The following methods are applicable for MRV of mitigation actions and national inventories.
The IPCC (2006) decision tree for adoption of a higher tier approach suggests that a Tier 2 approach should be used where livestock emissions are a key source and data is available, or if data is not available then data should be collected. The IPCC (2006, Vol. 1 Ch. 2) also gives general guidance on data collection approaches, including gathering existing data and collecting new data, and specific guidance on data sources for the Tier 2 approach for livestock (IPCC 2006 Vol. 4 Ch.10). For countries considering adopting a Tier 2 approach in their livestock inventory, limited data availability is often considered to be a major constraint. Common questions include:
- Do we need to have official agricultural statistics for each parameter in the IPCC equations?
- Do we need nationally representative survey data if there are no official statistics?
- If our country lacks data for certain parameters, can we still adopt a Tier 2 approach?
- Can we still use IPCC default values for certain parameters?
- If we have a national feed standard, can we use this instead of the IPCC’s recommended approach?
This chapter provides insight into these and other questions by summarizing the actual data sources reported in Tier 2 inventories for cattle. It also provides links to case studies of inventory practices illustrating how countries have dealt with practical challenges in data collection and inventory compilation. The information presented is based on a review of the initial and latest inventory reports (2017) available for 63 countries that have used a Tier 2 approach. The examples are limited to Tier 2 approaches for cattle, because most Tier 2 approaches have been applied to cattle. The review covers livestock population data, and data for estimating enteric fermentation and manure management methane emissions.
Livestock population data sources
Table 6 shows the frequency of different sources of data used for livestock populations. Most countries obtain the data from national statistical agencies, ministry of agriculture or other government agencies. In four countries, producer organisations hold the data on livestock populations and three countries used animal registration databases.
Table 6: Frequency of sources of livestock population data (n=63)
|Ministry of Agriculture||15|
|Other government agency||6|
|Animal registration database||3|
However, full population data is not always available for a complete time series for all livestock population types. Alternative data sources and methods to fill data gaps used by some countries include
- extrapolation from years with data (e.g. Inventory Practice: Dealing with missing data for livestock characterization in Austria, Inventory Practice: Estimating livestock population time series in Romania, Inventory Practice: Livestock population estimates in Croatia);
- estimating the population of livestock sub-categories using models of herd dynamics (e.g. Inventory Practice: Livestock characterization and herd structure modeling in Georgia);
- expert judgement; and
Frequent issues that need to be addressed include:
- alignment with sub-categories defined in national statistics (e.g. Inventory Practice: Livestock population estimates in Croatia), and
- estimating number of days alive (see Inventory Practice: Estimating number of days alive).
Lack of activity data on livestock populations or sub-populations is common in many developing countries that might wish to adopt a Tier 2 approach. When collection of new data is required, agricultural or livestock censuses provide an opportunity to collect data on livestock populations and herd or animal characteristics. The FAO operates the World Program on Agricultural Censuses, which supports countries to carry out census and provides methodological guidance, and the World Bank and FAO have produced a guidebook for designing the livestock component of household survey questionnaires (Zezza et al. 2016). Further practical guidance is provided by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS).
Energy intake and methane emissions data sources
The sub-sections that follow describe the types of data sources for specific parameters used by countries to compile data for estimation of energy intake and methane emissions from cattle. It summarizes data sources used in countries‘ initial Tier 2 inventory submissions as well as data sources used in the latest submissions, and describes the improvement pathways that countries have undergone (Text Box 5).
