h, GHG emission reduction ratio under the ALL scenario compared to BAU. The base map was applied without endorsement using data from the Database of Global Administrative Areas (GADM;

Source data

Fig. 5: Costs and benefits under assumed scenarios.
figure 5

The benefits of reducing Nr and GHG emissions are superimposed in this figure. The benefits arising from GHG emission reduction are included in the climate impact.

Source data

Under the SYS scenario, we found that increasing ruminant production by 24% (Extended Data Fig. 7) could maximize the use of cellulose and reduce by 2.3 TgN (3%) losses to the environment (Fig. 4c), reducing feed grain demand by 2.9 TgN (Extended Data Fig. 8a) compared with the BAU scenario globally. As a consequence, 39 Mha of cropland could be released, with the potential to feed up to 525 million people (Extended Data Fig. 8c,e) worldwide or sequestrate 862 TgCO2e emission through reforestation. In total, 495 TgCO2e emissions per year could be reduced under the SYS scenario. Surprisingly, 40% more ruminants would be produced under SYS2, and there would be 4 TgN emissions reduced and 73 Mha of cropland released, which means 940 million more people could be fed (Extended Data Fig. 8d,f). However, the amount of carbon sequestration resulting from reforestation is estimated to be larger than the total GHG reduction in the SYS scenario, that is, there is a trade-off between reducing emissions and supporting more people. This mainly occurs because the increased ruminant production would generate large amounts of CH4 emission, and thus the simultaneous implementation of targeted abatement measures is also essential.

Nr emissions from feed production and livestock raising are projected to be decreased by 20 Tg and 3 Tg under the FED scenario, respectively (Fig. 4e), as a consequence of reducing livestock emissions to the global average level. About 10.5 TgN emitted from fertilizer application and 9.8 TgN from recycled manure would be reduced, and GHG emissions could be reduced by 709 Tg through improved feed management and by 936 Tg through livestock raising. This indicates that feed and manure management still have substantial potential for optimization globally, especially in countries with current levels of Nr and GHG emissions above global average levels. Oceania shows the largest Nr emission reduction (47%) because of feed production optimization. The largest decrease in GHG emissions (40%) is found in sub-Saharan Africa due to emission reduction of CH4 stemming from enteric fermentation (Extended Data Fig. 9).

The ALL scenario was designed to combine elements of system optimization and feeding improvement, providing insight into the potential of response to emerging food security pressures, for example, as a result of the COVID-19 pandemic. This scenario could maximize the use of cellulose and save croplands for grain production for direct human consumption, while it could also reduce emissions of Nr and GHG through improving feeding strategies and manure management. Through the integration of SYS and FED scenarios, the benefits of both scenarios could be amplified and thus contribute to the increasingly efficient management of livestock production globally. The ALL scenario has a mitigation potential of 25 TgN, and results in an 87% Nr emission reduction in feed production, achieving benefits for food security and environmental protection at the same time. Furthermore, global GHG emissions would be reduced by 2.2 PgCO2e a year if the cropland saved was all reforested. Approximately 1.3 PgCO2e of GHG emissions would be reduced if the cropland was not returned to forest land use. However, several countries might experience increased GHG emissions without reforestation and a trade-off between emission reduction and feeding more people may occur (Supplementary Fig. 1). Alternatively, the cropland saved in these countries could be partly reforested and partly utilized to grow food to achieve a win–win situation for emission reduction and food security.

Cost–benefit analysis of optimizing livestock production

A cost–benefit analysis was undertaken to assess the feasibility of scenario implementation. Reduction of Nr and GHG emissions has the potential to benefit human health through less exposure to air pollution such as fine particulate matter (PM2.5), as well as improve ecosystem health, while mitigating climate change. However, it is important to consider socioeconomic impacts, for example, exemplified through implementation costs.

Ruminants have high production costs due to the long feeding period and continuous feed inputs required, resulting in a low benefit-to-cost ratio under the SYS scenario (2.4) (Fig. 5). However, the cropland saved under this scenario could contribute to feeding 525 million more humans and thus help to achieve the global zero hunger goal. At the same time, GHG emissions of ruminants could be reduced through the feed production and manure management optimization as illustrated by the FED scenario. Overall lower costs could generate large huge environmental benefits (US$443 billion) from an optimized application of synthetic fertilizers and manures from croplands and grasslands (US$6 billion), and improvements in livestock farming and manure management (US$5 billion). The combination of the SYS and FED scenarios could achieve the largest environmental benefits (US$468 billion) and food security benefits as documented in the ALL scenario, with a benefit-to-cost ratio of 13.5, mainly through optimizing the relative shares of livestock types across the whole system and improving production efficiency.


