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Project AC0115 Final Report

Appendix C: Report on data mining existing datasets of enteric methane emissions

April 2014

Introduction This work was completed and originally reported in April 2013 and is provided here for information.

AC0115 Final Report Appendix C -

Emission Factor Synthesis

National Methane Emissions Data Mining

Statistical modelling of enteric methane emissions

L A Crompton, J A N Mills and C K Reynolds

Animal Science Research Group,

School of Agriculture, Policy and Development,

University of Reading, PO Box 237, Whiteknights, Reading RG6 6AR

Revised April 2013


Contents

Contents..................................................................................................................................3

List of Tables..........................................................................................................................4

List of Figures ........................................................................................................................5

Executive Summary ...............................................................................................................8

Recommendations for revised national inventory ..............................................................9

Introduction ..........................................................................................................................12

Objectives.............................................................................................................................13

Database development..........................................................................................................14

Linear model development...................................................................................................19

Non-linear model development............................................................................................19

Model evaluation..................................................................................................................20

Results – emission factors ....................................................................................................21

All cattle ...........................................................................................................................22

Lactating cattle .................................................................................................................23

Non-lactating cattle...........................................................................................................25

Results - methane mitigation................................................................................................57

Conclusions ..........................................................................................................................63

Recommendations for revised national inventory ............................................................65

References ............................................................................................................................67


List of Tables

Table 1. Table of Abbreviations. .............................................................................................11

Table 2. Summary of live-weight, dry matter intake, methane production and milk yield and composition for the database. Mean values with observation number in parentheses. ...........17

Table 3. Summary of diet composition for the database. Mean values. ..................................18

Table 4. Description of models of methane emission for all cattle. ........................................22

Table 5. Evaluation of models of methane emission against the independent data sub set for all cattle....................................................................................................................................22

Table 6. Description of models of methane emission for lactating cattle................................23

Table 7. Evaluation of models of methane emission against the independent data sub set for lactating cattle. .........................................................................................................................24

Table 8. Description of models of methane emission for non-lactating cattle. .......................25

Table 9. Evaluation of models of methane emission against the independent data sub set for non-lactating cattle...................................................................................................................25


List of Figures

Figure 1. The relationship between dry matter intake and milk yield for lactating cattle. ......16

Figure 2. An example of correcting the data set for the effects of location and experiment using the relationship between dry matter intake and methane production for all cattle. .......20

Figure 3. The relationship between dry matter intake and methane production for all cattle. The solid line represents model DMI vs CH4 (Equation M1).................................................26

Figure 4. Observed versus predicted methane production for the all cattle model DMI vs CH4 (Equation M1) for the evaluation data set (dashed line). The solid line represents the line of unity. ........................................................................................................................................27

Figure 5. The relationship between dry matter intake and neutral detergent fibre content of the diet and methane production for all cattle. The solid line represents model DMI + NDF vs CH4 (Equation M2).............................................................................................................28

Figure 6. Observed versus predicted methane production for the all cattle model DMI + NDF vs CH4 for the evaluation data set (Equation M2) for the evaluation data set (dashed line). The solid line represents the line of unity................................................................................29

Figure 7. The relationship between dry matter intake and methane production for lactating cattle. The solid line represents model DMI vs CH4 (Equation M6). ....................................30

Figure 8. Observed versus predicted methane production for lactating cattle model DMI vs CH4 (Equation M6) for the evaluation data set (dashed line). The solid line represents the line of unity. .............................................................................................................................31

Figure 9. The relationship between dry matter intake and neutral detergent fibre content of the diet and methane production for lactating cattle. The solid line represents model DMI + NDF vs CH4 (Equation M7)....................................................................................................32

Figure 10. Observed versus predicted methane production for lactating cattle model DMI + NDF vs CH4 (Equation M7) for the evaluation data set (dashed line). The solid line represents the line of unity. ......................................................................................................33

Figure 11. The relationship between dry matter intake and methane production for non-lactating cattle. The solid line represents model DMI vs CH4 (Equation M6). .....................34

Figure 12. Observed versus predicted methane production for non-lactating cattle model DMI vs CH4 (Equation M6) for the evaluation data set (dashed line). The solid line represents the line of unity. .............................................................................................................................35

Figure 13. The relationship between dry matter intake and neutral detergent fibre content of the diet and methane production for non-lactating cattle. The solid line represents model DMI + NDF vs CH4 (Equation M7)........................................................................................36


Figure 14. Observed versus predicted methane production for non-lactating cattle model DMI +NDF vs CH4 (Equation M7) for the evaluation data set (dashed line). The solid line represents the line of unity. ......................................................................................................37

Figure 15. The relationship between digestible energy intake and methane production for all cattle. The solid line represents model DEI vs CH4 (Equation M3). .....................................38

Figure 16. Observed versus predicted methane production for cattle model DEI vs CH4 (Equation M3) for the evaluation data set (dashed line). The solid line represents the line of unity. ........................................................................................................................................39

Figure 17. The relationship between digestible energy intake and neutral detergent fibre content of the diet and methane production for all cattle. The solid line represents model DEI + NDF vs CH4 (Equation M4). ...............................................................................................40

Figure 18. Observed versus predicted methane production for cattle model DEI + NDF vs CH4 (Equation M4) for the evaluation data set (dashed line). The solid line represents the line of unity. .............................................................................................................................41

Figure 19. The relationship between digestible energy intake and methane production for lactating cattle. The solid line represents model DEI vs CH4 (Equation M10). ....................42

Figure 20. Observed versus predicted methane production for lactating cattle model DEI vs CH4 (Equation M10) for the evaluation data set (dashed line). The solid line represents the line of unity. .............................................................................................................................43

Figure 21. The relationship between digestible energy intake and the neutral detergent fibre content of the diet and methane production for lactating cattle. The solid line represents model DEI + NDF vs CH4 (Equation M11)............................................................................44

Figure 22. Observed versus predicted methane production for lactating cattle model DEI + NDF vs CH4 (Equation M11) for the evaluation data set (dashed line). The solid line represents the line of unity. ......................................................................................................45

Figure 23. The relationship between metabolisable energy requirement and methane production for lactating cattle. The solid line represents model MER vs CH4 (Equation M8)...................................................................................................................................................46

Figure 24. Observed versus predicted methane production for cattle model MER vs CH4 (Equation M8) for the evaluation data set (dashed line). The solid line represents the line of unity. ........................................................................................................................................47

Figure 25. The relationship between metabolisable energy requirement and the neutral detergent fibre content of the diet and methane production for lactating cattle. The solid line represents model MER + NDF vs CH4 (Equation M9)...........................................................48


