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Livestock inventory practice: Characterization of dairy cattle

Keywords: Livestock characterization | dairy cattle

Enteric fermentation and manure management emissions from dairy cattle are a key category in many countries’ national GHG inventories. Countries have developed different methods of categorizing dairy cattle. The following provide examples of different categorization methods.

One category (mature dairy cows only): Until 2018, the UK’s national inventory categorized only 1 cattle sub-type as ‘dairy cows’. Animal population data come from the annual agricultural survey. This survey collects data on the ‘dairy breeding herd’, which is defined as dairy cows over two years of age with offspring. Dairy heifers, dairy replacements >1 year, and dairy calves <1 year are included along with beef heifers and beef calves in the category ‘other cattle’. Dairy cow emissions were estimated using a Tier 2 approach, while emissions from ‘other cattle’ used a Tier 1 approach.

Sub-categories based on age, sex and physiological status:

  • Japan’s inventory applies a Tier 2 approach to all sub-categories of cattle. Dairy cattle are divided into lactating and non-lactating cows, and heifers. Calves of dairy breeds are included in the ‘non-dairy cattle’ category.
  • South Africa’s inventory, based on research by Du Toit et al. (2013) categorizes dairy cattle by age, physiological status and production system (Table 1).

Table 1: Enteric fermentation emission factors of dairy cattle sub-categories in South Africa’s inventory

Total mixed ration based production systemPasture-based production system
Emission factor (kgCH4/head/year)Emission factor (kgCH4/head/year)
Lactating cows132127
Lactating heifers127116
Dry cows80.483.4
Pregnant heifers67.761.8
Heifers >1 year62.652.6
Heifers 6-12 months42.137.1
Heifers 2-6 months22.524.5
Calves21.520.0

Source: Du Toit et al. 2013

Sub-categories by region: New Zealand’s agricultural statistics report numbers of dairy cows and heifers in milk or calf, non-milking cows, heifers, yearlings and bulls, and calves born alive in each year. Before 2010, the inventory estimated emissions for each sub-type of dairy cattle at the national level. An assessment found that because development dairy cattle population numbers and productivity had been uneven across the country, using a single national approach was no longer the most accurate way of estimating dairy cattle emissions. A time series of data on dairy cattle populations, live weight, milk yield and milk fat and protein contents were available at a regional level. Since 2010, the national inventory separately estimates emissions from different sub-types of dairy cattle in 17 regions, which are then aggregated to the national level.

Further Resources

Clark H. 2008. A comparison of greenhouse gas emissions from the New Zealand dairy sector calculated using either a national or regional approach.

Du Toit CJL, Van Niekerk WA. 2013. Direct methane and nitrous oxide emissions of South African dairy and beef cattle. South African Journal of Animal Science.

Author: Andreas Wilkes, Values for development Ltd (2019)

Inventory practice: Prioritization of key categories in the United Kingdom’s inventory

Keywords: Key category analysis | ranking and scoring

What data needs were addressed? To prioritize which inventory key categories should be the focus for improvement.

Why was the data needed? The UK’s inventory applies Approaches 1 and 2 to key category analysis. In the latest inventory, 39 inventory categories are identified as key categories. With limited resources for inventory improvement, a method was needed to help prioritize key categories.

Methods used: Ranking and scoring.

How was the data gap addressed? The UK has developed a ranking system to prioritize key categories. The Key Category Analysis (KCA) ranking system works by allocating a score based on how high categories rank in the base year and most recent year level assessments and the trend assessment for the Approach 1 KCA, including LULUCF. For example, in the base year (1990) level assessment, enteric fermentation from cattle was the 10th largest emission source; the 7th largest in the most recent (2018) level assessment; and ranked 14th in the most recent trend assessment. This category is therefore given a score of 10+7+14=22. The categories are then ranked from lowest score to highest, with scores that are equally resolved by the most recent year level assessment. In the 2018 KCA ranking results, enteric fermentation from cattle was ranked 9th out of all key categories.

The assessments used in this ranking exercise are only those including LULUCF because if the additional excluding LULUCF assessments were also used, the LULUCF sectors would only be included in half of the assessments and would, therefore, give an unrepresentative weighting.

