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| sector:agriculture:manure_management:start [2026/02/12 13:05] – kotzulla | sector:agriculture:manure_management:start [2026/04/01 11:40] (current) – [Uncertainty] kotzulla | ||
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| - | ^ ^ NO< | + | ^ |
| - | | 3.B.1.a | + | | 3.B.1.a |
| - | | 3.B.1.b | + | | 3.B.1.b |
| - | | 3.B.2 | -/- | + | | 3.B.2 |
| - | | 3.B.3 | -/- | + | | 3.B.3 |
| - | | 3.B.4.d | + | | 3.B.4.d |
| - | | 3.B.4.e | + | | 3.B.4.e |
| - | | 3.B.4.g.i | + | | 3.B.4.g.i |
| - | | 3.B.4.g.ii | + | | 3.B.4.g.ii |
| - | | 3.B.4.g.iii | + | | 3.B.4.g.iii |
| - | | 3.B.4.g.iv | + | | 3.B.4.g.iv |
| - | | {{page> | + | | {{page> |
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| ===== Country specifics ===== | ===== Country specifics ===== | ||
| - | |||
| {{ : | {{ : | ||
| Line 41: | Line 40: | ||
| - | NO< | + | NO< |
| NMVOC emissions from category 3.B (manure management) contributed 97.1 % (291.7 kt) from total agricultural NMVOC emissions (300.6 kt). | NMVOC emissions from category 3.B (manure management) contributed 97.1 % (291.7 kt) from total agricultural NMVOC emissions (300.6 kt). | ||
| Line 49: | Line 48: | ||
| ==== Activity data for all pollutants ==== | ==== Activity data for all pollutants ==== | ||
| - | The Federal Statistical Agency and the Statistical Agencies of the federal states carry out surveys in order to collect, along with other data, the head counts of animals. The results of these surveys are used for emission calculations, | + | The Federal Statistical Agency and the Statistical Agencies of the federal states carry out surveys in order to collect, along with other data, the head counts of animals. The results of these surveys are used for emission calculations, |
| - | The animal population figures used in the inventory are presented in Table 1. Buffaloes are included in the cattle population figures, mules and asses are included in the horse population figures (IE), see Vos et al. (2026), Chapter 2.3. In the first years after the German reunification in 1990 animal livestock decreased markedly. The head counts for cattle continued to decrease significantly until 2006/2007, followed by a more or less stable period until 2014. Since 2015 a slight decrease occurred. In 2024, dairy cattle numbers are 56.5 % of 1990 numbers, while the total population of other cattle is at 52.3 % of 1990. Swine numbers decreased until 1995 and then increased slightly. Since 2014 a new decrease occurred which became significant between 2020 and 2022 (total swine numbers were reduced by around 18 % within two years). 2024 swine numbers are 66.6 % of 1990 numbers. The 2024 numbers of horses, sheep and goats are, respectively, | + | The animal population figures used in the inventory are presented in Table 1. |
| + | Buffaloes are included in the cattle population figures, mules and asses are included in the horse population figures (IE), see Vos et al. (2026), Chapter 2.3. In the first years after the German reunification in 1990 animal livestock decreased markedly. The head counts for cattle continued to decrease significantly until 2006/2007, followed by a more or less stable period until 2014. Since 2015 a slight decrease occurred. | ||
| + | In 2024, dairy cattle numbers are 56.5 % of 1990 numbers, while the total population of other cattle is at 52.3 % of 1990. Swine numbers decreased until 1995 and then increased slightly. | ||
| + | Since 2014 a new decrease occurred which became significant between 2020 and 2022 (total swine numbers were reduced by around 18 % within two years). | ||
| + | 2024 swine numbers are 66.6 % of 1990 numbers. The 2024 numbers of horses, sheep and goats are, respectively, | ||
| Figures for broilers and turkeys are showing a massive increase since 1990. Since the year 2013, there have been only minor changes of total poultry numbers. In total, 2024 poultry population figures are at 147.2 % of 1990. | Figures for broilers and turkeys are showing a massive increase since 1990. Since the year 2013, there have been only minor changes of total poultry numbers. In total, 2024 poultry population figures are at 147.2 % of 1990. | ||
| Line 57: | Line 60: | ||