Figure 26. Observed versus predicted methane production for lactating cattle model MER + NDF vs CH4 (Equation M9) for the evaluation data set (dashed line). The solid line represents the line of unity. ......................................................................................................49

Figure 27. The relationship between dry matter intake and methane energy as percentage of gross energy intake for all cattle. The solid line represents model DMI vs CH4 as %GEI (Equation M5)..........................................................................................................................50

Figure 28. Observed versus predicted methane energy as percentage of gross energy intake for cattle model DMI vs CH4 as %GEI (Equation M5) for the evaluation data set (dashed line). The solid line represents the line of unity......................................................................51

Figure 29. The relationship between dry matter intake and methane energy as percentage of gross energy intake for lactating cattle. The solid line represents model DMI vs CH4 as %GEI (Equation M12).............................................................................................................52

Figure 30. Observed versus predicted methane energy as percentage of gross energy intake for lactating cattle model DMI vs CH4 as %GEI (Equation M12) for the evaluation data set (dashed line). The solid line represents the line of unity. .......................................................53

Figure 31. The relationship between dry matter intake and methane energy as percentage of gross energy intake for non-lactating cattle. The solid line represents model DMI vs CH4 as %GEI (Equation M15).............................................................................................................54

Figure 32. Observed versus predicted methane energy as percentage of gross energy intake for non-lactating cattle model DMI vs CH4 %GEI (Equation M15) for the evaluation data set (dashed line). The solid line represents the line of unity. .......................................................55

Figure 33. Models of methane emissions as percentage of gross energy intake relative to dry matter intake.............................................................................................................................56

Figure 34. The relationship between dietary ether extract and methane production for all cattle.........................................................................................................................................58

Figure 35. The relationship between dietary ether extract and methane energy as a percentage of gross energy intake for all cattle..........................................................................................59

Figure 36. The relationship between dietary starch:ADF ratio and methane production for all cattle.........................................................................................................................................60

Figure 37. The relationship between dietary forage:concentrate ratio and methane production for all cattle. .............................................................................................................................61

Figure 38 The relationship between dietary forage:concentrate ratio and methane yield for all cattle.........................................................................................................................................62


Executive Summary

The objectives of this research were:

to expand an existing database to include additional individual observations of methane emissions by dairy and beef cattle;

to undertake a meta-analysis of the data to determine relationships between diet parameters and methane excretion;

to define a shortlist of robust statistical models suitable for predicting enteric methane excretion within a revised greenhouse gas inventory structure;

to quantify the known effects of mitigation strategies on methane production within a revised inventory structure.

An existing database was updated and expanded to include more recent data from University of Reading and Agri-Food and Biosciences Institute together with existing data from the USA, the Netherlands and Rothamsted Research, giving a total of 2682 individual measurements of methane emission from cattle. Emissions from sheep were not included in this analysis.

A multivariate analysis was conducted, with appropriate adjustments for variance associated with location and trial effects, to determine the most important dietary factors that influence methane emission, based on both linear and non-linear models.

Total feed dry matter intake has an overriding effect on the amount of methane produced by lactating and non-lactating cattle across a broad range of diet types and productive states. In contrast to the clear effect of the level of dry matter intake, most measures of dietary nutrient composition were found to be non-significant factors in determining methane emissions across the dataset.

Including a measure of diet quality through the use of neutral detergent fibre in the models does lead to a small improvement in prediction in some cases. However, the errors associated with estimating neutral detergent fibre level in any given diet in the absence of direct measurement may outweigh any improvements to the model.

The current IPCC tier 2 method uses a constant 6.5% of gross energy intake being lost as methane. For lactating cattle in this analysis, this represents the observed methane output from an animal eating 13 kg dry matter per day. However, a lactating animal eating 20 kg dry matter per day (typical intake in mid lactation) would emit only 5.7% of the gross energy intake as methane. The proportion of gross energy intake lost as methane declines as the level of intake rises at a rate of 0.12% per kg dry matter intake.

For lactating cattle, we recommend adopting models that predict methane emission as grams per day from known intake, either dry matter or digestible energy. When reliable estimates


of intake are unavailable, an alternative relationship between feed intake and methane production is required. The improved inventory structure has proposed using the UK metabolisable energy system for ruminant livestock.

Metabolisable energy requirement is linearly correlated to methane output for lactating cattle, although the model demonstrates a higher level of prediction error than those relating known intake to methane output.

The ability to estimate the metabolisable energy requirement of lactating animals in different scenarios means that models of methane output based on metabolisable energy requirement may be the most practical solution.

For non-lactating animals the best new model from this analysis requires the estimation of methane output as a percentage of gross energy intake and this model also requires the dietary neutral detergent fibre concentration to be known. However, the limited data availability leads us to recommend continued use of the IPCC (2006) method for non-lactating cattle until further data from on-going studies becomes available for integration into this analysis.

Overall, the data set is heavily biased towards lactating animals with the result that the models for lactating cattle are significantly more reliable than for non-lactating animals due to the comparative differences in the availability of data for model development.

Non-lactating models contain significantly greater bias in the overall predicted mean methane output. They tend to under or over predict (depending on model used) across the whole range of intake for any given group of animals.

There was no evidence of a relationship between dietary ether extract and methane production or methane as a proportion of gross energy intake. The database did not distinguish between fat concentration of the basal diet and supplementary additives or between the different types of fat used. There was no indication of a relationship between the balance of starch and fibre in the diet and methane production. Variation within the dataset did not allow the development of suitable models describing the influence of dietary fat and dietary starch and fibre balance on methane production.


Recommendations for revised national inventory

Based on the analysis in this report and the probable data availability for the construction of the revised national inventory we recommend the following models for calculation of enteric methane emissions from cattle.

Lactating cattle

CH4 = 0.860 × MER + 193

where CH4 is enteric methane production (g/d) and MER is metabolisable energy requirement (MJ/d) (FiM calculations net of pregnancy).

Non-lactating cattle (as per IPCC, 2006)

CH4 = 0.065 × GEI

where CH4 is enteric methane production (MJ/d) and GEI is Gross Energy Intake (MJ/d).


Table 1. Table of Abbreviations.

ADF Acid Detergent Fibre AFBI Agri-Food and Biosciences Institute, Belfast and Hillsborough CEDAR University of Reading Centre for Dairy Research DEI Digestible Energy Intake DMI Dry Matter Intake EE Ether Extract GE Gross Energy GEI Gross Energy Intake ME Metabolisable Energy MER Metabolisable Energy Requirement MSPE Mean Square Prediction Error N Nitrogen N/A Data Not Available NDF Neutral Detergent Fibre OM Organic Matter WSC Water Soluble Carbohydrate


Introduction

The objective of this work was to extract, integrate and interpret existing experimental emissions data to derive revised and new country specific appropriate emission factors for enteric methane emissions.

