Uncertainty Analysis Supports Prioritization of Greenhouse Gas Inventory Improvement in Indonesia’s Livestock Sector

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However, uncertainties in emission factors and activity data can significantly affect the accuracy of national GHG inventories. This study conducts a comprehensive uncertainty analysis of GHG emissions from Indonesia’s cattle sector between 1994 and 2022 calculated using a Tier 2 approach and Monte Carlo simulation based on the IPCC 2019 guidelines. The cattle production systems assessed were intensive, semi-intensive and extensive systems. The analysis covers three major emission sources: enteric methane (CH₄), methane and nitrous oxide (N₂O) emissions from manure management (including deposit on pasture). Results show that uncertainty levels have increased over time, with uncertainty of enteric fermentation – the largest emission source – increasing from ca. ±15 for 1994 to ± 20% for 2022. This increase was mainly due to the rising share of cattle in the intensive system. Although uncertainties of other emission sources were higher, ranging between ± 36% and ± 104% for different sources in different years, their contributions to total emission uncertainty were limited because of the small proportion of total emissions from these sources. Uncertainty of key parameters in the intensive system, such as the enteric methane conversion factor, feed digestibility, liveweight, and proportions of manure managed in different systems, had the greatest contributions to overall inventory uncertainty. To improve inventory accuracy, the study emphasizes the need for improved data availability and quality, especially for intensive production systems. Strengthening national capacity to monitor animal performance, feed characteristics, and manure handling will significantly reduce uncertainty in future inventories. These improvements are crucial for enhancing accuracy and credibility in Indonesia’s climate reporting in the livestock sector. greenhouse gas inventory livestock methane nitrous oxide uncertainty analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Greenhouse gas (GHG) emissions from the agriculture sector, particularly from livestock, contribute significantly to global climate change. Globally, the livestock sector is responsible for approximately 14.5% of total anthropogenic GHG emissions, with methane (CH₄) from enteric fermentation in ruminants accounting for the largest share (Gerber et al. 2013 ). In Indonesia, the agriculture sector contributed around 7.8% of the country’s total GHG emissions in 2020, and livestock alone accounted for a major portion of those emissions (Ministry of Environment and Forestry 2021 ). Accurate quantification of these emissions is essential for national reporting under the United Nations Framework Convention on Climate Change (UNFCCC), and can support development of mitigation strategies in line with Indonesia’s Nationally Determined Contributions (NDCs). However, GHG inventories often suffer from uncertainties caused by limited or inconsistent data, the use of default emission factors, and assumptions in models (Tubiello et al. 2013 ). In Indonesia, livestock systems vary significantly across regions, ranging from intensive operations to extensive smallholder farming, which further complicates emission estimation (Widiawati et al. 2016 ). Uncertainty analysis is a methodological approach used to assess the accuracy and precision of GHG emission estimates (IPCC 2006 ). It helps identify critical parameters that influence inventory accuracy and guides efforts to improve data quality and model robustness (Huijbregts et al. 2003 ; Lloyd and Ries 2007 ). For livestock emissions, key sources of uncertainty include dry matter intake (DMI), feed digestibility, liveweight, animal productivity, and methane conversion factors (Ym) (Monni et al. 2007 ; Milne et al. 2014 ). Despite its importance, uncertainty analysis in livestock GHG inventories remains underexplored in Indonesia. Previous national reports have largely relied on default Tier 1 IPCC emission factors, which fail to capture the complexity of local livestock production systems (Ministry of Environment and Forestry 2021 ). Transitioning to higher-tier approaches (Tier 2 or Tier 3) requires comprehensive data on livestock populations, feeding regimes, and productivity metrics, which are often lacking or fragmented. Thus, although higher tier methods may improve inventory accuracy and better reflect national circumstances, they may not increase inventory precision (IPCC 2006 ). This study addresses these gaps by implementing a structured uncertainty analysis for cattle GHG emissions in Indonesia estimated using the Tier 2 method. Drawing on previous examples of uncertainty analysis for livestock emissions (e.g. Monni et al. 2007 ; Karimi-Zindashty et al. 2012 ; Milne et al. 2014 ) the study used Monte Carlo (MC) Simulation to quantify uncertainties and identify critical parameters affecting the uncertainty of GHG emission estimates. By highlighting the factors with the greatest influence on emission estimates, this research supports Indonesia’s efforts to improve its national GHG inventory system and implement evidence-based mitigation strategies in the livestock sector. Materials and Methods Data curation Emissions were estimated using the IPCC Tier 2 method (IPCC 2019 ). Data for input into the IPCC equations were collated from official statistics, published studies and reports. The studies were identified through systematic web searches via Google Scholar and by reviewing bibliographic references, including peer-reviewed journals, research papers, official government publications, and postgraduate theses (M.Sc. and Ph.D.). Eligible studies focused on beef cattle and were conducted from 1994 onward, which is the base year of Indonesia’s national inventory. Studies were included if they provided parameter values for one or more of the following: production system, live weight (LW), mature weight, weight gain, feed type, diet digestibility, or overall diet nutrient composition. Preference was given to data sources that were nationally representative or based on large-scale samples at the national, agro-ecological zone, or regional levels, and that provided multi-year data to ensure consistency in time series analysis. For data that were not available, values were determined based on IPCC default values and/or expert judgment involving senior researchers who are competent in the field of GHG emissions from livestock. Table 1 summarizes the data sources used. Table 1 Data included in the analysis and sources No Parameter Description / definition Data Sources 1 Population Population of animals of different types (head) Indonesia Statistic 2 Body weight Live weight per animal type (kg) Published journals 3 Mature weight Weight of mature animals (kg). Defined as shrunken body weight of mature animals in moderate body condition. Published journals 4 Weight gain Average daily weight gain (kg per day). Applied only to growing animals. Published journals 5 Milk yield Annual average daily milk yield (kg per day) Equation based on calves weight and daily gain (National Research Council 2001 ) 6 Fat content of milk Average fat content of milk (%) Default value (IPCC 2019 ) 7 Fraction of adult females pregnant Used in applying coefficient for pregnancy and coefficient for maintenance Official survey data (BPS and Ministry of Agriculture) 8 Feeding situation Stall-fed, grazing confined pasture or extensive grazing. Used in applying coefficient for activity Official survey data (BPS and Ministry of Agriculture) 9 Hours worked Annual average number of hours of work per day Default value (IPCC 2019 ) 10 Feed digestibility Digestible energy as a % of gross energy Published journals 11 Fraction of manure managed in different systems Fraction of manure from each type of livestock managed in different manure management system in different climate regions Expert judgment 12 Crude protein content of diet Average crude protein content of the diet (%) Published journals 13 Protein content of milk Protein content of milk (%) Default value (IPCC 2019 ) Beef cattle production systems in Indonesia are divided into three systems: intensive, semi-intensive, and extensive. This classification is based on the Ministry of Agriculture of the Republic of Indonesia ( 2015 ). Differences between the three systems are presented in Table 2 . Table 2 Definition of beef cattle production systems in Indonesia Production System Confinement Feeding Intensive Stall-fed Fully provisioned Semi-Intensive Day grazing, night stalled Not fully provided Extensive No confinement Not provided Uncertainty analysis Uncertainty analysis was accomplished using Monte Carlo (MC) simulation implemented in Palisade @Risk software. The key inputs to the uncertainty analysis were: Mean values: The values of all activity data, coefficients and emission factors were exactly as implemented in the inventory; Margins of error: Margins of error (MoE) around the mean values were estimated for each input parameter. Probability Density Functions (PDFs): For each parameter, PDFs were chosen either by reference to IPCC guidelines or other literature. The same data sources were used for each production system, and several animal sub-category population time series were highly correlated, so correlations between the time series for populations of each animal sub-category were included in the model. For activity data inputs into emission factors, it was assumed that there are no correlations. Uncertainty was estimated as the margin of error (e.g. ±18%) with a confidence interval of 95%. Calculation of margins of error used a z-score of 1.96 corresponding to an α value of 0.05. Uncertainty analysis was conducted for the base year (1994), the latest year in the inventory (2022), and for the uncertainty in the trends 1994–2022 for each emission source. In order to understand the contribution of input parameters to uncertainty, we used the regression coefficient function in @Risk. This function uses linear regression to calculate the size of the effect of each input variable on an output variable. Input variables with higher regression coefficients are assumed to have a greater influence on uncertainty of the output variable (Milne et al. 2014 ). In the figures presented, a longer bar represents a greater contribution than a shorter bar; bars to the left of centre indicate a negative relationship between the input and output variables, and those to the right of centre indicate a positive relationship. Uncertainty in livestock population activity data There was no data source that could be used to estimate the uncertainty of animal sub-category populations in 2022. The previous national inventory estimated activity data uncertainty of ± 15% (Rachmawaty et al. 2017 ). Cattle imports are relatively highly regulated, so population uncertainty is assumed to be zero in both 1994 and 2022. For 2022, for the other 12 cattle sub-categories, the uncertainty of each sub-category’s population was assumed to be ± 15%. For 1994, the following assumptions were made. A comparison of current official statistics for 1995 with official statistics for 1995 used in a World Bank livestock sector study (Brandenburg and Sukobagyo 2002 ) indicates a margin of error from the two sources of ± 6.23%. We assume the same margin of error for 1994 population estimates and for each subcategory we combined the two errors (i.e., ± 6.23% and ± 15%) by error propagation to estimate uncertainty of each sub-category population (except imported cattle) of ± 21.23% in 1994. Uncertainty in animal performance data The Tier 2 emission factors were calculated following the IPCC 2019 Refinement using activity data on animal performance and management. For parameters in the model to calculate enteric fermentation emission factors, the MoE (with a 95% CI) and PDFs and their justifications are given in the sub-sections that follow. Live weight and weight gain The MoE for live weight and weight gain in different cattle subcategories were derived from the respective data sources. Identical MoE values were applied for the years 1994 and 2022. The MoE values applied in the uncertainty analysis are presented in Table 3 , under the assumption of a normal distribution. Table 3 Margins of error (MoE) for cattle sub-category live weight (LW) and weight gain (WG) estimates used in uncertainty analysis (%) System Sub-category LW WG Extensive Weaning 6.32 29.22 Yearling 5.28 20.44 Adult Male 7.93 Adult Female 15.39 Semi-intensive Weaning 8.66 17.28 Yearling 7.54 35.03 Adult Male 10.33 Adult Female 11.97 Intensive Weaning 8.57 15.32 Yearling 7.13 33.03 Adult Male 9.67 8.55 Adult Female 11.76 Imported cattle 2.98 7.31 Proportion of cows pregnant There were few data sources for the proportions of cows pregnant, so variation could not be estimated from the available data. The MoE of the proportion of adult females pregnant was estimated by expert judgment at ± 10% in each production system (Table 4 ). A beta distribution was used, because the proportion can only take positive values. Milk yield The MoE associated with milk yield estimates were calculated based on available data sources and expert judgement (Table 4 ). A normal distribution was assumed for the uncertainty analysis. Milk fat and protein content The MoE for the fat and protein content of milk in the extensive production system was estimated based on the variability observed in the available data. For other systems, the IPCC default values, and uncertainty analysis assumed a MoE of ± 10%, and a normal distribution was used. Table 4 Margins of error (MoE) for the proportion