Choice of emission factors

Tier 1 Approach for methane emissions from Enteric Fermentation

This Tier 1 method is simplified so that only readily-available animal population data are needed to estimate emissions. Default emission factors are presented for each of the recommended population subgroups. Each step is discussed in turn.

Step 1: Animal population

The animal population data should be obtained using the approach described in Section 10.2.

Step 2: Emission factors

The purpose of this step is to select emission factors that are most appropriate for the country's livestock characteristics. Default emission factors for enteric fermentation have been drawn from previous studies, and are organised by region for ease of use.

The data used to estimate the default emission factors for enteric fermentation are presented in Annex 10A.1 at the end of this section.

Table 10.9

Suggested emissions inventory methods for enteric fermentation

Livestock

Suggested emissions inventory methods

Dairy Cow

Tier 2a/Tier 3

Other Cattle

Tier 2a/Tier 3

Buffalo

Tier 1/Tier 2

Sheep

Tier 1/Tier 2

Goats

Tier 1

Camels

Tier 1

Horses

Tier 1

Mules and Asses

Tier 1

Swine

Tier 1

Poultry

Not developed

Other (e.g., Llamas, Alpacas, Deer)

Tier 1

a The Tier 2 method is recommended for countries with large livestock populations. Implementing the Tier 2 method for additional livestock subgroups may be desirable when the category emissions are a large portion of total methane emissions for the country.

Table 10.10 shows the enteric fermentation emission factors for each of the animal species except cattle. As shown in the table, emission factors for sheep and swine vary for developed and developing countries. The differences in the emission factors are driven by differences in feed intake and feed characteristic assumptions (see Annex 10A.1). Table 10.11 presents the enteric fermentation emission factors for cattle. A range of emission factors is shown for typical regional conditions. As shown in the table, the emission factors vary by over a factor of four on a per head basis.

Table 10.10 shows the enteric fermentation emission factors for each of the animal species except cattle. As shown in the table, emission factors for sheep and swine vary for developed and developing countries. The differences in the emission factors are driven by differences in feed intake and feed characteristic assumptions (see Annex 10A.1). Table 10.11 presents the enteric fermentation emission factors for cattle. A range of emission factors is shown for typical regional conditions. As shown in the table, the emission factors vary by over a factor of four on a per head basis.

While the default emission factors shown in Table 10.11 are broadly representative of the emission rates within each of the regions described, emission factors vary within each region. Animal size and milk production are important determinants of emission rates for dairy cows. Relatively smaller dairy cows with low levels of production are found in Asia, Africa, and the Indian subcontinent. Relatively larger dairy cows with high levels of production are found in North America and Western Europe.

Animal size and population structure are important determinants of emission rates for other cattle. Relatively smaller other cattle are found in Asia, Africa, and the Indian subcontinent. Also, many of the other cattle in these regions are young. Other cattle in North America, Western Europe and Oceania are larger, and young cattle constitute a smaller portion of the population.

To select emission factors from Tables 10.10 and 10.11, identify the region most applicable to the country being evaluated. Scrutinise the tabulations in Annex 10A.1 to ensure that the underlying animal characteristics such as weight, growth rate and milk production used to develop the emission factors are similar to the conditions in the country. The data collected on the average annual milk production by dairy cows should be used to help select a dairy cow emission factor. If necessary, interpolate between dairy cow emission factors shown in the table using the data collected on average annual milk production per head.

Note that using the same Tier 1 emission factors for the inventories of successive years means that no allowance is being made for changing livestock productivity, such as increasing milk productivity or trend in live weight. If it is important to capture the trend in methane emission that results from a trend in livestock productivity, then livestock emissions can become a key source category based on trend and a Tier 2 calculation should be used.

Table 10.10

Enteric fermentation emission factors for Tier 1 method1 (kg CH4 head-1 yr-1)

Livestock

Developed countries

Developing countries

Liveweight

Buffalo

55

55

300 kg

Sheep

8

5

65 kg - developed countries; 45 kg - developing countries

Goats

5

5

40 kg

Camels

46

46

570 kg

Horses

18

18

550 kg

Mules and Asses

10

10

245 kg

Deer

20

20

120 kg

Alpacas

8

8

65 kg

Swine

1.5

1.0

Poultry

Insufficient data for calculation

Insufficient data for calculation

Other (e.g., Llamas)

To be determined1

To be determined1

All estimates have an uncertainty of +30-50%.

Sources: Emission factors for buffalo and camels from Gibbs and Johnson (1993). Emission factors for other livestock from Crutzen et al, (1986), Alpacas from Pinares-Patino et al, 2003; Deer from Clark et al, 2003 .

1 One approach for developing the approximate emission factors is to use the Tier 1 emissions factor for an animal with a similar digestive system and to scale the emissions factor using the ratio of the weights of the animals raised to the 0.75 power. Liveweight values have been included for this purpose. Emission factors should be derived on the basis of characteristics of the livestock and feed of interest and should not be restricted solely to within regional characteristics.