Text Box 5 – Data sources for analysis of Tier 2 livestock inventories
By 2017, 63 out of the 197 Parties to the UNFCCC have used a Tier 2 approach in their livestock inventories. Submissions by developed (Annex 1) countries since 2003 are available on the UNFCCC inventory submission website. Submissions by developing countries are available on the websites for national communication and BUR submissions. These documents contain summaries of national inventories, and where publicly available, full national inventory reports from developing countries were also accessed. The transparency of national inventories by both developed and developing countries has improved over time, with more details on data sources available for later submissions than initial submissions. Thus, not all inventories reported data sources for all parameters used every year. About two thirds of Tier 2 applications used the IPCC model, while one third used a country-specific model. The IPCC model uses coefficients (Cf, Ca, C, Cp) and default values are provided in the IPCC guidelines. Countries that use the IPCC model always use the default values for these coefficients, and no further analysis of data sources for these coefficients is given below. Country-specific approaches often do not use these coefficients (1), and some other variables in the IPCC model are also not estimated. As a result, for each of the parameters reviewed below, the total number of countries using each type of data source varies. Where the parameters listed were estimated but no data source is given, this is indicated by “no information“. Where parameters were not estimated in a country-specific model, this is indicated by “not estimated“.
Starting points for initial Tier 2 inventories: Lack of national data for some parameters is common when countries first adopt a Tier 2 approach. Some countries‘ initial Tier 2 inventory using the IPCC model was mainly populated by default factors or expert judgement (Country Case Studies Bulgaria & Estonia). A few countries were able to use mainly national data for most animal performance parameters in the IPCC model. For countries that use country-specific Tier 2 models, even though data availability can be considered in the design of the approach, IPCC default values, literature from other countries and expert judgement were also widely used in some initial Tier 2 inventories (e.g. Country case study: New Zealand).
Improvements over time: Countries‘ inventory submissions reveal two main types of improvement over time:
(1) Improvements in data within the same model: Each of the sections below describes examples of how countries have improved inventory estimates by changing data sources or data analysis methods to those that are more nationally appropriate or reliable. In particular, some countries that started out with an IPCC model mainly populated by default values and subsequently substituted several default values with national data sources (e.g. Country Case Study: Estonia).
(2) Adjustments in the model: Almost all countries that started using the IPCC model have continued to use that model. Refinements have been made to some components to account for national conditions (e.g. increasing use of concentrate in dairy feed) and to make use of existing knowledge and resources in the livestock sector (e.g. national feed tables, national energy balance models) (see e.g. Inventory Practice: Accounting for the effects of increased concentrate on gross energy intake and digestible energy; Inventory practice: Estimating digestibility using a country-specific approach in the UK). Some countries have adopted country-specific models after having used the IPCC model (e.g. Country Case Study: UK). Countries using a country-specific approach have also improved and revised the methodological approaches or models over time (e.g. Country Case Studies: Netherlands, Sweden, & Japan).
Both incremental and more significant changes are often enabled by specific inventory processes, such as commissioned design of a country-specific approach, commissioned inventory review, inventory working groups to link research with inventory agencies and other institutional arrangements for continual improvement. These revisions are often enabled by advances in the livestock sector or other policy fields (e.g. nitrogen management), which are then used to improve the national GHG inventory (e.g. Country Case studies: Denmark, Ireland, United Kingdom).
Animal weight estimates
For the 45 countries whose initial inventory reports indicated data sources for live weight estimates, expert judgement, literature from the country and commissioned studies (often as part of Tier 2 inventory design) accounted for about 50% of all data sources referred to (Table 7). About 30% of data sources were regularly reported statistics, agriculture or other government agencies or producer organizations. These sources include animal performance databases (Country Case Study: Denmark, Inventory Practice: The role of cow recording systems in Norway’s Tier 2 approach). When government agencies or producer organizations are cited as data sources, this is not always officially reported data derived from measurements, but may be similar to expert judgement. Four countries used calculation, equations or models to estimate animal weight. Over time, the number of countries reporting weights based on regularly reported statistical data, agriculture ministries or other government sources increased from 12 to 18, as did the number of countries using commissioned inventory studies to obtain data for this parameter.
Table 7: Data sources and methods for cattle animal weight estimates
|Initial Tier 2 NIR data sources||Latest Tier 2 NIR data sources|
|Regularly reported statistics||3||4|
|Ministry of agriculture||7||11|
|Other government agency||2||3|
|Literature from own country||8||6|
|Estimated by calculation||3||3|
|Value from other country’s inventory||1||1|
|Equation or model||1||2|
How have countries improved their estimates of animal live weight over time?