Taking a full life cycle perspective and feed production into consideration, we found that ruminants have comparatively lower Nr and GHG emissions than monogastric livestock. Emphasizing high Nr and GHG emissions from ruminants during the raising stage without a full life cycle analysis has misled policy recommendations on optimal livestock development. Land use change and synthetic nitrogen fertilizer use for grain feed production for monogastric livestock result in higher relative Nr and GHG emissions. Ruminants are able to convert human-inedible cellulose into high-quality protein, thus saving energy and nutrient otherwise required for feed production4,10—with benefits for human food security, environment and climate8. Therefore, shifting the balance in livestock production from ruminants to monogastric livestock requires more grain feed with substantial land use change implications and conversion from forest to croplands. This would lead to exacerbating biodiversity loss and threatening valuable carbon sinks, thus having clear negative impacts on a global strategy for achieving net zero carbon. Moreover, ruminants would deposit most manure on grasslands, which are directly used as nutrient for forage production6. Therefore, despite the low NUE of meat production, ruminants on aggregate only contribute to a small extent to Nr pollution to the environment, since the manure is recycled in grasslands. However, feedlots still have a substantial reduction potential with regard to Nr pollution from ruminants.

We determined the grassland carrying capacity using grassland cover share data to adjust the reasonable utilization of grassland due to the lack of country-specific data on grassland degradation (Methods). The calculation of implementation costs under the SYS scenario should utilize the production costs of all livestock in each country, but we used producer price data derived from FAOSTAT, which may be slightly higher than the actual implementation costs. Also, the 12% increase in ruminant production under the SYS scenario may potentially reduce and increase the production price of ruminants and monogastric livestock, respectively, but this is beyond the scope of this study and thus not considered here due to the complexity of the economic principles involved. The calculation of the potential for feeding more people would ideally have been based on the per-capita protein demand, but detailed data for all countries are lacking, so we utilized well-documented per-capita protein supply data in 2019 provided by FAOSTAT instead. Moreover, the cost of reducing Nr emissions was quantified in detail, while the implementation of Nr reduction (NH3, NOx, NO3 and N2O) measures will have co-benefits due to associated carbon emissions reductions (CH4 and CO2). However, these co-benefits are complex to assess at a global scale and are outside the scope of this study.

The increase in ruminant livestock production could be accompanied by a change in primary crop types to increase the overall efficiency. Soybean production has a comparatively low yield and generates only a small amount of straw compared with maize production, although soybean has a higher NUE than maize11. Therefore, switching soybean for whole corn silage may benefit ruminant production while increasing the yield and reducing pollution. Nevertheless, more quantitative studies are still needed to assess the impacts of such a change at the farm-gate level. Moreover, to optimize the shares of ruminants and monogastric livestock at the local scale to maximize the use of grain and straw, it is important to spatially integrate croplands with animals for both feed use and manure recycling.

Without a fundamental change in existing production methods, about 24% more ruminants could be reared using current grass and straw production levels. However, improving production practices across cropland, grassland and livestock systems is an essential step, provided that production efficiency is improved during the raising stage. Compared to the SYS scenario, feed and manure optimization in the FED scenario have greater potential to reduce Nr and GHG emissions during animal food production. The specific emission reduction measures for cropland and grassland areas include enhancing nitrogen fertilizer application techniques12,13 and fallow cultivation14. Emission reduction options for livestock raising focus on livestock dietary15 and manure management16. Compared to system optimization, feeding practice improvement has lower costs and comparatively larger environmental benefits. It is an easy-to-implement approach to reduce emissions quickly to achieve mitigation targets.

The long breeding cycle induced a high production cost for ruminants, leading to a high price of red meat and a low consumption level on the household level. Hence, it is important to reduce the production cost to make it feasible to increase the proportion of ruminants globally. Governments should properly guide the breeding of cattle and sheep: (1) increase cattle and sheep breeding subsidies, such as risk subsidies, and subsidies for related breeding companies to reduce breeding costs17; (2) pay attention to the cultivation of new varieties of animals to improve feed use efficiency and reduce manure production; and (3) guide the coupling of crop planting and animal raising, especially in regions dominated by small-scale farming, to reduce transportation costs for feed and manure18.