of cows pregnant, milk yield and milk fat and protein content in each production system used in uncertainty analysis (%) System Proportion giving birth 1994 & 2022 Milk yield 1994 & 2022 Fat content 1994 & 2022 Protein content 1994 & 2022 Extensive ± 10% ± 35.79% ± 5.91% ± 6.60% Semi-intensive ± 10% ± 7.51% ± 10% ± 10% Intensive ± 10% ± 7.51% ± 10% ± 10% Work hours The IPCC default number of work hours for cattle was applied to male cattle in the intensive system. Uncertainty was assumed to be ± 50%. Feed digestibility and crude protein content of the diet The MoE of the energy digestibility of feed was estimated using expert judgement, taking the inventory value as the most likely value in a triangular distribution, and using expert judgement to estimate the minimum and maximum likely values (Table 5 ). A similar method was used to estimate the most likely, minimum and maximum crude protein content in the diet. Uncertainty of ash content of feed was estimated from available data for the intensive system assuming a normal distribution, and due to the lack of multiple data points in other production systems expert judgement was used to estimate the minimum and maximum of triangular distributions. Table 5 Margins of error (MoE) for energy digestibility, crude protein, and ash content of the diet in each production system used in uncertainty analysis (%) System Digestibility 1994 & 2022 Crude protein 1994 & 2022 Ash content 1994 & 2022 Extensive Min: 49.58% Max: 51.86% Min: 4% Max: 10% Min: 7.3% Max: 11.3% Semi-intensive Min: 62.58% Max: 67.19% Min: 8.62% Max: 9.8% Min: 14.2% Max: 18.2% Intensive Min: 49.23% Max: 60.86% Min: 5.6% Max: 13.16% Normal ± 14.78% Imported cattle Min: 70.1% Max: 86.9% Min: 9.3% Max: 15.5% Min: 8% Max: 10% Other coefficients Table 6 shows the MoEs used for other coefficients in the IPCC enteric fermentation model. Table 6 Margins of error (MoE) and PDFs used for Y m and other coefficients used in uncertainty analysis (%) Parameter Margin of error 1990, 2021 PDF Explanation Y m (%) (all sub-categories) ± 20% Normal Normal, s.e. small. Margin of error from IPCC ( 2019 ). Cf i (all sub-categories) ± 15% Beta Beta, proportion, cannot have negative values. 15% from Monni et al. ( 2007 ). C a (all sub-categories) ± 15% Beta Beta, proportion, cannot have negative values. 15% from Monni et al. ( 2007 ). C p (all sub-categories) ± 15% Beta Beta, proportion, cannot have negative values. 15% from Monni et al. ( 2007 ). C (all sub-categories) ± 15% Beta Beta, proportion, cannot have negative values. 15% from Monni et al. ( 2007 ). Manure management The parameters used for uncertainty analysis of methane and nitrous oxide emissions from manure management and deposit of dung and urine on pasture are shown in Table 7 . Table 7 Margin of error (MoE) and PDFs used in the uncertainty analysis for manure management and managed soils Parameter Margin of error 1990 & 2021 PDF Explanation Ash content ± 10% Normal s.e. small compared to mean B o ± 15% Normal s.e. small compared to mean. Uncertainty ranges from IPCC ( 2019 ) Table 10.16 MMS%, various manure management systems ± 50% Normal Uncertainty range ± 50% from (IPCC 2006 ) Ch. 10, p. 10.50. MCF, various manure management systems ± 50% Normal Uncertainty range ± 50% chosen, slightly higher than MCF uncertainty range used in Karimi-Zindashty et al. ( 2012 ) of ± 45% MCF, pasture, cattle ± 23.6% Normal Calculated from supplementary data in IPCC ( 2019 ) Annex 10B.6 EF 3 , pasture deposit, cattle See explanation PERT Uncertainty ranges from IPCC ( 2019 ) Table 11.1 of 0.007–0.06, which were taken as min and max of PERT distribution, with 0.02 as most likely EF 3 , other manure management systems ± 100% Beta Uncertainty ranges from IPCC ( 2019 ) Table 10.21 Frac gasm , pasture deposit See explanation PERT IPCC ( 2019 ) Ch. 11 Table 11.3 gives uncertainty range of 0.05–0.5, which were taken as min and max of PERT distribution, with 0.2 as most likely. Frac gas , other manure management systems See explanation PERT Uncertainty ranges taken from IPCC ( 2019 ) Ch. 10 Table 10.22 taken as min and max of PERT distribution, with default value as most likely. Frac gas , biogas ± 50% Triangular Inventory value is maximum of the range given in IPCC ( 2019 ) Table 10.22. EF 4 See explanation PERT (IPCC 2019 ) Ch. 11 Table 11.3 gives uncertainty range of 0.011–0.017, which were taken as min and max of PERT distribution, with 0.014 as most likely. For pastoral/agro-pastoral system, Table 11.3 gives uncertainty range of 0.000–0.011, with 0.005 as most likely Frac leach , solid storage, composting See explanation PERT IPCC ( 2019 ) Annex 10B.7 suggests a range of 0 to 38% for solid storage and composting, which are taken as the min and max of the PERT distribution EF 5 See explanation PERT IPCC ( 2006 ) Ch. 11 Table 11.3 Frac GASM uncertainty range of 0.0005–0.025, which were taken as min and max of PERT distribution, with 0.0075 as most likely. Frac LEACH , pasture See explanation PERT (IPCC 2006 ) Ch. 11 Table 11.3 Frac LEACH uncertainty range of 0.1–0.8, which were taken as min and max of PERT distribution, with 0.3 as most likely. Results Emissions estimated using the Tier 2 method Table 8 shows the estimated emissions from each emission source in 1994 and 2022 and the uncertainty of these estimates. CH 4 and N 2 O emissions were converted into CO 2 equivalent (CO 2 e) using global warming potentials from the IPCC Fifth Assessment Report (AR5) (IPCC 2014 ). Table 8 Estimated GHG emissions from cattle in 1994 and 2022 1994 2022 Source Emissions (Gg CO 2 e) Uncertainty (%) Emissions (Gg CO 2 e) Uncertainty (%) Enteric fermentation 13,735.34 + 15.2%, -13.4% 21,478.31 + 19.7%, -17.1% Manure management CH 4 694.34 + 56.9%, -41.0% 1,300.90 + 62.7%, -45.2% Manure management direct N 2 O 708.96 + 67.8%, -57.3% 1,282.41 + 75.4%, -61.9% Manure management indirect N 2 O 517.35 + 45.1%, -36.0% 960.99 + 47.4%, − 38.5% Direct N 2 O from deposit on pasture 186.33 + 103.9%, -70.8% 134.70 + 98.2%, -69.9% Indirect N 2 O from deposit on pasture 167.70 + 69.0%, -48.4% 121.23 + 65.6%, -46.8% All emissions 16,010.02 + 15.9%,-13.9% 25,278.54 + 20.7%,-17.4% The total aggregated emissions increased by 57.9% over the inventory period, rising from 16,010.02 Gg CO 2 e in 1994 to 25,278.54 Gg CO 2 e in 2022. Among the specific sources, enteric fermentation was the largest contributor in both years, accounting for 85.8% of the total emissions in 1994 and 85.0% in 2022. Emissions from this source grew from 13,735.34 Gg CO 2 e to 21,478.31 CO 2 e Gg over this period. Total emissions from manure management and pasture deposits increased by 67% from 1994 to 2022, and their contribution to total emissions increased slightly from 14.2% in 1994 to 15.0% in 2022. Methane and direct N 2 O emissions from manure management were the largest contributors to total emissions from these sources, and increased from 61.7% of emissions from manure management and pasture deposits to 68.0% in 2022. Both the absolute value and the proportional contribution of dung and urine deposits on pasture to total emissions decreased over time. The uncertainty ranges associated with the emission estimates varied by source and year. The uncertainty for the total emission inventory increased from 95% CI = [+ 15.9%, -13.9%] in 1994 to [+ 20.7%, -17.4%] in 2022. Enteric fermentation presented uncertainty intervals of [+ 15.2%, -13.4%] in 1994 and [+ 19.7%, -17.1%] in 2022. The highest uncertainty was observed in direct emissions from deposits on pasture, which ranged from [+ 103.9%, -70.8%] in 1994 to [+ 98.2%, -69.9%] in 2022. Generally, uncertainties of manure management and pasture deposit emission estimates were greater than those for enteric fermentation. Activity data uncertainty The results of uncertainty analysis of activity data for 1994 and 2022 are shown in Table 9 . There was an increase in the uncertainty of total cattle population estimates between 1994 to 2022. The reason for this increase is likely due to the significant increase in the proportion of cattle in the intensive system, which after accounting for correlations among data sources has a relatively high level of uncertainty. Table 9 Uncertainty from activity data 1994 and 2022 Production system 1994 2022 Extensive + 15.8%, -15.3% + 14.3%, − 14.1% Semi-intensive + 13.0%, − 13.0% + 14.3%, − 14.1% Intensive + 16.0%, − 15.8% + 14.5%, − 14.5% Total cattle population + 6.9%, -6.9% + 9.2%, -9.3% Considering both uncertainties and estimated population size, Fig. 1 shows the population sub-categories contributing most to the overall uncertainty of the total population in 2022 (see Section 2.1 for explanation of figures). Figure 1 , shows that four of the top five most influential sub-categories are in the intensive system, while one is in the semi-intensive system. This indicates that obtaining accurate sub-category population estimates for the intensive production system would have the greatest contribution to reducing activity data uncertainty, followed by the semi-intensive system. The regression coefficients for the extensive system are all relatively low, indicating that better population data in the extensive system would not have large contributions to reducing inventory uncertainty. Enteric fermentation emission factors The results of uncertainty analysis for enteric emission factors from all livestock sub-categories in 1994 and 2022 are presented in Table 10 . The uncertainty values for enteric emission factors in 2022 are higher than those in 1994. Changes in management systems and variations in feed types have contributed to this increased uncertainty. Table 10 Uncertainty of implied emission factors for enteric fermentation 1994 2022 All cattle + 12.8%, -11.6% + 16.0%, -14.1% Note: Implied emission factor is the population-weighted emission factor. Figure 2 . shows that the parameters with the greatest influence on uncertainty of the implied emission factor in 2022 are all in the intensive system, and include Ym, Cfi, digestibility and live weight for adult females, and the same parameters for yearlings and weaned cattle. Overall, to reduce the uncertainty of emission factors, the focus should be on the intensive system. Factors driving emission factor uncertainty in the extensive and semi-intensive systems are not among the most important factors for overall inventory uncertainty (except Ym for adult females in the semi-intensive system) because of the relatively smaller portion of these two systems in the total national cattle population. Combining both activity data and emission factor uncertainty, there was an increase in the uncertainty of enteric emissions from [+ 15.2%, -13.4%] in 1994 to [+ 19.7%, -17.1%] in 2022 (Table 8 ). The increase in uncertainty of enteric emissions is due to the shift in the cattle population structure. There has been an increase in the proportion of the cattle population in the intensive system, for which emission factor uncertainty is greater. The uncertainty of the trend 1994–2022 for enteric emissions is [+ 65.2%, -53.8%]. CH 4 manure management emissions The results of the uncertainty analysis for manure management methane emission factors for all cattle are presented in Table 11 . The uncertainty values were slightly higher in 2022 compared to those in 1994. This difference may be related to a shift in production systems, as most cattle were managed under an intensive production system in 2022. Table 11 Uncertainty of implied emission factors for manure management Cattle 1994 2022 All cattle + 54.7%, -40.3% + 60.8%, -44.8% Figure 3 shows the parameters influencing CH 4 manure management emission factors in 2022. Longer bars indicate a greater influence on emission factor estimates, which can be either positive or negative. Assuming the factors with the longest bars contribute the most to the uncertainty value, they should be a priority to address. Except for the proportion of manure managed in solid storage (MCF solid storage, Fig. 3 ) and the population of adult males in the semi-intensive system (SINT_AdMale-pop, Fig. 3 ), all the other influential factors are in the intensive system. These include the methane conversion factors for solid storage, biogas and compost; manure management practices (e.g. MMS% in solid storage, composting and biogas) in the intensive system; as well as factors that influence volatile solid excretion (e.g. feed digestibility for intensive adult females, yearlings and weaned cattle, Cfi and live weight for adult females). Similar to enteric fermentation, this indicates that improving data on cattle feed and performance and manure management in the intensive system can make the biggest contribution to reducing inventory uncertainty. There was an increase in the total uncertainty of CH 4 manure management emissions from [+ 56.9%, -41.0%] in 1994 to [+ 62.7%, -45.2%] in 2022 (Table 8 ). The increase in uncertainty is due to the increase in the proportion of cattle in the intensive system. This system has the most influential factors driving inventory estimates. The uncertainty of the 1994–2022 trend was [+ 103.1%, -89.8%]. N 2 O emissions from manure management and pasture deposit of dung and urine The result of uncertainty analysis of various direct and indirect sources of nitrous oxide emission is shown in Table 12 . Table 12 Uncertainty of nitrous oxide emission from manure management N 2 O Emission Source 1994 2022 1994–2022 trend Total UNC EF UNC Total UNC EF UNC Direct N 2 O MM + 67.8%, -57.3% ± 62.1% + 75.4%, -61.9% ± 68.0% + 112.2%, -82.6% Indirect N 2 O MM + 45.1%, -36.0% ± 40.0% + 47.4%, − 38.5% ± 41.9% + 95.3%, -70.2% Direct N 2 O PRP + 103.9%, -70.8% ± 87.1% + 98.2%, -69.9% ± 83.5% + 72.6%, -36.9% Indirect N 2 O PRP + 69.0%, -48.4% ± 58.3% + 65.6%, -46.8% ± 55.5% + 69.8%, -46.9% Note: “MM” denotes manure management, “PRP” denotes deposit of dung and urine on pasture, rangeland and paddock, “EF” denotes emission factor, and “UNC” denotes uncertainty. Between 45% and 50% of total N 2 O emissions in these years were direct N 2 O emissions from manure management (Table 8 ). The main factors driving direct manure management N 2 O emissions are (Fig. 4 ): Emission factors for solid storage in the intensive and semi-intensive systems and for composting in the intensive system; Animal production parameters, such as crude protein and digestible energy content of the diets for adult females and yearlings in the intensive system; Manure management activity data in the intensive system and semi-intensive system. Improving data quality for these parameters would also mostly reduce uncertainty of methane manure management emissions, and in some cases also for enteric emission estimates. Parameters contributing to total indirect nitrous oxide emission from manure management for 2022 can be seen in Fig. 5 . Indirect N 2 O manure management emission uncertainty is mainly affected by: Fractions of gas volatilized and fractions of N leached in solid storage and composting systems; The emission factor (EF 4 ) for volatilisation; and Animal production parameters (e.g. crude protein and feed energy digestibility, live weight) in the intensive system. Total direct nitrous oxide emission from pasture, range, and paddock in 2022 can be seen on Fig. 6 . For nitrous oxide emissions from pasture deposits, the key factors include: EF 3,PRP in the extensive and semi-intensive systems Proportion of dung and urine deposited on pasture in the semi-intensive system; and Animal production parameters influencing nitrogen excretion, including crude protein content of the diet in the extensive systems; Cfi for adult females in the intensive and semi-intensive systems; live weight for adult females in the extensive and semi-intensive systems, etc. Note, however, that improving estimates for the animal diet and performance parameters in the semi-intensive system would not significantly reduce the uncertainty of enteric fermentation or manure management emissions, for which uncertainty is mostly influenced by parameters in the intensive system. Combining all emission sources by converting CH 4 and N 2 O to CO 2 e equivalents using the AR5 GWP values (IPCC 2014 ), the uncertainty of total cattle emissions increased from [+ 15.9%, -13.9%] in 1994 to [+ 20.7%,-17.4%] in 2022 (Table 8 ). Because enteric emissions accounted for about 85% of total emissions in both years, the uncertainty of total emissions from all sources is only slightly higher than the uncertainty of enteric emissions. Consequently, most of the input parameters affecting total cattle emissions (Fig. 7 ) are the same as those affecting enteric emissions (Fig. 2 ). Notable exceptions shown in Fig. 7 include the direct N 2 O emission factor for solid storage (EF 3 ) and the proportions of manure managed in solid storage in the intensive system. Discussion The uncertainty analysis conducted in this study provides critical insights into the need and priorities for improvement of Indonesia’s inventory of GHG emissions from cattle, including enteric fermentation, CH 4 from manure management, and N 2 O emissions. The results highlight that the intensive production system, while constituting a smaller portion of total livestock in the past, now represents the largest contributor to the overall uncertainty in the national inventory due to its rapid expansion and greater data uncertainty. Trends in Inventory Uncertainty The comparison between 1994 and 2022 clearly shows a rising trend in uncertainty, particularly for methane emissions from enteric fermentation and manure management. For instance, uncertainty in enteric CH₄ emissions increased from ± 15.2% in 1994 to ± 19.7% in 2022 (Table 8 ). Similarly, uncertainty in CH₄ from manure management increased from ± 56.9% to ± 62.7% over the same period. This trend correlates with the increasing proportion of cattle in intensive production systems from 50.9% in 1994 to 76.0% in 2022. The growth of intensive systems reflects structural shifts within the livestock sector. While margins of error for many key parameters in the intensive system are comparable to or lower than those in the other production systems (Tables 3 , 4 and 5 ), considerable uncertainty was assumed for some key parameters in the intensive system (e.g. feed digestibility, adult female live weights, yearling weight gain). When combined with the rapid increase in population numbers, uncertainty of these parameters has a large effect on the uncertainty of total emissions. This finding contrasts with some other analyses of inventory uncertainty in developed countries, where change in animal performance and emission factors has been the main driver of change in inventory uncertainty (e.g. Milne et al. 2014 ). This can be explained as follows. First, rapid transformation of production systems is much more prevalent in developing countries and intensification through changing feeding and housing systems is an important driver of productivity increases at sector level (McDermott et al. 2010 ). It is therefore a key GHG mitigation option in the longer-term (Bateki et al. 2023 ). Second, the effect of change in population structure on inventory uncertainty is in part a result of the data sources and assumptions used in this inventory. While it would be ideal to calculate emission factors for each year in the time series using input data specific to that year, complete and regular statistics are not available in countries such as Indonesia. Therefore, the same input variables were used to calculate emission factors in 1994 and 2022, and the main change in emissions over time was due to change in the population data. Thus the attribution of change in uncertainty to population structure is in part biased by the lack of regular statistics. standardized data collection in Indonesia’s cattle production systems. Key Drivers of Uncertainty Because enteric fermentation contributes a significant proportion of total GHG emissions from cattle (Table 8 ), factors driving uncertainty of emission estimates from this source have a strong impact on overall inventory uncertainty. Key drivers of uncertainty include: Feed digestibility and methane conversion factors: Feed digestibility is a key determinant of gross energy intake (as well as volatile solid excretion affecting manure methane emissions). The uncertainty of feed digestibility in all production systems is a consequence both of the high variability in feeding practices among farms and uncertainty and variability in the quality of the feeds fed. The high variability in diets and feed quality across regions and between seasons is common in tropical countries (Jayanegara et al. 2012 ; Salah et al. 2014 ). The methane conversion factor (Ym) also has a central role in determining enteric methane emissions, and varies considerably based on diet composition, feed intake level, and animal physiology (Knapp et al. 2014 ; Niu et al. 2018 ). In addition to variability among diets, measurement variability and error in prediction models contributes to the uncertainty of this parameter (Tee et al. 2022 ). The lack of country-specific Ym values in Indonesia further increases the uncertainty of using default values. Other coefficients affecting gross energy intake: Under low productivity conditions, the majority of net energy intake needs are for maintenance. In the IPCC model, this is affected by live weight and a coefficient for maintenance (Cfi) which varies by cattle sex and physiological status (IPCC 2019 ). The uncertainties of these parameters for major cattle sub-categories in the intensive system were key drivers of uncertainty in enteric emissions and total emissions from all sources (Figs. 2 and 7 ). In Indonesia, which possesses a wide range of local cattle breeds as well as crossbreeds with exotic cattle (Sutarno and Setyawan 2016 ), some of the uncertainty in live weight estimates reflects inherent variation in the population, while some reflects the relative lack of reported measurements. Factors affecting manure emissions: The uncertainties of manure methane and nitrous oxide emissions were larger than that of enteric emissions. However, most input parameters were not important drivers of overall uncertainty due to the relatively smaller contributions of manure emission sources to total emissions. Due to its assumed prevalence in the intensive system, parameters associated with solid storage were important drivers of manure methane and nitrous oxide emissions (Figs. 3 , 4 and 5 ), including the proportion of manure managed in solid storage, methane conversion factors for solid storage and direct N 2 O emission factor (EF 3 ) for solid storage. Activity data on manure management systems is relatively scarce, and expert judgement associated with wide uncertainty margins was used in this study. In the absence of sufficient country-specific measurements, use of the IPCC default factors may lead to significant uncertainty when applied in tropical environments, with higher ambient temperatures and varied moisture conditions that significantly affect manure decomposition and associated emissions (Chadwick et al. 2011 ; Cardoso et al. 2016 , Mancia et al. 2022). Measurements of emission factors in tropical countries are scarce (Hassouna et al. 2023 ). Measurements from developed countries both methane and N 2 O emissions show considerable variation between climate zones and measurement conditions (e.g. animal housing, diets, measurement methods)(Qu et al. 2021 ; Çinar et al. 2023 ). Implications for Policy and Reporting High uncertainty levels can erode confidence in reported reductions, limit the credibility of mitigation claims, and potentially affect access to climate finance mechanisms such as carbon markets or result-based payments under Article 6 of the Paris Agreement. This study has clear implications for improvement of national GHG inventory systems. It has highlighted the production systems and animal sub-categories for which more accurate data is required on animal performance and animal and manure management in order to reduce inventory uncertainty. To improve accuracy and reduce inventory uncertainty, the following priority actions are recommended: Invest in regular, region-specific surveys on animal performance, feed quality, and manure handling, especially in the intensive production system. Develop localized emission factors for enteric fermentation and manure emissions, building upon the IPCC 2019 Refinement guidelines. Integrate field measurement campaigns (e.g., digestibility trials, and methane flux measurements) into national agricultural monitoring systems. Strengthen institutional collaboration between ministries (e.g., agriculture, environment, statistics) to standardize and streamline livestock data collection and management. Capacity building for uncertainty analysis, including training in probabilistic modelling tools and expert elicitation methods. In a diverse and smallholder-dominated country such as Indonesia, diversity in key factors such as diets and feed quality, which are often influenced by economic factors and local conditions, will inevitably lead to high variation in parameter value estimates. Representative data to capture this variation can contribute to increasing inventory accuracy and better quantification of inventory uncertainty. Conclusion This study provides a detailed uncertainty analysis of greenhouse gas emissions from cattle in Indonesia, with a focus on enteric fermentation, CH 4 from manure management, and N 2 O emissions from manure management and pasture deposition. Using Monte Carlo simulation and IPCC guidelines, it identifies the key parameters driving uncertainty and quantifies the overall uncertainty of cattle GHG emissions in Indonesia from 1994 to 2022. The findings demonstrate a slight increase in uncertainty over time, largely due to the growing dominance of intensive livestock systems. Parameters such as populations of animal sub-categories, methane conversion factors (Ym), feed digestibility, live weight, coefficients for maintenance, and manure management system proportions (MMS%) in the intensive production system are the most influential in driving emission uncertainty. To improve the accuracy and transparency of national GHG inventories, the study recommends prioritizing data collection and methodological refinement in the intensive production system. Enhancing field-level monitoring, developing localized emission factors, and integrating expert-based assessments with empirical data will be critical for reducing inventory uncertainty. By targeting the most sensitive parameters and production systems, Indonesia can significantly enhance the credibility of its emission reporting and better support its climate mitigation commitments under the Paris Agreement. Declarations Funding This research was funded by the New Zealand Climate Smart Agriculture Initiative to support the objectives of the Global Research Alliance on Agricultural Greenhouse Gases through the project “Improving National GHG Inventory for Livestock Using the IPCC Tier 2 Method - phase 2”. Conflict of Interest Statement The authors declare no conflict of interest Author Contributions SW, YW, AW, MCH, and EHW: Conceptualization, Methodology; YW, BT, MIH and SP: Investigation; SW, MCH, GET, BT, YW, EHW: curation; SW and AW: data analysis; SW, YW, MCH, EHW: writing original draft; AW, SP, GET, and RH: reviewing; BT, YW, and MCH: supervision, project administration. Data availability Data will be made available on reasonable request . References Bateki CA, Wassie SE, Wilkes A (2023) The contribution of livestock to climate change mitigation: a perspective from a low-income country. 