Author: Andreas Wilkes, Values for development Ltd (2019)

Livestock inventory practice: Assessing sources of uncertainty in Finland‘s livestock inventory

Keywords: Uncertainty analysis | Monte Carlo analysis| sensitivity analysis

What data needs were addressed? Identifying the key sources of uncertainty in the national inventory.

Why was the data needed? Finland began reporting cattle emissions using a Tier 2 approach in the 1990s, but Tier 1 was used for other livestock. Uncertainty analysis was used to identify emission sources and parameters in the Tier 2 model for which improved estimation methods could reduce overall uncertainty of the inventory.

Methods used: Monte Carlo analysis, sensitivity analysis

How was the data gap addressed? In the early 2000’s, Finnish researchers applied uncertainty analysis to the national inventory in order to identify emission sources to target for improved estimation. The analysis, reported in Monni et al. (2007), used Monte Carlo analysis. The uncertainty of activity data was estimated by examining the data for representativeness and possible bias, informed by interviews with relevant experts. For example, cattle have individual ear marks that enable very accurate assessment of animal numbers (uncertainty of ±3%), but uncertainty in animal numbers for other species on farms is higher (±5%). For animal weight, the researchers divided the standard deviation of the total population by the square root of the number of animals in each category to obtain a standard deviation of the mean value. Additional uncertainty was added, based on expert judgement, to reflect the effects of estimating animal weights using heart girth measurements. The distribution of data for each parameter was established following IPCC guidelines, i.e. assume normal distribution for empirical data unless other distributions fit the data better. Monte Carlo simulation was used to combine uncertainties, and sensitivity analysis was used to identify the most important factors affecting uncertainty. The analysis identified higher uncertainty of emission factors for bulls, heifers and calves than for dairy cattle, mostly affected by digestibility and net energy for maintenance. It concluded that using a Tier 2 approach for all animal types would reduce uncertainty in the agriculture inventory by 3%.

Further Resources

Monni S, Perälä P, Regina K. 2007. Uncertainty in agricultural CH4 and N2O emissions from Finland–possibilities to increase accuracy in emission estimates. Mitigation and adaptation strategies for global change.

Author: Andreas Wilkes, Values for development Ltd (2019)

Livestock inventory practice: Assessing sources of uncertainty in the livestock inventory of the United Kingdom

Keywords: Uncertainty analysis | Monte Carlo analysis | sensitivity analysis

What data needs were addressed? Identifying the key sources of uncertainty in the national inventory.

Why was the data needed? The UK adopted a Tier 2 approach for livestock in 2000. However, no analysis of the sources of uncertainty in the inventory had been undertaken. In 2010, the UK government agency responsible for inventory compilation funded a project aiming to provide fundamental improvements in the accuracy and resolution of the UK national inventory and the development of a more detailed reporting methodology. As part of this project, a study was undertaken to quantify the uncertainty in the emissions of CH4 and N2O from agriculture for the year 2010 and the baseline year (1990), and the uncertainty in the trend between these two years, and to identify the inputs that had the greatest effect on uncertainty in the total emissions. Because the UK inventory uses activity data separately provided by devolved administrations in England, Scotland, Wales and Northern Ireland, the analysis also identified regional contributions to inventory uncertainty.

Methods used: Monte Carlo analysis, sensitivity analysis

How was the data gap addressed? Milne et al. (2014) report the methods and results of uncertainty analysis of CH4 and N2O emissions in the UK national GHG inventory. To quantify and identify the sources of uncertainty, Monte Carlo analysis was used. This method was chosen because it is straightforward to use, and can account for dependencies between inputs. In Monte Carlo simulation, model inputs are treated as random variables and are described by a probability density function (PDF). The mean of the PDF describes the expected value of the input and the variance reflects the uncertainty. A value for each input is pseudo-randomly sampled from the PDFs and the model is run to produce an output value. This process is repeated thousands of times, resulting in a set of output values which form an empirical distribution that describes the uncertainty. Statistics such as the mean, variance and 95% confidence intervals 96 can be derived from this distribution.

If the inventory is to use more Tier 3 calculations that use data at a higher resolution, this can be time- and resource-intensive. Therefore, to identify the inputs that had the greatest effect on uncertainty, sensitivity analysis was used. The effect of reducing uncertainty in the key parameters was tested by reducing the standard deviation of the PDFs associated with each input parameter by 50% in turn.