| Emissions of deer, rabbits, ostrich and fur-bearing animals are reported since submission 2024. The underlying animal numbers of these categories were estimated in different ways because there are no surveys which collect those animal numbers. However, the impact of those animal categories on the total emissions is small. | Emissions of deer, rabbits, ostrich and fur-bearing animals are reported since submission 2024. The underlying animal numbers of these categories were estimated in different ways because there are no surveys which collect those animal numbers. However, the impact of those animal categories on the total emissions is small. | ||
| - | A detailed description of the animal numbers used can be found in Vos et al. (2026), chapter 2.3. | + | A detailed description of the animal numbers used can be found in Vos et al. (2026), chapter 2.3[(VOSETAL2026)]. |
| __Table 1: Population of animals, in [1,000 individuals]__ | __Table 1: Population of animals, in [1,000 individuals]__ | ||
| Line 81: | Line 84: | ||
| ==== Additional data ==== | ==== Additional data ==== | ||
| - | Emission calculations in accordance with a Tier 2 or Tier 3 method require data on animal performance (animal weight, weight gain, milk yield, milk protein content, milk fat content, numbers of births, numbers of eggs and weights of eggs) and on the relevant feeding details (phase feeding, feed components, protein and energy content, digestibility and feed efficiency). To subdivide officially recorded total numbers of turkeys into roosters and hens, the respective population percentages need to be known. Details on data requirements for the modelling of emissions from livestock husbandry in the German inventory can be found in Vos et al. (2026), Chapter 2. | + | |
| + | Emission calculations in accordance with a Tier 2 or Tier 3 method require data on animal performance (animal weight, weight gain, milk yield, milk protein content, milk fat content, numbers of births, numbers of eggs and weights of eggs) and on the relevant feeding details (phase feeding, feed components, protein and energy content, digestibility and feed efficiency). To subdivide officially recorded total numbers of turkeys into roosters and hens, the respective population percentages need to be known. Details on data requirements for the modelling of emissions from livestock husbandry in the German inventory can be found in Vos et al. (2026), Chapter 2[(VOSETAL2026)]. | ||
| Most of the data regarding feed and performance is not available from official statistics and was obtained from literature, from publications by agricultural associations, | Most of the data regarding feed and performance is not available from official statistics and was obtained from literature, from publications by agricultural associations, | ||
| - | For 1991, 1995 and 1999, frequency distributions of feeding strategies, husbandry systems (shares of pasturing/ | + | For 1991, 1995 and 1999, frequency distributions of feeding strategies, husbandry systems (shares of pasturing/ |
| RAUMIS did not model complete time series but only selected years. RAUMIS data for the years 1991, 1995, and 1999 are used in the inventory for the respective years. For 1990, the data for 1991 is adopted, for the intervening years (1992-1994 and 1996-1998) data gaps were closed by linear interpolation on district level. | RAUMIS did not model complete time series but only selected years. RAUMIS data for the years 1991, 1995, and 1999 are used in the inventory for the respective years. For 1990, the data for 1991 is adopted, for the intervening years (1992-1994 and 1996-1998) data gaps were closed by linear interpolation on district level. | ||
| Line 91: | Line 95: | ||
| For the year 2009, respective data are used that were derived from the 2010 official agricultural census and the simultaneous survey of agricultural production methods (Landwirtschaftliche Zählung 2010, Statistisches Bundesamt/ Federal Statistical Office) as well as the 2011 survey on manure application practices (Erhebung über Wirtschaftsdüngerausbringung, | For the year 2009, respective data are used that were derived from the 2010 official agricultural census and the simultaneous survey of agricultural production methods (Landwirtschaftliche Zählung 2010, Statistisches Bundesamt/ Federal Statistical Office) as well as the 2011 survey on manure application practices (Erhebung über Wirtschaftsdüngerausbringung, | ||
| For the year 2015, data on techniques of farm manure spreading from the 2016 official agricultural census (Agrarstrukturerhebung 2016, Statistisches Bundesamt / Federal Statistical Office) are used. | For the year 2015, data on techniques of farm manure spreading from the 2016 official agricultural census (Agrarstrukturerhebung 2016, Statistisches Bundesamt / Federal Statistical Office) are used. | ||