There have been a number of summarisations and reviews of existing experimental data from cattle wherein the dietary and management factors that determine amounts of methane and/or nitrogen excreted have been investigated (Wilkerson et al., 1995; Mills et al., 2003; Kebreab et al., 2006; Ellis et al., 2009; Huhtanen & Hristov, 2009).

For methane emission from ruminants, there have been a number of summarizations of data from measurements of energy balance of cattle from laboratories in the UK, other parts of Europe and the US (e.g. Moe and Tyrrell, 1979; Holter and Young, 1992; Kirchgeßner et al., 1995; Wilkerson et al., 1995; Yan et al., 2000; Mills et al., 2001; Mills et al., 2003; Ellis et al. 2009). For all of the models developed thus far, the major determinant of total methane excretion is the amount of dry matter intake, or more precisely the fermentable organic matter consumed, but numerous other dietary factors have significant effects on methane excretion, including the amount and type of fibre, starch, sugars, fat and other nutrients or dietary additives (Kebreab et al., 2006).

As reviewed by Kebreab et al. (2006), predictions of methane production by ruminants have been based on dry matter intake (Kriss, 1930; Bratzler and Forbes, 1940), intake of digestible carbohydrates (Bratzler and Forbes, 1940; Moe and Tyrrell, 1979), diet digestibility and level of feed intake (Blaxter and Clapperton, 1965; Murray et al., 1978), as well as diet intake and composition (Holter and Young, 1992; Yan et al., 2000). The statistical modelling of Mills et al. (2003) confirmed the importance of the balance of structural and non-structural dietary carbohydrate as a determinant of methane emissions, but their study was limited to data from dairy cattle across a narrow range of diet types. Ellis et al. 2009 performed a similar exercise on data from beef cattle and showed that whilst equation forms used in earlier studies were transferable, model parameter values were significantly different, presumably due to the lower intake demonstrated by the beef cattle.

A major shortcoming of the extant statistical models of methane emission relates to the relatively narrow data ranges upon which the existing models were constructed. This limits the applicability of each model to the range of intake and diet or animal types characteristic of the development data and it compromises the application of these models for purposes such as compilation of national emissions inventories for farming systems at regional and national levels. To improve our scientific understanding and to facilitate practical application of models when considering methane emissions from both growing and mature animals (lactating or dry), a much broader database structure is required for construction and evaluation.

In addition to the variation in emissions due to dietary composition stated previously, it has long been known that feeding supplemental fat reduces methane output (Czerkawski et al,


1966; Andrew et al., 1991). However, the global concern to reduce anthropogenic sources of greenhouse gas emissions to the atmosphere has recently led scientists to investigate this further. Several studies, both in vitro and in vivo have demonstrated marked reductions in methanogenesis as levels of supplemental fat are increased. The effects vary according to basal diet, type of fat and level of inclusion in the diet. Recent research has focussed specifically on the methane suppressing effects of the medium chain fatty acids due to their apparent ability to achieve suppression in methanogenesis whilst exerting lesser influences on dry matter intake and animal performance than longer chain alternatives. Many studies have demonstrated large reductions in methane emission for C12 and C14 rich oils added to ruminant diets (Machmüller et al., 1998; Machmüller et al., 2000; Dohme et al., 2000; Solvia et al., 2003), but many of the conclusions drawn thus far are dependent on purely in vitro work or studies conducted with smaller ruminants. At the same time this research has tended to focus on feeding supplemental fat to nutritionally balanced diets. What is still not clear is whether there is a relationship between the level of fat provided by the basal diet (forages, protein and energy concentrates excluding supplemental fat) and methane emissions. In order to resolve this question a meta-analysis of a sufficiently broad range of diets fed to beef and dairy cattle varying in dietary fat level is required.

Other dietary supplements are also known to reduce the total amount of methane produced relative to metabolisable energy or milk yield (Johnson & Johnson, 1995; Kebreab et al., 2006). These previous reviews have provided a basis for predicting methane output, but each of the individual summarizations have in general focused on distinct approaches for predicting total methane output, depending on the available dietary composition data within the datasets analysed. As regards the effect of dietary protein on methane excretion, the data are equivocal. Significant effects of dietary crude protein level have been reported for some analyses of available data (e.g. Holter & Young, 1995), but not all (e.g. Moe & Tyrrell, 1979), but the database of methane excretion in lactating dairy cows is largely based on rations containing protein in excess of requirements.

Objectives

Therefore, the objectives of this research were:

to expand an existing database to include additional individual observations of methane emissions by dairy and beef cattle;

to undertake a meta-analysis of the data to determine relationships between diet parameters and methane excretion;

to define a shortlist of robust statistical models suitable for predicting methane excretion within a revised greenhouse gas inventory structure and finally:

to quantify the known effects of mitigation strategies on methane production within a revised inventory structure.


In addition, an existing database of individual measurements of energy balance from the University of Reading Centre for Dairy Research (CEDAR) and Agri-Food and Biosciences Institute (AFBI), Belfast and Hillsborough were updated and expanded using data from other laboratories as appropriate. A multivariate analysis was conducted, with appropriate adjustments for trial effects, to determine the most important dietary factors that influence methane and nitrogen excretion relative to growth rate and milk yield, based in part on response surface analysis. The meta-analysis also considered non-linear biological relationships in the manner proposed by Mills et al. (2003).

Database development

Within the project consortium there were two major data sets available for formulating new and revised methane emission factors from cattle, namely the calorimetric databases held by CEDAR and AFBI. These data sets comprised individual measurements of energy balance including measurements of methane emission across a broad range of diet types, animal characteristics and treatments. The starting point was an existing data summary of 672 individual measurements that was integrated for the Feed Into Milk project (Thomas, 2004). Each measurement represents the simultaneous metabolism of energy and nitrogen, including methane and nitrogen excretion, measured in cattle on a given diet or other treatment over the course of a 5 to 7 day collection period using open-circuit respiration calorimeters (e.g. Reynolds et al., 2001). This dataset was updated and expanded using recent data from CEDAR and AFBI and further historic data from other locations to produce a final database. Measurements of energy and/or nitrogen balance accompanied by adequate dietary and animal descriptions were accumulated into a revised database for a meta-analysis of effects of key parameters on methane emission, with practical relevance to the management of growing and lactating ruminants in the UK. The final database was as follows:

1. A total of 1208 measurements of energy and nitrogen balance from AFBI and Queens University, Belfast were provided by T. Yan. These data were obtained from lactating and dry dairy cattle and growing beef cattle fed grass silage diets and contains a large number of observations from fresh forage based diets (i.e. grazed grass). The AFBI data contained methane emissions measurements from different breeds of cows (Holstein-Friesian, Jersey cross and Norwegian) and different levels of feeding (from maintenance to production). Measurements were obtained from respiration calorimeters and using the SF6 tracer system.