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17:05:08","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49819,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8216844/v1/05e74f5cee4c074d06796914.png"},{"id":98385435,"identity":"8a21f824-940b-4be3-ba85-ee6554c06c57","added_by":"auto","created_at":"2025-12-17 08:31:35","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":140727,"visible":true,"origin":"","legend":"","description":"","filename":"TROPD25023200structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8216844/v1/f3214ccd307e9e3a5818e617.xml"},{"id":98441372,"identity":"9cccafdf-ebe5-4265-9c3a-a7249cb8958f","added_by":"auto","created_at":"2025-12-17 17:05:15","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146839,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8216844/v1/b1b756ef16ab99019cb40ce4.html"},{"id":98385406,"identity":"e533fc7d-b058-4a09-93b4-7c6e2fa9227a","added_by":"auto","created_at":"2025-12-17 08:31:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":258127,"visible":true,"origin":"","legend":"\u003cp\u003eContributions of cattle sub-categories to total population uncertainty in 2022\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8216844/v1/828cc65ccfdb530412caefc8.png"},{"id":98385410,"identity":"e6827b2f-fdb5-46a6-aa8b-7be15bd0d905","added_by":"auto","created_at":"2025-12-17 08:31:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":294173,"visible":true,"origin":"","legend":"\u003cp\u003eContributions of input parameters to enteric emission factor uncertainty in 2022\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8216844/v1/db095fa68e2b2120862b37d7.png"},{"id":98385408,"identity":"5b8208e3-63ae-4b42-b36c-ff6e496acabe","added_by":"auto","created_at":"2025-12-17 08:31:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":337655,"visible":true,"origin":"","legend":"\u003cp\u003eContributions of input parameters to manure management methane uncertainty in 2022\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8216844/v1/b1f625598350b122e7ade722.png"},{"id":98440305,"identity":"85d8a3a4-5231-40c2-8a85-339750ba2f64","added_by":"auto","created_at":"2025-12-17 17:03:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":333674,"visible":true,"origin":"","legend":"\u003cp\u003eContribution of input parameters to manure management direct nitrous oxide emission in 2022\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8216844/v1/1f76d0d5645ccd49e5a507ac.png"},{"id":98385414,"identity":"cd247de9-ff63-4de9-9f97-6f01e07012b2","added_by":"auto","created_at":"2025-12-17 08:31:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":319426,"visible":true,"origin":"","legend":"\u003cp\u003eContribution of input parameters to manure management indirect nitrous oxide emission in 2022\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8216844/v1/06e1579f957dddad1fcc152b.png"},{"id":98441178,"identity":"edf2cd7f-c485-4fa6-bab9-47ee1899682a","added_by":"auto","created_at":"2025-12-17 17:05:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":306747,"visible":true,"origin":"","legend":"\u003cp\u003eContribution of input parameters to direct nitrous oxide emission from pasture, range, and paddock in 2022\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8216844/v1/762887742a18a0f1d54f5944.png"},{"id":98440251,"identity":"20b2feaf-d664-467c-bb4c-55acda0c1ab3","added_by":"auto","created_at":"2025-12-17 17:03:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":295532,"visible":true,"origin":"","legend":"\u003cp\u003eContribution of inputs to total CO\u003csub\u003e2\u003c/sub\u003ee emissions from cattle in 2022\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8216844/v1/d89736199725213c74a40afd.png"},{"id":98775034,"identity":"f84f1090-4384-46cc-95dd-43cb92837b05","added_by":"auto","created_at":"2025-12-22 12:18:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3565859,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8216844/v1/e78b0914-3ce8-4b57-b9ce-d655891280b6.pdf"}],"financialInterests":"","formattedTitle":"Uncertainty Analysis Supports Prioritization of Greenhouse Gas Inventory Improvement in Indonesia’s Livestock Sector","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGreenhouse gas (GHG) emissions from the agriculture sector, particularly from livestock, contribute significantly to global climate change. Globally, the livestock sector is responsible for approximately 14.5% of total anthropogenic GHG emissions, with methane (CH₄) from enteric fermentation in ruminants accounting for the largest share (Gerber et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In Indonesia, the agriculture sector contributed around 7.8% of the country\u0026rsquo;s total GHG emissions in 2020, and livestock alone accounted for a major portion of those emissions (Ministry of Environment and Forestry \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Accurate quantification of these emissions is essential for national reporting under the United Nations Framework Convention on Climate Change (UNFCCC), and can support development of mitigation strategies in line with Indonesia\u0026rsquo;s Nationally Determined Contributions (NDCs). However, GHG inventories often suffer from uncertainties caused by limited or inconsistent data, the use of default emission factors, and assumptions in models (Tubiello et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In Indonesia, livestock systems vary significantly across regions, ranging from intensive operations to extensive smallholder farming, which further complicates emission estimation (Widiawati et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUncertainty analysis is a methodological approach used to assess the accuracy and precision of GHG emission estimates (IPCC \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). It helps identify critical parameters that influence inventory accuracy and guides efforts to improve data quality and model robustness (Huijbregts et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Lloyd and Ries \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). For livestock emissions, key sources of uncertainty include dry matter intake (DMI), feed digestibility, liveweight, animal productivity, and methane conversion factors (Ym) (Monni et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Milne et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Despite its importance, uncertainty analysis in livestock GHG inventories remains underexplored in Indonesia. Previous national reports have largely relied on default Tier 1 IPCC emission factors, which fail to capture the complexity of local livestock production systems (Ministry of Environment and Forestry \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Transitioning to higher-tier approaches (Tier 2 or Tier 3) requires comprehensive data on livestock populations, feeding regimes, and productivity metrics, which are often lacking or fragmented. Thus, although higher tier methods may improve inventory accuracy and better reflect national circumstances, they may not increase inventory precision (IPCC \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study addresses these gaps by implementing a structured uncertainty analysis for cattle GHG emissions in Indonesia estimated using the Tier 2 method. Drawing on previous examples of uncertainty analysis for livestock emissions (e.g. Monni et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Karimi-Zindashty et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Milne et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) the study used Monte Carlo (MC) Simulation to quantify uncertainties and identify critical parameters affecting the uncertainty of GHG emission estimates. By highlighting the factors with the greatest influence on emission estimates, this research supports Indonesia\u0026rsquo;s efforts to improve its national GHG inventory system and implement evidence-based mitigation strategies in the livestock sector.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData curation\u003c/h2\u003e \u003cp\u003eEmissions were estimated using the IPCC Tier 2 method (IPCC \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Data for input into the IPCC equations were collated from official statistics, published studies and reports. The studies were identified through systematic web searches via Google Scholar and by reviewing bibliographic references, including peer-reviewed journals, research papers, official government publications, and postgraduate theses (M.Sc. and Ph.D.). Eligible studies focused on beef cattle and were conducted from 1994 onward, which is the base year of Indonesia\u0026rsquo;s national inventory. Studies were included if they provided parameter values for one or more of the following: production system, live weight (LW), mature weight, weight gain, feed type, diet digestibility, or overall diet nutrient composition. Preference was given to data sources that were nationally representative or based on large-scale samples at the national, agro-ecological zone, or regional levels, and that provided multi-year data to ensure consistency in time series analysis. For data that were not available, values were determined based on IPCC default values and/or expert judgment involving senior researchers who are competent in the field of GHG emissions from livestock. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the data sources used.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData included in the analysis and sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription / definition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData Sources\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation of animals of different types (head)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndonesia Statistic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLive weight per animal type (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePublished journals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMature weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeight of mature animals (kg). Defined as shrunken body weight of mature animals in moderate body condition.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePublished journals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight gain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage daily weight gain (kg per day). Applied only to growing animals.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePublished journals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMilk yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual average daily milk yield (kg per day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEquation based on calves weight and daily gain (National Research Council \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2001\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFat content of milk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage fat content of milk (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDefault value (IPCC \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFraction of adult females pregnant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUsed in applying coefficient for pregnancy and coefficient for maintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOfficial survey data (BPS and Ministry of Agriculture)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeeding situation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStall-fed, grazing confined pasture or extensive grazing. Used in applying coefficient for activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOfficial survey data (BPS and Ministry of Agriculture)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHours worked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual average number of hours of work per day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDefault value (IPCC \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeed digestibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigestible energy as a % of gross energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePublished journals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFraction of manure managed in different systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFraction of manure from each type of livestock managed in different manure management system in different climate regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpert judgment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude protein content of diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage crude protein content of the diet (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePublished journals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein content of milk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein content of milk (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDefault value (IPCC \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBeef cattle production systems in Indonesia are divided into three systems: intensive, semi-intensive, and extensive. This classification is based on the Ministry of Agriculture of the Republic of Indonesia (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Differences between the three systems are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDefinition of beef cattle production systems in Indonesia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduction System\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConfinement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeeding\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFully provisioned\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemi-Intensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay grazing, night stalled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot fully provided\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo confinement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUncertainty analysis\u003c/h3\u003e\n\u003cp\u003eUncertainty analysis was accomplished using Monte Carlo (MC) simulation implemented in Palisade @Risk software. The key inputs to the uncertainty analysis were:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMean values: The values of all activity data, coefficients and emission factors were exactly as implemented in the inventory;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMargins of error: Margins of error (MoE) around the mean values were estimated for each input parameter.