Quantifying Greenhouse Gas Emissions from Livestock in Pastoral Areas based on Unmanned Aerial Vehicles

Quantifying Greenhouse Gas Emissions from Livestock in Pastoral Areas based on Unmanned Aerial Vehicles | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Quantifying Greenhouse Gas Emissions from Livestock in Pastoral Areas based on Unmanned Aerial Vehicles Pengfei Liu, Min Liu, Wanman Mei, Pengfei Duan, Lifeng Dong, Dongliang Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6170333/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurate measurement of greenhouse gas (GHG) emissions from livestock is essential for developing effective emission reduction strategies. This study introduces the use of Unmanned Aerial Vehicles (UAVs) and deep learning technology to estimate methane (CH₄) and nitrous oxide (N₂O) emissions from enteric fermentation and manure management in pastoral areas. More accurate animal GHG emission factors are derived by considering animal weight, feed quality, breeding methods, and grassland types, which improves the precision of measuring GHG emissions from livestock. Potential emission reductions from different strategies, along with their associated costs and benefits, are projected to identify the optimal emission reduction strategy. We also discuss appropriate carbon pricing mechanisms for mitigating livestock-related GHG emissions. These findings offer valuable guidance for shaping effective emission reduction strategies in pastoral livestock production. Social science/Environmental studies Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation Earth and environmental sciences/Environmental social sciences/Climate-change mitigation Unmanned Aerial Vehicle deep learning GHG inventory livestock pastoral areas Figures Figure 1 Figure 2 Introduction Carbon emissions from livestock production received significant attention at the 29th United Nations Climate Change Conference (COP29) in 2024. Various initiatives and discussions highlighted the importance of reducing greenhouse gas (GHG) emissions from the livestock sector. Livestock production is estimated to contribute 15–18% of global GHG emissions (Dangal et al., 2017 ; Rojas-Downing et al., 2017 ; Bellarby et al., 2013 ) and is one of the largest global sources of methane emissions, prompting scientists to call for actions to reduce methane emissions for climate change mitigation (e.g., Ripple et al., 2014 ; Reisinger et al., 2021 ; Sun et al., 2022 ). The mitigation potential of livestock systems is estimated at 0.1 to 7.8 gigatons of CO₂-equivalent per year, accounting for up to 50% of the total mitigation potential within the agriculture, forestry, and land use sectors (Herrero et al., 2016 ). Accurate measurement of greenhouse gas (GHG) emissions from livestock is the foundation for achieving GHG emission reductions, though continues to face significant challenges especially in pastoral areas (Herzon et al., 2024 ). This paper improves the accuracy of measuring livestock GHG emissions and contributes to design of effective GHG emission reduction strategies. Existing studies on measuring GHG emissions from livestock mainly use instrumental measurements and emission factor approaches (Huhtanen et al., 2015 ). The instrumental measurement approach involves direct methods such as respiration metabolism chambers, mask methods, and SF6 (sulfur hexafluoride) tracer methods (Deighton et al., 2014 ; Place et al., 2011 ; Williams et al., 2011 ), as well as indirect methods such as in vitro fermentation and in vitro gas production techniques (Krizsan et al., 2012 ). While the instrumental measurement approach provides per-animal GHG emissions data, the high economic and time costs restrict its practical application for large-scale implementations. Measuring GHG emissions from pastoral livestock is particularly challenging using the instrumental measurement approach, especially in extensive livestock production systems, such as those where animals graze on vast, permanent grasslands and move across varied environmental conditions (Garnsworthy et al., 2019 ). The emission factor approach relies on GHG emission factors published by the Organization for Economic Cooperation and Development (OECD) and the Intergovernmental Panel on Climate Change (IPCC) in the Guidelines for National GHG Emission Inventories (IPCC, 2006; 2019). Many national governments have also published localized GHG emission factors for livestock at the country or regional level (NDRC, 2005). The emission factor approach has been widely used to calculate large-scale GHG emissions from livestock (Wang et al., 2021 ). However, existing animal GHG emission factors do not account for variations in animal characteristics and rely on average characteristic values at the national or regional level instead (Herrero et al., 2013 ; 2016 ). For example, the CH 4 and N 2 O emission factors for enteric fermentation and manure management in cattle and buffalo are roughly averaged at an intercontinental level in the IPCC Tier 1 guidelines (IPCC, 2006; 2019). The animal GHG emission factors provided by national governments are more specific than the IPCC Tier 1 guidelines but remain averaged across broad regions (NDRC, 2005), which fail to reflect differences among individual animals. As a result, the existing GHG emission factors may lead to imprecise GHG emission estimates when using the emission factor approach. Crosson et al. ( 2011 ) found that the GHG emission estimates for livestock in China had a 50% margin of error when calculated using the IPCC method. Although the emission factor approach is feasible for estimating GHG emissions from pastoral livestock, the emission factors provided by the OECD, IPCC, or most national governments are not specifically tailored to pastoral livestock production, which differs from the more common intensive livestock production in cropping areas. Key factors affecting animal emissions, such as animal weight, feed quality, and breeding methods, vary between extensive pastoral systems and intensive systems (Herrero et al., 2013 ). Estimating GHG emissions for pastoral livestock based on the existing emission factors would result in substantial bias. We propose a novel approach for quantifying GHG emissions from pastoral livestock and deriving more accurate GHG emission factors tailored to pastoral livestock. Unmanned Aerial Vehicles (UAVs) and machine learning technology are used to estimate methane (CH₄) and nitrous oxide (N₂O) emissions from enteric fermentation and manure management for 12,945 cattle and 69,272 sheep in pastoral areas. More accurate animal GHG emission factors are derived by considering animal weight, feed quality, breeding methods, and grassland types, which improves the precision of measuring GHG emissions from livestock in pastoral areas. Potential emission reductions from different strategies, along with their associated costs and benefits, are projected to identify the optimal emission reduction strategy. Moreover, we provide carbon pricing mechanisms for mitigating livestock-related GHG emissions. This study makes three primary contributions. First, we improve the accuracy of quantifying GHG emissions from pastoral livestock by using Unmanned Aerial Vehicles (UAVs) combined with deep learning technology to address the uncertainty associated with animal weight in GHG emission inventories. Second, we propose new GHG emission factors tailored to pastoral livestock production by considering animal weight, feed quality, and breeding methods. Existing GHG emission factors fail to account for the heterogeneity in production systems, management practices, and resource-use efficiencies (Herrero et al., 2013 ). Third, our findings provide valuable insights into identifying optimal emission reduction strategies for mitigating livestock-related GHG emissions by considering efficiency and cost-effectiveness under different conditions, which provide valuable guidance for shaping effective emission reduction strategies in pastoral livestock production. Results Animal weights derived from UAV images. We employed unmanned aerial vehicles (UAVs) to collect animal images in pastoral areas. To extract information on animal species, population, and body weights, we developed a real-time monitoring system based on the TensorFlow open-source deep learning framework. This system integrates modules for data acquisition, transmission, livestock identification, information extraction, and result output (Wang et al., 2021). Initially, UAVs captured 1,151 images of animals from pastoral areas, covering 414 sample households. After screening, the qualified images were selected to create UAV image blocks and establish a sample database. During this process, Labelme software was used to annotate cattle and sheep in the images by outlining polygons around each animal. Next, the labeled image blocks were fed into the Mask RCNN (Region-based Convolutional Neural Network) model for iterative training. This training continued until the model achieved optimal accuracy, resulting in a robust deep learning model for livestock identification. The trained Mask RCNN model was subsequently used to accurately recognize cattle and sheep in the images. Finally, based on the livestock identified by the Mask RCNN model, their body weights were estimated using a livestock weight estimation model. This model was developed using 2019 data from Inner Mongolia, which included cattle and sheep weights measured with scales and corresponding head-to-body length measurements taken with a ruler. A linear regression model was constructed to ensure precise weight estimation for the identified livestock. Figures 1a and 1b show the outlined animals and the corresponding weight estimation for each animal. The accuracy of animal weight estimation using UAV images exceeded 90%. Figures 1c and 1d illustrate the weight distribution of 12,945 cattle and 69,272 sheep that were collected by the UAV images, respectively. Table S1 indicates that the cattle had an average weight of 336.2 kg, which is consistent with the cattle weight measured in the field by Hu et al. (2022) and Zhang et al. (2023), and is 9.3% higher than the reference weight used for calculating GHG emission factors for Asian cattle according to the IPCC Tier 1 guidelines. Our estimate is also 4.8% higher than the reference weight for northern China's cattle provided by the government. The average weight of the sheep was 36.3 kg, which is 14.6% higher than the reference weight provided by IPCC Tier 1 (IPCC, 2019) and 3.6% higher than the reference weight provided by the government (NDRC, 2014). These discrepancies suggest that the reference weights used by IPCC Tier 1 and the government for estimating GHG emission factors cause an underestimation of GHG emissions. Animal GHG emission factors for pastoral areas of China. Following the IPCC Tier 2 approach, we estimated animal GHG emissions using animal weights derived from UAV images and livestock production data collected through household surveys. Caro et al. (2014) presented that discrepancies with higher tiers highlight the value of more detailed analyses and caution against overinterpreting smaller-scale trends in the Tier 1 results. Fig. S1 illustrates the distribution of CH 4 emissions from enteric fermentation, CH 4 emissions from manure management, and N 2 O emissions from manure management for 12,945 sampled cattle and 69,272 sampled sheep. The emissions for each animal are converted into CO 2 -equivalent emissions, and the total greenhouse gas (GHG) emissions per animal are calculated as the sum of these contributions. We calculated the average GHG emissions for our sample to determine the animal GHG emission factors for pastoral areas, as shown in Table 1. We find that the values of the animal GHG emission factors provided by IPCC Tier 1 in 2006 and 2019 fall within the range of our calculated GHG emissions for individual sample animals. Additionally, our average GHG emissions per animal, i.e., the estimated animal GHG emission factors based on our survey, are closely aligned with the animal GHG emission factors reported by IPCC Tier 1 in 2006 and 2019, as well as those provided by the government, indicating that our estimates are consistent with the IPCC and national government standards. Our animal GHG emission factors provide a more accurate assessment of animal GHG emissions in pastoral areas because they account for variations in animal weight, feed quality, breeding methods, and grassland types specific to pastoral areas. Existing research indicates that animal CH₄ and N₂O emissions depend heavily on animal characteristics and management practices (Herrero et al., 2016). The GHG emission factors issued by IPCC Tier 1 and the national government are not specifically tailored to the pastoral areas of China. For instance, the CH₄ emission factors for cattle in China, as reported by IPCC Tier 1 in 2006 and 2019, are 55 and 54 kg CH₄ per head per year, respectively, which are the same as those for Asia as a whole, without specific reference to China. Similarly, the CH₄ emission factors for cattle issued by the Chinese government vary only by breeding method: 52.90 kg CH₄ per head per year for intensive breeding and 85.30 kg CH₄ per head per year for extensive breeding, without specific reference to the pastoral areas. Table 1 Animal GHG emission factors of pastoral areas ( kg CH₄/N₂O head⁻¹ year⁻¹ ) GHG emissions for individual sample animals Average GHG emissions per animal Animal GHG emission factors issued by IPCC Tier1 in 2006 Animal GHG emission factors issued by IPCC Tier1 in 2019 Animal GHG emission factors issued by the Chinese national government Cattle CH 4 emission from enteric fermentation [22.78, 121.20] 67.70 55.00 54.00 52.90, 85.30 CH 4 emission from manure management [0.51, 2.71] 1.51 1.57 1.34 1.02, 2.82 N 2 O emission from manure management [0.19, 0.90] 0.52 1.37 0.58 0.79, 0.91 Sheep CH 4 emission from enteric fermentation [2.72, 18.01] 7.30 5.00 5.00 7.50, 8.20 CH 4 emission from manure management [0.05, 0.31] 0.13 0.16 0.26 0.15, 0.15 N 2 O emission from manure management [0.02, 0.11] 0.05 0.03 0.05 0.09, 0.06 Notes: The estimated GHG emissions for individual sample animals are presented as ranges because we accounted for variations in animal weight, feed quality, breeding methods, and grassland types for all sample animals. Based on the location and climatic conditions of our study area, we applied the following animal GHG emission factors from the IPCC 2006 and 2019 Tier 1 guidelines for the pastoral areas of Inner Mongolia: CH₄ emission factors from enteric fermentation for cattle in Asia were 55.00 and 54.00 kg CH₄ head⁻¹ year⁻¹, and for sheep in developing countries/low-productivity systems, 5.00 kg CH₄ head⁻¹ year⁻¹; CH₄ and N₂O emission factors from manure management for cattle in Asia and moderate climate areas were 1.57 and 1.34 kg CH₄ head⁻¹ year⁻¹ and 1.37 and 0.58 kg N₂O head⁻¹ year⁻¹, respectively; CH₄ and N₂O emission factors from manure management for sheep in developing countries/low-productivity systems and moderate climate areas were 0.16 and 0.26 kg CH₄ head⁻¹ year⁻¹ and 0.03 and 0.05 kg N₂O head⁻¹ year⁻¹, respectively. China’s Greenhouse Gas Inventory Study provides CH₄ emission factors for enteric fermentation based on breeding methods and CH₄ and N₂O emission factors for manure management based on geographic areas in China. Accordingly, we used the following animal GHG emission factors: CH₄ emission factors from enteric fermentation for cattle were 52.90 and 85.30 kg CH₄ head⁻¹ year⁻¹, and for sheep, 7.50 and 8.20 kg CH₄ head⁻¹ year⁻¹, reflecting the mix of intensive and extensive breeding methods in the pastoral areas of Inner Mongolia. CH₄ emission factors from manure management for cattle were 1.02 and 2.82 kg CH₄ head⁻¹ year⁻¹, and for sheep, 0.15 kg CH₄ head⁻¹ year⁻¹, based on the study area’s location in Northern and Northeastern China. N₂O emission factors from manure management for cattle were 0.79 and 0.91 kg N₂O head⁻¹ year⁻¹, and for sheep, 0.09 and 0.06 kg N₂O head⁻¹ year⁻¹. Additionally, Table S1 provides the reference weights of animals used for calculating GHG emission factors in this study, as well as those used by IPCC Tier 1, IPCC Tier 2, and China’s Greenhouse Gas Inventory Study. Specified animal GHG emission factors for different conditions . We classified the sample animals based on their specific characteristics to derive more precise animal GHG emission factors. Animals were classified into young and adult groups based on their weight according to the animal growth curve in Table 2 (da Silva et al., 2012). Feed quality was divided into low-quality, medium-quality, and high-quality feed based on the ratio of pasture to feed. Low-quality feed has the highest GHG emission factors, followed by medium-quality and high-quality feed. Breeding methods were divided into stall-feeding, grazing on flat pastures, and grazing on hilly pastures. For cattle, grazing results in higher GHG emissions than stall-feeding, while for sheep, grazing results in lower emissions than stall-feeding, consistent with findings by Stanley et al. (2018) and Ripoll-Bosch et al. (2013). Grassland types also influence the GHG emission factors of animals as the grassland differs in biological characteristics such as grass species, regeneration, and yield (Chang et al., 2021; Pei et al., 2008). Emission factors differ significantly across these grassland types, with meadow grasslands producing the highest GHG emissions, followed by typical grasslands, and desert grasslands producing the lowest. Table 2 Specified animal GHG emission factors under different conditions ( kg CH₄/N₂O head⁻¹ year⁻¹ ) CH 4 emission from enteric fermentation CH 4 emission from manure management N 2 O emission from manure management cattle sheep cattle sheep cattle sheep Young 64.47 6.17 1.44 0.11 0.50 0.04 Adult 81.06 10.29 1.81 0.18 0.62 0.07 High-quality feed 33.54 3.67 1.00 0.04 0.43 0.03 Medium-quality feed 57.84 5.76 1.29 0.10 0.48 0.04 Low-quality feed 74.15 8.32 1.66 0.15 0.55 0.05 Stall-feeding 