Countries that started with no data: In their initial inventories, 3 countries (Estonia, Hungary, Croatia) used IPCC default data presented in the IPCC 1996 Guidelines in place of national values for animal weight. One country (Slovenia) used an equation to estimate dairy cow weight based on a relationship between milk yield and live weight and continues to use this method to date (2). Expert judgement was also a common source of initial values for cattle weight. Among those countries that started with default values, Croatia later obtained a national dataset (2010-2014) on cattle weights as part of a thorough review to replace default values with national values in the inventory for enteric fermentation. Annual updates to this dataset are now used in updating the GHG inventory on an annual basis. Both Estonia and Hungary subsequently adopted methods whereby expert judgement and scientific literature or other reported data were used to estimate the average weight for each breed within the herd. This was then combined with population data from national breed registries (Estonia) or expert judgement on the proportion of breeds in the herd (Hungary) to estimate the weighted average animal weight. (See also Inventory Practice: livestock characterization in Georgia). Portugal also filled missing data on animal weight with data from a 2004 published summary of breed registry data. Although the registries contained data on only 20% of the national breeding herd, it was assumed that much of the remaining 80% had derived from these registered breeding animals and would thus have similar characteristics. As a result of these methodological choices, some countries’ inventories do not reflect change in animal weight over time (e.g. Portugal). However, other countries have been able to estimate change in average weight on the basis of annually collected data or expert judgement on the changing breed composition of the herd (Estonia, Hungary).
Countries that started with estimated values or expert judgement: UK, Canada and Finland began with data sources that used estimation or expert judgement in the initial years. In some cases, initial expert judgements remained unchanged over several inventory submissions (e.g. Finland, dairy cattle in Canada). The UK began by applying an assumed 1% annual increase in animal weight to the initial value to produce a trend over time. Subsequently, all three of these countries adopted a method based on analysis of slaughter data. The UK and Finland now use annual slaughter data and a constant carcass ratio value (from literature or expert judgement) to estimate live weight (see Inventory Practice: Estimating cattle weights in the UK). For beef cattle, Canada began with initial live weight estimates based on expert judgement, but subsequently estimated the trend in live weight by applying the trend in slaughter weight to the initial live weight estimate. With use of regularly reported slaughter data, weight estimates now vary year on year in Canada’s inventory. Revisions to animal live weight estimates in New Zealand’s inventory also illustrate how the best available data can be used in the absence of statistically representative national data (see Inventory Practice: Improving estimates of cattle weights in New Zealand).
Some other countries that began with expert judgement (e.g. as part of commissioned reviews) have continued to use expert judgement to update animal weight estimates. Lithuania uses expert judgement to update weight estimates annually, while the Czech Republic has updated estimates every few years during commissioned inventory reviews, which results in revision of historical estimates for the intervening years. Expert judgement can thus also be used to produce a time series and trend for animal weight.
Seasonal weight loss is common in many countries. IPCC (2000) suggested that seasonal weight loss or weight loss during early lactation could be addressed by separately estimating feed intake for the different seasons or lactation periods. In 2006, IPCC revised this by suggesting that reduced intakes and emissions associated with weight loss are largely balanced by increased intakes and emissions during the periods of gain in body weight. Very few countries’ inventories explicitly account for weight loss. One example is Canada, whose initial Tier 2 inventory estimated net energy mobilized per kg of weight loss using Equations 4.4a and 4.4b from IPCC (2000).
Milk yield estimates
For the 40 countries whose initial inventory reports indicated data sources for milk yield estimates, regularly reported statistics were by far the most common source (Table 8). In the absence of regularly reported statistics, 8 countries used other types of report from the ministry of agriculture, other government agencies or producer organizations; 5 used literature values or values from other countries’ inventories; and 4 estimated milk yield by expert judgment or calculation.