Human meat consumption is the fundamental driver of livestock production19. We have shown that reducing ruminant production while increasing monogastric livestock production at the current stage would lead to adverse effects on food security and environmental health, while human-inedible cellulose would go unused for food production through conversion via ruminants. However, overconsumption of red meat is associated with several chronic diseases, obesity and premature death20. Despite this association being short of strong evidence21, we do not simply advocate eating more meat such as beef and mutton, but provide supporting evidence for achieving a balanced healthy diet, shifting towards ruminant protein in the current structure of meat protein demand and not exceeding the upper limit of recommendation for red meat consumption. Balancing dietary structure and relocating an increased production of ruminant products to countries where needed, especially in African countries, could help to achieve Sustainable Development Goals as a whole (such as zero hunger, good health and well-being). We advocate for an integrated assessment across the whole life cycle and all human and environmental determinants when developing recommendations for the whole production chain efficiency and the cellulose amount that we would use for ruminants. Related policy development should account for a more balanced dietary structure, including both ruminant and monogastric livestock products, based on a whole-system cost–benefit assessment.

Although replacing ruminants with monogastric livestock would release grasslands, natural and seminatural grassland (occupying most of the total grassland area) cannot be converted into croplands or forests everywhere due to climatic22, soil fertility and topographical factors23. Grasslands are more stable carbon sinks than forests because of their inherent resilience to drought and wildfire24. Changes in albedo caused by afforestation may outweigh the benefits of carbon capture, resulting in a net warming effect25. Moreover, healthy grasslands can store an amount of organic carbon comparable to that of forests, mainly due to their rich underground carbon sinks24. Grasslands are also more conducive than forests to alleviating soil erosion and adding water conservation in semi-arid ecosystems26, and forming habitats for a range of wildlife species27. Only a small portion of artificial grasslands may be suitable for conversion, but a lack of detailed information available about the area of convertible artificial grasslands, potential changes in productivity after reclamation into croplands and the degree of carbon sequestration after afforestation make it difficult to accurately project the potential benefits of grassland conversion. The conversion of artificial grassland to cropland would release a large amount of GHG emissions28,29 and grassland afforestation is complex, and therefore it is unclear whether carbon sequestration would decrease30,31 or increase32. Therefore, this study may only slightly underestimate the potential food security and climate benefits of monogastric livestock. Furthermore, the main purpose of this study is not to remove all ruminants, but to provide an extreme case to demonstrate the importance of ruminants in the context of maximum utilization of cellulose resources. Our aim is to provide guidance for policymakers—not to criticize or even eliminate ruminants—and highlight that ruminants can utilize human-inedible cellulose, freeing up large areas of cropland for conversion into forest or for human food production.


Data sources

FAOSTAT (Crop and Livestock Products, provides numbers (Producing Animals/Slaughtered), production (Production Quantity) and slaughtered weight yield (Yield) of each livestock3. We used the protein content of livestock products from Global Livestock Environment Assessment Model (GLEAM)6. Human protein demand for all products was from FAOSTAT (Food Balances)3. Proportion information for specific livestock systems, such as pig production systems containing backyard, intermediate and industrial systems, was obtained from GLW (Gridded Livestock of the World,

The yield and production quantity of feed crops were derived from FAOSTAT data3. For grass yield, we acquired the data from the literature33. The nitrogen content of each crop and grass was from GLEAM6. We estimated synthetic fertilizer consumption of each crop and grass from FAO3 (, IFA34 and the literature35. Nitrogen deposition rates on cropland and grassland were derived from the literature36,37,38. The irrigation nitrogen rates were obtained from Lesschen et al.39 and the irrigation water use data was from AQUASTAT ( For the natural biological fixation from grass and crop biological nitrogen fixation, we used data from Lassaletta et al.35 and Zhang et al.37. Global land cover data and land use data were from the GLC-SHARE database ( and FAOSTAT data3.

Counterfactual analysis

We compare the environmental impacts between ruminants and monogastric livestock by estimating and comparing the Nr and GHG emissions from current ruminants and monogastric livestock replaced by the ruminants according to the standard of protein equality. Using this approach, we can analyse the huge land use change risks for growing feed crops and evaluate the contribution of ruminants and monogastric livestock to humans from a new perspective. The allocation of pigs and chickens among monogastric livestock is based on the human protein requirements of pigs, chicken and eggs (from FAOSTAT, Protein Supply Quantity)3.