| - | For the year 2019 data from the 2020 official agricultural census (Landwirtschaftszählung 2020, LW20) are used for housing systems, storage systems and manure spreading systems. | + | For the year 2019 data from the 2020 official agricultural census (Landwirtschaftszählung 2020, LW20)[(DESTATIS2020)] |
| For 2010 to 2018 the housing and storage systems data was linearly interpolated between the censuses of 2010 and 2020.The data on manure spreading techniques was linearly interpolated between the census data from 2009 and 2015, and for 2016 to 2018 between the censuses conducted in 2016 and 2020. In addition, it was taken into account that, as of 2012, slurry spread on bare soil has to be incorporated within four hours. | For 2010 to 2018 the housing and storage systems data was linearly interpolated between the censuses of 2010 and 2020.The data on manure spreading techniques was linearly interpolated between the census data from 2009 and 2015, and for 2016 to 2018 between the censuses conducted in 2016 and 2020. In addition, it was taken into account that, as of 2012, slurry spread on bare soil has to be incorporated within four hours. | ||
| - | The data from the 2020 official agricultural census (DESTATIS, 2020)((Statistisches Bundesamt (2020): LW20, Landwirtschaftszählung 2020. https:// | + | The data from the 2020 official agricultural census (DESTATIS, 2020) is being used for subsequent years until more current data is available[(DESTATIS2020)] |
| - | For a description of the RAUMIS data, the data from official surveys and additional data from other sources see Vos et al. (2026), Chapter 2.5. Time series of frequency distributions of housing systems, storage systems and application techniques as well as the corresponding emission factors are provided in NID 2026((NID (2026): National Inventory Report 2026 for the German Greenhouse Gas Inventory 1990-2024. Available in April 2026.)), Chapter 17.3.1. | + | For a description of the RAUMIS data, the data from official surveys and additional data from other sources see Vos et al. (2026), Chapter 2.5. Time series of frequency distributions of housing systems, storage systems and application techniques as well as the corresponding emission factors are provided in NID 2026[(UBA2026)], Chapter 17.3.1. |
| - | Since submission 2026 transports of manure, energy crops and digestates between NUTS 3 regions are simulated in the calculation model. This does not have major influence on the emissions of the whole country, but changes the distribution of emissions between single NUTS 3 regions. For details on the methodology, | + | Since submission 2026 transports of manure, energy crops and digestates between NUTS 3 regions are simulated in the calculation model. This does not have major influence on the emissions of the whole country, but changes the distribution of emissions between single NUTS 3 regions. For details on the methodology, |
| Line 108: | Line 112: | ||
| == N excretion == | == N excretion == | ||
| - | In order to determine NH< | + | In order to determine NH< |
| - | For dairy cows N excretion is estimated based on milk urea content since submission 2026, for details see Vos et al. (2026) Chapter 4.1.2. This allows an estimation that is based on the actual feeding of the dairy cows, thus it is possible to depict the effect of feeding changes in N excretions. Milk urea contents are obtained from the agricultural information services company vit (Vereinigte Informationssysteme Tierhaltung w.V.) and directly by four state control associations, | + | |
| + | For dairy cows N excretion is estimated based on milk urea content since submission 2026, for details see Vos et al. (2026) Chapter 4.1.2[(VOSETAL2026)]. This allows an estimation that is based on the actual feeding of the dairy cows, thus it is possible to depict the effect of feeding changes in N excretions. Milk urea contents are obtained from the agricultural information services company vit (Vereinigte Informationssysteme Tierhaltung w.V.) and directly by four state control associations, | ||
| + | For the time between 2005 and 2022 over 10 million milk urea contents at herd-level were obtained. For the purpose of the inventory these were aggregated on district level, weighted by herd size. | ||
| + | For the years from 1990-2004 the values of 2005 were taken, for 2023 and 2024 the values of 2022. | ||
| + | |||
| + | \\ | ||
| __Table 2: National means of N excretions and TAN contents__ | __Table 2: National means of N excretions and TAN contents__ | ||