Table 8: Data sources and methods for milk yield estimates
|Initial NIR data sources||Latest NIR data sources|
|Regularly reported statistics||22||18|
|Ministry of agriculture||3||6|
|Other government agency||1||0|
|Literature from own country||3||0|
|Estimated by calculation||1||0|
|Value from other country’s inventory||2||0|
|Equation or model||0||1|
*2 countries report using a country-specific Tier 2 model that does not require milk yield as an input value: UKR, BLD. And data source was not mentioned in 16 countries’ latest inventory submissions.
Those countries that used literature values (e.g. Mongolia, Bolivia) mostly do not have a dairy cow emission factor time series that tracks change in emissions per head over time. In the absence of regularly reported milk yield data, Georgia estimates milk yield on the basis of expert judgement of milk yield by breed. A herd dynamics model then results in change in average milk yield reflecting the change breed structure (see Inventory Practice: livestock characterization and herd structure in Georgia).
Several countries have regularly reported milk yield data, but not a whole historical time series. For example, Croatia had data for 2008-2015 on milk yields, but not for 1990-2007. This earlier period was estimated by extrapolation from the existing data and expert judgement. Slovakia applied a linear function to existing data to extrapolate missing data for 1990-1996. Canada also used extrapolation methods to estimate a time series for milk yields based on a partial dataset (see Inventory practice: Estimating a time series for milk yields in Canada). Where a country lacks nationwide data on milk yields, estimates have been made on the basis of data for part of the herd in animal recording databases that are then extrapolated to the national herd (e.g. Inventory practice Estimating milk yields in Slovenia).
Countries use different types of regularly reported data for milk yield: some have sub-national reports of average milk yield per cow that are then aggregated to national level (e.g. Estonia prior to 2017). Other countries estimate per cow milk yields based on disaggregation of national total milk output data (see Inventory practice: estimating milk yield in Luxembourg).
Proportion of cows giving birth
Relatively few countries explicitly report in their GHG inventory the source of data for the proportion of cows giving birth in a year (Table 9). Official and industry sources account for about half the total sources referred to in NIRs. Expert judgement and literature from the country are also used by about one third of countries. When data is lacking, alternative estimation methods are used, including:
IPCC default values: Greece began by using the IPCC default (0.9 for western Europe, IPCC 1996 reference manual table A-1) in its initial Tier 2 national inventory, and continues to use that value in its current inventory.
Calculation: Calculation methods vary, depending on the available data from which the proportion giving birth is estimated.
- Ukraine’s initial Tier 2 model calculated the proportion pregnant on the basis of the annual number of cows reported in national statistics as calving and inseminated cows, and the number of calves at the beginning of the year.
- Namibia estimates the proportion pregnant on the basis of the estimated number of young females in the population.
- Canada’s initial Tier 2 inventory used the equation: Percent cows pregnant = (gestation length/calving interval X 100) percent cows culled due to reproductive failure.
Table 9: Data sources and methods for estimates of % giving birth
|Initial NIR data source||Latest NIR data source|
|Regularly reported statistics||3||5|
|Ministry of agriculture||3||3|
|Other government agency||0||2|
|Literature from own country||2||1|
|Estimated by calculation||1||2|
|Value from other country’s inventory||1||1|
Feed digestibility estimates
In initial Tier 2 submissions, estimates of feed digestibility came from ministries of agriculture or other government agencies and producer organizations in 7 countries, although information provided by these agencies is sometimes similar to expert judgements and may not all be based on direct measurements of feed digestibility (Table 10). Literature from the country, mostly official feed tables, was used in 5 countries. Countries, such as Poland, that commissioned a study for elaboration of their whole Tier2 approach, also obtained national data on feed digestibility through this commissioned study.