Nitrogen loss from livestock supply chains

Feed production emissions

To estimate Nr emissions from each livestock supply chain, we mainly used GLEAM. A detailed description of the GLEAM model can be found in the literature6. For feed production, we first estimated the dry matter feed by food conversion ratio (FCR)4 and crop nitrogen content to calculate grass (Ngrass feed), crop (Ncrop feed) and crop residues (Ncrop residue feed) feed nitrogen, respectively, following the feed ration percentage from the GLEAM model6, and then Nr emissions from cropland and grassland were calculated.

We calculated cropland Nr emissions from synthetic fertilizer, recycled manure and decomposed crop residues. First, we used the CHANS model40 to calculate all cropland NUE (NEUcrop, without differentiating between feed and ration cultivation) across global countries (equation (1)). Then, we used the NUEcrop to calculate the nitrogen input of crop feed, especially fertilizer and manure nitrogen input which were used to estimate nitrogen loss. We calculated the amount of nitrogen from decomposed crop residues returned to the field following equations in the GLEAM model, and used its removed fraction of above-ground residues of the cropland to estimate the crop residue feed quantity (Ncrop residue feed). The available manure nitrogen recycled on cropland from each livestock system was estimated following Uwizeye et al.1 Finally, the Nr emissions from synthetic fertilizer, manure and crop residues were calculated following equations in the GLEAM model.

$${{NUE}}_{\rm{crop}} = \frac{{N_{{\rm{crop}}\;{\rm{products}}}}}{{N_{\rm{CBNF}} + N_{\rm{fertilizer}} + N_{\rm{manure}} + N_{\rm{irrigation}} + N_{\rm{deposition}}}}$$

where the nitrogen inputs of cropland consist of crop biological nitrogen fixation (NCBNF), fertilizer nitrogen (Nfertilizer), manure nitrogen (Nmanure), irrigation nitrogen (Nirrigation) and deposition nitrogen (Ndeposition), and the nitrogen outputs of cropland are crop products (Ncrop products).

We calculated grassland Nr emissions from synthetic fertilizer and manure deposited on pastures. The manure deposited on pastures from all livestock was calculated based on Uwizeye et al.1 The nitrogen loss from grassland was calculated in the GLEAM model.

Livestock raising emissions

The Nr emissions from manure management systems were estimated as livestock raising emissions6. We calculated nitrogen excretion following IPCC methods41,42 and the total ammoniacal nitrogen (TAN) from Uwizeye et al.1, and used the fraction of manure management system (MSS) and EF (emission factor) to calculate Nr emissions at the livestock raising stage.

Nitrogen balance

Feed production stage

The nitrogen inputs of cropland consist of crop biological nitrogen fixation (NCBNF), fertilizer nitrogen (Nfertilizer), manure nitrogen (Nmanure), irrigation nitrogen (Nirrigation) and deposition nitrogen (Ndeposition), and nitrogen outputs of cropland are crop (Ncrop feed) and crop residues (Ncrop residue feed) as feed, other crop residues (Nother crop residue, not as feed for this livestock system, as feed for other livestock or for other uses) and Nr emissions (Nemission, including NH3-N, NOx-N, NO3-N, N2O-N). NUE in a cropland system (NUEcropland) is calculated as equation (2). The nitrogen inputs of grassland contain natural biological nitrogen fixation (NNBNF), fertilizer nitrogen (Nfertilizer), deposited manure nitrogen (Nmanure) and deposition nitrogen (Ndeposition), and nitrogen outputs of grassland are grass feed (Ngrass feed) and Nr emission (Nemission). NUE in a grassland system (NUEgrassland) is taken from equation (3).

$$NUE_{\rm{cropland}} = \frac{{N_{{{\rm{crop}}\;{\rm{feed}}}} + N_{{{\rm{crop}}\;{\rm{residue}}\;{\rm{feed}}}}}}{{N_{\rm{CBNF}} + N_{\rm{fertilizer}} + N_{\rm{manure}} + N_{\rm{irrigation}} + N_{\rm{deposition}}}}$$
$$NUE_{\rm{grassland}} = \frac{{N_{{{\rm{grass}}\;{\rm{feed}}}}}}{{N_{\rm{NBNF}} + N_{\rm{fertilizer}} + N_{{{\rm{deposited}}\;{\rm{manure}}}} + N_{\rm{deposition}}}}$$