58.67 7.80 1.33 0.14 0.47 0.05 Grazing on flat pasture 67.54 7.28 1.51 0.13 0.52 0.05 Grazing on hilly pasture 81.51 7.40 1.82 0.13 0.62 0.05 Desert grassland 64.22 5.60 1.43 0.98 0.51 0.38 Typical grassland 66.05 8.34 1.48 0.15 0.51 0.05 Meadow grassland 69.06 8.03 1.54 0.14 0.53 0.05 Notes: According to animal weight, we categorized animals into two groups: young and adult. Following the animal growth curve in the literature, sheep over 46 kg and cattle over 485 kg are considered adult animals (Feng et al., 2022; Li et al., 2021). Consequently, young animals are those weighing below 46 kg for sheep and below 485 kg for cattle after weaning. It should be noted that sample animals do not include unweaned animals that have not yet undergone digestion of food, as their GHG emissions are typically negligible. Regarding feed quality, which significantly impacts the intestinal emissions of livestock, we divided it into three categories: low-quality, medium-quality, and high-quality feed. These categories are based on different roughage quality standards corresponding to the digestibility rates in the IPCC guidelines. High-quality feed consists of a mixture with 0-15% pasture and more than 75% feed; medium-quality feed includes 15-75% feed; and low-quality feed consists of more than 75% pasture. Animals under different breeding methods require varying amounts of energy to obtain food, water, and shelter, which in turn determines the net energy of livestock activity. Different breeding methods are distinguished by the respective activity levels corresponding to different feeding conditions of animals, as outlined in the IPCC 2019 guidelines (Xue et al., 2014), including stall-feeding, grazing on flat pastures, and grazing on hilly pastures. Estimated animal GHG emissions at the household level based on the specified animal GHG emission factors . Animal GHG emission factors can be further refined based on multiple conditions. In Table 3, the CH 4 emission factor from enteric fermentation in pastoral areas is 29.77 kg CH 4 head -1 year -1 for young cattle with high-quality feed and stall-feeding. For young cattle with medium-quality feed and stall-feeding, the emission factor increases to 50.40 kg CH 4 head -1 year -1 , and further rises to 67.72 kg CH 4 head -1 year -1 with low-quality feed. Tables S2-S4 in the Appendix present the animal GHG emission factors under different conditions for multiple grassland types, including desert grasslands, typical grassland, and meadow grasslands. Based on the specified animal GHG emission factors under different conditions presented in Table 3, we estimate animal GHG emissions at household level. The household survey data include information on animal weight, feed quality, breeding methods, and grassland types, enabling the accurate estimation of GHG emissions at the household level based on the GHG emission factors in Table 3. As a result, the average animal GHG emissions per household were approximately 155.4 tons of CO 2 e. These emissions varied significantly across households, reflecting differences in animal numbers, weight, feed quality, breeding methods, and grassland types. According to the animal GHG emission factors from the IPCC Tier 1 guidelines (2006), the average GHG emissions from livestock across households were approximately 67.3 tons of CO 2 e per household. Using the updated IPCC 2019 guidelines, the average emissions slightly increased to 71.3 tons of CO 2 e per household. According to the animal GHG emission factors from China’s Greenhouse Gas Inventory Study, the reported average emissions were higher at 103.8 tons of CO 2 e per household. Our results indicate that animal GHG emissions in pastoral areas were underestimated when calculated using the IPCC Tier 1 guidelines and China’s Greenhouse Gas Inventory Study, which rely on lower reference animal weights for refining GHG emission factors and only use animal numbers in the calculation of emissions. This result is in line with the research finding by He et al. (2023) and Du et al. (2024). T able 3 Specified animal GHG emission factors under multiple interacting conditions (kg CH₄/N₂O head⁻¹ year⁻¹ ) Breeding methods High-quality feed Medium-quality feed Low-quality feed Cattle Sheep Cattle Sheep Cattle Sheep Young Adult Young Adult Young Adult Young Adult Young Adult Young Adult CH 4 emission from enteric fermentation Stall-feeding 29.77 34.86 3.30 4.94 50.40 61.19 5.88 8.44 67.72 78.72 6.90 10.86 Grazing on flat pasture 32.06 39.39 3.17 5.00 55.66 69.66 5.08 8.34 70.31 86.94 7.00 11.12 Grazing on hilly pasture 36.92 44.63 3.30 5.56 60.30 81.25 5.07 9.11 82.55 97.85 7.26 12.40 CH 4 emission from manure management Stall-feeding 0.89 1.04 0.04 0.06 1.12 1.36 0.10 0.15 1.52 1.76 0.12 0.19 Grazing on flat pasture 0.96 1.18 0.04 0.06 1.24 1.55 0.09 0.15 1.57 1.95 0.12 0.19 Grazing on hilly pasture 1.10 1.33 0.04 0.07 1.34 1.81 0.09 0.16 1.85 2.19 0.13 0.22 N 2 O emission from manure management Stall-feeding 0.38 0.45 0.03 0.04 0.41 0.51 0.04 0.06 0.50 0.59 0.04 0.07 Grazing on flat pasture 0.41 0.50 0.03 0.04 0.46 0.58 0.04 0.06 0.52 0.65 0.04 0.07 Grazing on hilly pasture 0.48 0.57 0.03 0.05 0.50 0.67 0.04 0.07 0.61 0.73 0.05 0.08 Notes: Following the animal growth curve described in the literature, we categorized animals into two groups based on weight: young and adult. Feed quality was classified into three categories—low-quality, medium-quality, and high-quality—based on roughage quality standards and corresponding digestibility rates outlined in the IPCC guidelines. Breeding methods were differentiated based on the activity levels associated with various feeding conditions, as specified in the IPCC 2019 guidelines, including stall-feeding, grazing on flat pastures, and grazing on hilly pastures. Predicting GHG emission reductions under different emission reduction methods. We further simulate the reduction in animal GHG emissions using various mitigation measures, based on the emission factors provided in Table 2. Existing studies have identified several effective measures for reducing animal GHG emissions, primarily focusing on reducing livestock size and improving technology (Cheng et al., 2022; Jonker et al., 2020). The animal GHG emission factors issued by the IPCC and national governments model reductions associated with a decrease in animal numbers and do not account for other types of improvements, such as enhancements in feed quality and breeding methods. Our specified animal GHG emission factors address this gap by estimating emissions with different feeding qualities and breeding methods. We use our surveyed households to predict the GHG emission reductions at the household level with different GHG emission reduction methods. Reducing livestock size. Reducing livestock numbers is a primary method for lowering animal GHG emissions (Wang et al., 2024). Using household survey data and the animal GHG emission factors presented in Table 2, we predicted GHG emission reductions under different scenarios involving reductions in sheep and cattle numbers. As shown in Table 4, S1-1, a 25% reduction in cattle numbers (reducing the household average from 48 to 36 head) results in a decrease of 22.59 tons of CO₂e per household, including 20.28 tons of CO₂e from methane emissions due to enteric fermentation, 0.45 tons of CO₂e from methane emissions related to manure management, and 1.86 tons of CO₂e from nitrous oxide emissions associated with manure management. Figure 2 shows that reducing cattle numbers by 25% results in a 14.3% decrease in animal GHG emissions, where methane emissions from enteric fermentation account for 12.8%, methane emissions from manure management for 0.3%, and nitrous oxide emissions from manure management for 1.2%. In S1-2, a 25% reduction in sheep numbers (reducing the household average from 333 to 250 head) results in a reduction of 16.88 tons of CO₂e per household (10.8%), including 15.41 tons of CO₂e from methane emissions due to enteric fermentation (9.8%), 0.27 tons of CO₂e from methane emissions related to manure management (0.2%), and 1.19 tons of CO₂e from nitrous oxide emissions associated with manure management (0.8%). In S1-3 and S1-4, we evaluated a 50% reduction in cattle and sheep numbers, respectively. In S1-3, reducing the household cattle average from 48 to 24 head results in a decrease of 45.20 tons of CO₂e per household (28.7%), including 40.55 tons of CO₂e from methane emissions due to enteric fermentation (25.7%), 0.93 tons of CO₂e from manure-related methane emissions (0.6%), and 3.72 tons of CO₂e from manure-related nitrous oxide emissions (2.4%). In S1-4, reducing the household sheep average from 333 to 167 head results in a decrease of 33.76 tons of CO₂e per household (21.3%), including 30.83 tons of CO₂e from enteric fermentation (19.5%), 0.54 tons of CO₂e from manure-related methane emissions (0.3%), and 2.39 tons of CO₂e from manure-related nitrous oxide emissions (1.5%). The results indicate that reducing cattle numbers achieves a greater GHG emission reduction compared to reducing sheep numbers when the livestock numbers are converted to sheep units for comparison (1 cattle equals 5 sheep units, and 1 sheep equals 1 sheep unit). Improving feed quality. Existing studies indicate that improving feed quality can significantly reduce carbon emissions from animals (Pelton et al., 2024; Hristov et al., 2013; Martin et al., 2010; Beauchemin et al., 2008) by adjusting the daily rations of forage and fodder. Dietary changes influence the fermentation and metabolic pathways of animal nutrients, which, in turn, affect GHG emissions (Gastelen et al., 2019). Table 4 presents the predicted results of GHG emission reductions achieved by improving feed quality. According to our survey data, the current feed quality among herder households in the pastoral area comprises 64.6% low-quality feed, 35.4% medium-quality feed, and no households using high-quality feed. In S2-1, converting all herder households currently using low-quality feed to medium-quality feed reduces GHG emissions by 31.81 tons of CO₂e per household (19.5%), including 29.66 tons of CO₂e from methane emissions due to enteric fermentation (18.2%), 0.56 tons of CO₂e from methane emissions related to manure management (0.3%), and 1.58 tons of CO₂e from nitrous oxide emissions associated with manure management (1.0%). In S2-2, converting all herder households currently using low-quality feed to medium-quality feed and currently using medium-quality feed to high-quality feed results in a reduction of 45.74 tons of CO₂e per household (28.1%), including 42.81 tons of CO₂e from methane emissions due to enteric fermentation (26.3%), 0.82 tons of CO₂e from methane emissions related to manure management (0.5%), and 2.12 tons of CO₂e from nitrous oxide emissions associated with manure management (1.3%). In S2-3, converting all herder households currently using low-quality and using medium-quality feed to high-quality feed achieves a reduction of 81.03 tons of CO₂e per household (49.8%), including 76.31 tons of CO₂e from methane emissions due to enteric fermentation (46.9%), 1.40 tons of CO₂e from methane emissions related to manure management (0.9%), and 3.32 tons of CO₂e from nitrous oxide emissions associated with manure management (2.0%). These results demonstrate that the reduction in GHG emissions becomes increasingly significant as the proportion of higher-quality feed increases. Changing breeding methods. Stalling, grazing on flat pastures, and grazing on hilly pastures require varying energy inputs for food, water, and shelter, resulting in differences in animal GHG emissions (Samsonstuen, 2019). Table 4 presents the predicted results of GHG emission reductions through improved breeding methods. Our survey data indicates that 3.7% of cattle and 1.4% of sheep are currently stalled. In S3-1, stalling 50% of cattle while leaving sheep unchanged reduces emissions by 5.22 tons of CO₂e per household (3.3%), comprising 4.75 tons of CO₂e from methane emissions due to enteric fermentation (3.0%), 0.11 tons of CO₂e from methane emissions related to manure management (0.1%), and 0.36 tons of CO₂e from nitrous oxide emissions associated with manure management (0.2%). Conversely, in S3-2, leaving cattle unchanged while stalling 50% of sheep increases emissions by 1.86 tons of CO₂e per household, as stalling produces more GHGs than grazing for sheep, as shown in Table 2. In S3-3, stalling 100% of cattle while leaving sheep unchanged reduces emissions by 10.46 tons of CO₂e per household (6.7%), including 9.50 tons of CO₂e from methane emissions due to enteric fermentation (6.0%), 0.25 tons of CO₂e from methane emissions related to manure management (0.2%), and 0.71 tons of CO₂e from nitrous oxide emissions associated with manure management (0.5%). In S3-4, stalling 100% of sheep while leaving cattle unchanged increases emissions by 3.72 tons of CO₂e per household. These results indicate that shifting cattle breeding methods to stall-feeding has a moderate impact on reducing GHG emissions, while changing sheep breeding methods to stall-feeding actually increases GHG emissions (Kiggundu et al., 2019; Recktenwald & Ehrhardt, 2024). Different types of grasslands. Table 4 illustrates GHG emissions under different reduction methods across various types of grasslands. Reducing 50% of cattle and sheep numbers results in a reduction of 49.01 tons of CO₂e per household in desert grasslands, 90.41 tons of CO₂e per household in typical grasslands, and 88.50 tons of CO₂e per household in meadow grasslands. Switching from low- and medium-quality feed to high-quality feed results in reductions of 48.1 tons of CO₂e per household in desert grasslands, 86.50 tons of CO₂e per household in typical grasslands, and 85.57 tons of CO₂e per household in meadow grasslands. Changing breeding methods to stall-feeding for both cattle and sheep decreases emissions by 0.79 tons of CO₂e per household in desert grasslands, 6.6 tons of CO₂e per household in typical grasslands, and 11.24 tons of CO₂e per household in meadow grasslands. These results demonstrate that reducing livestock numbers and improving feed quality are significantly more effective for reducing GHG emissions compared to changing breeding methods. Furthermore, the effects of GHG emission reductions are more pronounced in typical and meadow grasslands compared to desert grasslands, while typical and meadow grasslands also generate more animal GHG emissions than desert grasslands. Table 4 Predicted animal GHG emission reductions with different reduction measures Emissions reduction measures Scenarios CH 4 from enteric fermentation (ton/CO 2 e) CH 4 from manure management (ton/CO 2 e) N 2 O from manure management (ton/CO 2 e) Total reduction (ton/CO 2 e) (S1) Reducing livestock size S1-1: Cattle number (-25%) -20.28 -0.45 -1.86 -22.59 S1-2: Sheep number (-25%) -15.41 -0.27 -1.19 -16.88 S1-3: Cattle number (-50%) -40.55 -0.93 -3.72 -45.20 S1-4: Sheep number (-50%) -30.83 -0.54 -2.39 -33.76 (S2) Improving feed quality S2-1: Low—Medium -29.66 -0.56 -1.58 -31.81 S2-2: Low—Medium and Medium—High -42.81 -0.82 -2.12 -45.74 S2-3: Low—High and Medium—High -76.31 -1.40 -3.32 -81.03 (S3) Changing breeding methods S3-1: Stalling cattle (50%) -4.75 -0.11 -0.36 -5.22 S3-2: Stalling sheep (50%) 1.67 0.04 0.15 1.86 S3-3: Stalling cattle (100%) -9.50 -0.25 -0.71 -10.46 S3-4: Stalling sheep (100%) 3.34 0.08 0.29 3.72 On different types of grasslands: Desert grassland (S1-3)+(S1-4) -44.50 -0.85 -3.65 -49.01 (S2-3) -44.91 -0.96 -2.23 -48.09 (S3-3)+(S3-4) -0.76 -0.01 -0.02 -0.79 Typical grassland (S1-3)+(S1-4) -81.86 -1.64 -6.91 -90.41 (S2-3) -81.42 -1.57 -3.51 -86.50 (S3-3)+(S3-4) -6.05 -0.13 -0.40 -6.58 Meadow grassland (S1-3)+(S1-4) -79.79 -1.68 -7.02 -88.50 (S2-3) -80.94 -1.45 -3.19 -85.57 (S3-3)+(S3-4) -10.26 -0.23 -0.74 -11.24 Notes: According to the conversion coefficients of global warming potential over a 100–year time scale (GWP100), the emission of 1 kg of CH 4 is equivalent to the emission of 25 kg of CO 2 , and the emission of 1 kg of N 2 O is equivalent to the emission of 298 kg of CO 2 (IPCC, 2019). Reducing livestock size (S1): In S1-1, the number of cattle per household decreases by 25%. In S1-2, the number of sheep per household decreases by 25%. In S1-3, the number of cattle per household decreases by 50%. In S1-4, the number of sheep per household decreases by 50%. Improving feed quality (S2): In S2-1, all low-quality forage is converted into medium-quality forage. In S2-2, all low-quality forage is converted into medium-quality forage, and medium-quality forage is converted into high-quality forage. In S2-3, all low- and medium-quality forage is converted into high-quality forage. Improving breeding method (S3): In S3-1, 50% of cattle are converted to stall-feeding, while the grazing method for sheep remains unchanged. In S3-2, 50% of sheep are converted to stall-feeding, while the grazing method for cattle remains unchanged. In S3-3, 100% of cattle are converted to stall-feeding, while the grazing method for sheep remains unchanged. In S3-4, 100% of sheep are converted to stall-feeding, while the grazing method for cattle remains unchanged. GHG emission reduction in different grassland types: This includes desert grasslands, typical grasslands and meadow grasslands. In (S1-3)+(S1-4), the number of both sheep and cattle is reduced by 50%. In (S2-3), low- and medium-quality feed is converted to high-quality feed. In (S3-3)+(S3-4), 100% of both cattle and sheep are converted to stall-feeding. Cost-Benefit Analysis of Animal GHG Emission Reduction. Based on the amount of GHG reductions achieved through various emission reduction methods, we calculated both the revenue and costs associated with reducing animal GHG emissions. Using current carbon trading prices in China, we estimated the revenue generated from these emission reductions. The costs were then determined by accounting for the expenses incurred in implementing different reduction methods, such as reducing livestock numbers, improving feed quality, and modifying breeding