| Line 147: | Line 156: | ||
| ^ ostrich | ^ ostrich | ||
| ^ fur animals | ^ fur animals | ||
| + | |||
| + | |||
| == N mass flow and emission assessment == | == N mass flow and emission assessment == | ||
| - | The calculation of the emissions of NH< | + | The calculation of the emissions of NH< |
| - | This approach differentiates between N excreted with faeces (organic nitrogen Norg, i. e. undigested feed N) and urine (total ammoniacal nitrogen TAN, i. e. fraction of feed N metabolized). The N flow within the manure management system is treated as depicted in the figure below. This method reconciles the requirements of both the Atmospheric Emission Inventory Guidebook for NH< | + | This approach differentiates between N excreted with faeces (organic nitrogen Norg, i. e. undigested feed N) and urine (total ammoniacal nitrogen TAN, i. e. fraction of feed N metabolized). The N flow within the manure management system is treated as depicted in the figure below. This method reconciles the requirements of both the Atmospheric Emission Inventory Guidebook for NH< |
| Not explicitly shown in the N mass flow scheme is air scrubbing in housing and anaerobic digestion of manure. These issues are separately described further below. Note that emissions from grazing and application are reported in sector 3.D. | Not explicitly shown in the N mass flow scheme is air scrubbing in housing and anaerobic digestion of manure. These issues are separately described further below. Note that emissions from grazing and application are reported in sector 3.D. | ||
| Line 161: | Line 172: | ||
| The model allows tracing of the pathways of the two N fractions after excretion. The various locations where excretion may take place are considered. The partial mass flows through the livestock systems are represented. | The model allows tracing of the pathways of the two N fractions after excretion. The various locations where excretion may take place are considered. The partial mass flows through the livestock systems are represented. | ||
| - | During storage Norg can be transformed into TAN and vice versa. Both, the way and the magnitude of such transformations may be influenced by manure treatment processes like, e. g., anaerobic digestion where a considerable fraction of Norg is mineralized to TAN. For details see Vos et al. (2026), Chapter 4.2. Wherever NH< | + | |
| + | During storage Norg can be transformed into TAN and vice versa. Both, the way and the magnitude of such transformations may be influenced by manure treatment processes like, e. g., anaerobic digestion where a considerable fraction of Norg is mineralized to TAN. For details see Vos et al. (2026), Chapter 4.2[(VOSETAL2026)]. Wherever NH< | ||
| == Air scrubber systems in swine and poultry housings == | == Air scrubber systems in swine and poultry housings == | ||
| + | |||
| For pig and poultry production the inventory model considers the effect of air scrubbing. Data on frequencies of air scrubbing facilities and the removal efficiency are provided by KTBL (Kuratorium für Technik und Bauwesen in der Landwirtschaft / Association for Technology and Structures in Agriculture) supplemented by data from the 2020 agricultural census. The average removal efficiency of NH< | For pig and poultry production the inventory model considers the effect of air scrubbing. Data on frequencies of air scrubbing facilities and the removal efficiency are provided by KTBL (Kuratorium für Technik und Bauwesen in der Landwirtschaft / Association for Technology and Structures in Agriculture) supplemented by data from the 2020 agricultural census. The average removal efficiency of NH< | ||
| According to KTBL, 7.6 % of all pig places were equipped with ”first class” systems in 2024, another 12.6 % were equipped with “second class” systems. For poultry 0.9 % of all laying hen places and 2.5 % of all broiler places were equipped with air scrubbers that remove both NH< | According to KTBL, 7.6 % of all pig places were equipped with ”first class” systems in 2024, another 12.6 % were equipped with “second class” systems. For poultry 0.9 % of all laying hen places and 2.5 % of all broiler places were equipped with air scrubbers that remove both NH< | ||
| - | The amounts of NH< | + | |
| + | The amounts of NH< | ||
| == Anaerobic digestion of manure == | == Anaerobic digestion of manure == | ||