Livestock raising stage

Crop (Ncrop feed), crop residues (including Ncrop residue feed and Nother crop residue feed from the production of human rations or crop feed for other livestock), grass feed (Ngrass feed), swill (Nswill) and other feed (Nother feed, including synthetic amino acids and fishmeal) are the nitrogen inputs, and the nitrogen output contained livestock products (Nlivestock products, including meat, eggs and milk), Nr emission and manure nitrogen recycling to croplands and grassland. The NUE in the livestock system (NUElivestock) is derived based on equation (4).

$$\begin{array}{l}NUE_{\rm{livestock}} =\\ \frac{{N_{{{\rm{livestock}}\;{\rm{products}}}}}}{{N_{{{\rm{crop}}\;{\rm{feed}}}} + N_{{{\rm{crop}}\;{\rm{residue}}\;{\rm{feed}}}} + N_{{{\rm{grass}}\;{\rm{feed}}}} + N_{{{\rm{other}}\;{\rm{crop}}}}\;_{{{\rm{residue}}\;{\rm{feed}}}} + N_{{\rm{{swill}}}} + N_{{{\rm{other}}\;{\rm{feed}}}}}}\end{array}$$

Whole livestock production chain

We defined NUEwhole chain based on the total livestock supply chain, including feed production and livestock raising stages (equation (5)). NBNF contains NCBNF from croplands and NNBNF from grassland. Nother manure is the manure nitrogen recycling to the cropland from other livestock, for instance, monogastric livestock that require more crop feed could not produce enough manure of their own and need manure nitrogen from other livestock.

$$\begin{array}{l}NUE_{{\rm{whole}}\;{\rm{chain}}} =\\ \frac{{N_{{\rm{livestock}}\;{\rm{products}}}}}{{N_{\rm{BNF}} + N_{\rm{irrigation}} + N_{\rm{fertilizer}} + N_{\rm{deposition}} + N_{{\rm{other}}\;{\rm{manure}}} + N_{{\rm{other}}\;{\rm{crop}}\;{\rm{residue}}} + N_{\rm{swill}} + N_{{\rm{other}}\;{\rm{feed}}}}}\end{array}$$

GHG emissions from livestock supply chains

The major GHG emissions from livestock systems in the GLEAM model are: (1) CH4 emissions from enteric fermentation in ruminants and pigs; (2) CH4 emissions arising from manure management; (3) N2O emissions released from manure management, done in the calculation of Nr emissions; (4) CH4 emissions from rice production; (5) CO2 emissions from fertilizer manufacture; (6) CO2 emissions from field operations; (7) CO2 emissions from feed blending, processing and transport; (8) CO2 emissions from land use change. Items (1)–(7) were calculated by the following methods described in Supplementary Table 1 and item (8) was calculated following equations (6) and (7).

We calculated the cropland area by monogastric livestock more than ruminants and take the cropland area as the relative land use change with monogastric livestock, as shown in equations (6) and (7).

$${\mathrm{Land}}_c = \frac{{{\mathrm{DM}}_c}}{{{\mathrm{DMYG}}_c \times {\mathrm{FUE}}_c}} \times \frac{{{\mathrm{EFA}}_c}}{{{\mathrm{MFA}}_c}}$$

where Landc is the land area of feed c, DMYGc is the dry matter yield of feed c, in kg ha−1, and is calculated based on crop yield according to the GLEAM method, FUEc is feed use efficiency of feed c, and MFAc and EFAc are the mass fraction and economic fraction, respectively, and are derived from GLEAM.


where Croplandruminant and Croplandmonogastric are the areas of cropland required for ruminant and monogastric feed, respectively, and LUC is the land use change value43, representing annual GHG emissions released from forest to cropland, in tCO2 ha−1 yr−1. The LUC value takes into account the long-term effects and was discounted to an average value for each year43. There was a large uncertainty in the calculation of GHG emission changes from forest conversion to cropland and reforestation (Uncertainty analysis).

Scenario analysis

The baseline scenario has been established as BAU, assuming the total amount of ruminant and monogastric protein produced in 2019 is maintained at a constant level. Three optimized scenarios were designed to assess the impact on mitigating livestock nitrogen and GHG emissions, including SYS, FED and ALL scenarios.