Initially, there was limited empirical evidence on the magnitude and form of uncertainty for many input variables. The researchers made assumptions about the distribution of variables, often based on previous literature in particular, a previous analysis conducted for Finland (Monni et al. 2007) or IPCC guidance. For example, expected values and standard errors for livestock population data were calculated from national survey data. Where standard errors were less than 25% of the mean, a normal distribution was assumed, otherwise a lognormal distribution was used. For the uncertainty of input parameters to the IPCC Tier 2 enteric fermentation model various sources were used to estimate the standard errors and form of PDF (Table 1).

Table 1: References used for uncertainty estimates in Monte Carlo analysis

ParameterSource of uncertainty estimate
Cfi, Ca, C, Cpregnancy, milk fat content, animal weight, digestible energyMonni et al. (2007)
Feed energy densityMcDonald et al (1981)
Milk yieldFarm business survey

Source: Milne et al. (2014)

Summarizing the main results for livestock methane emissions, the study found that:

  • the inputs that most affected the uncertainty in CH4 emissions were similar across the UK’s constituent countries, although the order of importance varied slightly from country to country. In Wales and Scotland the emission factor for enteric fermentation from adult sheep had the largest impact on uncertainty, whereas in England and Northern Ireland model inputs for cattle emissions were more important.
  • The most important inputs are: emission factors for enteric fermentation for dairy replacements, adult sheep, beef (other >1 year) and beef calves; the maintenance parameter for lactating cattle (Cfi); and feed digestibility for both beef and dairy cows.
  • Reducing the uncertainty in the emission factor for enteric fermentation in dairy replacements in England by halving the standard deviation in its associated PDF resulted in a reduction in the standard deviation of modeled CH4 from England of 10% in 1990 and 14% in 2010. The same reduction in the uncertainty for the emission factor for enteric fermentation in adult sheep in England (i.e. 50%) resulted in a 7% reduction in the standard deviation of the modeled emissions CH4 from England in both 1990 and 2010.
  • Literature values for uncertainty of model inputs were used for many parameters, so future uncertainty analysis could be improved by using country-specific estimates of uncertainty.

Further Resources

McDonald P, Edwards RA, Greenhalgh JFD. 1981. Animal Nutrition, 3rd Edition, Longman, London and New York

Milne AE, Glendining MJ, Bellamy P, Misselbrook T, Gilhespy S, Casado MR, Hulin A, Van Oijen M, Whitmore AP. 2014. Analysis of uncertainties in the estimates of nitrous oxide and methane emissions in the UK’s greenhouse gas inventory for agriculture. Atmospheric Environment.

Monni S, Perälä P, Regina K. 2007. Uncertainty in agricultural CH4 and N2O emissions from Finland–possibilities to increase accuracy in emission estimates. Mitigation and adaptation strategies for global change.

Author: Andreas Wilkes, Values for development Ltd (2019)

Inventory practice: Estimating digestible energy and methane conversion rates for feedlot cattle in the USA

Keywords: Expert judgement | interpolation | digestibility | methane conversion rates

What data needs were addressed? An estimate of digestible energy (DE as a % of GE [MJ/Day]) was required for feedlot cattle.

Why was the data needed? Diet composition for feedlot cattle in the USA has been changing rapidly as feedlots change their practices based on new nutritional information and changing feed availability. Therefore, values for DE and Ym in the USA’s GHG inventory are adjusted over time.

Methods used: interpolation of available data, expert surveys, expert opinion, modeling.

How was the data gap addressed? Feedlot diets are assumed to not differ significantly by region within the country, so a single set of national diet values is used each year.

For 1990, DE and Ym values used in the inventory were provided by a leading academic on methane production in cattle.

For 1991-1999, values were linearly extrapolated based on values for 1990 and 2000.

For 2000 onwards, values for Ym were estimated using the MOLLY model, as described in Kebreab et al. (2008). This model is a dynamic mechanistic model of nutrient utilization in cattle. Methane production is predicted based on hydrogen balance. Excess hydrogen produced during fermentation of carbohydrates and protein to lipogenic VFA (acetate and butyrate) is partitioned between use for microbial growth, biohydrogenation of unsaturated fatty acids, and production of glucogenic VFA (propionate and valerate). It is assumed that the remaining hydrogen is used for methanogenesis.