| - | According to IPCC (2006), anaerobic digestion of manure is treated like a particular storage type. In the German Inventory it comprises three sub-compartments (pre-storage, | + | According to IPCC (2006), anaerobic digestion of manure is treated like a particular storage type [(IPCC2006)]. In the German Inventory it comprises three sub-compartments (pre-storage, |
| NH< | NH< | ||
| Line 180: | Line 194: | ||
| The detailed NH< | The detailed NH< | ||
| - | For the detailed emission factors of livestock husbandry see Vos et al. (2026), Chapter 4.3. | ||
| - | The detailed emission factors for N< | + | For the detailed emission factors of livestock husbandry see Vos et al. (2026), Chapter 4.3[(VOSETAL2026)]. |
| + | |||
| + | The detailed emission factors for N< | ||
| Table 3 shows the implied emission factors of NH< | Table 3 shows the implied emission factors of NH< | ||
| __Table 3: IEF for NH< | __Table 3: IEF for NH< | ||
| - | ^ ^ 1990 | + | ^ |
| ^ Ammonia | ^ Ammonia | ||
| ^ dairy cattle | ^ dairy cattle | ||
| Line 225: | Line 240: | ||
| == Trend discussion for Key Sources == | == Trend discussion for Key Sources == | ||
| - | Dairy cattle, other cattle and swine are key sources of NH< | + | Dairy cattle, other cattle and swine are key sources of NH< |
| + | |||
| + | The time series of the total NH< | ||
| For NO< | For NO< | ||
| Line 233: | Line 250: | ||
| All timeseries of the emission inventory have completely been recalculated. Tables 4 and 5 compare the recalculated time series for NH< | All timeseries of the emission inventory have completely been recalculated. Tables 4 and 5 compare the recalculated time series for NH< | ||
| - | For NH3 there are many reasons for very different emissions compared to last year’s submission. For dairy cows the new methodology to claculate N and TAN excretions (see [[sector: | + | For NH3 there are many reasons for very different emissions compared to last year’s submission. For dairy cows the new methodology to claculate N and TAN excretions (see [[sector: |
| - | The total emissions of NO< | + | The adjusted N excretion for horses after 2010 (**recalculation No. 6**) is the main reason for higher emissions from other animals. The addition of substrate transports to biogas plants (**recalculation No. 1**) has a smaller impact on emissions than the other recalculations. This recalculation is the main reason for the changes for emissions from other cattle and poultry but it also affects dairy cattle and swine emissions. Many of the other recalculations have much smaller effects. Overall, the changes result in lower emissions compared with last year’s submission. |
| + | |||
| + | The total emissions of NO< | ||
| __Table 4: Comparison of NH< | __Table 4: Comparison of NH< | ||
| - | ^ NFR Total emissions | + | ^ |
| - | ^ ^ Submission | + | ^ NFR-Total: NH< |
| - | ^ Total | + | | ::: |
| - | ^ ::: | + | | ::: |
| - | ^ ::: | + | | ::: |
| - | ^ ::: | + | | thereof: **Dairy cattle** ^ current |
| - | ^ Dairy cattle | + | ^ ::: |
| - | ^ ::: | + | | thereof: **Other cattle** ^ current |
| - | ^ Other cattle | + | ^ ::: |
| - | ^ ::: | + | | thereof: **Swine** |
| - | ^ Swine | + | ^ ::: |
| - | ^ ::: | + | | thereof: **Poultry** |
| - | ^ poultry | + | ^ ::: |
| - | ^ ::: | + | | thereof: **Other animals** |
| - | ^ Other animals | + | ^ ::: |
| - | ^ ::: | + | |
| + | \\ | ||
| __Table 5: Comparison of NO< | __Table 5: Comparison of NO< | ||
| - | ^ NFR Total emissions | + | ^ |
| - | ^ ^ Submission | + | ^ NFR-Total: NO< |
| - | ^ Total | + | | ::: |
| - | ^ ::: | + | | ::: |
| - | ^ ::: | + | | ::: |
| - | ^ ::: | + | | thereof: **Dairy cattle** ^ current |
| - | ^ Dairy cattle | + | | ::: |
| - | ^ ::: | + | | thereof: **Other cattle** ^ current |
| - | ^ Other cattle | + | | ::: |
| - | ^ ::: | + | | thereof: **Swine** |
| - | ^ Swine | + | | ::: |
| - | ^ ::: | + | | thereof: **Poultry** |
| - | ^ poultry | + | | ::: |
| - | ^ ::: | + | | thereof: **Other animals** |
| - | ^ Other animals | + | | ::: |
| - | ^ ::: | + | |
| == Planned improvements == | == Planned improvements == | ||
| + | |||
| No improvements are planned at present. | No improvements are planned at present. | ||
| ===== NMVOC ===== | ===== NMVOC ===== | ||
| + | |||
| In 2024, NMVOC emissions from manure management amount to 291.7 kt which is 97.1 % of total NMVOC emissions from the agricultural sector. 84.1 % thereof originate from cattle, 15.9 % from other animals. | In 2024, NMVOC emissions from manure management amount to 291.7 kt which is 97.1 % of total NMVOC emissions from the agricultural sector. 84.1 % thereof originate from cattle, 15.9 % from other animals. | ||