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eProbability Density Functions (PDFs): For each parameter, PDFs were chosen either by reference to IPCC guidelines or other literature.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe same data sources were used for each production system, and several animal sub-category population time series were highly correlated, so correlations between the time series for populations of each animal sub-category were included in the model. For activity data inputs into emission factors, it was assumed that there are no correlations. Uncertainty was estimated as the margin of error (e.g. \u0026plusmn;18%) with a confidence interval of 95%. Calculation of margins of error used a z-score of 1.96 corresponding to an α value of 0.05. Uncertainty analysis was conducted for the base year (1994), the latest year in the inventory (2022), and for the uncertainty in the trends 1994\u0026ndash;2022 for each emission source.\u003c/p\u003e \u003cp\u003eIn order to understand the contribution of input parameters to uncertainty, we used the regression coefficient function in @Risk. This function uses linear regression to calculate the size of the effect of each input variable on an output variable. Input variables with higher regression coefficients are assumed to have a greater influence on uncertainty of the output variable (Milne et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the figures presented, a longer bar represents a greater contribution than a shorter bar; bars to the left of centre indicate a negative relationship between the input and output variables, and those to the right of centre indicate a positive relationship.\u003c/p\u003e\n\u003ch3\u003eUncertainty in livestock population activity data\u003c/h3\u003e\n\u003cp\u003eThere was no data source that could be used to estimate the uncertainty of animal sub-category populations in 2022. The previous national inventory estimated activity data uncertainty of \u0026plusmn;\u0026thinsp;15% (Rachmawaty et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Cattle imports are relatively highly regulated, so population uncertainty is assumed to be zero in both 1994 and 2022. For 2022, for the other 12 cattle sub-categories, the uncertainty of each sub-category\u0026rsquo;s population was assumed to be \u0026plusmn;\u0026thinsp;15%. For 1994, the following assumptions were made. A comparison of current official statistics for 1995 with official statistics for 1995 used in a World Bank livestock sector study (Brandenburg and Sukobagyo \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) indicates a margin of error from the two sources of \u0026plusmn;\u0026thinsp;6.23%. We assume the same margin of error for 1994 population estimates and for each subcategory we combined the two errors (i.e., \u0026plusmn;\u0026thinsp;6.23% and \u0026plusmn;\u0026thinsp;15%) by error propagation to estimate uncertainty of each sub-category population (except imported cattle) of \u0026plusmn;\u0026thinsp;21.23% in 1994.\u003c/p\u003e\n\u003ch3\u003eUncertainty in animal performance data\u003c/h3\u003e\n\u003cp\u003eThe Tier 2 emission factors were calculated following the IPCC \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e Refinement using activity data on animal performance and management. For parameters in the model to calculate enteric fermentation emission factors, the MoE (with a 95% CI) and PDFs and their justifications are given in the sub-sections that follow.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLive weight and weight gain\u003c/strong\u003e \u003cp\u003eThe MoE for live weight and weight gain in different cattle subcategories were derived from the respective data sources. Identical MoE values were applied for the years 1994 and 2022. The MoE values applied in the uncertainty analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, under the assumption of a normal distribution.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMargins of error (MoE) for cattle sub-category live weight (LW) and weight gain (WG) estimates used in uncertainty analysis (%)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSub-category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eExtensive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeaning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYearling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdult Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdult Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eSemi-intensive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeaning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYearling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdult Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdult Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eIntensive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeaning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYearling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdult Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdult Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImported cattle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProportion of cows pregnant\u003c/strong\u003e \u003cp\u003eThere were few data sources for the proportions of cows pregnant, so variation could not be estimated from the available data. The MoE of the proportion of adult females pregnant was estimated by expert judgment at \u0026plusmn;\u0026thinsp;10% in each production system (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A beta distribution was used, because the proportion can only take positive values.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMilk yield\u003c/strong\u003e \u003cp\u003eThe MoE associated with milk yield estimates were calculated based on available data sources and expert judgement (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A normal distribution was assumed for the uncertainty analysis.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMilk fat and protein content\u003c/strong\u003e \u003cp\u003eThe MoE for the fat and protein content of milk in the extensive production system was estimated based on the variability observed in the available data. For other systems, the IPCC default values, and uncertainty analysis assumed a MoE of \u0026plusmn;\u0026thinsp;10%, and a normal distribution was used.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMargins of error (MoE) for the proportion of cows pregnant, milk yield and milk fat and protein content in each production system used in uncertainty analysis (%)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion giving birth\u003c/p\u003e \u003cp\u003e1994 \u0026amp; 2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMilk yield\u003c/p\u003e \u003cp\u003e1994 \u0026amp; 2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFat content\u003c/p\u003e \u003cp\u003e1994 \u0026amp; 2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProtein content 1994 \u0026amp; 2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;35.79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;5.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;6.60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemi-intensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;7.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;7.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eWork hours\u003c/strong\u003e \u003cp\u003eThe IPCC default number of work hours for cattle was applied to male cattle in the intensive system. Uncertainty was assumed to be \u0026plusmn;\u0026thinsp;50%.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFeed digestibility and crude protein content of the diet\u003c/strong\u003e \u003cp\u003eThe MoE of the energy digestibility of feed was estimated using expert judgement, taking the inventory value as the most likely value in a triangular distribution, and using expert judgement to estimate the minimum and maximum likely values (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). A similar method was used to estimate the most likely, minimum and maximum crude protein content in the diet. Uncertainty of ash content of feed was estimated from available data for the intensive system assuming a normal distribution, and due to the lack of multiple data points in other production systems expert judgement was used to estimate the minimum and maximum of triangular distributions.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMargins of error (MoE) for energy digestibility, crude protein, and ash content of the diet in each production system used in uncertainty analysis (%)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigestibility\u003c/p\u003e \u003cp\u003e1994 \u0026amp; 2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrude protein\u003c/p\u003e \u003cp\u003e1994 \u0026amp; 2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAsh content\u003c/p\u003e \u003cp\u003e1994 \u0026amp; 2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin: 49.58% Max: 51.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin: 4% Max: 10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin: 7.3% Max: 11.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemi-intensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin: 62.58% Max: 67.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin: 8.62% Max: 9.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin: 14.2% Max: 18.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin: 49.23% Max: 60.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin: 5.6% Max: 13.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal\u0026thinsp;\u0026plusmn;\u0026thinsp;14.78%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImported cattle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin: 70.1% Max: 86.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin: 9.3% Max: 15.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin: 8% Max: 10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOther coefficients\u003c/strong\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the MoEs used for other coefficients in the IPCC enteric fermentation model.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMargins of error (MoE) and PDFs used for Y\u003csub\u003em\u003c/sub\u003e and other coefficients used in uncertainty analysis (%)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMargin of error\u003c/p\u003e \u003cp\u003e1990, 2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExplanation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY\u003csub\u003em\u003c/sub\u003e (%) (all sub-categories)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal, s.e. small. Margin of error from IPCC (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCf\u003csub\u003ei\u003c/sub\u003e (all sub-categories)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta, proportion, cannot have negative values. 15% from Monni et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003csub\u003ea\u003c/sub\u003e (all sub-categories)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta, proportion, cannot have negative values. 15% from Monni et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003csub\u003ep\u003c/sub\u003e (all sub-categories)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta, proportion, cannot have negative values. 15% from Monni et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC (all sub-categories)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta, proportion, cannot have negative values. 15% from Monni et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eManure management\u003c/strong\u003e \u003cp\u003eThe parameters used for uncertainty analysis of methane and nitrous oxide emissions from manure management and deposit of dung and urine on pasture are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMargin of error (MoE) and PDFs used in the uncertainty analysis for manure management and managed soils\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMargin of error\u003c/p\u003e \u003cp\u003e1990 \u0026amp; 2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExplanation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsh content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es.e. small compared to mean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003csub\u003eo\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es.e. small compared to mean. Uncertainty ranges from IPCC (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Table\u0026nbsp;10.