practices. Finally, we conducted a comparative analysis of the net revenue per ton of emission reductions for each method. The results show that the revenue generated by all reduction methods is substantially lower than the associated costs. Consequently, we estimated the break-even carbon prices required for animal GHG emission reductions to offset their costs. These break-even prices represent the carbon prices at which the revenue from emission reductions equals the costs of implementing the reduction methods. Reducing livestock size. Table 5 illustrates that both the revenue and costs associated with emission reductions from decreasing the cattle population are significantly higher than those from reducing the sheep population. As a result, the net revenue from reducing the cattle population is lower compared to that of reducing the sheep population. In S1-1, a 25% reduction in the cattle population results in an average reduction of 22.6 tons of CO₂e per household, valued at 1,894 yuan based on the current carbon trading price in China. However, the cost of reducing 25% of the cattle population per household amounts to 110,220 yuan, based on the cattle trading price in pastoral areas. Consequently, the net revenue from GHG emission reduction by reducing 25% of the cattle population is -4,795 yuan per ton of CO₂e. The break-even price required for animal GHG emission reductions to offset the costs is 4,879 yuan per ton of CO₂e. Similarly, in S1-2, a 25% reduction in the sheep population results in an average reduction of 16.9 tons of CO₂e per household, valued at 1,415 yuan. The cost of reducing 25% of the sheep population per household is 74,119 yuan, based on the sheep trading price in pastoral areas. Thus, the net revenue from GHG emission reduction by reducing 25% of the sheep population is -4,307 yuan per ton of CO₂e, and the break-even price is 4,391 yuan per ton of CO₂e. In S1-3 and S1-4, where 50% of the cattle and sheep populations are reduced, the net revenue from GHG emission reduction is -4,793 yuan and -4,307 yuan per ton of CO₂e, respectively. The break-even prices for these reductions are 4,877 yuan per ton of CO₂e for cattle and 4,391 yuan per ton of CO₂e for sheep. Detailed calculations are provided in Table S8 of Appendix 7. Improving feed quality. Table 5 illustrates that converting all low-quality forage into medium-quality forage results in the lowest emission reduction cost and the highest net revenue. It also reveals that as feed quality increases, net revenue decreases, and the break-even price rises. In S2-1, converting all households currently using low-quality forage to medium-quality forage results in a reduction of 31.8 tons of CO₂e per household, valued at 2,667 yuan. However, the average cost of improving feed quality from low-quality to medium-quality forage is 14,066 yuan per household per year, based on the current forage trading price in pastoral areas. As a result, the net revenue from GHG emission reduction by converting low-quality forage to medium-quality forage is -358 yuan per ton of CO₂e. The break-even price required for animal GHG emission reductions to offset the costs is 442 yuan per ton of CO₂e. In S2-2, converting all low-quality forage to medium-quality forage and all medium-quality forage to high-quality forage results in a net revenue of -684 yuan per ton of CO₂e. The break-even price is 768 yuan per ton of CO₂e. In S2-3, converting all low- and medium-quality forage to high-quality forage results in a net revenue of -906 yuan per ton of CO₂e. The break-even price for this conversion is 990 yuan per ton of CO₂e. Detailed calculations are provided in Table S9 of Appendix 7. Changing breeding methods. In S3-1, converting 50% of grazing cattle to stall-feeding while keeping the grazing method for sheep unchanged results in a reduction of 5.22 tons of CO₂e per household, valued at 438 yuan. However, the average cost of converting grazing cattle to stall-feeding per household is 19,163 yuan per year, primarily due to increased labor requirements. Consequently, the net revenue from GHG emission reduction by converting 50% of grazing cattle to stall-feeding is -3,587 yuan per ton of CO₂e. The break-even price required for animal GHG emission reductions to offset the costs is 3,671 yuan per ton of CO₂e. In S3-3, converting 100% of grazing cattle to stall-feeding while keeping the grazing method for sheep unchanged results in a net revenue of -3,580 yuan per ton of CO₂e. The break-even price is 3,664 yuan per ton of CO₂e. However, in S3-2 and S3-4, converting 50% or 100% of sheep to stall-feeding while cattle remain grazing does not yield break-even prices, as these scenarios increase GHG emissions. Detailed calculations are provided in Table S10 of Appendix 7. Different types of grasslands. Table 5 illustrates that improving feed quality in each type of grassland results in the highest net revenue and lowest break-even price for GHG emission reductions, followed by reducing animal numbers. Changing breeding methods yields the lowest net revenue and highest break-even price. Specifically, converting low-quality and medium-quality feed into high-quality feed in meadow grasslands provides the highest net revenue at -410 yuan per ton of CO₂e, followed by typical grasslands at -462 yuan per ton of CO₂e. Desert grasslands have the lowest net revenue at -532 yuan per ton of CO₂e. Correspondingly, converting low-quality and medium-quality feed into high-quality feed in meadow grasslands also results in the lowest break-even price at 493 yuan per ton of CO₂e, followed by typical grasslands at 546 yuan per ton of CO₂e, and desert grasslands at 616 yuan per ton of CO₂e. In contrast, changing breeding methods in desert grasslands incurs the highest break-even price at 82,268 yuan per ton of CO₂e, followed by typical grasslands at 16,260 yuan per ton of CO₂e, and meadow grasslands at 8,780 yuan per ton of CO₂e. Additionally, the break-even carbon price for reducing livestock numbers is 4,527 yuan per ton of CO₂e in desert grasslands, which is lower than 4,666 yuan per ton of CO₂e in typical grasslands and 4,931 yuan per ton of CO₂e in meadow grasslands. Detailed calculations are provided in Table S11 of Appendix 7. Table 5 Predicted revenue and costs of different GHG emission reduction methods at household level Emissions reduction measures Scenarios Revenue (yuan) Costs (yuan) Net revenue (yuan/ton) Carbon price (yuan/ton) (S1) Reducing livestock size S1-1: Cattle number (-25%) 1,894 -110,220 -4,795.31 4,879.15 S1-2: Sheep number (-25%) 1,415 -74,119 -4,307.11 4,390.94 S1-3: Cattle number (-50%) 3,789 -220,440 -4,793.16 4,876.99 S1-4: Sheep number (-50%) 2,830 -148,238 -4,307.11 4,390.94 (S2) Improving feed quality S2-1: Low—Medium 2,667 -14,066 -358.35 442.19 S2-2: Low—Medium and Medium—High 3,834 -35,109 -683.76 767.58 S2-3: Low—High and Medium—High 6,793 -80,235 -906.36 990.19 (S3) Changing breeding methods S3-1: Stalling cattle (50%) 438 -19,163 -3587.16 3,671.07 S3-2: Stalling sheep (50%) -156 -26,588 N/A N/A S3-3: Stalling cattle (100%) 877 -38,325 -3580.11 3,663.96 S3-4: Stalling sheep (100%) -312 -53,716 N/A N/A Desert grassland (S1-3)+(S1-4) 4,109 -221,846 -4,442.72 4526.55 (S2-3) 4,031 -29,625 -532.20 616.03 (S3-3)+(S3-4) 66 -64,992 -82,184.52 82268.35 Typical grassland (S1-3)+(S1-4) 7,579 -421,875 -4,582.41 4666.24 (S2-3) 7,251 -47,195 -461.78 545.61 (S3-3)+(S3-4) 552 -106,990 -16,176.05 16259.88 Meadow grassland (S1-3)+(S1-4) 7,419 -436,417 -4,847.44 4931.27 (S2-3) 7,173 -42,221 -409.58 493.41 (S3-3)+(S3-4) 942 -98,687 -8,696.15 8779.98 Notes: N/A stands for 'not available.' The emission reduction revenue is equal to the national carbon market trading price multiplied by the reduced carbon emissions, with the carbon price referring to the comprehensive price of 83.83 yuan/ton on the national carbon market as of March 21, 2024. Reducing livestock size involves four scenarios: a 25% reduction in cattle (S1-1), a 25% reduction in sheep (S1-2), a 50% reduction in cattle (S1-3), and a 50% reduction in sheep (S1-4). Improving feed quality involves three scenarios: converting all low-quality forage to medium-quality (S2-1), converting low-quality to medium-quality and medium-quality to high-quality (S2-2), and converting all low- and medium-quality forage to high-quality (S2-3). Improving breeding methods involves four scenarios: switching 50% of cattle to stall-feeding while sheep remain grazing (S3-1), switching 50% of sheep to stall-feeding while cattle remain grazing (S3-2), switching 100% of cattle to stall-feeding while sheep remain grazing (S3-3), and switching 100% of sheep to stall-feeding while cattle remain grazing (S3-4). GHG emission reduction across grassland types (desert, typical, and meadow) includes reducing animals by 50% in (S1-3)+(S1-4), converting low- and medium-quality feed to high-quality in (S2-3), and switching 100% of both cattle and sheep to stall-feeding in (S3-3)+(S3-4). Discussion and Conclusion Accurately estimating GHG emissions from animals is crucial for developing an effective strategy to reduce animal-related emissions. Our research improves the calculation of livestock carbon emissions in pastoral areas where we first extracted the specified GHG emission factors for cattle and sheep, accounting for animal weight, feed quality, and breeding methods. Based on specified emission factors, we measured animal GHG emissions at the household level. The results showed that the average greenhouse gas (GHG) emissions from livestock per household in the pastoral areas of Inner Mongolia were approximately 155.4 tons of CO2e. These emissions were underestimated when calculated using the GHG emission factors from the IPCC Tier 1 guidelines and China’s Greenhouse Gas Inventory Study, as these methods only consider animal numbers, resulting in emissions of 71.3 and 103.8 tons of CO2e, respectively. We further predicted the potential for GHG emission reductions through various methods, including decreasing animal populations, improving feed quality, and changing breeding methods. A cost-benefit analysis was conducted to compare the net revenue of these emission reduction strategies across different scenarios. Our findings indicate that improving feed quality is more effective and cost-efficient in reducing GHG emissions from livestock than reducing animal numbers or changing breeding methods at the household level in pastoral areas. Therefore, prioritizing adjustments to the feeding structure to enhance animal digestibility is essential for reducing GHG emissions. Meanwhile, the lowest break-even price occurs when herders convert all low-quality forage into medium-quality forage, at 442 yuan per ton, which is slightly lower than the current carbon price in the European Union at 483 yuan per ton, suggesting a viable market opportunity to reduce animal GHG emissions by improving feed quality. Moreover, enhancing feed quality can increase animal productivity, while the costs of these improvements can be offset through the carbon market. Our results provide important information for herders to potentially engage in GHG emission reduction through carbon markets. The pastoral areas of Inner Mongolia had 596,400 herder households, of which 64.6% were using low-quality feed in 2020. Shifting these households to medium-quality feed would lead to a reduction of 31.8 tons of CO₂e per household, resulting in a total reduction of 12.2 million tons of CO₂e, including 11.6 million tons of CO₂e from methane and 0.6 million tons of CO₂e from nitrous oxide. Conversely, changing breeding methods has a minimal impact on reducing animal GHG emissions, while the associated costs are significantly higher, indicating inefficacy for emission reduction in pastoral areas. The break-even is approximately eight times higher than the current carbon price in the European market. The results also show that GHG emission reductions are more pronounced and less costly in typical and meadow grasslands compared to desert grasslands. Consequently, GHG emission reduction could be piloted in typical and meadow grasslands first. Finally, reducing the number of cattle is more effective for GHG emission reduction than reducing the number of sheep, though the cost of reducing cattle is higher. Therefore, when reducing animal numbers to lower GHG emissions, the choice between cattle and sheep depends on whether the priority is greater emission reduction efficiency or cost-effectiveness. There are several avenues for future research. First, the data obtained by UAVs are static. Future research can use cameras to monitor changes in animals over time to calculate dynamic GHG emissions and assess temporal changes. Second, the reduction in animal emissions applies only to the livestock rearing stage in this study. A life cycle assessment that takes into account additional stages such as feed production, slaughter and processing, and product transportation, may provide related policy implications from a different perspective. Lastly, this study only calculates emission factors for cattle and sheep in Inner Mongolia, though our methods can be easily applied to other pastoral areas with different types of grasslands and modes of livestock production, such as the Qinghai-Tibet Plateau. Future research can expand the study areas and implement similar methods across a broader geographical region to improve the accuracy of animal GHG emission factors in pastoral areas throughout China, while also incorporating the impacts of grasslands on animal GHG emissions in pastoral areas. Methodology We calculate livestock GHG emissions from the procedure of livestock production, including CH 4 emissions from enteric fermentation and manure management, and N 2 O emissions from manure management of ruminant animals based on the IPCC Tier 2 approach. CH 4 emission from enteric fermentation. CH₄ emission from enteric fermentation refers to the CH₄ produced by microorganisms in the animal’s digestive tract during the normal process of metabolism and fermentation of feed (McAuliffe et al., 2016 ; Swamy & Bhattacharya, 2006 ), which accounts for 42.6% of GHG emissions in livestock production (Sugar et al., 2012 ) and includes CH₄ expelled from the animal’s mouth, nose, and rectum. The CH₄ emission factor for enteric fermentation is estimated using Eq. ( 1 ): $$\:{CH}_{4\left(ef\right)}=[GE\times\:{(Y}_{m}/100)\times\:365/55.65],$$ 1 where \(\:{CH}_{4\left(ef\right)}\) is the CH 4 emission factor from enteric fermentation (kg CH 4 head − 1 year − 1 ). GE is the total energy intake (MJ head − 1 day − 1 ), which depends on animal weight, feed quality, breeding methods, and grassland types. The detailed calculation for GE is presented in Appendix 2. Y m is the CH 4 conversion factor, which represents the percentage of GE intake converted into CH 4 , and 55.65 (MJ kg − 1 ) is the energy content of CH 4 . CH 4 emission from manure management. CH₄ emissions from manure management account for 1.4% of GHG emissions from livestock production (Sugar et al., 2012 ). CH₄ is produced during the storage and management of animal manure and composting on farms. The main factors affecting CH₄ emissions are the amount of manure produced and the proportion of anaerobic degradation during manure management. The CH₄ emission from manure management is estimated using Eq. ( 2 ): $$\:{CH}_{4\left(mm\right)}=VS\times\:365\times\:\left[{B}_{0}\times\:\frac{0.67kg}{{m}^{3}}\times\:\sum\:_{s,k}\frac{{MCF}_{\left(s,k\right)}}{100}\times\:{MS}_{\left(s,k\right)}\right]\times\:\left(\frac{44}{28}\right),$$ 2 where \(\:{CH}_{4\left(mm\right)}\:\) is the CH 4 emission factor from manure management (kg CH 4 head − 1 year − 1 ). VS is the daily volatile solid excrement of livestock (kg dry matter head − 1 day − 1 ). The detailed calculation on VS is presented in Appendix 3 . B o represents the maximum CH 4 production capacity (m 3 kg − 1 ) of the excrement produced by animals. 