2. A total of 484 measurements of energy and nitrogen balance from CEDAR were included. These were obtained from high yielding lactating dairy cattle fed predominantly total mixed rations containing conserved forages (i.e. maize, grass and whole crop cereal silages).

3. A total of 366 measurements of energy and nitrogen balance from the USDA ARS Energy Metabolism Unit from the personal research of C.K. Reynolds were included. These were obtained primarily from growing beef cattle fed pelleted diets based on maize meal, soybean meal and alfalfa meal in varying proportions. In addition, measurements from lactating beef cows (Reynolds and Tyrrell, 2000) and Holstein and Jersey cows (Tyrrell et al., 1990) were included.


4. A total of 615 measurements of energy and nitrogen balance from research centres at Wageningen and Lelystad in the Netherlands were included by kind permission of colleagues at Wageningen University (A. Bannink, A. van Vuuren, Y. van der Honing and S. Tamminga). These data included data from lactating cows’ fed fresh and frozen grass and grass silage.

5. A total of 9 measurements of methane production from the Rothamsted Research, North Wyke were provided by T. Misselbrook. These data were obtained from growing beef cattle fed fresh grass and measurements made using the polytunnel system.

In total, the database included 2682 individual records of methane excretion, along with varying amounts of supplementary information on diet formulation, diet composition and the cattle used in each experiment. The data set contained 2154 records from lactating cattle and 528 records from non-lactating cattle (growing beef cattle and dry dairy cattle). Additional information on the individual contributing data sets is detailed in Defra project AC0114 Scoping Phase Report July to December 2011, Appendix A, Record of United Kingdom Methane Emission Factor Data sets. The availability of supplemental information ranged from extensive to very limited, which reflected the availability of resources for the measurements when the studies were conducted, or the resources needed for accessing the measurements from archives. In many cases, funds were only available for essential measurements of diet composition when the studies were conducted, whilst for older data in many cases the data had not been retained.

Additional databases exist, for example additional USDA data from Beltsville (Wilkerson et al., 1995), data from the Ritzman Laboratory in New Hampshire (Holter et al., 1992) and data from other locations in Europe with large animal calorimeters (e.g. Kirchgeßner et al., 1995, Külling et al., 2002) and around the world. Data from many of these other locations were also sought, but ultimately were not obtained due to intellectual property restrictions and resource availability.


Figure 1 shows the range of dry matter intake and milk yield for the lactating cattle in the data set. Intake ranged from 6.5 to 30 kg/d and milk yield ranged from 0.6 to 59.7 kg/d.

Figure 1. The relationship between dry matter intake and milk yield for lactating cattle.

Dry matter intake (kg/d)

0 5 10 15 20 25 30 35

Milk

yie

ld (k

g/d)

0

10

20

30

40

50

60

70

AFBI Beltsville CEDARLelystad Queens Wageningen

A summary description of the complete dataset is shown in Tables 2 and 3.


Table 2. Summary of live-weight, dry matter intake, methane production and milk yield and composition for the database. Mean values with observation number in parentheses.

Centre Live-

weight (kg)

DMI (kg/d)

Methane (g/d)

Milk yield (kg/d)

Milk fat

(g/kg)

Milk protein (g/kg)

Milk lactose (g/kg)

All cattle

AFBI 539

(1088) 14.18 (1088)

328 (1088)

Beltsville 440

(361) 8.16 (361)

168 (366)

CEDAR 631

(372) 20.63 (436)

436 (484)

Lelystad 555

(152) 15.44 (152)

262 (152)

North Wyke 347 (9)

6.43 (9)

130 (9)

Queens 517

(120) 13.67 (120)

354 (120)

Wageningen 529

(463) 14.60 (463)

275 (463)

Min 196 2.09 32 Max 826 29.95 516

Lactating cattle

AFBI 543

(849) 16.16 (849)

368 (849)

21.20 (814)

43.60 (342)

34.90 (360)

46.98 (324)

Beltsville 569 (86)

16.06 (86)

325 (86)

17.96 (86)

42.83 (86)

36.11 (86)

CEDAR 631

(484) 20.63 (484)

436 (484)

33.43 (456)

38.92 (456)

32.26 (456)

46.88 (456)

Lelystad 555

(152) 15.44 (152)

262 (152)

22.53 (152)

39.36 (148)

30.86 (152)

Queens 517

(120) 13.67 (120)

354 (120)

21.86 (120)

Wageningen 529

(463) 14.6 0 (463)

275 (463)

19.35 (463)

40.28 (463)

30.07 (463)

Min 341 6.54 142 0.57 12.10 20.63 38.75 Max 826 29.95 704 59.70 75.83 53.70 54.40

Non-lactating cattle

AFBI 522

(239) 7.12 (239)

185 (239)

Beltsville 400

(280) 5.69 (280)

120 (280)

North Wyke 347 (9)

6.43 (9)

130 (9)

Min 196 2.09 44 Max 788 17.75 331 DMI, Dry Matter Intake.


Table 3. Summary of diet composition for the database. Mean values.

Diet composition, gross energy (MJ/kg DM); nutrients (g/kg DM)

Centre GE N NDF ADF EE Ash OM Starch WSC

All cattle AFBI 18.47 28 433 255 80 Beltsville 18.56 26 327 178 28 70 930 413 CEDAR 18.99 27 367 192 33 76 917 235 70 Lelystad 18.39 31 50 909 North Wyke n/a n/a n/a n/a n/a n/a n/a n/a n/a Queens 19.31 28 Wageningen 18.34 28 39 910 Min 15.14 10 175 75 7 32 833 71 29 Max 22.63 46 672 438 119 141 968 641 235 Lactating cattle AFBI 18.51 29 383 238 81 Beltsville 18.76 29 415 251 32 81 CEDAR 18.99 27 367 192 33 76 917 235 Lelystad 18.39 31 50 909 Queens 19.31 28 Wageningen 18.34 28 39 910 Min 15.14 16 179 106 17 50 833 71 29 Max 22.63 46 672 438 119 138 944 566 235 Non-lactating cattle AFBI 18.36 23 502 280 78 Beltsville 18.50 25 299 155 27 66 934 451 North Wyke n/a n/a n/a n/a n/a n/a n/a n/a n/a Min 17.01 10 175 75 7 32 863 174 Max 19.95 41 656 375 62 141 968 641

GE, Gross Energy; N, Nitrogen; NDF, Neutral Detergent Fibre; ADF, Acid Detergent Fibre; EE, Ether Extract; OM, Organic Matter; WSC; Water Soluble Carbohydrate; n/a, data not available.