| ==== Method ==== | ==== Method ==== | ||
| - | The Tier 2 methodology provided by EMEP (2023)-3B-26 was used to assess the NMVOC emissions from manure management for dairy cattle and other cattle. For all other animals the Tier 1 methodology (EMEP (2023)-3B-17) was used. The use of the Tier 2 methodology yields NMVOC emissions which formally could be reported in the sectors 3.D.a.2.a (application of manure to soils) and 3.D.a.3 (grazing emissions). However, to be congruent with the NMVOC emissions for other animal categories, Germany reports these emissions in the NMVOC emissions reported from manure management (3.B). For the NFR codes 3.D.a.2.a | + | |
| + | The Tier 2 methodology provided by EMEP/EEA (2023), Chapter 3.B, page 26 [(EMEPEEA2023)] | ||
| + | |||
| + | However, to be congruent with the NMVOC emissions for other animal categories, Germany reports these emissions in the NMVOC emissions reported from manure management (3.B). For the NFR codes 3.D.a.2.a | ||
| === Activity data === | === Activity data === | ||
| + | |||
| Animal numbers serve as activity data, see Table 1. | Animal numbers serve as activity data, see Table 1. | ||
| === Emission factors === | === Emission factors === | ||
| + | |||
| For the Tier 2 methodology applied to dairy cattle and other cattle the following data was used: | For the Tier 2 methodology applied to dairy cattle and other cattle the following data was used: | ||
| - | * gross feed intake in MJ per year, country specific data from the annual reporting of greenhouse gas emissions, see NID 2026, Chapter 5.1.3.3, | + | * gross feed intake in MJ per year, country specific data from the annual reporting of greenhouse gas emissions, see NID 2026, Chapter 5.1.3.3[(UBA2026)], |
| - | * proportion x< | + | * proportion x< |
| - | * FRAC< | + | * FRAC< |
| - | * FRAC< | + | * FRAC< |
| - | * EF< | + | * EF< |
| * EF< | * EF< | ||
| - | For all other animal categories the Tier 1 emission factors for NMVOC were used as provided in EMEP (2023)-3B-17, Table 3.4. For horses the emission factors for feeding with silage was chosen, for all other animals the emission factors for feeding without silage. Due to missing country-specific emission factors or emission factors that do not correspond to the inventory’s animal categories, the emission factors provided in EMEP (2023)-3B-17, Table 3.4, were used to define specific emission factors for weaners, boars, lambs, ponies/ | + | For all other animal categories the Tier 1 emission factors for NMVOC were used as provided in EMEP/EEA (2023), Ch. 3.B, p. 17, Table 3.4[(EMEPEEA2023)]. For horses the emission factors for feeding with silage was chosen, for all other animals the emission factors for feeding without silage. Due to missing country-specific emission factors or emission factors that do not correspond to the inventory’s animal categories, the emission factors provided in EMEP/EEA (2023), Ch. 3.B, p. 17, Table 3.4, were used to define specific emission factors for weaners, boars, lambs, ponies/ |
| - | The implied emission factors given in Table 4 relate the overall NMVOC emissions to the number of animals in each animal category. The IEFs for dairy cattle and other cattle are much higher than the EMEP Tier 1 EF, which are 17.937 kg NMVOC for dairy cattle and 8.902 kg NMVOC for other cattle. The only possible explanation for those huge differences is that the EMEP Tier 2 and Tier 1 methods are not consistent. | + | |
| + | The implied emission factors given in Table 4 relate the overall NMVOC emissions to the number of animals in each animal category. The IEFs for dairy cattle and other cattle are much higher than the EMEP/EEA Tier 1 EF, which are 17.937 kg NMVOC for dairy cattle and 8.902 kg NMVOC for other cattle. The only possible explanation for those huge differences is that the EMEP Tier2 and Tier1 methods are not consistent. | ||
| + | |||
| + | The IEFs for the other categories provided in Table 6 correspond to the EMEP Tier1 emission factors, except for horses, sheep and swine. These categories comprise subcategories with different emission factors so that their overall IEFs in Table 4 represent subpopulation-weighted national mean values. | ||