BAU scenario

The BAU scenario assumes the amount of protein produced by ruminants and monogastric livestock to be 7.06 and 6.64 TgN-protein in 2019, respectively. Currently, there are partially underutilized grassland areas (Supplementary Fig. 2) and sustainable unused crop residue resources.

SYS scenario

In this scenario, we model the effect of maximizing ruminant production and reducing monogastric production accordingly while keeping total livestock protein production constant (13.7 TgN-protein, calculated from FAOSTAT) in 2019. The SYS scenario represents a switch of 12.3% of global livestock production from monogastric to ruminant livestock. The resource constraints for maximizing ruminant protein production are current maximum production of total cellulose while considering the carrying capacity of the grass and the total amount of crop residues. As for the maximum available value of grass nitrogen, we take into account the degradation conditions (grass degradation adjustment rate (DAR))44 of grazing grassland to adjust the utilization efficiency (UE)6 of grassland. For DAR, we set mild degradation to 80%, slightly mild degradation to 65%, moderate degradation to 50% and severe degradation to 30% (Extended Data Fig. 10). The country’s average grassland cover share was calculated from the GLC-Share database45 to reflect the extent of grassland degradation, and using 3/4 value, 1/2 value and 1/4 value quadrature into four intervals, the adjustment factors were set to 80%, 65%, 50% and 30%, respectively. In addition, grassland degradation is not considered for non-grazed grasslands. The maximum available value of grass nitrogen (Nmaxgrass) is calculated as shown in equation (8).

$$\begin{array}{l}N{\mathrm{max}}_{\mathrm{grass}}= {\mathrm{Production}}_{\mathrm{grassN}} \times R_{\mathrm{grazing}} \times {\mathrm{UE}} \times {\mathrm{DAR}}\\ \qquad \qquad \quad + {\mathrm{Production}}_{\mathrm{grassN}} \times (1 - R_{\mathrm{grazing}}) \times {\mathrm{UE}}\end{array}$$

where ProductiongrassN is the total grass nitrogen production and Rgrazing is the grazing ratio of ruminants.

The maximum crop residue nitrogen removed from croplands was calculated from crop nitrogen (ProductioncropN), crop residues to crop ratio (Rresidue–crop)6 and the proportion of crop residues removed (Rremoved)6, as shown in equation (9).

$$N{\mathrm{max}}_{\mathrm{crop}\;\mathrm{residues}} = {\mathrm{Production}}_{\mathrm{cropN}} \times R_{\mathrm{residue} - \mathrm{crop}} \times R_{\mathrm{removed}}$$

The maximum cellulose nitrogen production was obtained from the sum of Nmaxgrass and Nmaxcrop residues, dividing by the current amount of cellulose utilized by the ruminants and getting the maximum available ruminant production multiplier. Meanwhile, in the context of the Nr emission intensity of ruminants being lower than that of monogastric livestock, we can obtain the proportion of ruminants that maximizes cellulose utilization.

SYS2 scenario

This scenario is an extreme variant of the SYS scenario; it assumes the crop residues are not returned to the cropland and all removed crop residues are used to produce feed for ruminants, that is, Rremoved = 1. In SYS2, it is also a prerequisite that the Nr emission intensity of ruminants is lower than that of monogastric livestock. The SYS2 scenario reflects a switch of 20.7% of global livestock production from monogastric to ruminant livestock. To fully realize the benefits of this scenario it would be necessary to account for the fact that cropland may be deprived of nutrients from recycled crop residues; however, the deficit could be supplemented by the ruminant manure. This scenario would release more croplands and feed more people than the SYS scenario.

FED scenario

In this scenario, the production of ruminants and monogastric livestock remained was kept consistent with the BAU scenario. All Nr emissions (nitrogen from NH3, NOx and NO3) from feed production and livestock raising are reduced to the global average, and those countries that are already below the global average remain unchanged. The FED scenario was designed to produce substantial emission reductions and could be achieved through targeted abatement measures on cropland, grassland and livestock system (Supplementary Table 2), but no additional croplands would be released.