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMS%, various manure management systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncertainty range\u0026thinsp;\u0026plusmn;\u0026thinsp;50% from (IPCC \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) Ch. 10, p. 10.50.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCF, various manure management systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncertainty range\u0026thinsp;\u0026plusmn;\u0026thinsp;50% chosen, slightly higher than MCF uncertainty range used in Karimi-Zindashty et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) of \u0026plusmn;\u0026thinsp;45%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCF, pasture, cattle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;23.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCalculated from supplementary data in IPCC (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Annex 10B.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u003csub\u003e3\u003c/sub\u003e, pasture deposit, cattle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSee explanation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePERT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncertainty ranges from IPCC (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Table\u0026nbsp;11.1 of 0.007\u0026ndash;0.06, which were taken as min and max of PERT distribution, with 0.02 as most likely\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u003csub\u003e3\u003c/sub\u003e, other manure management systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncertainty ranges from IPCC (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Table\u0026nbsp;10.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrac\u003csub\u003egasm\u003c/sub\u003e, pasture deposit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSee explanation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePERT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIPCC (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Ch. 11 Table\u0026nbsp;11.3 gives uncertainty range of 0.05\u0026ndash;0.5, which were taken as min and max of PERT distribution, with 0.2 as most likely.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrac\u003csub\u003egas\u003c/sub\u003e, other manure management systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSee explanation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePERT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncertainty ranges taken from IPCC (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Ch. 10 Table\u0026nbsp;10.22 taken as min and max of PERT distribution, with default value as most likely.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrac\u003csub\u003egas\u003c/sub\u003e, biogas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTriangular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInventory value is maximum of the range given in IPCC (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Table\u0026nbsp;10.22.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSee explanation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePERT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(IPCC \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Ch. 11 Table\u0026nbsp;11.3 gives uncertainty range of 0.011\u0026ndash;0.017, which were taken as min and max of PERT distribution, with 0.014 as most likely. For pastoral/agro-pastoral system, Table\u0026nbsp;11.3 gives uncertainty range of 0.000\u0026ndash;0.011, with 0.005 as most likely\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrac\u003csub\u003eleach\u003c/sub\u003e, solid storage, composting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSee explanation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePERT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIPCC (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Annex 10B.7 suggests a range of 0 to 38% for solid storage and composting, which are taken as the min and max of the PERT distribution\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSee explanation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePERT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIPCC (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) Ch. 11 Table\u0026nbsp;11.3 Frac\u003csub\u003eGASM\u003c/sub\u003e uncertainty range of 0.0005\u0026ndash;0.025, which were taken as min and max of PERT distribution, with 0.0075 as most likely.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrac\u003csub\u003eLEACH\u003c/sub\u003e, pasture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSee explanation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePERT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(IPCC \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) Ch. 11 Table\u0026nbsp;11.3 Frac\u003csub\u003eLEACH\u003c/sub\u003e uncertainty range of 0.1\u0026ndash;0.8, which were taken as min and max of PERT distribution, with 0.3 as most likely.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEmissions estimated using the Tier 2 method\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the estimated emissions from each emission source in 1994 and 2022 and the uncertainty of these estimates. CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions were converted into CO\u003csub\u003e2\u003c/sub\u003e equivalent (CO\u003csub\u003e2\u003c/sub\u003ee) using global warming potentials from the IPCC Fifth Assessment Report (AR5) (IPCC \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated GHG emissions from cattle in 1994 and 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1994\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmissions (Gg CO\u003csub\u003e2\u003c/sub\u003ee)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUncertainty (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmissions (Gg CO\u003csub\u003e2\u003c/sub\u003ee)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUncertainty (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnteric fermentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,735.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;15.2%, -13.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21,478.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;19.7%, -17.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManure management CH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e694.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;56.9%, -41.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,300.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;62.7%, -45.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManure management direct N\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e708.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;67.8%, -57.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,282.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;75.4%, -61.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManure management indirect N\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e517.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;45.1%, -36.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e960.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;47.4%, \u0026minus;\u0026thinsp;38.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect N\u003csub\u003e2\u003c/sub\u003eO from deposit on pasture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;103.9%, -70.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;98.2%, -69.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect N\u003csub\u003e2\u003c/sub\u003eO from deposit on pasture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;69.0%, -48.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;65.6%, -46.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll emissions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,010.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;15.9%,-13.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25,278.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;20.7%,-17.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe total aggregated emissions increased by 57.9% over the inventory period, rising from 16,010.02 Gg CO\u003csub\u003e2\u003c/sub\u003ee in 1994 to 25,278.54 Gg CO\u003csub\u003e2\u003c/sub\u003ee in 2022. Among the specific sources, enteric fermentation was the largest contributor in both years, accounting for 85.8% of the total emissions in 1994 and 85.0% in 2022. Emissions from this source grew from 13,735.34 Gg CO\u003csub\u003e2\u003c/sub\u003ee to 21,478.31 CO\u003csub\u003e2\u003c/sub\u003ee Gg over this period. Total emissions from manure management and pasture deposits increased by 67% from 1994 to 2022, and their contribution to total emissions increased slightly from 14.2% in 1994 to 15.0% in 2022. Methane and direct N\u003csub\u003e2\u003c/sub\u003eO emissions from manure management were the largest contributors to total emissions from these sources, and increased from 61.7% of emissions from manure management and pasture deposits to 68.0% in 2022. Both the absolute value and the proportional contribution of dung and urine deposits on pasture to total emissions decreased over time.\u003c/p\u003e \u003cp\u003eThe uncertainty ranges associated with the emission estimates varied by source and year. The uncertainty for the total emission inventory increased from 95% CI = [+\u0026thinsp;15.9%, -13.9%] in 1994 to [+\u0026thinsp;20.7%, -17.4%] in 2022. Enteric fermentation presented uncertainty intervals of [+\u0026thinsp;15.2%, -13.4%] in 1994 and [+\u0026thinsp;19.7%, -17.1%] in 2022. The highest uncertainty was observed in direct emissions from deposits on pasture, which ranged from [+\u0026thinsp;103.9%, -70.8%] in 1994 to [+\u0026thinsp;98.2%, -69.9%] in 2022. Generally, uncertainties of manure management and pasture deposit emission estimates were greater than those for enteric fermentation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eActivity data uncertainty\u003c/h3\u003e\n\u003cp\u003eThe results of uncertainty analysis of activity data for 1994 and 2022 are shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. There was an increase in the uncertainty of total cattle population estimates between 1994 to 2022. The reason for this increase is likely due to the significant increase in the proportion of cattle in the intensive system, which after accounting for correlations among data sources has a relatively high level of uncertainty.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUncertainty from activity data 1994 and 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduction system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1994\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;15.8%, -15.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;14.3%, \u0026minus;\u0026thinsp;14.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemi-intensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;13.0%, \u0026minus;\u0026thinsp;13.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;14.3%, \u0026minus;\u0026thinsp;14.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;16.0%, \u0026minus;\u0026thinsp;15.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;14.5%, \u0026minus;\u0026thinsp;14.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cattle population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;6.9%, -6.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;9.2%, -9.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConsidering both uncertainties and estimated population size, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the population sub-categories contributing most to the overall uncertainty of the total population in 2022 (see Section 2.1 for explanation of figures). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, shows that four of the top five most influential sub-categories are in the intensive system, while one is in the semi-intensive system. This indicates that obtaining accurate sub-category population estimates for the intensive production system would have the greatest contribution to reducing activity data uncertainty, followed by the semi-intensive system. The regression coefficients for the extensive system are all relatively low, indicating that better population data in the extensive system would not have large contributions to reducing inventory uncertainty.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEnteric fermentation emission factors\u003c/h3\u003e\n\u003cp\u003eThe results of uncertainty analysis for enteric emission factors from all livestock sub-categories in 1994 and 2022 are presented in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. The uncertainty values for enteric emission factors in 2022 are higher than those in 1994. Changes in management systems and variations in feed types have contributed to this increased uncertainty.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUncertainty of implied emission factors for enteric fermentation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1994\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll cattle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;12.8%, -11.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;16.0%, -14.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Implied emission factor is the population-weighted emission factor.