Step 3: Total emission

To estimate total emission, the selected emission factors are multiplied by the associated animal population (Equation 10.19) and summed (Equation 10.20):

Where:

Emissions = methane emissions from Enteric Fermentation, Gg CH4 yr-1 EF(T) = emission factor for the defined livestock population, kg CH4 head-1 yr-1 N(T) = the number of head of livestock species / category T in the country T = species/category of livestock

Equation 10.20 Total emissions from livestock enteric fermentation

Total CH4Enteric =£ El i

Where:

Total CH^ = total methane emissions from Enteric Fermentation, Gg CH4 yr-1

Ei = is the emissions for the ith livestock categories and subcategories

Table 10.11

Tier 1 enteric fermentation emission factors for Cattle1

Regional characteristics

Cattle category

Emission factor 2,3 (kg CH4 head-1 yr-1)

Comments

North America: Highly productive commercialized dairy sector feeding high quality forage and grain. Separate beef cow herd, primarily grazing with feed supplements seasonally. Fast-growing beef steers/heifers finished in feedlots on grain. Dairy cows are a small part of the population.

Dairy

Other Cattle

128 53

Average milk production of 8,400 kg head-1 yr-1.

Includes beef cows, bulls, calves, growing steers/heifers, and feedlot cattle.

Western Europe: Highly productive commercialised dairy sector feeding high quality forage and grain. Dairy cows also used for beef calf production. Very small dedicated beef cow herd. Minor amount of feedlot feeding with grains.

Dairy

Other Cattle

117 57

Average milk production of 6,000 kg head-1 yr-1.

Includes bulls, calves, and growing steers/heifers.

Eastern Europe: Commercialised dairy sector feeding mostly forages. Separate beef cow herd, primarily grazing. Minor amount of feedlot feeding with grains.

Dairy

Other Cattle

99 58

Average milk production of 2,550 kg head-1 yr-1.

Includes beef cows, bulls, and young.

Oceania: Commercialised dairy sector based on grazing. Separate beef cow herd, primarily grazing rangelands of widely varying quality. Growing amount of feedlot feeding with grains. Dairy cows are a small part of the population.

Dairy

Other Cattle

90 60

Average milk production of 2,200 kg head-1 yr-1.

Includes beef cows, bulls, and young.

Latin America: Commercialised dairy sector based on grazing. Separate beef cow herd grazing pastures and rangelands. Minor amount of feedlot feeding with grains. Growing non-dairy cattle comprise a large portion of the population.

Dairy

Other Cattle

56

Average milk production of 800 kg head-1 yr-1

Includes beef cows, bulls, and young.

Asia: Small commercialised dairy sector. Most cattle are multi-purpose, providing draft power and some milk within farming regions. Small grazing population. Cattle of all types are smaller than those found in most other regions.

Dairy

Other Cattle

68 47

Average milk production of 1,650 kg head-1 yr-1

Includes multi-purpose cows, bulls, and young

Africa and Middle East: Commercialised dairy sector based on grazing with low production per cow. Most cattle are multi-purpose, providing draft power and some milk within farming regions. Some cattle graze over very large areas. Cattle are smaller than those found in most other regions.

Dairy

Other Cattle

46 31

Average milk production of 475 kg head-1 yr-1

Includes multi-purpose cows, bulls, and young

Indian Subcontinent: Commercialised dairy sector based on crop by-product feeding with low production per cow. Most bullocks provide draft power and cows provide some milk in farming regions. Small grazing population. Cattle in this region are the smallest compared to cattle found in all other regions.

Dairy

Other Cattle

58 27

Average milk production of 900 kg head-1 yr-1

Includes cows, bulls, and young. Young comprise a large portion of the population

1 Emission factors should be derived on the basis of the characteristics of the cattle and feed of interest and need not be restricted solely to within regional characteristics.

2 IPCC Expert Group, values represent averages within region, where applicable the use of more specific regional milk production data is encouraged. Existing values were derived using Tier 2 method and the data in Tables 10 A. 1 and 10A. 2.

3 The following assumptions have been made in deriving these values: i) mature weights of animals have been used; ii) cows have been assumed to be non-lactating as lactation levels were low and, iii) the mix of bulls and castrates among "males" was undetermined as Cfi value for castrates was not specified.

Tier 2 Approach for methane emissions from Enteric Fermentation

The Tier 2 method is applied to more disaggregated livestock population categories and used to calculate emission factors, as opposed to default values. The key considerations for the Tier 2 method are the development of emission factors and the collection of detailed activity data.

Step 1: Livestock population

The animal population data and related activity data should be obtained following the approach described in Section 10.2.