0.67 is the conversion factor from the volume of CH 4 to the quality of CH 4 . MCF (s,k) is the CH 4 conversion factor for each type of manure management system s in climate zone k , and MS (s,k) is the proportion of manure managed by system s in climate zone k . N 2 O emission from manure management. N 2 O emission from animal manure management accounts for 3.6% of total GHG emissions from livestock production (Sugar et al., 2012 ). N₂O emissions from manure management \(\:\left({{N}_{2}O}_{\left(mm\right)}\right)\) are estimated using Eq. ( 3 ): $$\:{{N}_{2}O}_{\left(mm\right)}={N}_{2}{O}_{D}+{N}_{2}{O}_{ID}\:\:\:\:\:\:$$ 3 where \(\:{{N}_{2}O}_{\left(mm\right)}\) is the N 2 O emission factor of manure management (kg N 2 O head − 1 year − 1 ), N 2 O D refers to the direct emissions of N 2 O from manure management, and N 2 O ID refers to the indirect emissions of N 2 O from manure management. Direct emission of N 2 O from animal manure management refers to the N 2 O generated during the storage and handling of animal manure before it is applied to the soil. Eq. ( 4 ) below estimates the direct emission of N 2 O ( N 2 O D ): $$\:{N}_{2}{O}_{D}=[\sum\:_{S}\left[\sum\:_{T}\left({N}_{\left(T\right)}\times\:{Nex}_{\left(T\right)}\times\:{MS}_{\left(T,S\right)}\right)\right]\times\:{EF}_{3}]\times\:(44/28)$$ 4 where N 2 O D refers to direct emissions of N 2 O (in kg year − 1 ) from manure management. N (T) represents the number of livestock of a certain species/category T . Nex (T) is the average annual nitrogen excretion (in kg head − 1 year − 1 ) of each livestock in the specific species/category T . MS (T, S) i s the dimensionless proportion of the total annual nitrogen excretion from each livestock species/category T managed by fecal management system S . EF 3 is the emission factor for direct emissions of N 2 O from livestock fecal management system S , and 44/28 is the conversion factor from N 2 O-N emissions to N 2 O emissions. Indirect emission of N 2 O from animal feces management refers to the loss of nitrogen starting from the excretion points in the sheds and other livestock production areas and continuing to be lost through the on-site management of storage and management systems (i.e., the feces management system). In outdoor areas (i.e., the feeding areas and grazing areas of livestock farms), the nitrogen lost from solid fecal storage can also enter the soil through leaching and runoff, resulting in indirect emissions of N 2 O. N 2 O ID emissions can be estimated using equations ( 5 ) to ( 7 ): $$\:{N}_{2}{O}_{ID}={N}_{2}{O}_{G\left(gas\right)}+{N}_{2}{O}_{L\left(leach\right)}\:$$ 5 $$\:{N}_{2}{O}_{G\left(gas\right)}=[\sum\:_{S}\left[{(N}_{\left(T\right)}\times\:{Nex}_{\left(T\right)}\times\:{MS}_{\left(T,S\right)}\times\:({Frac}_{gasMS}/100))\right]\times\:{EF}_{4}]\times\:(44/28)$$ 6 The N 2 O G (volatile) refers to the indirect N 2 O emissions (kg year − 1 ) caused by volatilization in the fecal management system, Frac gasMS represents the proportion (%) of nitrogen from animal excrement managed by category T in the fecal management system S that volatilizes through NH 3 and NOx, while EF 4 is the emission factor for N 2 O generated in nitrogen deposition from soil and water surfaces to the atmosphere. $$\:{N}_{2}{O}_{L\left(leach\right)}=[\sum\:_{S}\left[{(N}_{\left(T\right)}\times\:{Nex}_{\left(T\right)}\times\:{MS}_{\left(T,S\right)}\times\:({Frac}_{leachMS}/100))\right]\times\:{EF}_{5}]\times\:(44/28)$$ 7 The N 2 O L (leaching and runoff) is the indirect N 2 O emission caused by leaching and runoff in the fecal management system, measured in kg N 2 O per year. Frac leachMS represents the percentage (usually ranging from 1–20%) of nitrogen loss from animal manure due to leaching and runoff during the storage of solid and liquid feces and livestock management. EF 5 is the emission factor for N 2 O caused by nitrogen leaching and runoff. Data Data source. We conducted the surveys in Inner Mongolia, China. Inner Mongolia has the second-largest grassland area in China, accounting for 20.06% of the total national grassland area (Steinfeld et al., 2006 ) and raising 10% and 25% of China’s cattle and sheep, respectively. We first utilized a stratified random sampling method to select household samples. There are three types of grasslands in Inner Mongolia, including desert grasslands, typical grassland, and meadow grasslands. We selected 1–2 sample counties from each type of grassland. Sonid Right County was selected to represent desert grasslands, West Ujimqin County for typical grassland, and Ewenki Autonomous County and Xin Barag Left County for meadow grasslands. We divided all towns in each county into three groups based on their grassland quality, such as good, medium, and poor. One sample town was randomly selected from each group. Three sample villages from each selected town were chosen following a similar approach. We then randomly selected 12 households from each village. Finally, we obtained 414 sample households across four counties, 10 towns, and 30 villages, where 108 sample households are from desert grasslands, 162 households from typical grassland, and 144 households from meadow grasslands. The sample area is shown in Fig. S2 in Appendix 1. More detailed sample information is summarized in Table S6 in Appendix 4 . We used UAVs to collect 1,151 images of the animals from the pastoral area from these 414 sample herders. 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Frontiers in Microbiology , 14 , 1076011. https://doi.org/10.3389/fmicb.2023.1076011 Additional Declarations There is NO Competing Interest. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6170333","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":430047831,"identity":"40cfcd6a-efc2-4439-8f52-45b90322f640","order_by":0,"name":"Pengfei Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYLCCCgYJBj5mBsYHDEAGA0MCEVrOAFWyMTMwG5CihYGBDYgkIFwCWszZew+/OFBhwcDGzmNWzbvHgoGfPccArxbLnnNpFgfOgBzGY3ab55kEg2TPG/xaDG7kmBl/bINpOSABEiGg5f4bM4OD/yBaikFa7AlqucFj/OBgA0QLM9gWCYJ+yTFjOHAMpIWtWHLOAQkeiTPPCvBqMWc/Y/zhQE0dAz//4Y0f3hyok+NvT96A32HQ6KhvgArw4FUO1cL8gaCqUTAKRsEoGNkAAIchO3nuEErGAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5217-0760","institution":"University of Rhode Island","correspondingAuthor":true,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Liu","suffix":""},{"id":430047832,"identity":"4e5329a6-dc86-4f5a-a029-0f05ecc33fd2","order_by":1,"name":"Min Liu","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Liu","suffix":""},{"id":430047833,"identity":"003e630d-21d0-42bf-bb60-3364cf79b36d","order_by":2,"name":"Wanman Mei","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Wanman","middleName":"","lastName":"Mei","suffix":""},{"id":430047834,"identity":"7edb9dd7-a20e-4c03-8e8e-e77cbe0dfc05","order_by":3,"name":"Pengfei Duan","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Duan","suffix":""},{"id":430047835,"identity":"0fa919d1-6024-44ad-ba77-6e93e640403d","order_by":4,"name":"Lifeng Dong","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lifeng","middleName":"","lastName":"Dong","suffix":""},{"id":430047836,"identity":"43fbcdc1-7e53-49e5-9165-862202d7ed33","order_by":5,"name":"Dongliang Wang","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Dongliang","middleName":"","lastName":"Wang","suffix":""},{"id":430047837,"identity":"76cd8da2-9c9f-4d39-b5cc-b27d7e2d6054","order_by":6,"name":"David Wuepper","email":"","orcid":"https://orcid.org/0000-0002-1344-6023","institution":"University of Bonn","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Wuepper","suffix":""}],"badges":[],"createdAt":"2025-03-06 12:06:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6170333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6170333/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78726045,"identity":"4b65efec-1530-4ae0-ba1e-5419e0ed3b13","added_by":"auto","created_at":"2025-03-18 06:12:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1016406,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentifying animal species, animal number, and animal weight through UVA images and deep learning technology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes: \u003c/strong\u003ea. Outlined cattle with annotation tool. b. Outlined sheep with annotation tool. c. Distribution of cattle weight. d. Distribution of sheep weight. Sample animals exclude unweaned animals, as they have not yet undergone food digestion and their GHG emissions are typically negligible (Beauchemin et al., 2011). Therefore, sheep weights start at 15 kg and cattle weights at 100 kg. Sheep weights are less than 90 kg, and cattle weights are less than 750 kg, consistent with the weight distributions of Chinese cattle and sheep reported in previous studies (Ge et al., 2023; Long et al, 2024).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6170333/v1/ea4699cf35321ee2f135c0ef.png"},{"id":78725353,"identity":"6a354177-579d-4a63-8563-33f645270abb","added_by":"auto","created_at":"2025-03-18 06:04:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":186262,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProportion of animal GHG emission reductions by different GHG emission reduction measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes: \u003c/strong\u003eThe amounts of methane (CH₄) and nitrous oxide (N₂O) are converted into the common unit of carbon dioxide equivalent (CO₂e) using the 100-year Global Warming Potential (GWP100) coefficients. The GWP100 coefficients for methane (CH₄) and nitrous oxide (N₂O) are 25 and 298, respectively (IPCC, 2019). The entire circle represents the actual animal carbon emissions on average at the household level, with the colored segments indicating the reductions in animal carbon emissions based on different GHG emission reduction measures.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6170333/v1/1dea4c1e241db2f5005702a2.png"},{"id":80299145,"identity":"9568a848-0a82-4aef-a957-b7cb9bd43b01","added_by":"auto","created_at":"2025-04-10 08:58:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2725307,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6170333/v1/ee51de53-fbe3-489e-a7a2-b63ec851f95a.pdf"},{"id":78726047,"identity":"87fd26d7-9a4f-43c9-9f60-be3187770e01","added_by":"auto","created_at":"2025-03-18 06:12:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":472595,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6170333/v1/e0f50cedefaa1f3da4801191.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"\u003cp\u003eQuantifying Greenhouse Gas Emissions from Livestock in Pastoral Areas based on Unmanned Aerial Vehicles\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCarbon emissions from livestock production received significant attention at the 29th United Nations Climate Change Conference (COP29) in 2024. Various initiatives and discussions highlighted the importance of reducing greenhouse gas (GHG) emissions from the livestock sector. Livestock production is estimated to contribute 15\u0026ndash;18% of global GHG emissions (Dangal et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rojas-Downing et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bellarby et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and is one of the largest global sources of methane emissions, prompting scientists to call for actions to reduce methane emissions for climate change mitigation (e.g., Ripple et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Reisinger et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The mitigation potential of livestock systems is estimated at 0.1 to 7.8 gigatons of CO₂-equivalent per year, accounting for up to 50% of the total mitigation potential within the agriculture, forestry, and land use sectors (Herrero et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Accurate measurement of greenhouse gas (GHG) emissions from livestock is the foundation for achieving GHG emission reductions, though continues to face significant challenges especially in pastoral areas (Herzon et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This paper improves the accuracy of measuring livestock GHG emissions and contributes to design of effective GHG emission reduction strategies.\u003c/p\u003e \u003cp\u003eExisting studies on measuring GHG emissions from livestock mainly use instrumental measurements and emission factor approaches (Huhtanen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The instrumental measurement approach involves direct methods such as respiration metabolism chambers, mask methods, and SF6 (sulfur hexafluoride) tracer methods (Deighton et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Place et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Williams et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), as well as indirect methods such as in vitro fermentation and in vitro gas production techniques (Krizsan et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). While the instrumental measurement approach provides per-animal GHG emissions data, the high economic and time costs restrict its practical application for large-scale implementations. Measuring GHG emissions from pastoral livestock is particularly challenging using the instrumental measurement approach, especially in extensive livestock production systems, such as those where animals graze on vast, permanent grasslands and move across varied environmental conditions (Garnsworthy et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe emission factor approach relies on GHG emission factors published by the Organization for Economic Cooperation and Development (OECD) and the Intergovernmental Panel on Climate Change (IPCC) in the Guidelines for National GHG Emission Inventories (IPCC, 2006; 2019). Many national governments have also published localized GHG emission factors for livestock at the country or regional level (NDRC, 2005). The emission factor approach has been widely used to calculate large-scale GHG emissions from livestock (Wang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, existing animal GHG emission factors do not account for variations in animal characteristics and rely on average characteristic values at the national or regional level instead (Herrero et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For example, the CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emission factors for enteric fermentation and manure management in cattle and buffalo are roughly averaged at an intercontinental level in the IPCC Tier 1 guidelines (IPCC, 2006; 2019). The animal GHG emission factors provided by national governments are more specific than the IPCC Tier 1 guidelines but remain averaged across broad regions (NDRC, 2005), which fail to reflect differences among individual animals. As a result, the existing GHG emission factors may lead to imprecise GHG emission estimates when using the emission factor approach. Crosson et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) found that the GHG emission estimates for livestock in China had a 50% margin of error when calculated using the IPCC method.\u003c/p\u003e \u003cp\u003eAlthough the emission factor approach is feasible for estimating GHG emissions from pastoral livestock, the emission factors provided by the OECD, IPCC, or most national governments are not specifically tailored to pastoral livestock production, which differs from the more common intensive livestock production in cropping areas. Key factors affecting animal emissions, such as animal weight, feed quality, and breeding methods, vary between extensive pastoral systems and intensive systems (Herrero et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Estimating GHG emissions for pastoral livestock based on the existing emission factors would result in substantial bias.\u003c/p\u003e \u003cp\u003eWe propose a novel approach for quantifying GHG emissions from pastoral livestock and deriving more accurate GHG emission factors tailored to pastoral livestock. Unmanned Aerial Vehicles (UAVs) and machine learning technology are used to estimate methane (CH₄) and nitrous oxide (N₂O) emissions from enteric fermentation and manure management for 12,945 cattle and 69,272 sheep in pastoral areas. More accurate animal GHG emission factors are derived by considering animal weight, feed quality, breeding methods, and grassland types, which improves the precision of measuring GHG emissions from livestock in pastoral areas. Potential emission reductions from different strategies, along with their associated costs and benefits, are projected to identify the optimal emission reduction strategy. Moreover, we provide carbon pricing mechanisms for mitigating livestock-related GHG emissions.\u003c/p\u003e \u003cp\u003eThis study makes three primary contributions. First, we improve the accuracy of quantifying GHG emissions from pastoral livestock by using Unmanned Aerial Vehicles (UAVs) combined with deep learning technology to address the uncertainty associated with animal weight in GHG emission inventories. Second, we propose new GHG emission factors tailored to pastoral livestock production by considering animal weight, feed quality, and breeding methods. Existing GHG emission factors fail to account for the heterogeneity in production systems, management practices, and resource-use efficiencies (Herrero et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Third, our findings provide valuable insights into identifying optimal emission reduction strategies for mitigating livestock-related GHG emissions by considering efficiency and cost-effectiveness under different conditions, which provide valuable guidance for shaping effective emission reduction strategies in pastoral livestock production.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAnimal weights derived from UAV images.\u0026nbsp;\u003c/strong\u003eWe employed unmanned aerial vehicles (UAVs) to collect animal images in pastoral areas. To extract information on animal species, population, and body weights, we developed a real-time monitoring system based on the TensorFlow open-source deep learning framework. This system integrates modules for data acquisition, transmission, livestock identification, information extraction, and result output (Wang et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInitially, UAVs captured 1,151 images of animals from pastoral areas, covering 414 sample households. After screening, the qualified images were selected to create UAV image blocks and establish a sample database. During this process, Labelme software was used to annotate cattle and sheep in the images by outlining polygons around each animal. Next, the labeled image blocks were fed into the Mask RCNN (Region-based Convolutional Neural Network) model for iterative training. This training continued until the model achieved optimal accuracy, resulting in a robust deep learning model for livestock identification. The trained Mask RCNN model was subsequently used to accurately recognize cattle and sheep in the images. Finally, based on the livestock identified by the Mask RCNN model, their body weights were estimated using a livestock weight estimation model. This model was developed using 2019 data from Inner Mongolia, which included cattle and sheep weights measured with scales and corresponding head-to-body length measurements taken with a ruler. A linear regression model was constructed to ensure precise weight estimation for the identified livestock.\u003c/p\u003e\n\u003cp\u003eFigures 1a and 1b show the outlined animals and the corresponding weight estimation for each animal. The accuracy of animal weight estimation using UAV images exceeded 90%. Figures 1c and 1d illustrate the weight distribution of 12,945 cattle and 69,272 sheep that were collected by the UAV images, respectively. Table S1 indicates that the cattle had an average weight of 336.2 kg, which is consistent with the cattle weight measured in the field by Hu et al. (2022) and Zhang et al. (2023), and is 9.3% higher than the reference weight used for calculating GHG emission factors for Asian cattle according to the IPCC Tier 1 guidelines. Our estimate is also 4.8% higher than the reference weight for northern China\u0026apos;s cattle provided by the government. The average weight of the sheep was 36.3 kg, which is 14.6% higher than the reference weight provided by IPCC Tier 1 (IPCC, 2019) and 3.6% higher than the reference weight provided by the government (NDRC, 2014). These discrepancies suggest that the reference weights used by IPCC Tier 1 and the government for estimating GHG emission factors cause an underestimation of GHG emissions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal GHG emission factors for pastoral areas of China.