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. shows that the parameters with the greatest influence on uncertainty of the implied emission factor in 2022 are all in the intensive system, and include Ym, Cfi, digestibility and live weight for adult females, and the same parameters for yearlings and weaned cattle. Overall, to reduce the uncertainty of emission factors, the focus should be on the intensive system. Factors driving emission factor uncertainty in the extensive and semi-intensive systems are not among the most important factors for overall inventory uncertainty (except Ym for adult females in the semi-intensive system) because of the relatively smaller portion of these two systems in the total national cattle population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCombining both activity data and emission factor uncertainty, there was an increase in the uncertainty of enteric emissions from [+\u0026thinsp;15.2%, -13.4%] in 1994 to [+\u0026thinsp;19.7%, -17.1%] in 2022 (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The increase in uncertainty of enteric emissions is due to the shift in the cattle population structure. There has been an increase in the proportion of the cattle population in the intensive system, for which emission factor uncertainty is greater. The uncertainty of the trend 1994\u0026ndash;2022 for enteric emissions is [+\u0026thinsp;65.2%, -53.8%].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCH\u003csub\u003e4\u003c/sub\u003e manure management emissions\u003c/h2\u003e \u003cp\u003eThe results of the uncertainty analysis for manure management methane emission factors for all cattle are presented in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. The uncertainty values were slightly higher in 2022 compared to those in 1994. This difference may be related to a shift in production systems, as most cattle were managed under an intensive production system in 2022.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUncertainty of implied emission factors for manure management\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCattle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1994\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll cattle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;54.7%, -40.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;60.8%, -44.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the parameters influencing CH\u003csub\u003e4\u003c/sub\u003e manure management emission factors in 2022. Longer bars indicate a greater influence on emission factor estimates, which can be either positive or negative. Assuming the factors with the longest bars contribute the most to the uncertainty value, they should be a priority to address. Except for the proportion of manure managed in solid storage (MCF solid storage, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and the population of adult males in the semi-intensive system (SINT_AdMale-pop, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), all the other influential factors are in the intensive system. These include the methane conversion factors for solid storage, biogas and compost; manure management practices (e.g. MMS% in solid storage, composting and biogas) in the intensive system; as well as factors that influence volatile solid excretion (e.g. feed digestibility for intensive adult females, yearlings and weaned cattle, Cfi and live weight for adult females). Similar to enteric fermentation, this indicates that improving data on cattle feed and performance and manure management in the intensive system can make the biggest contribution to reducing inventory uncertainty.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere was an increase in the total uncertainty of CH\u003csub\u003e4\u003c/sub\u003e manure management emissions from [+\u0026thinsp;56.9%, -41.0%] in 1994 to [+\u0026thinsp;62.7%, -45.2%] in 2022 (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The increase in uncertainty is due to the increase in the proportion of cattle in the intensive system. This system has the most influential factors driving inventory estimates. The uncertainty of the 1994\u0026ndash;2022 trend was [+\u0026thinsp;103.1%, -89.8%].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eN\u003csub\u003e2\u003c/sub\u003eO emissions from manure management and pasture deposit of dung and urine\u003c/h2\u003e \u003cp\u003eThe result of uncertainty analysis of various direct and indirect sources of nitrous oxide emission is shown in Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUncertainty of nitrous oxide emission from manure management\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO Emission Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1994\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1994\u0026ndash;2022 trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal UNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEF UNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal UNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEF UNC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect N\u003csub\u003e2\u003c/sub\u003eO MM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;67.8%, -57.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;62.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;75.4%, -61.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;68.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;112.2%, -82.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect N\u003csub\u003e2\u003c/sub\u003eO MM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;45.1%, -36.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;40.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;47.4%, \u0026minus;\u0026thinsp;38.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;41.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;95.3%, -70.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect N\u003csub\u003e2\u003c/sub\u003eO PRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;103.9%, -70.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;87.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;98.2%, -69.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;83.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;72.6%, -36.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect N\u003csub\u003e2\u003c/sub\u003eO PRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;69.0%, -48.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;58.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;65.6%, -46.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;55.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;69.8%, -46.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: \u0026ldquo;MM\u0026rdquo; denotes manure management, \u0026ldquo;PRP\u0026rdquo; denotes deposit of dung and urine on pasture, rangeland and paddock, \u0026ldquo;EF\u0026rdquo; denotes emission factor, and \u0026ldquo;UNC\u0026rdquo; denotes uncertainty.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBetween 45% and 50% of total N\u003csub\u003e2\u003c/sub\u003eO emissions in these years were direct N\u003csub\u003e2\u003c/sub\u003eO emissions from manure management (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The main factors driving direct manure management N\u003csub\u003e2\u003c/sub\u003eO emissions are (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eEmission factors for solid storage in the intensive and semi-intensive systems and for composting in the intensive system;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAnimal production parameters, such as crude protein and digestible energy content of the diets for adult females and yearlings in the intensive system;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eManure management activity data in the intensive system and semi-intensive system.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eImproving data quality for these parameters would also mostly reduce uncertainty of methane manure management emissions, and in some cases also for enteric emission estimates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eParameters contributing to total indirect nitrous oxide emission from manure management for 2022 can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Indirect N\u003csub\u003e2\u003c/sub\u003eO manure management emission uncertainty is mainly affected by:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFractions of gas volatilized and fractions of N leached in solid storage and composting systems;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe emission factor (EF\u003csub\u003e4\u003c/sub\u003e) for volatilisation; and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAnimal production parameters (e.g. crude protein and feed energy digestibility, live weight) in the intensive system.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTotal direct nitrous oxide emission from pasture, range, and paddock in 2022 can be seen on Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. For nitrous oxide emissions from pasture deposits, the key factors include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eEF\u003csub\u003e3,PRP\u003c/sub\u003e in the extensive and semi-intensive systems\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProportion of dung and urine deposited on pasture in the semi-intensive system; and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAnimal production parameters influencing nitrogen excretion, including crude protein content of the diet in the extensive systems; Cfi for adult females in the intensive and semi-intensive systems; live weight for adult females in the extensive and semi-intensive systems, etc.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eNote, however, that improving estimates for the animal diet and performance parameters in the semi-intensive system would not significantly reduce the uncertainty of enteric fermentation or manure management emissions, for which uncertainty is mostly influenced by parameters in the intensive system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCombining all emission sources by converting CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO to CO\u003csub\u003e2\u003c/sub\u003ee equivalents using the AR5 GWP values (IPCC \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), the uncertainty of total cattle emissions increased from [+\u0026thinsp;15.9%, -13.9%] in 1994 to [+\u0026thinsp;20.7%,-17.4%] in 2022 (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Because enteric emissions accounted for about 85% of total emissions in both years, the uncertainty of total emissions from all sources is only slightly higher than the uncertainty of enteric emissions. Consequently, most of the input parameters affecting total cattle emissions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) are the same as those affecting enteric emissions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notable exceptions shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e include the direct N\u003csub\u003e2\u003c/sub\u003eO emission factor for solid storage (EF\u003csub\u003e3\u003c/sub\u003e) and the proportions of manure managed in solid storage in the intensive system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe uncertainty analysis conducted in this study provides critical insights into the need and priorities for improvement of Indonesia\u0026rsquo;s inventory of GHG emissions from cattle, including enteric fermentation, CH\u003csub\u003e4\u003c/sub\u003e from manure management, and N\u003csub\u003e2\u003c/sub\u003eO emissions. The results highlight that the intensive production system, while constituting a smaller portion of total livestock in the past, now represents the largest contributor to the overall uncertainty in the national inventory due to its rapid expansion and greater data uncertainty.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTrends in Inventory Uncertainty\u003c/h2\u003e \u003cp\u003eThe comparison between 1994 and 2022 clearly shows a rising trend in uncertainty, particularly for methane emissions from enteric fermentation and manure management. For instance, uncertainty in enteric CH₄ emissions increased from \u0026plusmn;\u0026thinsp;15.2% in 1994 to \u0026plusmn;\u0026thinsp;19.7% in 2022 (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Similarly, uncertainty in CH₄ from manure management increased from \u0026plusmn;\u0026thinsp;56.9% to \u0026plusmn;\u0026thinsp;62.7% over the same period. This trend correlates with the increasing proportion of cattle in intensive production systems from 50.9% in 1994 to 76.0% in 2022. The growth of intensive systems reflects structural shifts within the livestock sector. While margins of error for many key parameters in the intensive system are comparable to or lower than those in the other production systems (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), considerable uncertainty was assumed for some key parameters in the intensive system (e.g. feed digestibility, adult female live weights, yearling weight gain). When combined with the rapid increase in population numbers, uncertainty of these parameters has a large effect on the uncertainty of total emissions. This finding contrasts with some other analyses of inventory uncertainty in developed countries, where change in animal performance and emission factors has been the main driver of change in inventory uncertainty (e.g. Milne et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This can be explained as follows. First, rapid transformation of production systems is much more prevalent in developing countries and intensification through changing feeding and housing systems is an important driver of productivity increases at sector level (McDermott et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It is therefore a key GHG mitigation option in the longer-term (Bateki et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Second, the effect of change in population structure on inventory uncertainty is in part a result