In the absence of country-specific data, about half of countries used the appropriate IPCC default value for feed digestibility, while expert judgement (5) was also common. Moldova, for example, used expert judgement to reconstruct a time series for change in average digestibility in different historical periods (Inventory Practice: Reconstructing a time series for feed digestibility in Moldova). Literature from other countries was also used (e.g. Slovenia used data from German feed tables; Belgium used Dutch digestibility data) in the absence of national data.
Table 10: Data sources for feed digestibility estimates
|Initial NIR data sources||Latest NIR data sources|
|Regularly reported statistics||0||0|
|Ministry of agriculture||3||4|
|Other government agency||1||2|
|Literature from own country||5||12|
|Literature from other country||3||1|
|Estimated by calculation||0||1|
|Equation or model||0||5|
Improvement pathways for feed digestibility estimates:
Countries that started with no national data: More than half of the 13 countries that began by using IPCC default values for digestibility subsequently used other data sources to identify country-specific values. Two countries (Bulgaria and Estonia) used scientific publications, and a third (Latvia) commissioned a study that was then combined with expert judgement on typical diets to estimate feed digestibility for the national inventory (Inventory Practice: Improving feed digestibility estimates in Latvia). Four countries (Portugal, UK, Hungary, Slovenia) identified country-specific values on the basis of national feed tables. The particular ways in which these feed tables were used varied according to the way in which they relate feed composition and digestibility to livestock performance parameters (see Country case stud: UK & Sweden; Inventory Practice: Use of feed tables for estimating gross energy in Lithuania; Use of national feeding standards to estimate net energy requirements in Hungary).
Continual improvement in feed digestibility data: Several countries that did have national data to start with also improved or continually updated data sources over time. The USA provides an example of how annual surveys of small numbers of animal nutrition experts across the country are used to produce updated estimates of feed digestibility (Inventory practice: Estimating digestible energy and methane conversion rates for feedlot cattle in the USA).
Improvements through refinement of the IPCC model: A few countries have made particular refinements of the IPCC Tier 2 model in order to improve estimates of digestibility and to improve estimates of Ym in view of the composition of feed. With an increasing proportion of concentrate in dairy cattle feed, Slovenia has adopted the results of research by INRA that established a relationship between organic matter digestibility (dOM) and net energy for lactation (see Inventory Practice: Accounting for the effects of increased concentrate use of gross energy intake and digestible energy). The UK also changed from using IPCC default values to expert judgement and later used a country-specific energy balance model to improve its estimate of feed digestibility for dairy cattle (Inventory practice: Estimating digestibility using a country-specific approach in the UK).
(1) For Cfi, some countries apply an adjustment for cold climate, as recommended in IPCC (2006). For Ca, many countries use an estimate of the proportion of the year on pasture to adjust the default values for Ca.
(2) The relationship is: Weight (kg) = 418.8 + 0.0313 × [305 day milk yield (kg)]. However, a source for this equation is not given in the national GHG inventory.
Methane yield data sources
In initial Tier 2 inventories most countries used IPCC default values for the methane conversion factor (Ym), and almost 70% of countries continue to use default values in their most recent inventory submissions. Country-specific values have been obtained from government research agencies (1 in Italy) and published literature (3), and in the absence of national data, scientific publications from other countries, expert judgement and values in other countries’ inventories have also been used (e.g. Hungary cited values in the Swiss inventory report). In subsequent submissions, results from commissioned direct measurement studies were cited as a data source by 2 countries (Belgium and France). More commonly, scientific studies are used to validate the model used in the national GHG inventory to predict the methane conversion factor (5 countries).
The models and equations used range from simple to complex. Croatia recently began to estimate Ym using an equation from an FAO publication by Hristov (i.e. Ym=9.75 -0.05*DE%) (Hristov et al. 2013). Norway’s inventory estimates Ym for dairy cattle using an equation relating Ym to milk yield and the proportion of feed concentrate in the diet, data that are available from a cattle recording database (Inventory Practice: The role of cow recording systems in Norway’s Tier 2 approach). Denmark, Colombia and the USA, on the other hand, estimate Ym using more complex models based on feed chemical composition, while the model used in The Netherlands also includes more specific modeling of rumen processes.