\u0026nbsp;\u003c/strong\u003eFollowing the IPCC Tier 2 approach, we estimated animal GHG emissions using animal weights derived from UAV images and livestock production data collected through household surveys. Caro et al. (2014) presented that discrepancies with higher tiers highlight the value of more detailed analyses and caution against overinterpreting smaller-scale trends in the Tier 1 results. Fig. S1 illustrates the distribution of CH\u003csub\u003e4\u003c/sub\u003e emissions from enteric fermentation, CH\u003csub\u003e4\u003c/sub\u003e emissions from manure management, and N\u003csub\u003e2\u003c/sub\u003eO emissions from manure management for 12,945 sampled cattle and 69,272 sampled sheep. The emissions for each animal are converted into CO\u003csub\u003e2\u003c/sub\u003e-equivalent emissions, and the total greenhouse gas (GHG) emissions per animal are calculated as the sum of these contributions. We calculated the average GHG emissions for our sample to determine the animal GHG emission factors for pastoral areas, as shown in Table 1.\u0026nbsp;We find that the values of the animal GHG emission factors provided by IPCC Tier 1 in 2006 and 2019 fall within the range of our calculated GHG emissions for individual sample animals. Additionally, our average GHG emissions per animal, i.e., the estimated animal GHG emission factors based on our survey, are closely aligned with the animal GHG emission factors reported by IPCC Tier 1 in 2006 and 2019, as well as those provided by the government, indicating that our estimates are consistent with the IPCC and national government standards.\u003c/p\u003e\n\u003cp\u003eOur animal GHG emission factors provide a more accurate assessment of animal GHG emissions in pastoral areas because they account for variations in animal weight, feed quality, breeding methods, and grassland types specific to pastoral areas. Existing research indicates that animal CH₄ and N₂O emissions depend heavily on animal characteristics and management practices (Herrero et al., 2016). The GHG emission factors issued by IPCC Tier 1 and the national government are not specifically tailored to the pastoral areas of China. For instance, the CH₄ emission factors for cattle in China, as reported by IPCC Tier 1 in 2006 and 2019, are 55 and 54 kg CH₄ per head per year, respectively, which are the same as those for Asia as a whole, without specific reference to China. Similarly, the CH₄ emission factors for cattle issued by the Chinese government vary only by breeding method: 52.90 kg CH₄ per head per year for intensive breeding and 85.30 kg CH₄ per head per year for extensive breeding, without specific reference to the pastoral areas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Animal GHG emission factors of pastoral areas (\u003c/strong\u003e\u003cstrong\u003ekg CH₄/N₂O head⁻\u0026sup1; year⁻\u0026sup1;\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGHG emissions for individual sample animals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage GHG emissions per animal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnimal GHG emission factors issued by IPCC Tier1 in 2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnimal GHG emission factors issued by IPCC Tier1 in 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnimal GHG emission factors issued by the Chinese national government\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eCattle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e emission from enteric fermentation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[22.78, 121.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.90, 85.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e emission from manure management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[0.51, 2.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02, 2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO emission from manure management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[0.19, 0.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.79, 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eSheep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e emission from enteric fermentation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[2.72, 18.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.50, 8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e emission from manure management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[0.05, 0.31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.15, 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO emission from manure management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[0.02, 0.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09, 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e The estimated GHG emissions for individual sample animals are presented as ranges because we accounted for variations in animal weight, feed quality, breeding methods, and grassland types for all sample animals. Based on the location and climatic conditions of our study area, we applied the following animal GHG emission factors from the IPCC 2006 and 2019 Tier 1 guidelines for the pastoral areas of Inner Mongolia: CH₄ emission factors from enteric fermentation for cattle in Asia were 55.00 and 54.00 kg CH₄ head⁻\u0026sup1; year⁻\u0026sup1;, and for sheep in developing countries/low-productivity systems, 5.00 kg CH₄ head⁻\u0026sup1; year⁻\u0026sup1;; CH₄ and N₂O emission factors from manure management for cattle in Asia and moderate climate areas were 1.57 and 1.34 kg CH₄ head⁻\u0026sup1; year⁻\u0026sup1; and 1.37 and 0.58 kg N₂O head⁻\u0026sup1; year⁻\u0026sup1;, respectively; CH₄ and N₂O emission factors from manure management for sheep in developing countries/low-productivity systems and moderate climate areas were 0.16 and 0.26 kg CH₄ head⁻\u0026sup1; year⁻\u0026sup1; and 0.03 and 0.05 kg N₂O head⁻\u0026sup1; year⁻\u0026sup1;, respectively. China\u0026rsquo;s Greenhouse Gas Inventory Study provides CH₄ emission factors for enteric fermentation based on breeding methods and CH₄ and N₂O emission factors for manure management based on geographic areas in China. Accordingly, we used the following animal GHG emission factors: CH₄ emission factors from enteric fermentation for cattle were 52.90 and 85.30 kg CH₄ head⁻\u0026sup1; year⁻\u0026sup1;, and for sheep, 7.50 and 8.20 kg CH₄ head⁻\u0026sup1; year⁻\u0026sup1;, reflecting the mix of intensive and extensive breeding methods in the pastoral areas of Inner Mongolia. CH₄ emission factors from manure management for cattle were 1.02 and 2.82 kg CH₄ head⁻\u0026sup1; year⁻\u0026sup1;, and for sheep, 0.15 kg CH₄ head⁻\u0026sup1; year⁻\u0026sup1;, based on the study area\u0026rsquo;s location in Northern and Northeastern China. N₂O emission factors from manure management for cattle were 0.79 and 0.91 kg N₂O head⁻\u0026sup1; year⁻\u0026sup1;, and for sheep, 0.09 and 0.06 kg N₂O head⁻\u0026sup1; year⁻\u0026sup1;. Additionally, Table S1 provides the reference weights of animals used for calculating GHG emission factors in this study, as well as those used by IPCC Tier 1, IPCC Tier 2, and China\u0026rsquo;s Greenhouse Gas Inventory Study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecified animal GHG emission factors for different conditions\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e We classified the sample animals based on their specific characteristics to derive more precise animal GHG emission factors. Animals were classified into young and adult groups based on their weight according to the animal growth curve in Table 2 (da Silva et al., 2012). Feed quality was divided into low-quality, medium-quality, and high-quality feed based on the ratio of pasture to feed. Low-quality feed has the highest GHG emission factors, followed by medium-quality and high-quality feed. Breeding methods were divided into stall-feeding, grazing on flat pastures, and grazing on hilly pastures. For cattle, grazing results in higher GHG emissions than stall-feeding, while for sheep, grazing results in lower emissions than stall-feeding, consistent with findings by Stanley et al. (2018) and Ripoll-Bosch et al. (2013). Grassland types also influence the GHG emission factors of animals as the grassland differs in biological characteristics such as grass species, regeneration, and yield (Chang et al., 2021; Pei et al., 2008). Emission factors differ significantly across these grassland types, with meadow grasslands producing the highest GHG emissions, followed by typical grasslands, and desert grasslands producing the lowest.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Specified animal GHG emission factors under different conditions (\u003c/strong\u003e\u003cstrong\u003ekg CH₄/N₂O head⁻\u0026sup1; year⁻\u0026sup1;\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e emission from enteric fermentation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e emission from manure management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO emission from manure management\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ecattle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003esheep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ecattle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003esheep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ecattle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003esheep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eYoung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e64.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eAdult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e81.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e10.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eHigh-quality feed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e33.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMedium-quality feed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e57.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e5.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eLow-quality feed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e74.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e8.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eStall-feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e58.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eGrazing on flat pasture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e67.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e7.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eGrazing on hilly pasture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e81.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e7.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eDesert grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e64.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eTypical grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e66.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMeadow grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e69.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e8.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u0026nbsp;\u003c/strong\u003eAccording to animal weight, we categorized animals into two groups: young and adult. Following the animal growth curve in the literature, sheep over 46 kg and cattle over 485 kg are considered adult animals (Feng et al., 2022; Li et al., 2021). Consequently, young animals are those weighing below 46 kg for sheep and below 485 kg for cattle after weaning. It should be noted that sample animals do not include unweaned animals that have not yet undergone digestion of food, as their GHG emissions are typically negligible. Regarding feed quality, which significantly impacts the intestinal emissions of livestock, we divided it into three categories: low-quality, medium-quality, and high-quality feed. These categories are based on different roughage quality standards corresponding to the digestibility rates in the IPCC guidelines. High-quality feed consists of a mixture with 0-15% pasture and more than 75% feed; medium-quality feed includes 15-75% feed; and low-quality feed consists of more than 75% pasture. Animals under different breeding methods require varying amounts of energy to obtain food, water, and shelter, which in turn determines the net energy of livestock activity. Different breeding methods are distinguished by the respective activity levels corresponding to different feeding conditions of animals, as outlined in the IPCC 2019 guidelines (Xue et al., 2014), including stall-feeding, grazing on flat pastures, and grazing on hilly pastures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimated animal GHG emissions at the household level based on the specified animal GHG emission factors\u003c/strong\u003e. Animal GHG emission factors can be further refined based on multiple conditions. In Table 3, the CH\u003csub\u003e4\u003c/sub\u003e emission factor from enteric fermentation in pastoral areas is 29.77 kg CH\u003csub\u003e4\u003c/sub\u003e head\u003csup\u003e-1\u003c/sup\u003e year\u003csup\u003e-1\u003c/sup\u003e for young cattle with high-quality feed and stall-feeding. For young cattle with medium-quality feed and stall-feeding, the emission factor increases to 50.40 kg CH\u003csub\u003e4\u003c/sub\u003e head\u003csup\u003e-1\u003c/sup\u003e year\u003csup\u003e-1\u003c/sup\u003e, and further rises to 67.72 kg CH\u003csub\u003e4\u003c/sub\u003e head\u003csup\u003e-1\u003c/sup\u003e year\u003csup\u003e-1\u003c/sup\u003e with low-quality feed. Tables S2-S4 in the Appendix present the animal GHG emission factors under different conditions for multiple grassland types, including desert grasslands, typical grassland, and meadow grasslands.\u003c/p\u003e\n\u003cp\u003eBased on the specified animal GHG emission factors under different conditions presented in Table 3, we estimate animal GHG emissions at household level. The household survey data include information on animal weight, feed quality, breeding methods, and grassland types, enabling the accurate estimation of GHG emissions at the household level based on the GHG emission factors in Table 3. As a result, the average animal GHG emissions per household were approximately 155.4 tons of CO\u003csub\u003e2\u003c/sub\u003ee. These emissions varied significantly across households, reflecting differences in animal numbers, weight, feed quality, breeding methods, and grassland types. According to the animal GHG emission factors from the IPCC Tier 1 guidelines (2006), the average GHG emissions from livestock across households were approximately 67.3 tons of CO\u003csub\u003e2\u003c/sub\u003ee per household. Using the updated IPCC 2019 guidelines, the average emissions slightly increased to 71.3 tons of CO\u003csub\u003e2\u003c/sub\u003ee per household. According to the animal GHG emission factors from China\u0026rsquo;s Greenhouse Gas Inventory Study, the reported average emissions were higher at 103.8 tons of CO\u003csub\u003e2\u003c/sub\u003ee per household. Our results indicate that animal GHG emissions in pastoral areas were underestimated when calculated using the IPCC Tier 1 guidelines and China\u0026rsquo;s Greenhouse Gas Inventory Study, which rely on lower reference animal weights for refining GHG emission factors and only use animal numbers in the calculation of emissions. This result is in line with the research finding by He et al. (2023) and Du et al. (2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003cstrong\u003eable 3 Specified animal GHG emission factors under multiple interacting conditions (kg CH₄/N₂O head⁻\u0026sup1; year⁻\u0026sup1;\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"97%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 22px;\"\u003e\n \u003cp\u003eBreeding methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 20px;\"\u003e\n \u003cp\u003eHigh-quality feed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eMedium-quality feed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 19px;\"\u003e\n \u003cp\u003eLow-quality feed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eCattle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eSheep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eCattle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eSheep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eCattle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eSheep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eYoung\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003eAdult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eYoung\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003eAdult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eYoung\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003eAdult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eYoung\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003eAdult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003eYoung\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003eAdult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eYoung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003eAdult\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e emission from enteric fermentation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eStall-feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e29.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e34.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e50.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e61.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e8.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e67.