ALL scenario

The ALL scenario is a combination of the SYS scenario and the FED scenario to achieve both an optimal livestock production ratio and emission levels. In this scenario, maximizing ruminant production (ruminant protein production is consistent with the SYS scenario) and targeted abatement measures at all stages are needed. The ALL scenario could maximize the benefits of food security, environmental protection and climate mitigation. This is the scenario advocated in this study.

Cost–benefit analysis

Implementation cost

The implementation cost under the SYS scenario is considered to be equal to the change in protein quality of all livestock Pj (where j represents different livestock systems) multiplied by their unit product cost (PPricej, in US$ per kg protein), as shown in equation (10). Here PPricej is the regional animal producer price and is derived from the FAOSTAT database with regional producer prices.

$${\mathrm{Cost}}_{\mathrm{SYS}} = {\sum} {P_j} \times {\mathrm{PPrice}}_j$$

For the FED scenario, we used the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model ( to calculate the abatement costs from cropland (CostFED–cropland,k), grassland (CostFED–grassland,k) and livestock (CostFED–livestock,k) for each country. A detailed description of the GAINS model can be found in Klimont et al.46 The implementation cost under the FED scenario is calculated in equations (11)–(13).

$${\mathrm{Cost}}_{{\mathrm{FED - cropland}},k} = \nabla E_{{{\mathrm{N - cropland}}},k} \times C_{\mathrm{{cropland}},k}$$
$${\mathrm{Cost}}_{{\mathrm{FED - grassland}},k} = \nabla E_{{{\mathrm{N - grassland}}},k} \times C_{\mathrm{{grassland}},k}$$
$${\mathrm{Cost}}_{{\mathrm{FED - livestock}},j} = N_{i,j} \times C_{\mathrm{{livestock}},j} \times {\mathrm{AR}}_k$$

Where EN–cropland,k and EN–grassland,k are the Nr emission reduction from cropland and grassland in country k, respectively, Ccropland,k, Cgrassland,k and Clivestock,k are the unit abatement cost of the most appropriate mitigations (shown in Supplementary Tables 2 and 3) to reduce cropland nitrogen, grassland nitrogen and livestock nitrogen loss modified for the specific farming practices of country k, respectively; Cgrassland,k is set at one-fifth of Ccropland,k. ARk is the calculated abatement rate for country k.

For the ALL scenario, the abatement costs from cropland and grassland are assumed to be equal to those in the FED scenario, and the abatement costs from livestock are the sum of the FED and SYS scenarios.

Societal benefits assessment

The societal benefits of optimizing global ruminant production in this study are defined as the sum of avoided damage costs for ecosystem health (Ebenefit), human health (Hbenefit) and climate change mitigation (Cbenefit), as shown in equation (14).

$$SO_\mathrm{benefit} = E_\mathrm{benefit} + H_\mathrm{benefit} + C_\mathrm{benefit}$$

The Ebenefit is assumed to be the benefit of Nr mitigation on the ecosystem, which is also equal to reducing the avoided damage costs. We assume unit Nr damage cost in Europe and the United States is also applicable to other countries after adjusting for differences in the regional willingness to pay (WTP) and purchasing power parity (PPP) for the ecosystem services, as shown in equation (15).

$${\mathrm{UE}}_{{\mathrm{benefit,Nr}},k} = \partial _{\mathrm{EU}} \times \frac{{{\mathrm{WTP}}_k}}{{{\mathrm{WTP}}_{\mathrm{EU}}}} \times \frac{{{\mathrm{PPP}}_k}}{{{\mathrm{PPP}}_{\mathrm{EU}}}}$$

where ∂EU is the estimated unit ecosystem damage cost of Nr emissions based on the literature45,47; the value of UEbenefit,Nr,k can be found in Supplementary Table 4.

Then, the Ebenefit is summed according to equation (16).

$$E_{\mathrm{benefit}} = \nabla E_{\mathrm{N}_2\mathrm{O}} \times {\mathrm{UE}}_{\mathrm{benefit},\mathrm{N}_2\mathrm{O}} + \nabla E_{\mathrm{NH}_3} \times {\mathrm{UE}}_{\mathrm{benefit},\mathrm{NH}_3} + \nabla E_{\mathrm{NO}_{x}} \times {\mathrm{UE}}_{\mathrm{benefit},\mathrm{NO}_{x}}$$

where \(\nabla E_{\mathrm{N}_2\mathrm{O}}\), \(\nabla E_{\mathrm{NH}_3}\) and \(\nabla E_{\mathrm{NO}_{x}}\) are the calculated reduction in N2O, NH3 and NOx, and \({\mathrm{UE}}_{\mathrm{benefit},\mathrm{N}_2\mathrm{O}}\), \(\mathrm{UE}_{\mathrm{benefit},\mathrm{NH}_3}\) and \(\mathrm{UE}_{{\mathrm{benefit}},{\mathrm{NO}}_x}\) represent the unit ecosystem benefit of N2O, NH3 and NOx emission reduction, respectively, in US$ kgN−1 (values are listed in Supplementary Table 4).