Linear model development

For each relationship under investigation, the data were split into two sets. Two thirds of the data were used for model construction and the remaining third was allocated for model evaluation. The selection of the data subsets was random within each experiment.

A multivariate analysis was conducted, with appropriate adjustments for trial effects, to determine the most important dietary factors that influence methane emission. The first stage of analysis comprised correlations between dietary variables and methane emission using the Proc Corr procedure of SAS (SAS Inst. Inc., Cary, NC) as described by Mills et al. (2003). For methane, the variables analysed included methane excretion (g/d), dry matter intake (kg/d) and diet dry matter concentrations (g/kg) of nitrogen, starch, neutral detergent fibre, acid detergent fibre and ether extract. These measurements describe the balance of protein, carbohydrate fractions and fat in the diet and are widely accepted measures of nutritional quality within industry. The data set included reports from AFBI, Beltsville, CEDAR and the Netherlands, but diet concentrations of starch, neutral detergent fibre and acid detergent fibre were only available for reports from AFBI, Beltsville and CEDAR. The effect of changing concentrations of one of these nutrient classes may in part reflect indirect effects of associated changes in the other nutrients (e.g. increasing crude protein concentration may have resulted in a decrease in fibre concentration from co-product concentrates).

Next the backwards elimination multiple regression procedure was used to determine the most appropriate linear model for describing variation between diet variables and methane emission as described by Mills et al. (2003). A probability level of 0.15 was used for removal of independent variables from the starting model. Data were corrected for variation due to location and experiment using Mixed Models procedures of SAS and linear regression models as described by St-Pierre (2001). Covariance structures were selected based on fit criteria, but in most cases an unstructured model was used for the data reported. In all cases, there were significant effects of location. An example of correction for the effects of location and experiment is shown in Figure 2 for the relationship between dry matter intake and methane production. There is a particularly large spread of data at the higher levels of dry matter intake for the uncorrected data, but following correction, the spread of data was reduced. Where a significant biologically meaningful relationship could be established between an independent variable and methane emission, a model was developed exclusively from the construction data set. Outliers were not excluded from model development.

Non-linear model development

Although linear models provide a straightforward method of describing many processes, biological systems rarely display linear behaviour across a range of circumstances. Rumen fermentation is no exception and the complexities of methanogenesis have been studied and reported widely. With a view to gaining a more complete understanding of such systems, dynamic mechanistic models have been developed and applied as their structure can be tailored to represent the nature of the system (Dijkstra et al., 2002). However, these models are time consuming to develop and their application can be limited by their increased


complexity. Through non-linear statistical modelling, an alternative exists to incorporate some of the principal biological mechanisms that underlie a relationship whilst still maintaining much of the simplicity that enhances dissemination. For example Mills et al. (2003) and Ellis et al. (2009) demonstrated that even the strong relationship between dry matter intake and methane emission may be improved by adopting a non-linear function.

Figure 2. An example of correcting the data set for the effects of location and experiment using the relationship between dry matter intake and methane production for all cattle.


0 5 10 15 20 25 30 35

Met

hane

pro

duct

ion

(g/d

)

0

100

200

300

400

500

600

700

800

Uncorrected dataCorrected data

Model evaluation

The various models of methane excretion were evaluated against their ability to predict the evaluation data set. A comparison of observed and predicted values was made initially using linear regression. The mean square prediction error (MSPE) was then used to demonstrate the overall error associated with the model as well as any bias that might have been evident.


The MSPE is described as follows:

2

1

/n

iii

MSPE nO P

Where i=1, 2, ..., n; n is the number of observations; and Oi and Pi are the observed and predicted values respectively. The square root of the MSPE is expressed in the same units as the observed values and a comparison of the root MSPE as a percentage of the observed mean provides and indication of the overall error of prediction.

Results – emission factors For many of the relationships investigated, a linear model proved to be the best description of the data as might be expected from such a large dataset. Some relationships did exhibit non-linear patterns and in these cases, suitable non-linear functions were selected based on their ability to describe the data. However, when the non-linear models were tested with the data, none of the functions used demonstrated a significant improvement in performance relative to the equivalent linear function. Therefore, it was decided to concentrate the analysis on linear functions due to their ease of use and applicability. Results of the non-linear modelling are not presented in this report.

The results emphasise that, as highlighted in many previous meta-analyses and reviews of published data (Kebreab et al., 2006; Beauchemin et al., 2008; Ellis et al., 2009; Martin et al., 2010), total feed intake as either dry matter or digestible energy is the major determinant of methane production by cattle. Increased dry matter intake is associated with increased fermentation of organic matter in the rumen and methane is an inevitable by-product of this process. However, the variations in methane production at a set level of intake reflect the influence of diet composition and diet quality. In this analysis, neutral detergent fibre was chosen as the most readily available feed component which should provide an indication of diet quality in relation to fibre digestion and methane production at a set intake. The inclusion of dietary neutral detergent fibre content in certain models tended to improve the fit of the model and reduce the prediction error when tested against the evaluation data set.


All cattle

The linear models developed and evaluated for all cattle are presented in Tables 4 and 5. Parameter estimates and associated standard errors were derived from the construction data set after correction for variation due to location and experiment.

Table 4. Description of models of methane emission for all cattle.

Model Number

Model Description Parameter (standard error)

M1 DMI vs CH4 CH4 (g/d) = mDMI + c m = 16.4 (0.152) c = 77.6 (2.38)

M2 DMI + NDF vs CH4 CH4 (g/d) = m1DMI +m2NDF + c

m1 = 18.3 (0.201) m2 = 0.0930 (0.0117) c = 37.2 (3.44)

M3 DEI vs CH4 CH4 (g/d) = mDEI + c m = 1.21 (0.0116) c = 92.0 (2.51)

M4 DEI + NDF vs CH4 CH4 (g/d) = m1DEI +m2NDF + c

m1 = 1.35 (0.0145) m2 = 0.121 (0.0113) c = 29.3 (3.32)

M5 DMI vs CH4 as %GEI CH4 (% GEI) = mDMI + c m = -0.118 (0.00335) c = 8.03 (0.0523)

DMI, Dry Matter Intake; DEI, Digestible Energy Intake; GEI, Gross Energy Intake; NDF, Neutral Detergent Fibre.