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e78.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e6.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e10.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eGrazing on flat pasture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e32.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e39.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e55.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e69.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e70.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e86.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e11.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eGrazing on hilly pasture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e36.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e44.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e60.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e81.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e9.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e82.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e97.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e7.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e12.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e emission from manure management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eStall-feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eGrazing on flat pasture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eGrazing on hilly pasture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO emission from manure management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eStall-feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eGrazing on flat pasture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eGrazing on hilly pasture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Following the animal growth curve described in the literature, we categorized animals into two groups based on weight: young and adult. Feed quality was classified into three categories\u0026mdash;low-quality, medium-quality, and high-quality\u0026mdash;based on roughage quality standards and corresponding digestibility rates outlined in the IPCC guidelines. Breeding methods were differentiated based on the activity levels associated with various feeding conditions, as specified in the IPCC 2019 guidelines, including stall-feeding, grazing on flat pastures, and grazing on hilly pastures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredicting GHG emission reductions under different emission reduction methods.\u0026nbsp;\u003c/strong\u003eWe further simulate the reduction in animal GHG emissions using various mitigation measures, based on the emission factors provided in Table 2. Existing studies have identified several effective measures for reducing animal GHG emissions, primarily focusing on reducing livestock size and improving technology (Cheng et al., 2022; Jonker et al., 2020). The animal GHG emission factors issued by the IPCC and national governments model reductions associated with a decrease in animal numbers and do not account for other types of improvements, such as enhancements in feed quality and breeding methods. Our specified animal GHG emission factors address this gap by estimating emissions with different feeding qualities and breeding methods. We use our surveyed households to predict the GHG emission reductions at the household level with different GHG emission reduction methods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eReducing livestock size.\u003c/em\u003e Reducing livestock numbers is a primary method for lowering animal GHG emissions (Wang et al., 2024). Using household survey data and the animal GHG emission factors presented in Table 2, we predicted GHG emission reductions under different scenarios involving reductions in sheep and cattle numbers. As shown in Table 4, S1-1, a 25% reduction in cattle numbers (reducing the household average from 48 to 36 head) results in a decrease of 22.59 tons of CO₂e per household, including 20.28 tons of CO₂e from methane emissions due to enteric fermentation, 0.45 tons of CO₂e from methane emissions related to manure management, and 1.86 tons of CO₂e from nitrous oxide emissions associated with manure management. Figure 2 shows that reducing cattle numbers by 25% results in a 14.3% decrease in animal GHG emissions, where methane emissions from enteric fermentation account for 12.8%, methane emissions from manure management for 0.3%, and nitrous oxide emissions from manure management for 1.2%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn S1-2, a 25% reduction in sheep numbers (reducing the household average from 333 to 250 head) results in a reduction of 16.88 tons of CO₂e per household (10.8%), including 15.41 tons of CO₂e from methane emissions due to enteric fermentation (9.8%), 0.27 tons of CO₂e from methane emissions related to manure management (0.2%), and 1.19 tons of CO₂e from nitrous oxide emissions associated with manure management (0.8%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn S1-3 and S1-4, we evaluated a 50% reduction in cattle and sheep numbers, respectively. In S1-3, reducing the household cattle average from 48 to 24 head results in a decrease of 45.20 tons of CO₂e per household (28.7%), including 40.55 tons of CO₂e from methane emissions due to enteric fermentation (25.7%), 0.93 tons of CO₂e from manure-related methane emissions (0.6%), and 3.72 tons of CO₂e from manure-related nitrous oxide emissions (2.4%). In S1-4, reducing the household sheep average from 333 to 167 head results in a decrease of 33.76 tons of CO₂e per household (21.3%), including 30.83 tons of CO₂e from enteric fermentation (19.5%), 0.54 tons of CO₂e from manure-related methane emissions (0.3%), and 2.39 tons of CO₂e from manure-related nitrous oxide emissions (1.5%). The results indicate that reducing cattle numbers achieves a greater GHG emission reduction compared to reducing sheep numbers when the livestock numbers are converted to sheep units for comparison (1 cattle equals 5 sheep units, and 1 sheep equals 1 sheep unit).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImproving feed quality.\u0026nbsp;\u003c/em\u003eExisting studies indicate that improving feed quality can significantly reduce carbon emissions from animals (Pelton et al., 2024; Hristov et al., 2013; Martin et al., 2010; Beauchemin et al., 2008) by adjusting the daily rations of forage and fodder. Dietary changes influence the fermentation and metabolic pathways of animal nutrients, which, in turn, affect GHG emissions (Gastelen et al., 2019). Table 4 presents the predicted results of GHG emission reductions achieved by improving feed quality. According to our survey data, the current feed quality among herder households in the pastoral area comprises 64.6% low-quality feed, 35.4% medium-quality feed, and no households using high-quality feed.\u003c/p\u003e\n\u003cp\u003eIn S2-1, converting all herder households currently using low-quality feed to medium-quality feed reduces GHG emissions by 31.81 tons of CO₂e per household (19.5%), including 29.66 tons of CO₂e from methane emissions due to enteric fermentation (18.2%), 0.56 tons of CO₂e from methane emissions related to manure management (0.3%), and 1.58 tons of CO₂e from nitrous oxide emissions associated with manure management (1.0%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn S2-2, converting all herder households currently using low-quality feed to medium-quality feed and currently using medium-quality feed to high-quality feed results in a reduction of 45.74 tons of CO₂e per household (28.1%), including 42.81 tons of CO₂e from methane emissions due to enteric fermentation (26.3%), 0.82 tons of CO₂e from methane emissions related to manure management (0.5%), and 2.12 tons of CO₂e from nitrous oxide emissions associated with manure management (1.3%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn S2-3, converting all herder households currently using low-quality and using medium-quality feed to high-quality feed achieves a reduction of 81.03 tons of CO₂e per household (49.8%), including 76.31 tons of CO₂e from methane emissions due to enteric fermentation (46.9%), 1.40 tons of CO₂e from methane emissions related to manure management (0.9%), and 3.32 tons of CO₂e from nitrous oxide emissions associated with manure management (2.0%). These results demonstrate that the reduction in GHG emissions becomes increasingly significant as the proportion of higher-quality feed increases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eChanging breeding methods.\u0026nbsp;\u003c/em\u003eStalling, grazing on flat pastures, and grazing on hilly pastures require varying energy inputs for food, water, and shelter, resulting in differences in animal GHG emissions (Samsonstuen, 2019). Table 4 presents the predicted results of GHG emission reductions through improved breeding methods. Our survey data indicates that 3.7% of cattle and 1.4% of sheep are currently stalled.\u003c/p\u003e\n\u003cp\u003eIn S3-1, stalling 50% of cattle while leaving sheep unchanged reduces emissions by 5.22 tons of CO₂e per household (3.3%), comprising 4.75 tons of CO₂e from methane emissions due to enteric fermentation (3.0%), 0.11 tons of CO₂e from methane emissions related to manure management (0.1%), and 0.36 tons of CO₂e from nitrous oxide emissions associated with manure management (0.2%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConversely, in S3-2, leaving cattle unchanged while stalling 50% of sheep increases emissions by 1.86 tons of CO₂e per household, as stalling produces more GHGs than grazing for sheep, as shown in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn S3-3, stalling 100% of cattle while leaving sheep unchanged reduces emissions by 10.46 tons of CO₂e per household (6.7%), including 9.50 tons of CO₂e from methane emissions due to enteric fermentation (6.0%), 0.25 tons of CO₂e from methane emissions related to manure management (0.2%), and 0.71 tons of CO₂e from nitrous oxide emissions associated with manure management (0.5%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn S3-4, stalling 100% of sheep while leaving cattle unchanged increases emissions by 3.72 tons of CO₂e per household. These results indicate that shifting cattle breeding methods to stall-feeding has a moderate impact on reducing GHG emissions, while changing sheep breeding methods to stall-feeding actually increases GHG emissions (Kiggundu et al., 2019; Recktenwald \u0026amp; Ehrhardt, 2024). \u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDifferent types of grasslands.\u0026nbsp;\u003c/em\u003eTable 4 illustrates GHG emissions under different reduction methods across various types of grasslands. Reducing 50% of cattle and sheep numbers results in a reduction of 49.01 tons of CO₂e per household in desert grasslands, 90.41 tons of CO₂e per household in typical grasslands, and 88.50 tons of CO₂e per household in meadow grasslands. Switching from low- and medium-quality feed to high-quality feed results in reductions of 48.1 tons of CO₂e per household in desert grasslands, 86.50 tons of CO₂e per household in typical grasslands, and 85.57 tons of CO₂e per household in meadow grasslands. Changing breeding methods to stall-feeding for both cattle and sheep decreases emissions by 0.79 tons of CO₂e per household in desert grasslands, 6.6 tons of CO₂e per household in typical grasslands, and 11.24 tons of CO₂e per household in meadow grasslands. These results demonstrate that reducing livestock numbers and improving feed quality are significantly more effective for reducing GHG emissions compared to changing breeding methods. Furthermore, the effects of GHG emission reductions are more pronounced in typical and meadow grasslands compared to desert grasslands, while typical and meadow grasslands also generate more animal GHG emissions than desert grasslands.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Predicted animal GHG emission reductions with different reduction measures\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eEmissions reduction\u003c/p\u003e\n \u003cp\u003emeasures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eScenarios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e from enteric fermentation (ton/CO\u003csub\u003e2\u003c/sub\u003ee)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e from manure management (ton/CO\u003csub\u003e2\u003c/sub\u003ee)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO from manure management (ton/CO\u003csub\u003e2\u003c/sub\u003ee)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eTotal reduction\u003c/p\u003e\n \u003cp\u003e(ton/CO\u003csub\u003e2\u003c/sub\u003ee)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(S1) Reducing livestock size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS1-1: Cattle number (-25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-20.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-22.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS1-2: Sheep number (-25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-15.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-16.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS1-3: Cattle number (-50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-40.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-45.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS1-4: Sheep number (-50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-30.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-33.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(S2) Improving feed quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS2-1: Low\u0026mdash;Medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-29.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-31.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS2-2: Low\u0026mdash;Medium and Medium\u0026mdash;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-42.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-45.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS2-3: Low\u0026mdash;High and Medium\u0026mdash;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-76.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-81.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(S3) Changing breeding methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS3-1: Stalling cattle (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS3-2: Stalling sheep (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS3-3: Stalling cattle (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-9.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-10.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS3-4: Stalling sheep (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 864px;\"\u003e\n \u003cp\u003eOn different types of grasslands:\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eDesert grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S1-3)+(S1-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-44.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-49.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S2-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-44.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-48.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S3-3)+(S3-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTypical grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S1-3)+(S1-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-81.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-90.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S2-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-81.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-86.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S3-3)+(S3-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-6.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMeadow grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S1-3)+(S1-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-79.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-88.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S2-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-80.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-85.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S3-3)+(S3-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-10.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-11.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u0026nbsp;\u003c/strong\u003eAccording to the conversion coefficients of global warming potential over a 100\u0026ndash;year time scale (GWP100), the emission of 1 kg of CH\u003csub\u003e4\u003c/sub\u003e is equivalent to the emission of 25 kg of CO\u003csub\u003e2\u003c/sub\u003e, and the emission of 1 kg of N\u003csub\u003e2\u003c/sub\u003eO is equivalent to the emission of 298 kg of CO\u003csub\u003e2\u003c/sub\u003e (IPCC, 2019). Reducing livestock size (S1): In S1-1, the number of cattle per household decreases by 25%. In S1-2, the number of sheep per household decreases by 25%. In S1-3, the number of cattle per household decreases by 50%. In S1-4, the number of sheep per household decreases by 50%. Improving feed quality (S2): In S2-1, all low-quality forage is converted into medium-quality forage. In S2-2, all low-quality forage is converted into medium-quality forage, and medium-quality forage is converted into high-quality forage. In S2-3, all low- and medium-quality forage is converted into high-quality forage. Improving breeding method (S3): In S3-1, 50% of cattle are converted to stall-feeding, while the grazing method for sheep remains unchanged. In S3-2, 50% of sheep are converted to stall-feeding, while the grazing method for cattle remains unchanged. In S3-3, 100% of cattle are converted to stall-feeding, while the grazing method for sheep remains unchanged. In S3-4, 100% of sheep are converted to stall-feeding, while the grazing method for cattle remains unchanged. GHG emission reduction in different grassland types: This includes desert grasslands, typical grasslands and meadow grasslands. In (S1-3)+(S1-4), the number of both sheep and cattle is reduced by 50%. In (S2-3), low- and medium-quality feed is converted to high-quality feed. In (S3-3)+(S3-4), 100% of both cattle and sheep are converted to stall-feeding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCost-Benefit Analysis of Animal GHG Emission Reduction.