To run the MOLLY model, data on average diet feed compositions was taken from Galyean and Gleghorn (2001) for 2000 through 2006 and Vasconcelos and Galyean (2007) for 2007 onwards. These sources are an annual survey of consulting animal nutritionists. The survey is a postal or web-based survey, with respondent numbers generally between 19 and 31. The questionnaire asks the nutritionists to indicate, among other things:

  • the % of dry matter contributed by different types of grain, grain by-products, roughage and other supplements to the cattle finishing diet,
  • the recommended concentration of key nutrients in the diet for cattle at different growth stages,
  • other management practices recommended to feedlots (e.g. diet adjustment periods).

DE values and other parameters required as inputs to the MOLLY model are estimated from the survey responses. For example, in 2015, feedlot cattle DE was estimated at 82.5, and a Ym value predicted at 3.9%.

The methods used to estimate DE and Ym for other types of cattle in the USA differ from the methods summarized here and are described in the annexes to USA NIR 2017.

Further Resources

Galyean ML, Leghorn JF. 2001. Summary of the 2000 Texas Tech University Consulting Nutritionist Survey. Texas Tech University.

Kebreab E et al. 2008. Model for estimating enteric methane emissions from United States dairy and feedlot cattle. Journal of animal science.

Vasconcelos JT, Galyean ML. 2007. Nutritional recommendations of feedlot consulting nutritionists: The 2007 Texas Tech University Study. Journal of Animal Science.

Author: Andreas Wilkes, Values for development Ltd (2019)

Livestock inventory practice: Accounting for the effects of increased concentrate use on gross energy intake and digestible energy

Keywords: digestibility

What data needs were addressed? Estimating digestible energy when concentrate feed consists of a greater proportion of dairy cattle diet.

Why was the data needed? Dairy cattle in Slovenia are fed a greater proportion of concentrate in their diet than other types of cattle. When estimating gross energy, information on the concentration of net energy for lactation is critical to avoid under- or over-estimation of gross energy intake.

How was the data need addressed? Slovenia applies the IPCC Tier 2 model for dairy cattle, but has introduced refinements to the model and implements the model in a country-specific manner.

Step 1: Estimation of net energy requirements for the maintenance (NEm), activity (NEa), milk production (NEl) and pregnancy (NEp). These are determined following the IPCC guidance and using the default IPCC coefficients.

Step 2: Estimation of gross energy intake. For this, the concentration of net energy for lactation is estimated, considering energy concentration in the basal diet and the proportion of concentrates in the diet. The concentration of net energy for lactation in the diet was calculated as a quotient between the animal requirements for maintenance, milk production and pregnancy on the one hand and potential dry matter intake on another. National feeding standards were used to assess the requirements. Specifically, monthly milk recording data from 2000-2009 was used to construct more than 700,000 lactation curves, on the basis of which standard lactation curves at 500 kg intervals ranging between 3500 and 12000 kg were calculated. Based on daily milk yields and assumed concentrations of net energy for lactation in basal diet, the required proportions of concentrates in diets were estimated. Dry matter intake was then predicted on the basis of daily milk production, amount of concentrates and concentration of net energy for lactation in the basal diet, using equations developed by researchers in Germany (Gruber et al. 2005).

Step 3: Estimation of digestible energy: The estimated concentration of net energy for lactation was then transformed to organic matter digestibility (dOM) using an equation based on data in the German feeding tables (DLG 1997):

dOM = 24.12 + net energy for lactation × 7.9

Energy digestibility (DE%) was estimated as:

DE%= dOM 3.1 

The relation was obtained on the basis of equations presented in INRA (1989).

Step 4: Gross energy intake was then calculated using Equation 10.16 in IPCC (2006, Vol 4 Ch 10).

Further Resources

DLG 1997 Futterwertttabellen. Wiederkäuer. DLG Verlag, Frankfurt am Main, 1997, 212 p.

Gruber L, et al. 2005. Estimation of feed intake in dairy cows.

INRA Ruminant nutrition, Recommended allowance & feed tables, Paris, INRA, 1989, 389 p.