Table 5. Evaluation of models of methane emission against the independent data sub set for all cattle.

Model Number

Model Observed

mean Predicted

mean R2 Root MSPE (% of

observed mean)#

M1 DMI vs CH4 312 317 0.75 18.6%

M2 DMI + NDF vs CH4 299 317 0.87 17.8%

M3 DEI vs CH4 325 330 0.83 16.9%

M4 DEI + NDF vs CH4 300 318 0.89 17.1%

M5 DMI vs CH4 %GEI 6.52% 6.32% 0.16 18.4% #MSPE, mean square prediction error; DMI, Dry Matter Intake; DEI, Digestible Energy Intake; GEI, Gross Energy Intake; NDF, Neutral Detergent Fibre. The imbalance between the numbers of observations for lactating compared to non-lactating cattle in the data set means that the 'all cattle' models are heavily biased towards lactating animals. Therefore, the data set was divided into lactating cattle and non-lactating cattle for subsequent analysis.


Lactating cattle

The linear models developed and evaluated for lactating cattle are presented in Tables 6 and 7. Parameter estimates and associated standard errors were derived from the construction data set after correction for variation due to location and experiment.

Table 6. Description of models of methane emission for lactating cattle.

Model Number




m1 = 18.0 (0.424) m2 = 0.067 (0.0263) c = 52.2 (15.1)

M8 MER vs CH4 CH4 (g/d) = mMER + c m = 0.860 (0.0258) c = 193 (5.06)

M9 MER + NDF vs CH4 CH4 (g/d) = m1MER +m2NDF + c

m1 = 0.816 (0.0401) m2 = -0.119 (0.0317) c = 273 (17.0)

M10 DEI vs CH4 CH4 (g/d) = mDEI + c m = 1.19 (0.0229) c = 97.0 (5.65)

M11 DEI + NDF vs CH4 CH4 (g/d) = m1DEI +m2NDF + c

m1 = 1.31 (0.0348) m2 = 0.0707 (0.0277) c = 59.5 (17.1)


DMI, Dry Matter Intake; MER, Metabolisable Energy Requirement; DEI, Digestible Energy Intake; GEI, Gross Energy Intake; NDF, Neutral Detergent Fibre.


Table 7. Evaluation of models of methane emission against the independent data sub set for lactating cattle.

Model Number

Model Observed

Mean Predicted


observed mean)#

M6 DMI vs CH4 351 350 0.52 17.4%

M7 DMI + NDF vs CH4 388 396 0.56 15.1%

M8 MER vs CH4 349 355 0.39 20.4%

M9 MER + NDF vs CH4 386 395 0.40 17.4%

M10 DEI vs CH4 389 381 0.54 14.3%

M11 DEI + NDF vs CH4 400 405 0.55 13.9%

M12 DMI vs CH4 as %GEI 6.33% 6.08% 0.07 18.2% #MSPE, mean square prediction error; DMI, Dry Matter Intake; MER, Metabolisable Energy Requirement; DEI, Digestible Energy Intake; GEI, Gross Energy Intake; NDF, Neutral Detergent Fibre. The overall error is similar for models predicting methane emission as g/d or as percentage of gross energy intake, but R squared values are significantly different. Therefore, the models predicting methane as g/d appear to explain more of the observed variation for the evaluation data set. In addition to the overall error, end users need to consider practicality and likely variation between any diets being tested. The straightforward relationship between dry matter intake and methane is highly practical (probably more so than digestible energy intake) given the information available on farm.



The models developed and evaluated for non-lactating cattle are presented in Tables 8 and 9. Parameter estimates and associated standard errors were derived from the construction data set after correction for variation due to location and experiment.

Table 8. Description of models of methane emission for non-lactating cattle.

Model Number




m1 = 20.0 (0.675) m2 = 0.109 (0.00859) c = -20.0 (4.57)


DMI, Dry Matter Intake; GEI, Gross Energy Intake; NDF, Neutral Detergent Fibre.

Table 9. Evaluation of models of methane emission against the independent data sub set for non-lactating cattle.

Model Number

Model Observed

mean Predicted


observed mean)#

M13 DMI vs CH4 153 151 0.59 21.2%

M14 DMI + NDF vs CH4 148 143 0.73 16.0%

M15 DMI vs CH4 as %GEI 7.31% 7.14% 0.10 18.9% #MSPE, mean square prediction error; DMI, Dry Matter Intake; GEI, Gross Energy Intake; NDF, Neutral Detergent Fibre. Models for non-lactating cattle are generally less reliable predictors of methane emissions than those for lactating cattle even though these models were using observed measures of feed intake rather than the estimates of metabolisable energy requirement as applied to the models for lactating cattle. Models based on metabolisable energy requirement for non-lactating animals could not be formulated because live-weight change data were unavailable. For this group of animals, the live-weight change could have accounted for a significant proportion of the total energy requirement. Further data from non-lactating cattle describing the relationship between methane emissions and calculated feed intake will be available from studies in Defra project AC0115 and these data will allow the development of models that can be used in the revised inventory.


The construction and evaluation graphs associated with each of the models described in Tables 4 to 9 are shown in Figures 3 to 33.

Figure 3. The relationship between dry matter intake and methane production for all cattle. The solid line represents model DMI vs CH4 (Equation M1).


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Dry matter intake is a good predictor of methane emission across the entire range of intake for both lactating and non-lactating cattle. However, there is a small bias in the predictions with a tendency to under-predict the highest levels of methane output. Such under-prediction at high levels of observed methane output has been noted in many previous modelling studies (see Figure 4).


Figure 4. Observed versus predicted methane production for the all cattle model DMI vs CH4 (Equation M1) for the evaluation data set (dashed line). The solid line represents the line of unity.

Observed methane (g/d)

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Figure 5. The relationship between dry matter intake and neutral detergent fibre content of the diet and methane production for all cattle. The solid line represents model DMI + NDF vs CH4 (Equation M2).


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The inclusion of neutral detergent fibre as a measure of diet quality in the methane emissions model relating dry matter intake to methane output reduces the prediction error slightly (0.8%) and it improves the level of underlying bias to the predictions (c.f. Figures 4 and 6). However, the inclusion of neutral detergent fibre increases the tendency for the model to over-predict throughout the entire range of intake (MSPE decomposed into error due to overall bias of prediction increases from 0.8% to 11.8%).