Step 2: Emission factors

When the Tier 2 method is used, emission factors are estimated for each animal category using the detailed data developed in Step 1.

The emission factors for each category of livestock are estimated based on the gross energy intake and methane conversion factor for the category. The gross energy intake data should be obtained using the approach described in Section 10.2. The following two sub-steps need to be completed to calculate the emission factor under the Tier 2 method:

1. Obtaining the methane conversion factor (Ym)

The extent to which feed energy is converted to CH4 depends on several interacting feed and animal factors. If CH4 conversion factors are unavailable from country-specific research, the values provided in Table 10.12, Cattle/Buffalo CH4 conversion factors, can be used for cattle and buffalo. These general estimates are a rough guide based on the general feed characteristics and production practices found in many developed and developing countries. When good feed is available (i.e., high digestibility and high energy value) the lower bounds should be used. When poorer feed is available, the higher bounds are more appropriate. A CH4 conversion factor of zero is assumed for all juveniles consuming only milk (i.e., milk-fed lambs as well as calves).

Due to the importance of Ym in driving emissions, substantial ongoing research is aimed at improving estimates of Ym for different livestock and feed combinations. Such improvement is most needed for animals fed on tropical pastures as the available data are sparse. For example, a recent study (Kurihara et al., 1999) observed Ym values outside the ranges described in Table 10.12.

Table 10.12

Cattle/Buffalo CH4 conversion factors (Ym )

Livestock category

A m

Feedlot fed Cattle a

3.0% + 1.0%

Dairy Cows (Cattle and Buffalo) and their young

6.5% + 1.0%

Other Cattle and Buffaloes that are primarily fed low quality crop residues and byproducts

6.5% + 1.0%

Other Cattle or Buffalo - grazing

6.5% + 1.0%

a When fed diets contain 90 percent or more concentrates. b The ± values represent the range. Source: IPCC Expert Group.

Regional, national and global estimates of enteric methane generation rely on small scale determinations both of Ym and of the influence of feed and animal properties upon Ym. Traditional methods for measuring Ym include the use of respiration calorimeters for housing individual animals (Johnson and Johnson, 1995). A tracer technique using SF6 enables methane emissions from individual animals to be estimated under both housed or grazing conditions (Johnson et al., 1994). The results of recent measurements have been surveyed by Lassey ( 2006) who also examines the "upscaling" of such measurements to national and global inventories.

It is also important to examine the influences of feed properties and animal attributes on Ym. Such influences are important to better understand the microbiological mechanisms involved in methanogenesis with a view to designing emission abatement strategies, as well as to identify different values for Ym according to animal husbandry practices. To date, the search for such influences is equivocal, and consequently there is little variability evident both in the values reported in Table 10.12 as supported by the recent survey of Ym measurements in the literature (Lassey, 2006).

Table 10.13 proposes a common Ym value for all mature sheep irrespective of feed quality, but with different values for mature and juvenile sheep with demarcation at 1 year of age. These values are based on data by Lassey et al. (1997), Judd et al. (1999) and Ulyatt et al. (2002a, 2002b, 2005) and while consistent with measurements by other researchers (Murray et al., 1978; Leuning et al., 1999), may not span the full range of pastures to be found. The median value is appropriate for most applications, but for poor quality feed the upper limits may be more appropriate, and for high-digestibility high-energy feeds the lower limits may be used.

Table 10.13 proposes a common Ym value for all mature sheep irrespective of feed quality, but with different values for mature and juvenile sheep with demarcation at 1 year of age. These values are based on data by Lassey et al. (1997), Judd et al. (1999) and Ulyatt et al. (2002a, 2002b, 2005) and while consistent with measurements by other researchers (Murray et al., 1978; Leuning et al., 1999), may not span the full range of pastures to be found. The median value is appropriate for most applications, but for poor quality feed the upper limits may be more appropriate, and for high-digestibility high-energy feeds the lower limits may be used.

Table 10.13 Sheep CH4 conversion factors (YM)

Category

Y a m

Lambs (<1 year old)

4.5% + 1.0%

Mature Sheep

6.5% + 1.0%

a The + values represent the range.

Note that in some cases, CH4 conversion factors may not exist for specific livestock types. In these instances, CH4 conversion factors from the reported livestock that most closely resembles those livestock types can be reported. For examples, CH4 conversion factors for other cattle or buffalo could be applied to estimate an emission factor for camels.

2. Emission factor development

An emission factor for each animal category should be developed following Equation 10.21:

2. Emission factor development

Equation 10.21

CH4 emission factors for enteric fermentation from a livestock category

GE •( I» 365

EF =

1100 J

55.65

EF = emission factor, kg CH4 head-1 yr-1 GE = gross energy intake, MJ head-1 day-1

Ym = methane conversion factor, per cent of gross energy in feed converted to methane

The factor 55.65 (MJ/kg CH4) is the energy content of methane

This emission factor equation assumes that the emission factors are being developed for an animal category for an entire year (365 days). While a full year emission factor is typically used, in some circumstances the animal category may be defined for a shorter period (e.g., for the wet season of the year or for a 150-day feedlot feeding period). In this case, the emission factor would be estimated for the specific period (e.g., the wet season) and the 365 days would be replaced by the number of days in the period. The definition of the period to which the emission factor applies is described in Section 10.2.