| - | The IEFs for the other categories provided in Table 6 correspond to the EMEP Tier 1 emission factors, except for horses, sheep and swine. These categories comprise subcategories with different emission factors so that their overall IEFs in Table 4 represent subpopulation-weighted national mean values. | + | Note that other poultry in Germany includes not only geese and ducks but also pullets. For pullets no default EF is given in the 2023 EMEP/EEA guidebook (EMEP/EEA, 2023), hence the EF of broilers has been adopted (because of similar housing). This assumption significantly lowers the overall IEF of other poultry (in Table 6 the IEFs are listed separately for each poultry category). The IEF of the sheep category is significantly lower than the EMEP/EEA Tier 1 emission factor, because for lambs the EF is assumed to be 40% lower compared to an adult sheep in accordance with the difference in N excretion between lambs and adult sheep. |
| - | Note that other poultry in Germany includes not only geese and ducks but also pullets. For pullets no default EF is given in the EMEP guidebook (EMEP, 2023), hence the EF of broilers has been adopted (because of similar housing). This assumption significantly lowers the overall IEF of other poultry (in Table 6 the IEFs are listed separately for each poultry category). The IEF of the sheep category is significantly lower than the EMEP Tier 1 emission factor, because for lambs the EF is assumed to be 40% lower compared to an adult sheep in accordance with the difference in N excretion between lambs and adult sheep. | + | |
| __Table 6: IEF for NMVOC from manure management, in [kg NMVOC per animal place]__ | __Table 6: IEF for NMVOC from manure management, in [kg NMVOC per animal place]__ | ||
| Line 332: | Line 360: | ||
| __Table 7: Comparison of NMVOC emissions [kt] with previous submission__ | __Table 7: Comparison of NMVOC emissions [kt] with previous submission__ | ||
| - | ^ NFR Total emissions | + | ^ |
| - | ^ ^ Submission | + | ^ NFR-Total: NMVOC |
| - | ^ Total | + | | ::: |
| - | ^ ::: | + | | ::: |
| - | ^ ::: | + | | ::: |
| - | ^ ::: | + | | thereof: **Dairy cattle** ^ current |
| - | ^ Dairy cattle | + | ^ ::: |
| - | ^ ::: | + | | thereof: **Other cattle** ^ current |
| - | ^ Other cattle | + | ^ ::: |
| - | ^ ::: | + | | thereof: **Other animals** |
| - | ^ Other animals | + | ^ ::: |
| - | ^ ::: | + | |
| === Planned improvements === | === Planned improvements === | ||
| Line 355: | Line 382: | ||
| 66.4 % of total PM< | 66.4 % of total PM< | ||
| + | |||
| ==== Method ==== | ==== Method ==== | ||
| - | EMEP (2013-3B-26) provided a Tier 2 methodology. In the 2023 Guidebook (EMEP, 2023), this methodology has been replaced by a Tier 1 methodology. However, EF for cattle derived with the EMEP 2013 Tier 2 methodology remained unchanged. Therefore, the EMEP 2013((EMEP (2013): | + | |
| + | EMEP/EEA (2013), Ch. 3.B, p. 26[(EMEPEEA2013)] provided a Tier2 methodology. In the 2023 Guidebook (EMEP, 2023), this methodology has been replaced by a Tier1 methodology. However, EF for cattle derived with the EMEP/EEA 2013 Tier2 methodology remained unchanged. Therefore, the EMEP/EEA 2013[(EMEPEEA2013)] methodology was kept for cattle. For swine the EMEP 2013 methodology was formally kept but the EMEP/EEA 2023 Tier1 EF was used both for slurry and solid based manure management systems. | ||
| + | In case the EMEP 2023 EFs are simply rounded EMEP/EEA 2013 EFs, the unrounded EMEP/EEA 2013 EFs were kept. | ||
| + | For rabbits the EFs from The Netherlands’ inventory were adopted (Huis In’t Veld et al, 2011)[(HUISINTVELTETAL2011)], for ostriches the EFs of goats were used. The inventory considers air scrubber systems in swine and poultry husbandry. For animal places equipped with air scrubbing the emission factors are reduced according to the removal efficiency of the air scrubber systems (90 % for TSP and PM< | ||
| === Activity data === | === Activity data === | ||
| + | |||
| Animal numbers serve as activity data, see Table 1. | Animal numbers serve as activity data, see Table 1. | ||
| === Emission factors === | === Emission factors === | ||
| - | Tier 1 emission factors for TSP, PM< | + | Tier 1 emission factors for TSP, PM< |
| The implied emission factors given in Table 8 relate the overall TSP and PM emissions to the number of animals in each animal category. The Guidebook does not indicate whether EFs have considered the condensable component (with or without). | The implied emission factors given in Table 8 relate the overall TSP and PM emissions to the number of animals in each animal category. The Guidebook does not indicate whether EFs have considered the condensable component (with or without). | ||
| Line 419: | Line 451: | ||
| ^ ostrich | ^ ostrich | ||
| ^ fur animals | ^ fur animals | ||
| + | |||
| ==== Trend discussion for Key Sources ==== | ==== Trend discussion for Key Sources ==== | ||