Author: Andreas Wilkes, Values for development Ltd (2019)

Inventory practice: Improving feed digestibility estimates in Latvia

Keywords: Expert judgement | commissioned research | digestibility

What data needs were addressed? Producing a country-specific estimate of cattle feed digestibility.

Why was the data needed? Prior to 2017, Latvia had no country-specific data on feed digestibility and the GHG inventory used 65%, which is the mid-range of the representative values for pasture-fed cattle in the 2006 IPCC Guidelines (Ch. 10, Table 10.2).

Methods used: commissioned research, expert opinion.

How was the data gap addressed? The government of Norway operated a grant program to reduce disparities between members of the European Economic Area. In the program agreement on national climate policy with Latvia, the pre-defined project “Development of the National System for Greenhouse Gas Inventory and Reporting on Policies, Measures and Projections” included funding for improving estimates of feed digestibility. The research was conducted by the Latvia University of Agriculture (Degola et al. 2016).

Feed samples were taken from 38 farms in different regions of the country at different growth stages over 2015. The selection of farms was undertaken to represent farms with different scales of dairy cattle production. The samples covered hay, silage, haylage and total mixed ration. The samples were analyzed at the university’s Scientific Laboratory of Agronomic Analysis. Chemical analysis of feed was conducted for dry matter (DM) %, crude protein (CP) %, insoluble protein, %, soluble protein, %, undegraded intake protein (UIP) %, crude fiber (CF) %, acid detergent fiber (ADF) %, neutral detergent fiber (NDF) %, ash %, Ca and P %, according ISO 5983, ISO 6490/2 and ISO 6491 standards. Digestibility was determined using the cellulase method and by calculation of net energy for lactation.

The average determined digestibility of forage for natural meadow hay was 52.3±4.3% and 53.8±5.2% for cereal grass hay; for grass silage with preservative 65.2±6.1%, without preservative 62.8±4.9%; and for corn silage, respectively 71.1±0.6%, 68.2±3.1%; for haylage 62.6±4.1%, for TMR 71.7±5.7%.

For the national GHG inventory, interviews were conducted with agricultural and academic experts to identify the typical feed rations for dairy cows and other cattle. This suggested that the feed ration of dairy cows consists on average of 71% grass forage and 29% concentrates based on dry matter intake. Feed ration composition for other cattle types were also estimated. The results of cattle feed quality analysis and feeding ration composition estimates were combined, leading to a feed digestibility estimate of 67% for dairy cows in 2015. Considering that the proportion of concentrates in dairy cow diet had been gradually increasing, it was decided to use a digestibility value of 66% for dairy cows in the period 2010-2014. For other cattle, a value of 65% was estimated.

Furthermore, correlation analysis between digestibility determined using the cellulose method and the calculation method found a good correlation, leading to the conclusion that it is not necessary to determine forage digestibility in the laboratory with the cellulase method, but the formula DDM, % = 88.9 (0.779 x ADF %) can be used, where the digestibility is calculated from the ADF content in feed.

Further Resources

Degola L, Trupa A, Aplocina E. 2016. Forage quality and digestibility for calculation of enteric methane emission from cattle. In 15th International Scientific conference “Engineering for Rural Development”: Proceedings.

Author: Andreas Wilkes, Values for development Ltd (2019)

Inventory practice: Use of national feeding standards to estimate net energy requirements in Hungary

Keywords: Feed tables

What data needs were addressed? Estimating gross energy intake for dairy cattle.

Why was the data needed? Hungary’s national feed nutrition standards (Hungarian Nutrition Codex, 2004) are based on the NRC equations that underlie the IPCC model for enteric fermentation. However, there are some differences in the underlying methodology. This means that there are some differences in gross energy estimations made using the IPCC method and the Hungarian feed standards.

Methods used: national energy balance model.

How was the data gap addressed? The main difference between the Hungarian and the IPCC model is that the Hungarian model does not differentiate between net energy for maintenance and activity, but takes both energy requirements into account as net energy for maintenance. Hungary’s inventory compilers decided to estimate these separately using Eq. 10.5 of 2006 IPCC Guidelines. Calculation of net energy for lactation also differs from the IPCC methodology. Inventory compilers applied both equations and found that the Hungarian standards gave higher values than the IPCC model. They decided to calculate net energy for lactation using the Hungarian standards, on the grounds that this is more reliable for common Hungarian breeds. The equations used for net energy for pregnancy were different, but the result of the calculation was very similar, so the simpler IPCC equation was applied. Finally, for converting net energy requirements into gross energy intake, data on diet composition derived from a national Farm Accountancy Data Network (FADN), and digestibility values for the different dietary components were taken from the ‘feed database’ provided in the Hungarian Nutrition Codex (2004). This database contains results of laboratory measurements for feeds in Hungary.