\u0026nbsp;\u003c/strong\u003eBased on the amount of GHG reductions achieved through various emission reduction methods, we calculated both the revenue and costs associated with reducing animal GHG emissions. Using current carbon trading prices in China, we estimated the revenue generated from these emission reductions. The costs were then determined by accounting for the expenses incurred in implementing different reduction methods, such as reducing livestock numbers, improving feed quality, and modifying breeding practices. Finally, we conducted a comparative analysis of the net revenue per ton of emission reductions for each method. The results show that the revenue generated by all reduction methods is substantially lower than the associated costs. Consequently, we estimated the break-even carbon prices required for animal GHG emission reductions to offset their costs. These break-even prices represent the carbon prices at which the revenue from emission reductions equals the costs of implementing the reduction methods.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eReducing livestock size.\u003c/em\u003e Table 5 illustrates that both the revenue and costs associated with emission reductions from decreasing the cattle population are significantly higher than those from reducing the sheep population. As a result, the net revenue from reducing the cattle population is lower compared to that of reducing the sheep population. In S1-1, a 25% reduction in the cattle population results in an average reduction of 22.6 tons of CO₂e per household, valued at 1,894 yuan based on the current carbon trading price in China. However, the cost of reducing 25% of the cattle population per household amounts to 110,220 yuan, based on the cattle trading price in pastoral areas. Consequently, the net revenue from GHG emission reduction by reducing 25% of the cattle population is -4,795 yuan per ton of CO₂e. The break-even price required for animal GHG emission reductions to offset the costs is 4,879 yuan per ton of CO₂e.\u003c/p\u003e\n\u003cp\u003eSimilarly, in S1-2, a 25% reduction in the sheep population results in an average reduction of 16.9 tons of CO₂e per household, valued at 1,415 yuan. The cost of reducing 25% of the sheep population per household is 74,119 yuan, based on the sheep trading price in pastoral areas. Thus, the net revenue from GHG emission reduction by reducing 25% of the sheep population is -4,307 yuan per ton of CO₂e, and the break-even price is 4,391 yuan per ton of CO₂e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn S1-3 and S1-4, where 50% of the cattle and sheep populations are reduced, the net revenue from GHG emission reduction is -4,793 yuan and -4,307 yuan per ton of CO₂e, respectively. The break-even prices for these reductions are 4,877 yuan per ton of CO₂e for cattle and 4,391 yuan per ton of CO₂e for sheep. Detailed calculations are provided in Table S8 of Appendix 7.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImproving feed quality.\u0026nbsp;\u003c/em\u003eTable 5 illustrates that converting all low-quality forage into medium-quality forage results in the lowest emission reduction cost and the highest net revenue. It also reveals that as feed quality increases, net revenue decreases, and the break-even price rises. In S2-1, converting all households currently using low-quality forage to medium-quality forage results in a reduction of 31.8 tons of CO₂e per household, valued at 2,667 yuan. However, the average cost of improving feed quality from low-quality to medium-quality forage is 14,066 yuan per household per year, based on the current forage trading price in pastoral areas. As a result, the net revenue from GHG emission reduction by converting low-quality forage to medium-quality forage is -358 yuan per ton of CO₂e. The break-even price required for animal GHG emission reductions to offset the costs is 442 yuan per ton of CO₂e. In S2-2, converting all low-quality forage to medium-quality forage and all medium-quality forage to high-quality forage results in a net revenue of -684 yuan per ton of CO₂e. The break-even price is 768 yuan per ton of CO₂e. In S2-3, converting all low- and medium-quality forage to high-quality forage results in a net revenue of -906 yuan per ton of CO₂e. The break-even price for this conversion is 990 yuan per ton of CO₂e. Detailed calculations are provided in Table S9 of Appendix 7.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eChanging breeding methods.\u003c/em\u003e In S3-1, converting 50% of grazing cattle to stall-feeding while keeping the grazing method for sheep unchanged results in a reduction of 5.22 tons of CO₂e per household, valued at 438 yuan. However, the average cost of converting grazing cattle to stall-feeding per household is 19,163 yuan per year, primarily due to increased labor requirements. Consequently, the net revenue from GHG emission reduction by converting 50% of grazing cattle to stall-feeding is -3,587 yuan per ton of CO₂e. The break-even price required for animal GHG emission reductions to offset the costs is 3,671 yuan per ton of CO₂e. In S3-3, converting 100% of grazing cattle to stall-feeding while keeping the grazing method for sheep unchanged results in a net revenue of -3,580 yuan per ton of CO₂e. The break-even price is 3,664 yuan per ton of CO₂e. However, in S3-2 and S3-4, converting 50% or 100% of sheep to stall-feeding while cattle remain grazing does not yield break-even prices, as these scenarios increase GHG emissions. Detailed calculations are provided in Table S10 of Appendix 7.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDifferent types of grasslands.\u0026nbsp;\u003c/em\u003eTable 5 illustrates that improving feed quality in each type of grassland results in the highest net revenue and lowest break-even price for GHG emission reductions, followed by reducing animal numbers. Changing breeding methods yields the lowest net revenue and highest break-even price. Specifically, converting low-quality and medium-quality feed into high-quality feed in meadow grasslands provides the highest net revenue at -410 yuan per ton of CO₂e, followed by typical grasslands at -462 yuan per ton of CO₂e. Desert grasslands have the lowest net revenue at -532 yuan per ton of CO₂e.\u003c/p\u003e\n\u003cp\u003eCorrespondingly, converting low-quality and medium-quality feed into high-quality feed in meadow grasslands also results in the lowest break-even price at 493 yuan per ton of CO₂e, followed by typical grasslands at 546 yuan per ton of CO₂e, and desert grasslands at 616 yuan per ton of CO₂e. In contrast, changing breeding methods in desert grasslands incurs the highest break-even price at 82,268 yuan per ton of CO₂e, followed by typical grasslands at 16,260 yuan per ton of CO₂e, and meadow grasslands at 8,780 yuan per ton of CO₂e. Additionally, the break-even carbon price for reducing livestock numbers is 4,527 yuan per ton of CO₂e in desert grasslands, which is lower than 4,666 yuan per ton of CO₂e in typical grasslands and 4,931 yuan per ton of CO₂e in meadow grasslands. Detailed calculations are provided in Table S11 of Appendix 7. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 Predicted revenue and costs of different GHG emission reduction methods at household level\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"881\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eEmissions reduction\u003c/p\u003e\n \u003cp\u003emeasures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eScenarios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eRevenue\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCosts\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNet revenue\u003c/p\u003e\n \u003cp\u003e(yuan/ton)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eCarbon price (yuan/ton)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(S1) Reducing livestock size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS1-1: Cattle number (-25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1,894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-110,220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-4,795.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e4,879.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS1-2: Sheep number (-25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1,415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-74,119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-4,307.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e4,390.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS1-3: Cattle number (-50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3,789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-220,440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-4,793.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e4,876.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS1-4: Sheep number (-50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2,830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-148,238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-4,307.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e4,390.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(S2) Improving feed quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS2-1: Low\u0026mdash;Medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2,667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-14,066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-358.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e442.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS2-2: Low\u0026mdash;Medium and Medium\u0026mdash;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3,834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-35,109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-683.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e767.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS2-3: Low\u0026mdash;High and Medium\u0026mdash;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e6,793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-80,235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-906.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e990.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(S3) Changing breeding methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS3-1: Stalling cattle (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-19,163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-3587.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e3,671.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS3-2: Stalling sheep (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-26,588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS3-3: Stalling cattle (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-38,325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-3580.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e3,663.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eS3-4: Stalling sheep (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-53,716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eDesert grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S1-3)+(S1-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4,109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-221,846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-4,442.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e4526.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S2-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4,031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-29,625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-532.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e616.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S3-3)+(S3-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-64,992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-82,184.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e82268.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTypical grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S1-3)+(S1-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e7,579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-421,875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-4,582.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e4666.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S2-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e7,251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-47,195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-461.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e545.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S3-3)+(S3-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-106,990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-16,176.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e16259.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMeadow grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S1-3)+(S1-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e7,419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-436,417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-4,847.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e4931.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S2-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e7,173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-42,221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-409.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e493.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(S3-3)+(S3-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-98,687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-8,696.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e8779.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u0026nbsp;\u003c/strong\u003eN/A stands for \u0026apos;not available.\u0026apos; The emission reduction revenue is equal to the national carbon market trading price multiplied by the reduced carbon emissions, with the carbon price referring to the comprehensive price of 83.83 yuan/ton on the national carbon market as of March 21, 2024. Reducing livestock size involves four scenarios: a 25% reduction in cattle (S1-1), a 25% reduction in sheep (S1-2), a 50% reduction in cattle (S1-3), and a 50% reduction in sheep (S1-4). Improving feed quality involves three scenarios: converting all low-quality forage to medium-quality (S2-1), converting low-quality to medium-quality and medium-quality to high-quality (S2-2), and converting all low- and medium-quality forage to high-quality (S2-3). Improving breeding methods involves four scenarios: switching 50% of cattle to stall-feeding while sheep remain grazing (S3-1), switching 50% of sheep to stall-feeding while cattle remain grazing (S3-2), switching 100% of cattle to stall-feeding while sheep remain grazing (S3-3), and switching 100% of sheep to stall-feeding while cattle remain grazing (S3-4). GHG emission reduction across grassland types (desert, typical, and meadow) includes reducing animals by 50% in (S1-3)+(S1-4), converting low- and medium-quality feed to high-quality in (S2-3), and switching 100% of both cattle and sheep to stall-feeding in (S3-3)+(S3-4).\u003c/p\u003e"},{"header":"Discussion and Conclusion","content":"\u003cp\u003eAccurately estimating GHG emissions from animals is crucial for developing an effective strategy to reduce animal-related emissions. Our research improves the calculation of livestock carbon emissions in pastoral areas where we first extracted the specified GHG emission factors for cattle and sheep, accounting for animal weight, feed quality, and breeding methods. Based on specified emission factors, we measured animal GHG emissions at the household level. The results showed that the average greenhouse gas (GHG) emissions from livestock per household in the pastoral areas of Inner Mongolia were approximately 155.4 tons of CO2e. These emissions were underestimated when calculated using the GHG emission factors from the IPCC Tier 1 guidelines and China\u0026rsquo;s Greenhouse Gas Inventory Study, as these methods only consider animal numbers, resulting in emissions of 71.3 and 103.8 tons of CO2e, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe further predicted the potential for GHG emission reductions through various methods, including decreasing animal populations, improving feed quality, and changing breeding methods. A cost-benefit analysis was conducted to compare the net revenue of these emission reduction strategies across different scenarios. Our findings indicate that improving feed quality is more effective and cost-efficient in reducing GHG emissions from livestock than reducing animal numbers or changing breeding methods at the household level in pastoral areas. Therefore, prioritizing adjustments to the feeding structure to enhance animal digestibility is essential for reducing GHG emissions. Meanwhile, the lowest break-even price occurs when herders convert all low-quality forage into medium-quality forage, at 442 yuan per ton, which is slightly lower than the current carbon price in the European Union at 483 yuan per ton, suggesting a viable market opportunity to reduce animal GHG emissions by improving feed quality. Moreover, enhancing feed quality can increase animal productivity, while the costs of these improvements can be offset through the carbon market. Our results provide important information for herders to potentially engage in GHG emission reduction through carbon markets.