Enteric Fermentation

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Estimating emissions from enteric fermentation

What data are required?

Tier 1

Emissions (for a subcategory of livestock) = A * EF, where

  •  Emissions of CH4 = CH4 emissions from enteric fermentation, for a defined population, in kg CH4 yr-1
  • NT, P = the number of head of livestock species/category T in the country classified as productivity system P
  • EF = emission factor for the defined livestock population T and the productivity system P, expressed in units of kg CH4 head-1 yr-1 (IPCC, 2019; Table 10.10; Table 10.11)
  • T = species/category of livestock (IPCC, 2019; Table 10.10; Table 10.11)
  • P = productivity system, either high or low productivity for use in advanced Tier 1a – omitted if using Tier 1 approach (IPCC, 2019; Table 10.10; Table 10.11)

Generally, the following animal subcategories are included: dairy cattle, non-dairy cattle, buffaloes, sheep, goats, camels, llamas, horses, mules, asses, and pigs.

Because only the number of animals is used, Tier 1 emissions calculations are not sufficient to capture changes in emissions due to productivity improvements, which are the most common intervention in developing countries. Tier 2 methods are recommended in these cases.

Tier 2

Tier 2 inventories for livestock require more detailed country-specific data but are much more useful than Tier 1 because they reflect a country’s national circumstances and production systems. Inventories using Tier 1 methods cannot recognize changes in emissions resulting from changes in productivity. From a Tier 1 perspective, the only way to reduce emissions would be to reduce animal numbers. In contrast, Tier 2 based inventories use the information on the animal’s gross energy or dry matter intake to estimate their actual emissions. Using this approach, Tier 2 inventories more closely reflect a country’s actual farming systems and their productivity and automatically pick up any changes over time (GRA, CCAFS 2016; FAO, GRA, 2020; Wilkes et al. 2017; 2018).

A more detailed characterization of livestock sub-populations is required for Tier 2 approaches. This means distinguishing subcategories of animals by age, sex, productive use, or another factor likely to be associated with significant differences in average emissions. For example, cattle and buffalo populations should be classified into at least three main subcategories: mature dairy, other mature, and growing cattle.

Emission factors are then estimated for each animal subcategory based on the gross energy intake and methane conversion factor for the category.

  • EF = emission factor, kg CH4 head-1 yr-1
  • GE = gross energy intake, MJ head-1 day-1 (IPCC, 2019; Equation 10.16)
  • Ym = methane conversion factor, percent of gross energy in feed converted to methane, obtained from IPCC (2006; 2019) guidelines or EFDB, or from local or regional studies (IPCC, 2019; Table 10.12)
  • The factor 55.65 (MJ/kg CH4) is the energy content of CH4

In cases where the inventory compiler used the simplified Tier 2, the emission factors should be calculated using the following equation (IPCC, 2019; Equation 10.21a):

  • EF = emission factor, kg CH4 head-1 yr-1
  • DMI = dry matter intake, kg DMI day-1 (IPCC, 2019; Equation 10.17-10.18)
  • Ym = methane conversion factor, percent of gross energy in feed converted to methane, obtained from IPCC (2006; 2019) guidelines or EFDB, or from local or regional studies (IPCC, 2019; Table 10.12)
  • 365 = days per year
  • 1000 = conversion from g to kg

Gross energy intake is usually estimated based on the animal’s typical diet in each subcategory and data describing the animal’s typical performance (productivity) in each subcategory.

Data describing the animal performance or productivity include:

  • Weight (W), kg
  • Average weight gain per day (WG), kg day-1
  • Mature weight (MW), kg
  • Average number of hours worked per day (for draft animals)
  • Feeding situation (e.g. stall, pasture)
  • Mean winter temperature, °C (for detailed feed intake models)
  • Average daily milk production, kg day-1 (for dairy animals)
  • Fat content of milk, % (for dairy animals)
  • Percent of females that give birth in a year (for mature animals)
  • Number of offspring produced per year (for female livestock with multiple births)
  • Feed digestibility, % of gross energy
  • Average annual wool production, kg yr-1 (for sheep)

The parameters and equations used to calculate feed intake (usually called dry matter intake or DMI) from this activity data can be found in Chapter 10 of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories.

There are two ways to implement a Tier 2 inventory or MRV system for enteric methane emissions in practice. The first is to continuously (annually or every few years) track data on animal diets and animal performance according to the established sub-categories and update the equations for feed intake and emissions. The second is to collect animal diet and performance data once and calculate a set of the country- or region-specific Tier 2 emission factors (kg CH4 head-1 yr-1), as was done in Mongolia (see Box 1). This reduces the frequency and level of detail of activity data collection. Once these emission factors are calculated, estimating emissions only requires knowing the animals’ population in each sub-category.