Author: Andreas Wilkes, Values for development Ltd (2019)

Inventory practice: Use of feed tables to estimate gross energy in Lithuania

Keywords: feed tables | milk yield | dairy cattle

What data needs were addressed? Estimating gross energy intake for dairy cattle.

Why was the data needed? Lithuania’s inventory points out that gross energy and milk yield have a clear positive relationship. Estimation of gross energy in the inventory can be simplified if standards are used to relate annual milk yield data to gross energy intake.

Methods used: feed standards.

How was the data gap addressed? Gross energy estimates for dairy cattle are based on feed standards. National research has established that gross energy intake is related to crude protein, crude fat, crude fibre and nitrogen-free extracts in feed, and identified a relationship between these feed contents and milk yield (Table 1). Gross energy is estimated as a function of these feed contents:

𝐺𝐸=0.0239∙𝐶𝑃+0.0398∙𝐶𝐹𝑎𝑡+0.0201∙𝐶𝐹𝑖𝑏𝑒𝑟+0.0175∙𝑁𝐹𝐸

where:

GE gross energy, MJ kg in DM;
CP crude protein, g/kg in DM;
CFat crude fat, g/kg in DM;
CFibre crude fibre, g/kg in DM;
NFE nitrogen-free extracts, g/kg in DM.

Since the nutrition standards have established the relationship between milk yield and dietary nutrients, inventory estimates of gross energy intake can be made using only data on milk yield. Milk yield values between those shown in the table are interpolated.

Table 1. Nutrition standards for dairy cattle

Quantity of milk (4% milk fat), kg/day
101520
Dry matter, kg12.715.117.0
Crude protein, g1,5242,0382,550
Crude fat, g279362459
Crude fiber, g3,0483,4733,740
Nitrogen-free extract6,3507,4208,990

Source: Lithuania NIR 2017

Author: Andreas Wilkes, Values for development Ltd (2019)

Livestock inventory practice: Estimating a time series for cattle feed digestibility in Moldova

Keywords: Expert judgement | digestibility

What data needs were addressed? Estimation of a time series since 1990 for feed digestibility when digestibility data for previous years is missing.

Why was the data needed? When adopting a Tier 2 approach, the approach should be applied to the whole time series back to the base year (1990), but historical data on feed digestibility was missing, so alternative methods were needed.

Over recent decades, fodder and feed production in the Republic of Moldova has been affected by both general socio-economic conditions and natural conditions. Prior to the early 1990s, cattle production was organised in collective farms and fodder production was carefully managed. With reforms in the 1990s, the collective farms collapsed and livestock concentrated in the smallholder private sector. The average productivity of dairy cows decreased significantly as a consequence of poor organization of fodder production and inappropriate animal feeding and maintenance conditions. Since the early 2000’s, fodder and feed production and dairy cow productivity have improved. Fodder and feed production have also been affected by annual variability in growing conditions, such as droughts or other weather conditions in some years.

Methods used: expert opinion.

How was the data gap addressed? The IPCC 1996 Guidelines (Reference Manual, Ch 4, 4.16) provides representative feed digestibility values for different types of livestock: 50-60% for crop by-products and rangelands, 60-75% for good pastures, good preserved forages, and grain supplemented forage-based diets and 75-85% for grain-based diets fed in feedlots. Expert judgement was used to estimate the feed digestibility value for cattle in different historical periods. The approach used assumed that when livestock maintenance conditions, fodder and feed production conditions were optimal, the digestibility value would be 67%. Based on changes affecting fodder and feed production and cattle raising in the country, a time series for digestibility was estimated (Table 1).

Table 1: Cattle feed digestibility values for Republic of Moldova 1991-2013

Period1991-199219931994-19961997-20042005-20082009-2013
Digestibility (%)686765666768

Author: Andreas Wilkes, Values for development Ltd (2019)