The human health benefit (Hbenefit) refers to the benefit of prevented mortality derived from PM2.5 mitigation caused by animal Nr abatement. We derived the national-specific unit health damage costs of Nr emission from Gu et al.48, which connected the economic costs of mortality per unit of Nr emission with the population density, GDP per capita, urbanization and nitrogen share. The calculation of health benefits in this study is shown in equation (17).

$$H_{\mathrm{benefit}} = \nabla E_{\mathrm{Nr}} \times H_{\mathrm{benefit,Nr}}$$

where ENr is the Nr emission reduction in specific scenarios and Hcost,Nr represents the unit health benefit of Nr reduction in US$ kgN−1.

The climate-related benefits of optimizing ruminant production (Cbenefit) are considered to be the sum of the GHG mitigation benefits and the Nr (NH3 and NOx) mitigation impact on climate, as shown in equation (18).

$$\begin{array}{l}C_{\rm{benefit}} = \nabla E_{\rm{GHG}} \times {\rm{UC}}_{{{\rm{benefit}},{\rm{GHG}}}}\\ - (\nabla E_{{{\rm{NH}}}_{3}} \times C_{{{\rm{benefit}},{{{\rm{NH}}}_{3}}}} + \nabla E_{{\rm{NO}}_x} \times C_{{\rm{benefit}},{\rm{NO}}_x})\end{array}$$

where EGHG is the GHG emission reduction in a specific scenario, in kgCO2e, and the GWP100 for CH4 and N2O are 27.9 and 273 kgCO2e, respectively. UCbenefit,GHG represents the monetary climate benefit due GHG mitigation, which is assumed to be the carbon price, of US$40–80 tCO2−1 (ref. 49). \(C_{{\mathrm{benefit,NH}}_3}\) and \(C_{{\mathrm{benefit,NO}}_x}\) represent the monetary climate impact due to changed NH3 and NOx emissions, which is associated with the cooling effect of NH3 and NOx on the global climate based on previous studies.

Cropland recovered for human food production

We first calculated the recovered or ‘saved’ cropland area (Landsys and all) under the SYS and ALL scenarios (no cropland is released in the FED scenario). Next, we calculated crop nitrogen yield per unit of cropland (YieldcropN) by dividing all crop nitrogen production (ProductioncropN) using the total cropland area (Croplandtotal, from FAOSTAT), which is multiplied by Landsys and all to obtain the total value of saved crop nitrogen production. We estimate the additional number of people that could be sustained under the assumption of a purely vegetarian diet by extra food production from saved croplands (Populationsaved) by dividing the total saved crop nitrogen production by unit nitrogen nutrition requirements (Proteinunit/6.25, where 6.25 is the protein to nitrogen conversion ratio and Proteinunit is the per-capita protein requirement, in kg protein per capita per year, calculated from FAOSTAT). The calculation is depicted in equations (19) and (20).

$${\mathrm{Yield}}_{\mathrm{cropN}} = \frac{{{\mathrm{Production}}_{\mathrm{cropN}}}}{{{\mathrm{Cropland}}_{\mathrm{total}}}}$$
$$\mathrm{{Population}}_{\mathrm{saved}} = \frac{{{\mathrm{Yield}}_{\mathrm{cropN}} \times {\mathrm{Land}}_{\mathrm{sys\;and\;all}}}}{{{\mathrm{Protein}}_\mathrm{{unit}}}}$$

Uncertainty analysis

In this study, we estimated the uncertainties of nitrogen losses, GHG emissions, costs and benefits for each scenario in 166 countries using 10,000 Monte Carlo simulations. The 95% confidence intervals for all results were calculated. The coefficients of variation (CVs, %) of activity data and parameters are shown in Supplementary Tables 5 and 6, and the uncertainties of the final simulation results are shown in Supplementary Data.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.