Figure 6. Observed versus predicted methane production for the all cattle model DMI + NDF vs CH4 for the evaluation data set (Equation M2) for the evaluation data set (dashed line). The solid line represents the line of unity.


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Figure 7. The relationship between dry matter intake and methane production for lactating cattle. The solid line represents model DMI vs CH4 (Equation M6).


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The relationship between dry matter and methane output for lactating cattle is strong given the broad range of diet composition in the evaluation data set. There is a small bias with a tendency to under predict emissions for the highest emitters and an under-prediction at the opposite end of the spectrum (see Figure 8).


Figure 8. Observed versus predicted methane production for lactating cattle model DMI vs CH4 (Equation M6) for the evaluation data set (dashed line). The solid line represents the line of unity.


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Figure 9. The relationship between dry matter intake and neutral detergent fibre content of the diet and methane production for lactating cattle. The solid line represents model DMI + NDF vs CH4 (Equation M7).


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Including neutral detergent fibre in the model relating dry matter intake to methane output from lactating cattle reduces the total error of prediction by 2.4% with a noticeably reduced spread for the correlation, although the small bias in model predictions remains relatively unchanged (see. Figures 8 and 10).


Figure 10. Observed versus predicted methane production for lactating cattle model DMI + NDF vs CH4 (Equation M7) for the evaluation data set (dashed line). The solid line represents the line of unity.


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Figure 11. The relationship between dry matter intake and methane production for non-lactating cattle. The solid line represents model DMI vs CH4 (Equation M6).


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AFBI Beltsville

For non-lactating cattle, the model relating dry matter intake to methane output is less reliable than for the comparable model for lactating animals. This is due to the smaller and narrower data set. The model tends to over-predict emissions for all but the highest emitters and this tendency to over-predict accounts for the greatest proportion of the overall error. The relationship shows the relative imbalance in the distribution of the data across the range of dry matter intake with relatively few data points for animals consuming over 8 kg dry matter per day representing dry Holstein dairy cows. Much of the data at the lower end of the range of dry matter intake represents beef cattle from the Beltsville data set.


Figure 12. Observed versus predicted methane production for non-lactating cattle model DMI vs CH4 (Equation M6) for the evaluation data set (dashed line). The solid line represents the line of unity.


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Figure 13. The relationship between dry matter intake and neutral detergent fibre content of the diet and methane production for non-lactating cattle. The solid line represents model DMI + NDF vs CH4 (Equation M7).


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AFBI Beltsville

Including neutral detergent fibre in the model relating dry matter intake to methane output from non-lactating cattle reduces the inherent bias, with the observed versus predicted plot showing a regression line almost parallel to the line of unity as the model is better able to account for the differences in methane output for high forage versus high concentrate diets. However, the general tendency to over-predict emissions is still clear (see Figure 14).


Figure 14. Observed versus predicted methane production for non-lactating cattle model DMI +NDF vs CH4 (Equation M7) for the evaluation data set (dashed line). The solid line represents the line of unity.


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Figure 15. The relationship between digestible energy intake and methane production for all cattle. The solid line represents model DEI vs CH4 (Equation M3).

Digestible energy intake (MJ/d)

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Observed digestible energy intake is the best predictor of methane output with the lowest overall error of prediction of all the models. However, digestible energy intake may be more difficult to estimate accurately given the requirement to estimate diet quality. As with the relationships between dry matter intake and methane, there is a tendency for the model to over-predict for the lowest emitters and to under-predict for the highest observed methane emitters (see Figure 16).


Figure 16. Observed versus predicted methane production for cattle model DEI vs CH4 (Equation M3) for the evaluation data set (dashed line). The solid line represents the line of unity.


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Figure 17. The relationship between digestible energy intake and neutral detergent fibre content of the diet and methane production for all cattle. The solid line represents model DEI + NDF vs CH4 (Equation M4).


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Including neutral detergent fibre in conjunction with digestible energy intake does improve the predictions to a small degree, but it also introduces a tendency to over-predict emissions for a higher proportion of animals (c.f. Figures 16 and 18).


Figure 18. Observed versus predicted methane production for cattle model DEI + NDF vs CH4 (Equation M4) for the evaluation data set (dashed line). The solid line represents the line of unity.


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Figure 19. The relationship between digestible energy intake and methane production for lactating cattle. The solid line represents model DEI vs CH4 (Equation M10).


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As with the all cattle data set, the digestible energy intake versus methane output model was the most reliable predictor of methane output for lactating animals, although there is an element of bias as shown in the observed versus predicted plot (see Figure 20).


Figure 20. Observed versus predicted methane production for lactating cattle model DEI vs CH4 (Equation M10) for the evaluation data set (dashed line). The solid line represents the line of unity.


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Figure 21. The relationship between digestible energy intake and the neutral detergent fibre content of the diet and methane production for lactating cattle. The solid line represents model DEI + NDF vs CH4 (Equation M11).


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Neutral detergent fibre in conjunction with digestible energy intake leads to a small improvement in prediction of methane output, with overall error reducing from 14.3% to 13.9%. However, this is unlikely to be useful in practical circumstances where neutral detergent fibre is not measured and therefore errors in its estimation would be introduced to counteract such marginal improvement in model performance.


Figure 22. Observed versus predicted methane production for lactating cattle model DEI + NDF vs CH4 (Equation M11) for the evaluation data set (dashed line). The solid line represents the line of unity.


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Figure 23. The relationship between metabolisable energy requirement and methane production for lactating cattle. The solid line represents model MER vs CH4 (Equation M8).

Metabolisable energy requirement (MJ/d)

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Metabolisable energy requirement is linearly correlated to methane output for lactating cattle, although the model demonstrates a higher level of prediction error than those relating dry matter intake to methane output (see Table 7), due to the uncertainty introduced by estimating metabolisable energy requirement from a known dry matter intake.


Figure 24. Observed versus predicted methane production for cattle model MER vs CH4 (Equation M8) for the evaluation data set (dashed line). The solid line represents the line of unity.


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Figure 25. The relationship between metabolisable energy requirement and the neutral detergent fibre content of the diet and methane production for lactating cattle. The solid line represents model MER + NDF vs CH4 (Equation M9).

Metabolisable energy requirement (MJ/d)

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AFBI BeltsvilleCEDARQueens

Including neutral detergent fibre in the model relating metabolisable energy requirement to methane output reduces the overall error from 21.2% to 17.3% (Table 7), although the bias remains similar with over-prediction at low levels of methane output and under-prediction towards the highest level of intake and methane production (see Figures 24 and 26).