of the data sources and assumptions used in this inventory. While it would be ideal to calculate emission factors for each year in the time series using input data specific to that year, complete and regular statistics are not available in countries such as Indonesia. Therefore, the same input variables were used to calculate emission factors in 1994 and 2022, and the main change in emissions over time was due to change in the population data. Thus the attribution of change in uncertainty to population structure is in part biased by the lack of regular statistics. standardized data collection in Indonesia\u0026rsquo;s cattle production systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eKey Drivers of Uncertainty\u003c/h2\u003e \u003cp\u003eBecause enteric fermentation contributes a significant proportion of total GHG emissions from cattle (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), factors driving uncertainty of emission estimates from this source have a strong impact on overall inventory uncertainty. Key drivers of uncertainty include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFeed digestibility and methane conversion factors: Feed digestibility is a key determinant of gross energy intake (as well as volatile solid excretion affecting manure methane emissions). The uncertainty of feed digestibility in all production systems is a consequence both of the high variability in feeding practices among farms and uncertainty and variability in the quality of the feeds fed. The high variability in diets and feed quality across regions and between seasons is common in tropical countries (Jayanegara et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Salah et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The methane conversion factor (Ym) also has a central role in determining enteric methane emissions, and varies considerably based on diet composition, feed intake level, and animal physiology (Knapp et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Niu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition to variability among diets, measurement variability and error in prediction models contributes to the uncertainty of this parameter (Tee et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The lack of country-specific Ym values in Indonesia further increases the uncertainty of using default values.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOther coefficients affecting gross energy intake: Under low productivity conditions, the majority of net energy intake needs are for maintenance. In the IPCC model, this is affected by live weight and a coefficient for maintenance (Cfi) which varies by cattle sex and physiological status (IPCC \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The uncertainties of these parameters for major cattle sub-categories in the intensive system were key drivers of uncertainty in enteric emissions and total emissions from all sources (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In Indonesia, which possesses a wide range of local cattle breeds as well as crossbreeds with exotic cattle (Sutarno and Setyawan \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), some of the uncertainty in live weight estimates reflects inherent variation in the population, while some reflects the relative lack of reported measurements.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFactors affecting manure emissions: The uncertainties of manure methane and nitrous oxide emissions were larger than that of enteric emissions. However, most input parameters were not important drivers of overall uncertainty due to the relatively smaller contributions of manure emission sources to total emissions. Due to its assumed prevalence in the intensive system, parameters associated with solid storage were important drivers of manure methane and nitrous oxide emissions (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), including the proportion of manure managed in solid storage, methane conversion factors for solid storage and direct N\u003csub\u003e2\u003c/sub\u003eO emission factor (EF\u003csub\u003e3\u003c/sub\u003e) for solid storage. Activity data on manure management systems is relatively scarce, and expert judgement associated with wide uncertainty margins was used in this study. In the absence of sufficient country-specific measurements, use of the IPCC default factors may lead to significant uncertainty when applied in tropical environments, with higher ambient temperatures and varied moisture conditions that significantly affect manure decomposition and associated emissions (Chadwick et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cardoso et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Mancia et al. 2022). Measurements of emission factors in tropical countries are scarce (Hassouna et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Measurements from developed countries both methane and N\u003csub\u003e2\u003c/sub\u003eO emissions show considerable variation between climate zones and measurement conditions (e.g. animal housing, diets, measurement methods)(Qu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; \u0026Ccedil;inar et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Policy and Reporting\u003c/h2\u003e \u003cp\u003eHigh uncertainty levels can erode confidence in reported reductions, limit the credibility of mitigation claims, and potentially affect access to climate finance mechanisms such as carbon markets or result-based payments under Article 6 of the Paris Agreement. This study has clear implications for improvement of national GHG inventory systems. It has highlighted the production systems and animal sub-categories for which more accurate data is required on animal performance and animal and manure management in order to reduce inventory uncertainty. To improve accuracy and reduce inventory uncertainty, the following priority actions are recommended:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eInvest in regular, region-specific surveys on animal performance, feed quality, and manure handling, especially in the intensive production system.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDevelop localized emission factors for enteric fermentation and manure emissions, building upon the IPCC \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e Refinement guidelines.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIntegrate field measurement campaigns (e.g., digestibility trials, and methane flux measurements) into national agricultural monitoring systems.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStrengthen institutional collaboration between ministries (e.g., agriculture, environment, statistics) to standardize and streamline livestock data collection and management.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCapacity building for uncertainty analysis, including training in probabilistic modelling tools and expert elicitation methods.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIn a diverse and smallholder-dominated country such as Indonesia, diversity in key factors such as diets and feed quality, which are often influenced by economic factors and local conditions, will inevitably lead to high variation in parameter value estimates. Representative data to capture this variation can contribute to increasing inventory accuracy and better quantification of inventory uncertainty.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides a detailed uncertainty analysis of greenhouse gas emissions from cattle in Indonesia, with a focus on enteric fermentation, CH\u003csub\u003e4\u003c/sub\u003e from manure management, and N\u003csub\u003e2\u003c/sub\u003eO emissions from manure management and pasture deposition. Using Monte Carlo simulation and IPCC guidelines, it identifies the key parameters driving uncertainty and quantifies the overall uncertainty of cattle GHG emissions in Indonesia from 1994 to 2022.\u003c/p\u003e \u003cp\u003eThe findings demonstrate a slight increase in uncertainty over time, largely due to the growing dominance of intensive livestock systems. Parameters such as populations of animal sub-categories, methane conversion factors (Ym), feed digestibility, live weight, coefficients for maintenance, and manure management system proportions (MMS%) in the intensive production system are the most influential in driving emission uncertainty.\u003c/p\u003e \u003cp\u003eTo improve the accuracy and transparency of national GHG inventories, the study recommends prioritizing data collection and methodological refinement in the intensive production system. Enhancing field-level monitoring, developing localized emission factors, and integrating expert-based assessments with empirical data will be critical for reducing inventory uncertainty. By targeting the most sensitive parameters and production systems, Indonesia can significantly enhance the credibility of its emission reporting and better support its climate mitigation commitments under the Paris Agreement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the New Zealand Climate Smart Agriculture Initiative to support the objectives of the Global Research Alliance on Agricultural Greenhouse Gases through the project “Improving National GHG Inventory for Livestock Using the IPCC Tier 2 Method - phase 2”.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSW, YW, AW, MCH, and EHW: Conceptualization, Methodology; YW, BT, MIH and SP: Investigation; SW, MCH, GET, BT, YW, EHW: curation; SW and AW: data analysis; SW, YW, MCH, EHW: writing original draft; AW, SP, GET, and RH: reviewing; BT, YW, and MCH: supervision, project administration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on reasonable request\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBateki CA, Wassie SE, Wilkes A (2023) The contribution of livestock to climate change mitigation: a perspective from a low-income country. 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Jurnal Ilmu Ternak dan Veteriner 21(2):101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14334/jitv.v21i2.1358\u003c/span\u003e\u003cspan address=\"10.14334/jitv.v21i2.1358\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"greenhouse gas inventory, livestock, methane, nitrous oxide, uncertainty analysis","lastPublishedDoi":"10.21203/rs.3.rs-8216844/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8216844/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate estimation of greenhouse gas (GHG) emissions from the livestock sector is essential for informing climate policies and fulfilling international reporting obligations. However, uncertainties in emission factors and activity data can significantly affect the accuracy of national GHG inventories. This study conducts a comprehensive uncertainty analysis of GHG emissions from Indonesia\u0026rsquo;s cattle sector between 1994 and 2022 calculated using a Tier 2 approach and Monte Carlo simulation based on the IPCC \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e guidelines. The cattle production systems assessed were intensive, semi-intensive and extensive systems. The analysis covers three major emission sources: enteric methane (CH₄), methane and nitrous oxide (N₂O) emissions from manure management (including deposit on pasture). Results show that uncertainty levels have increased over time, with uncertainty of enteric fermentation \u0026ndash; the largest emission source \u0026ndash; increasing from ca. \u0026plusmn;15 for 1994 to \u0026plusmn;\u0026thinsp;20% for 2022. This increase was mainly due to the rising share of cattle in the intensive system. Although uncertainties of other emission sources were higher, ranging between \u0026plusmn;\u0026thinsp;36% and \u0026plusmn;\u0026thinsp;104% for different sources in different years, their contributions to total emission uncertainty were limited because of the small proportion of total emissions from these sources. Uncertainty of key parameters in the intensive system, such as the enteric methane conversion factor, feed digestibility, liveweight, and proportions of manure managed in different systems, had the greatest contributions to overall inventory uncertainty. To improve inventory accuracy, the study emphasizes the need for improved data availability and quality, especially for intensive production systems. Strengthening national capacity to monitor animal performance, feed characteristics, and manure handling will significantly reduce uncertainty in future inventories. These improvements are crucial for enhancing accuracy and credibility in Indonesia\u0026rsquo;s climate reporting in the livestock sector.\u003c/p\u003e","manuscriptTitle":"Uncertainty Analysis Supports Prioritization of Greenhouse Gas Inventory Improvement in Indonesia’s Livestock Sector","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 08:31:30","doi":"10.21203/rs.3.rs-8216844/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-05-07T00:46:29+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-11T13:07:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-30T14:08:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Animal Health and Production","date":"2025-11-27T19:45:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"06e5cc6f-adb0-4265-bd9b-b6c0a19da68e","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"","date":"2026-05-07T00:46:29+00:00","index":0,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-17T08:31:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 08:31:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8216844","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8216844","identity":"rs-8216844","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}