\u003c/p\u003e\n\u003cp\u003eThe pastoral areas of Inner Mongolia had 596,400 herder households, of which 64.6% were using low-quality feed in 2020. Shifting these households to medium-quality feed would lead to a reduction of 31.8 tons of CO₂e per household, resulting in a total reduction of 12.2 million tons of CO₂e, including 11.6 million tons of CO₂e from methane and 0.6 million tons of CO₂e from nitrous oxide. Conversely, changing breeding methods has a minimal impact on reducing animal GHG emissions, while the associated costs are significantly higher, indicating inefficacy for emission reduction in pastoral areas. The break-even is approximately eight times higher than the current carbon price in the European market. The results also show that GHG emission reductions are more pronounced and less costly in typical and meadow grasslands compared to desert grasslands. Consequently, GHG emission reduction could be piloted in typical and meadow grasslands first. Finally, reducing the number of cattle is more effective for GHG emission reduction than reducing the number of sheep, though the cost of reducing cattle is higher. Therefore, when reducing animal numbers to lower GHG emissions, the choice between cattle and sheep depends on whether the priority is greater emission reduction efficiency or cost-effectiveness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere are several avenues for future research. First, the data obtained by UAVs are static. Future research can use cameras to monitor changes in animals over time to calculate dynamic GHG emissions and assess temporal changes. Second, the reduction in animal emissions applies only to the livestock rearing stage in this study. A life cycle assessment that takes into account additional stages such as feed production, slaughter and processing, and product transportation, may provide related policy implications from a different perspective. Lastly, this study only calculates emission factors for cattle and sheep in Inner Mongolia, though our methods can be easily applied to other pastoral areas with different types of grasslands and modes of livestock production, such as the Qinghai-Tibet Plateau. Future research can expand the study areas and implement similar methods across a broader geographical region to improve the accuracy of animal GHG emission factors in pastoral areas throughout China, while also incorporating the impacts of grasslands on animal GHG emissions in pastoral areas.\u0026nbsp;\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eWe calculate livestock GHG emissions from the procedure of livestock production, including CH\u003csub\u003e4\u003c/sub\u003e emissions from enteric fermentation and manure management, and N\u003csub\u003e2\u003c/sub\u003eO emissions from manure management of ruminant animals based on the IPCC Tier 2 approach.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCH\u003c/b\u003e \u003csub\u003e \u003cb\u003e4\u003c/b\u003e \u003c/sub\u003e \u003cb\u003eemission from enteric fermentation.\u003c/b\u003e CH₄ emission from enteric fermentation refers to the CH₄ produced by microorganisms in the animal\u0026rsquo;s digestive tract during the normal process of metabolism and fermentation of feed (McAuliffe et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Swamy \u0026amp; Bhattacharya, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), which accounts for 42.6% of GHG emissions in livestock production (Sugar et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and includes CH₄ expelled from the animal\u0026rsquo;s mouth, nose, and rectum. The CH₄ emission factor for enteric fermentation is estimated using Eq.\u0026nbsp;(\u003cspan refid=\"Equ8\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{CH}_{4\\left(ef\\right)}=[GE\\times\\:{(Y}_{m}/100)\\times\\:365/55.65],$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CH}_{4\\left(ef\\right)}\\)\u003c/span\u003e\u003c/span\u003e is the CH\u003csub\u003e4\u003c/sub\u003e emission factor from enteric fermentation (kg CH\u003csub\u003e4\u003c/sub\u003e head\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). \u003cem\u003eGE\u003c/em\u003e is the total energy intake (MJ head\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), which depends on animal weight, feed quality, breeding methods, and grassland types. The detailed calculation for \u003cem\u003eGE\u003c/em\u003e is presented in Appendix 2. \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e is the CH\u003csub\u003e4\u003c/sub\u003e conversion factor, which represents the percentage of \u003cem\u003eGE\u003c/em\u003e intake converted into CH\u003csub\u003e4\u003c/sub\u003e, and 55.65 (MJ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) is the energy content of CH\u003csub\u003e4\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCH\u003c/b\u003e \u003csub\u003e \u003cb\u003e4\u003c/b\u003e \u003c/sub\u003e \u003cb\u003eemission from manure management.\u003c/b\u003e CH₄ emissions from manure management account for 1.4% of GHG emissions from livestock production (Sugar et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). CH₄ is produced during the storage and management of animal manure and composting on farms. The main factors affecting CH₄ emissions are the amount of manure produced and the proportion of anaerobic degradation during manure management. The CH₄ emission from manure management is estimated using Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{CH}_{4\\left(mm\\right)}=VS\\times\\:365\\times\\:\\left[{B}_{0}\\times\\:\\frac{0.67kg}{{m}^{3}}\\times\\:\\sum\\:_{s,k}\\frac{{MCF}_{\\left(s,k\\right)}}{100}\\times\\:{MS}_{\\left(s,k\\right)}\\right]\\times\\:\\left(\\frac{44}{28}\\right),$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CH}_{4\\left(mm\\right)}\\:\\)\u003c/span\u003e\u003c/span\u003eis the CH\u003csub\u003e4\u003c/sub\u003e emission factor from manure management (kg CH\u003csub\u003e4\u003c/sub\u003e head\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). \u003cem\u003eVS\u003c/em\u003e is the daily volatile solid excrement of livestock (kg dry matter head\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The detailed calculation on VS is presented in \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003eAppendix 3\u003c/span\u003e. \u003cem\u003eB\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e represents the maximum CH\u003csub\u003e4\u003c/sub\u003e production capacity (m\u003csup\u003e3\u003c/sup\u003e kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of the excrement produced by animals. 0.67 is the conversion factor from the volume of CH\u003csub\u003e4\u003c/sub\u003e to the quality of CH\u003csub\u003e4\u003c/sub\u003e. \u003cem\u003eMCF\u003c/em\u003e\u003csub\u003e\u003cem\u003e(s,k)\u003c/em\u003e\u003c/sub\u003e is the CH\u003csub\u003e4\u003c/sub\u003e conversion factor for each type of manure management system \u003cem\u003es\u003c/em\u003e in climate zone \u003cem\u003ek\u003c/em\u003e, and \u003cem\u003eMS\u003c/em\u003e\u003csub\u003e\u003cem\u003e(s,k)\u003c/em\u003e\u003c/sub\u003e is the proportion of manure managed by system \u003cem\u003es\u003c/em\u003e in climate zone \u003cem\u003ek\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eN\u003c/b\u003e \u003csub\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sub\u003e \u003cb\u003eO emission from manure management.\u003c/b\u003e N\u003csub\u003e2\u003c/sub\u003eO emission from animal manure management accounts for 3.6% of total GHG emissions from livestock production (Sugar et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). N₂O emissions from manure management \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left({{N}_{2}O}_{\\left(mm\\right)}\\right)\\)\u003c/span\u003e\u003c/span\u003e are estimated using Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{{N}_{2}O}_{\\left(mm\\right)}={N}_{2}{O}_{D}+{N}_{2}{O}_{ID}\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{N}_{2}O}_{\\left(mm\\right)}\\)\u003c/span\u003e\u003c/span\u003e is the N\u003csub\u003e2\u003c/sub\u003eO emission factor of manure management (kg N\u003csub\u003e2\u003c/sub\u003eO head\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e refers to the direct emissions of N\u003csub\u003e2\u003c/sub\u003eO from manure management, and \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003eID\u003c/em\u003e\u003c/sub\u003e refers to the indirect emissions of N\u003csub\u003e2\u003c/sub\u003eO from manure management. Direct emission of N\u003csub\u003e2\u003c/sub\u003eO from animal manure management refers to the N\u003csub\u003e2\u003c/sub\u003eO generated during the storage and handling of animal manure before it is applied to the soil. Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) below estimates the direct emission of N\u003csub\u003e2\u003c/sub\u003eO (\u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e):\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{N}_{2}{O}_{D}=[\\sum\\:_{S}\\left[\\sum\\:_{T}\\left({N}_{\\left(T\\right)}\\times\\:{Nex}_{\\left(T\\right)}\\times\\:{MS}_{\\left(T,S\\right)}\\right)\\right]\\times\\:{EF}_{3}]\\times\\:(44/28)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e refers to direct emissions of N\u003csub\u003e2\u003c/sub\u003eO (in kg year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) from manure management. \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003e(T)\u003c/em\u003e\u003c/sub\u003e represents the number of livestock of a certain species/category \u003cem\u003eT\u003c/em\u003e. \u003cem\u003eNex\u003c/em\u003e\u003csub\u003e\u003cem\u003e(T)\u003c/em\u003e\u003c/sub\u003e is the average annual nitrogen excretion (in kg head\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of each livestock in the specific species/category \u003cem\u003eT\u003c/em\u003e. \u003cem\u003eMS\u003c/em\u003e\u003csub\u003e\u003cem\u003e(T, S)\u003c/em\u003e\u003c/sub\u003e \u003cem\u003ei\u003c/em\u003es the dimensionless proportion of the total annual nitrogen excretion from each livestock species/category \u003cem\u003eT\u003c/em\u003e managed by fecal management system \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eEF\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e is the emission factor for direct emissions of N\u003csub\u003e2\u003c/sub\u003eO from livestock fecal management system \u003cem\u003eS\u003c/em\u003e, and 44/28 is the conversion factor from N\u003csub\u003e2\u003c/sub\u003eO-N emissions to N\u003csub\u003e2\u003c/sub\u003eO emissions.\u003c/p\u003e \u003cp\u003eIndirect emission of N\u003csub\u003e2\u003c/sub\u003eO from animal feces management refers to the loss of nitrogen starting from the excretion points in the sheds and other livestock production areas and continuing to be lost through the on-site management of storage and management systems (i.e., the feces management system). In outdoor areas (i.e., the feeding areas and grazing areas of livestock farms), the nitrogen lost from solid fecal storage can also enter the soil through leaching and runoff, resulting in indirect emissions of N\u003csub\u003e2\u003c/sub\u003eO. \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003eID\u003c/em\u003e\u003c/sub\u003e emissions can be estimated using equations (\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) to (\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e7\u003c/span\u003e):\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:{N}_{2}{O}_{ID}={N}_{2}{O}_{G\\left(gas\\right)}+{N}_{2}{O}_{L\\left(leach\\right)}\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:{N}_{2}{O}_{G\\left(gas\\right)}=[\\sum\\:_{S}\\left[{(N}_{\\left(T\\right)}\\times\\:{Nex}_{\\left(T\\right)}\\times\\:{MS}_{\\left(T,S\\right)}\\times\\:({Frac}_{gasMS}/100))\\right]\\times\\:{EF}_{4}]\\times\\:(44/28)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003eG\u003c/em\u003e\u003c/sub\u003e (volatile) refers to the indirect N\u003csub\u003e2\u003c/sub\u003eO emissions (kg year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) caused by volatilization in the fecal management system, \u003cem\u003eFrac\u003c/em\u003e\u003csub\u003e\u003cem\u003egasMS\u003c/em\u003e\u003c/sub\u003e represents the proportion (%) of nitrogen from animal excrement managed by category \u003cem\u003eT\u003c/em\u003e in the fecal management system \u003cem\u003eS\u003c/em\u003e that volatilizes through NH\u003csub\u003e3\u003c/sub\u003e and NOx, while \u003cem\u003eEF\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e is the emission factor for N\u003csub\u003e2\u003c/sub\u003eO generated in nitrogen deposition from soil and water surfaces to the atmosphere.\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:{N}_{2}{O}_{L\\left(leach\\right)}=[\\sum\\:_{S}\\left[{(N}_{\\left(T\\right)}\\times\\:{Nex}_{\\left(T\\right)}\\times\\:{MS}_{\\left(T,S\\right)}\\times\\:({Frac}_{leachMS}/100))\\right]\\times\\:{EF}_{5}]\\times\\:(44/28)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e (leaching and runoff) is the indirect N\u003csub\u003e2\u003c/sub\u003eO emission caused by leaching and runoff in the fecal management system, measured in kg N\u003csub\u003e2\u003c/sub\u003eO per year. Frac\u003csub\u003eleachMS\u003c/sub\u003e represents the percentage (usually ranging from 1\u0026ndash;20%) of nitrogen loss from animal manure due to leaching and runoff during the storage of solid and liquid feces and livestock management. \u003cem\u003eEF\u003c/em\u003e\u003csub\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sub\u003e is the emission factor for N\u003csub\u003e2\u003c/sub\u003eO caused by nitrogen leaching and runoff.\u003c/p\u003e\n\u003ch3\u003eData\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eData source.\u003c/b\u003e We conducted the surveys in Inner Mongolia, China. Inner Mongolia has the second-largest grassland area in China, accounting for 20.06% of the total national grassland area (Steinfeld et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and raising 10% and 25% of China\u0026rsquo;s cattle and sheep, respectively. We first utilized a stratified random sampling method to select household samples. There are three types of grasslands in Inner Mongolia, including desert grasslands, typical grassland, and meadow grasslands. We selected 1\u0026ndash;2 sample counties from each type of grassland. Sonid Right County was selected to represent desert grasslands, West Ujimqin County for typical grassland, and Ewenki Autonomous County and Xin Barag Left County for meadow grasslands. We divided all towns in each county into three groups based on their grassland quality, such as good, medium, and poor. One sample town was randomly selected from each group. Three sample villages from each selected town were chosen following a similar approach. We then randomly selected 12 households from each village. Finally, we obtained 414 sample households across four counties, 10 towns, and 30 villages, where 108 sample households are from desert grasslands, 162 households from typical grassland, and 144 households from meadow grasslands. The sample area is shown in Fig. S2 in Appendix 1. More detailed sample information is summarized in Table S6 in \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003eAppendix 4\u003c/span\u003e. We used UAVs to collect 1,151 images of the animals from the pastoral area from these 414 sample herders. In addition, we obtained information on livestock production, such as feed quality, breeding methods, and so forth, through household surveys. Statistical analysis on the sample households is presented in Table S7 of \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003eAppendix 6\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBeauchemin, K. A., Janzen, H. H., Little, S. M., McAllister, T. A., \u0026amp; McGinn, S. M. (2011). 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Effects of altitude on the gut microbiome and metabolomics of Sanhe heifers. \u003cem\u003eFrontiers in Microbiology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 1076011. https://doi.org/10.3389/fmicb.2023.1076011\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Unmanned Aerial Vehicle, deep learning, GHG inventory, livestock, pastoral areas","lastPublishedDoi":"10.21203/rs.3.rs-6170333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6170333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate measurement of greenhouse gas (GHG) emissions from livestock is essential for developing effective emission reduction strategies. This study introduces the use of Unmanned Aerial Vehicles (UAVs) and deep learning technology to estimate methane (CH₄) and nitrous oxide (N₂O) emissions from enteric fermentation and manure management in pastoral areas. More accurate animal GHG emission factors are derived by considering animal weight, feed quality, breeding methods, and grassland types, which improves the precision of measuring GHG emissions from livestock. Potential emission reductions from different strategies, along with their associated costs and benefits, are projected to identify the optimal emission reduction strategy. We also discuss appropriate carbon pricing mechanisms for mitigating livestock-related GHG emissions. These findings offer valuable guidance for shaping effective emission reduction strategies in pastoral livestock production.\u003c/p\u003e","manuscriptTitle":"Quantifying Greenhouse Gas Emissions from Livestock in Pastoral Areas based on Unmanned Aerial Vehicles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-18 06:04:03","doi":"10.21203/rs.3.rs-6170333/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a88e61bb-a80a-4637-a8e8-e0b321c45323","owner":[],"postedDate":"March 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45824161,"name":"Social science/Environmental studies"},{"id":45824162,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation"},{"id":45824163,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change mitigation"}],"tags":[],"updatedAt":"2025-04-10T08:50:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-18 06:04:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6170333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6170333","identity":"rs-6170333","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}