Modeling livestock emissions is a dynamic field and the models used vary considerably (Hiristov et al. 2013, Hristov et al. 2018, Niu et al. 2018). The models used have in each case been validated against individual cow measurements. While some models have been developed specifically for GHG inventories, most were developed for feed evaluation and provision of farm advisory services. The availability of increasing data for validation and improvement of the models is often therefore driven by progress in livestock research rather than specific inventory needs. Commissioned inventory reviews are one way in which inventories can capitalize on the increasing knowledge in the livestock sector.
Table 11: Data sources for methane conversion rate (Ym) estimates
|Initial NIR data sources||Latest NIR data sources|
|Other government agency||1||0|
|Literature from own country||3||3|
|Estimated by calculation||1||1|
|Value from other country’s inventory||0||1|
|Equation or model||4||5|
|Literature from other country||0||1|
Manure management data sources
Most countries that use a Tier 2 approach for enteric fermentation from cattle also use a Tier 2 approach to estimate methane emissions from manure management. Of these 57 countries, 48 use the IPCC model as set out in the IPCC guidelines. Three countries follow that model, but instead of calculating the amount of volatile solids input into manure management systems, volumes are estimated using normative standards for manure management. Six countries use country-specific models of methane emissions from manure management. For example, Japan has a considerable body of direct measurements of methane emission factors, which it uses alongside other data sources in its inventory (see Inventory Practice: Choice of emission factor for manure management in Japan).
Data on methane production potential and methane conversion factors
The IPCC model includes a parameter for the maximum methane production capacity (Bo) and a parameter for the methane conversion factor (MCF). The vast majority of countries that reported a data source use the IPCC default factor for both parameters (Table 12). Some countries’ inventories used values from published literature or commissioned studies, while some countries used values from studies in other countries. Where the model used requires data on ash content of manure, about 70% of countries used the IPCC default values. Other data sources included published literature from the same or another country, commissioned reviews and estimation using a model (e.g. national ammonia or nitrogen balance model).
Table 12: Data sources for methane manure management parameters Bo and MCF
|Maximum methane-producing capacity of manure (Bo)||Methane conversion factor (MCF)|
|Initial Tier 2 inventory||Latest Tier 2 inventory||Initial Tier 2 inventory||Latest Tier 2 inventory|
|No or unclear info||22||15||19||15|
|IPCC default value||26||35||30||36|
|Literature from own country||1||5||3||5|
|Estimate by calculation||1||1|
|Literature from other country||1||1||1||3|
Data on manure management systems
Sources of data for manure management systems are quite diverse, and many countries have improved their estimates over time. Among countries reporting data sources, the most common data sources were expert judgement, data provided by agricultural, statistics or other government agencies, and commissioned studies. In many cases, surveys conducted by government agencies or researchers were irregular (e.g. decadeal census, biannual or one-off surveys), and several countries use interpolation between survey dates to construct a consistent time series. Several countries use housing surveys together with expert judgement to estimate the manure management systems used in different types of housing system or farm. In addition, manure management categories reported in official surveys often differ from the categories used in the IPCC, so expert judgement is applied to convert available data into a times series consistent with IPCC categories.
Table 13: Data sources for the allocation of manure to manure management systems
|Initial NIR data sources||Latest NIR data sources|
|No or unclear info||20||21|
|IPCC default value||4||1|
|Other government agency||4||1|
|Literature from own country||4||8|
|Literature from other country||1||1|
Where data is unavailable, methods used to collect data include structured surveys to elicit expert judgement (Inventory Practice: Structured elicitation of expert judgement on manure management systems in Canada). More commonly, however, a variety of sources are drawn upon to provide the best available estimate of the distribution of manure in different management systems (Inventory Practice: Characterization of manure management systems in Finland). Some countries have managed to improve the availability of data on manure management systems by incorporating related questions in regular surveys (Country Case Study: Austria & Bulgaria).