While the second approach is much less data-intensive, it is minimally useful for measuring the impacts of mitigation projects or policies because it does not capture changes in animal production systems (e.g., implementing improved feeding practices) that would change emissions. An alternative approach, falling somewhere between these two approaches, is to develop a livestock system characterization that is detailed enough to capture differences in diet and productivity that would affect emissions. For example, growing cattle could be further subdivided into animals fed a high-grain diet and those grown and finished on pasture (ICC IPCC 2006). For large countries, it may be possible to define region subdivisions in order to represent differences in climate, feeding systems, and diet. Ideally, the classification system would also include differences in manure management, further described below. Static Tier 2 emission factors (kg CH4 head-1 yr--1 could then be calculated for each of these more detailed sub-categories. Going forward, an MRV or GHG inventory system would then track the change in livestock population within each sub-category, and develop factors for new sub-categories (or update old ones) as livestock management changes within the country or region.

Box 1. Different ways used by selected countries to structure application of the IPCC Tier 2 equations
Argentina: The country was divided into 8 regions, based on agro-ecological and climatic factors. In each region, a number of breeding and fattening systems was identified. Data to characterize production systems in terms of activity, diet, reproduction and production in each system were then procured from literature, and entered into a model structured around regions and production systems. The resulting preliminary model was then refined using other data sources, and the aggregate results cross-checked against regional, census and agricultural production data (Argentina, National Inventory Report 2012, Vol 3).

Bolivia: Cattle populations in three climatic regions (altiplano, valles and tropics) were identified according to the agro-ecological zonation of different departments (sub-regions) in the country. For cattle and sheep, the population was stratified into sub-classes (e.g. dairy cattle, non-dairy cattle, young cattle and oxen) based on consultations with livestock production experts in each region. In each region, data on feed rations and apparent digestibility of forage and feed was obtained from publications, and other production data (e.g. milk yields, live weights) were obtained from publications or government agencies (Bolivia, National Inventory Report 1999-2000).

Georgia: Common cattle breeds in Georgia include late maturing breeds (the Georgian Mountain and Red Mingrelian) characterized by low weight, low productivity and high milk fat content, as well as several high-productive early maturing breeds that were imported in the previous century. The IPCC equations were populated separately for early and late maturing breeds at different life stages using published data and expert opinion. Expert opinion was used to estimate the proportion of each breed in the total cattle population (Georgia, National Inventory Report 2010-2013).

Mongolia: Although Mongolia has diverse indigenous breeds of livestock, a small number of breeds dominate the total population of each livestock type. Published breed characterization studies were referred to, and used along with expert judgement of livestock experts and IPCC default factors to develop a single Tier 2 emission factor for each type of livestock in the country. Results were compared with Tier 2 factors from China (Prof. B. Namkhainyam, Mongolian University of Science and Technology, pers. comm.)

Source: Excerpt from Wilkes et al. (2017)

Tier 3

Some countries for which livestock emissions are particularly important may wish to go beyond the Tier 2 method and incorporate additional country-specific information in their estimates. This approach could employ sophisticated models that consider diet composition in detail, the concentration of products arising from ruminant fermentation, seasonal variation in animal population or feed quality and availability, and possible mitigation strategies. Many of these estimates would be derived from direct experimental measurements. A Tier 3 method should be subjected to a wide degree of international peer-review such as that which occurs in peer-reviewed publications to ensure that they improve the accuracy and/or precision of estimates (IPCC 2019). A mechanistic model has been developed in the Netherlands that employs a Tier 3 approach using a mechanistic model (Bannink et al. 2011) to estimate CH4 yield from dairy cattle. The US uses mechanistic models (Baldwin 1995Kebreab et al. 2008) to refine Ym estimates for dairy and beef in different US states.

Methods and sources of activity data

Livestock population

Livestock Geo-wiki provides 2005 global distribution maps of cattle, pigs and chickens, and a partial distribution map for ducks. It updates the Gridded Livestock of the World (GLW) database produced by FAO in 2007, and population data at a national level are consistent with FAOSTAT. The data can be used as an input into analyses of GHG emissions and livestock-related land-use change, such as in the GLEAM model. The data are available in a GIS raster image file (spatial resolution of 3 minutes of arc; about 5×5 km at the equator).

Animal productivity

A range of sources exists for productivity data. For example, a country may have no specific statistics on the number of offspring produced per year, but this could be estimated from the number of breeding females and their longevity. Examples of data sources include:

  • Annual or periodic census/surveys
  • Agricultural production/export statistics
  • Other information collected by Agriculture Ministries
  • Industry sources, consultations, reports
  • Research studies
  • Expert judgment

Feed composition

A range of sources exists for feed composition data. Examples of data sources include:

Methods and sources of emission factors

Chapter 10 of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories provides refined default methane conversion factors (Ym). Development of country-specific Ym values generally requires direct measurement of methane production by animals or the use of animal digestion models. See Measuring for an overview of direct measurement methodologies.

See case studies of the methods used to compile Tier 2 livestock GHG inventories at the AgMRV Platform for Agriculture.