| + | |||
| Swine and laying hens are key sources of TSP emissions from manure management. The total TSP emissions from swine mainly follow the animal numbers given in Table 1 for the earlier years of the time series. However, due to increases in places equipped with air scrubbing and different emission factors of the different housing systems of the five swine subcategories (sows (divided in gilts and old sows), weaners, fattening pigs, boars) and the varying population shares in those housing systems the R< | Swine and laying hens are key sources of TSP emissions from manure management. The total TSP emissions from swine mainly follow the animal numbers given in Table 1 for the earlier years of the time series. However, due to increases in places equipped with air scrubbing and different emission factors of the different housing systems of the five swine subcategories (sows (divided in gilts and old sows), weaners, fattening pigs, boars) and the varying population shares in those housing systems the R< | ||
| ==== Recalculations ==== | ==== Recalculations ==== | ||
| - | The following table 9 shows the effects of recalculations on emissions of particulate matter. Minimal differences compared with the previous submission are due to the correction of the number of animal places equipped with aur scrubbers (**recalculation No. 11**), see [[sector: | + | The following table 9 shows the effects of recalculations on emissions of particulate matter. Minimal differences compared with the previous submission are due to the correction of the number of animal places equipped with air scrubbers (**recalculation No. 11**), see [[sector: |
| __Table 9: Comparison of particle emissions (TSP, PM< | __Table 9: Comparison of particle emissions (TSP, PM< | ||
| ^ TSP, PM< | ^ TSP, PM< | ||
| - | ^ ^ Submission | + | ^ ^ ^ 1990 ^ 1995 ^ 2000 ^ 2005 ^ 2010 ^ 2015 ^ 2016 ^ 2017 ^ 2018 ^ 2019 ^ 2020 ^ 2021 ^ 2022 ^ 2023 ^ 2024 ^ |
| - | ^ TSP ^ current | + | ^ TSP ^ current |
| - | ^ ::: ^ previous | + | | ::: ^ previous |
| - | ^ ::: ^ absolute change | + | | ::: ^ absolute change |
| - | ^ ::: ^ relative change [%] | 0.00 | 0.00 | 0.00 | 0.00 | -0.01 | -0.01 | -0.01 | 0.00 | 0.00 | 0.13 | 0.00 | 0.00 | 0.00 | 0.00 | + | | ::: ^ relative change [%] | 0.00 | 0.00 | 0.00 | 0.00 | -0.01 | -0.01 | -0.01 | 0.00 | 0.00 | 0.13 | 0.00 | 0.00 | 0.00 | 0.00 |
| - | ^ PM< | + | ^ PM< |
| - | ^ ::: ^ previous | + | | ::: ^ previous |
| - | ^ ::: ^ absolute change | + | | ::: ^ absolute change |
| - | ^ ::: ^ relative change [%] | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.05 | 0.00 | 0.00 | 0.00 | 0.00 | | | + | | ::: ^ relative change [%] | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.05 | 0.00 | 0.00 | 0.00 | 0.00 | | |
| - | ^ PM< | + | ^ PM< |
| - | ^ ::: ^ previous | + | | ::: ^ previous |
| - | ^ ::: ^ absolute change | + | | ::: ^ absolute change |
| - | ^ ::: ^ relative change [%] | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | | | + | | ::: ^ relative change [%] | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | | |
| <WRAP center round info 65%> | <WRAP center round info 65%> | ||
| - | For **pollutant-specific information on recalculated emission estimates for Base Year and 2024**, please see the pollutant specific recalculation tables following [[general: | + | For **pollutant-specific information on recalculated emission estimates for Base Year and 2023**, please see the pollutant specific recalculation tables following [[general: |
| </ | </ | ||
| - | |||
| ===== Planned improvements ===== | ===== Planned improvements ===== | ||
| Line 453: | Line 486: | ||
| At the moment, no category-specific improvements are planned. | At the moment, no category-specific improvements are planned. | ||
| </ | </ | ||
| - | |||
| ===== Uncertainty ===== | ===== Uncertainty ===== | ||
| + | |||
| Details are described in [[general: | Details are described in [[general: | ||
| + | |||
| + | [(IPCC2006> | ||
| + | |||
| + | [(IPCC2019> | ||
| + | |||
| + | [(VOSETAL2026> | ||
| + | |||
| + | [(BITTMANETAL2014> | ||
| + | |||
| + | [(DESTATIS2020> | ||
| + | |||
| + | [(WEINGARTEN1995> | ||
| + | |||
| + | [(DAEMMGENHUTCHINGS2008> | ||
| + | Volume 154, Issue 3, 2008, pp. 488-497, ISSN 0269-7491, https:// | ||
| + | |||
| + | [(REIDYETAL2008> | ||
| + | |||
| + | [(EMEPEEA2013> | ||
| + | |||
| + | [(EMEPEEA2023> | ||
| + | |||
| + | [(UBA2026> | ||
| + | |||
| + | [(HUISINTVELTETAL2011> | ||
| + | |||
| + | [(HENRICHSMEYERETAL1996> | ||