Step 3: Total emissions

To estimate total emissions, the selected emission factors are multiplied by the associated animal population and summed. As described above under Tier 1, the emissions estimates should be reported in gigagrams (Gg).

Potential for refinement of Tier 2 or development of a Tier 3 method to enteric methane emission inventories

Increased accuracy and identification of causes of variation in emissions are at the heart of inventory purpose. Improvements in country methodology, whether as components of current Tier 1 or 2 or if additional refinements are implemented (Tier 3), are encouraged.

Current Tier 1 and Tier 2 enteric methane emissions factors and estimation procedures are driven by first estimating daily and annual gross energy consumption by individual animals within an inventory class which are then multiplied by an estimate of CH4 loss per unit of feed (Ym). There is considerable room for improvement in

Tier 2 prediction of both feed intake and in Ym. Factors potentially impacting feed requirements and/or consumption that are not considered include:

• breed or genotype variation in maintenance requirement;

• heat and cold stress effects on intake and maintenance requirements; and

• depression in digestibility with increasing levels of consumption, or diet composition limits to diet intake.

Likewise, a host of interacting factors that control variations in Ym are not included in Tier 2 methodology, including:

• diet dry matter intake as it relates to live body weight;

• diet chemical composition;

• particle passage and digestion kinetics, or plant microbial defensive compounds; and

• variation in the microbial populations within the digestive tract.

Accurate estimation of diet DE% is singularly important in the estimation of feed intake and thus emissions, as previously emphasized. A 10% error in the average diet DE% or TDN% will result in CH4 errors ranging from 12 to 20% depending on beginning circumstance. The depression in DE% with increasing daily amounts of diet consumed is not considered. This will underestimate feed intakes of high producing dairy cows consuming mixtures of concentrates and forages, e.g., as is common in the North America and Europe, although some of the resulting error in methane emission estimate will be compensated by reductions in Ym as intake per day increases. Methods to estimate digestibility depressions have been described (NRC, 1996; NRC, 2001).

There have been many attempts to refine estimates of Ym. Several researchers have developed models which relate the chemical composition of the diet consumed, or in more detail, the composition of digested carbohydrate and other chemical components to Ym. These models typically predict diet particle and chemical component rates of passage and digestion in each enteric compartment at varying intake and the resulting H2 balance, volatile fatty acids, and microbial and CH4 yields. These approaches have generated Ym values that are consistent with direct measurements using chamber and SF6 techniques.

The literature contains many examples of the positive relationship of plant cell wall digestion to high acetic to propionic end-product ratios, and to high CH4 yields. While fibrous carbohydrate digestion is undeniably the strongest single indicator of CH4 yield, the CH4 per digested fiber is not constant, e.g., when soyhulls or beet pulp are fed as single feed at varying levels of intake, Ym will vary from 8 to 11% when measured at restricted feed intakes and from 5 to 6% when measure at ad libitum intakes (Kujawa, 1994; Diarra, 1994). Thus, enteric fermentation of the same fibrous substrate can result in quite different Ym values. Perhaps the most severe limitation to development of more complex prediction models lies in the difficulty of applying them to broad country inventories. The difficulty is to provide the data needed to drive these more complex models of feed intake or Ym. It is often difficult to define animal characteristics, productivity, and %DE accurately for a class of livestock in a region of the country, let alone detailed carbohydrate fraction, rates of passage and digestion, etc.

The amount of global research on mitigation strategies currently going on, such as vaccines, ionophores, polyunsaturated vegetable oils, condensed tannins etc, suggests a need to address how they should be reflected in inventory compilation at Tier 2 or Tier 3. First, the inventory should reflect only those technologies that conform to QA/QC principles and have attracted a wide degree of international acceptance such as through peer-reviewed articles that include a description of the technology, its efficacy and its validation under field conditions. Second, the inventory should be accompanied by evidence of the take-up of the technology, and apply it only to emissions by those livestock where take-up can be validated. Third, for a newly implemented technology (such as an administered dose of a mitigating agent), the inventory could also present an accompanying calculation of the emissions in the absence of a mitigation measure in order to make transparent the magnitude of the emission reductions that are being claimed. Mitigation measures should be supported by peer-reviewed publications.

Approaches to improve estimates of feed intake and Ym and to consider mitigation approaches are to be encouraged, given due care on limitations of scope, production circumstance, etc. to which the predictive relationships apply.

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