Figure 26. Observed versus predicted methane production for lactating cattle model MER + NDF vs CH4 (Equation M9) for the evaluation data set (dashed line). The solid line represents the line of unity.


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Figure 27. The relationship between dry matter intake and methane energy as percentage of gross energy intake for all cattle. The solid line represents model DMI vs CH4 as %GEI (Equation M5).


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This figure shows the linear improvement in efficiency as dry matter intake increases and methane output per unit of gross energy intake declines. This observation is in contrast to the assumption of a static relationship in the current IPCC Tier 2 model for predicting methane emissions per animal. Whilst the trend is clear in the construction data (Figure 27), the relationship is not well suited as a model to predict emissions due to the large variability and significant bias seen in Figure 28.


Figure 28. Observed versus predicted methane energy as percentage of gross energy intake for cattle model DMI vs CH4 as %GEI (Equation M5) for the evaluation data set (dashed line). The solid line represents the line of unity.

Observed methane energy as percentage of gross energy intake

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Figure 29. The relationship between dry matter intake and methane energy as percentage of gross energy intake for lactating cattle. The solid line represents model DMI vs CH4 as %GEI (Equation M12).


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For lactating cattle, the relationship between dry matter intake and methane energy as a percentage of gross energy intake is generally inferior for predicting emissions to those models relating dry matter intake or metabolisable energy requirement directly to methane output per animal per day. The observed versus predicted plot demonstrates a significant bias (see Figure 30).


Figure 30. Observed versus predicted methane energy as percentage of gross energy intake for lactating cattle model DMI vs CH4 as %GEI (Equation M12) for the evaluation data set (dashed line). The solid line represents the line of unity.


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Figure 31. The relationship between dry matter intake and methane energy as percentage of gross energy intake for non-lactating cattle. The solid line represents model DMI vs CH4 as %GEI (Equation M15).


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AFBI Beltsville

Models for methane output from non-lactating cattle demonstrate significantly higher error of prediction due to the relatively narrow spread in feed intake level for the majority of the non-lactating data (see Tables 8 and 9). However, in contrast to the lactating cattle, the best model relates dry matter intake to methane emission expressed as a proportion of gross energy intake. Therefore, in such instances gross energy intake needs to be determined for a prediction of methane output for these animals. The relatively narrow range of dry matter intake for the majority of the data shown in Figure 31 means that the fitted model is subject to significant prediction error. Therefore, more data across a broad range of intake are required to refine this model.


Figure 32. Observed versus predicted methane energy as percentage of gross energy intake for non-lactating cattle model DMI vs CH4 %GEI (Equation M15) for the evaluation data set (dashed line). The solid line represents the line of unity.


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Figure 33. Models of methane emissions as percentage of gross energy intake relative to dry matter intake.


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IPCC Lactating cattleLactating cattle (MER model) Non-lactating cattle

The current IPCC Tier 2 method applies a static coefficient to determine methane output as a proportion of gross energy intake. This analysis suggests that for both lactating and non-lactating cattle the reality is a decline in the coefficient with rising intake as shown by the linear models fitted to these observations. Also shown on this figure is the non-linear relationship constructed from the predictions of model M8. In this scenario, it is assumed that the animals eat to satisfy their metabolisable energy requirement and that the energy density of the dry matter is 18.2 MJ/kg. These assumptions allow a direct comparison of the underlying nature of the IPCC model and the alternative based on metabolisable energy requirement.


Results - methane mitigation

As part of the current analysis, we also examined the database for evidence of known dietary factors that can influence methane emissions from cattle and which could potentially be used as mitigation options within the UK. The two chosen were dietary fat content and dietary forage proportion and composition.

Figures 34-38 illustrate the large variation in the data that remains unaccounted for by the chosen dietary factors. This variation prevented the construction of any reliable model describing the effect of either of these potential mitigation measures.


Figure 34. The relationship between dietary ether extract and methane production for all cattle.

Dietary ether extract (g/kg DM)

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Beltsville CEDARLelystad Wageningen

Whilst previous research has demonstrated the effect of added dietary fat on methane emissions within carefully controlled experimental conditions, the broad effect of total dietary fat concentration does not demonstrate a clear relationship with either total methane output (Figure 34) or methane as a proportion of energy intake (Figure 35). Within the database it is not possible to distinguish between fat concentration of the basal diet and supplementary additives or between the different types of fat. For this reason it is not currently possible to incorporate such effects in a model suitable for integration in an inventory scheme.


Figure 35. The relationship between dietary ether extract and methane energy as a percentage of gross energy intake for all cattle.

Dietary ether extract (g/kg DM)

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Figure 36. The relationship between dietary starch:ADF ratio and methane production for all cattle.

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The balance of starch and fibre in the diet could not be shown to exert a clear effect on the methane output. There is a clear split between the data sources of Beltsville and CEDAR representing the different, diets, animals and procedures used. As with dietary fat, this does not mean that no effect exists, merely that within the context of the available data the determination of the nature of the effect is not possible.


Figure 37. The relationship between dietary forage:concentrate ratio and methane production for all cattle.

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Figures 37 and 38 show the large range in forage proportion across the database. However Figure 38 shows that the variation in methane yield per unit of dry matter intake for any given level of consumption is too great to allow the development of a suitable model describing the influence of this factor.


Figure 38 The relationship between dietary forage:concentrate ratio and methane yield for all cattle.

Dietary forage : concentrate ratio

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Conclusions

Total feed dry matter intake has an overriding effect on the amount of methane produced by lactating and non-lactating cattle across a broad range of diet types and productive states. The prediction of methane production is only slightly improved on the basis of digestible energy, which is an indicator of the amount of digestible organic matter in the diet fed. The effect of individual diet components is relatively minor compared to the dominant effect of total feed dry matter intake. Provided information on an animal’s intake is available, a reliable estimate of methane emissions is possible for cattle.

Models using dry matter intake and digestible energy intake tended to display a lower error of prediction when compared to those using estimated metabolisable energy requirement (excluding pregnancy and live-weight change) as the independent variable. This was expected given that the metabolisable energy requirement calculation is based on several assumptions inherent to the Feed into Milk energy requirements model, whereas the dry matter intake and digestible energy intake data were based on direct experimental observations. However, where reliable estimates of dry matter intake are not available (such as in grazing situations), an alternative relationship between feed intake and methane production is required. As a starting point, the improved inventory structure has proposed using the UK metabolisable energy system for ruminant livestock as a means of predicting feed energy requirements, with the assumption that, over the long term, an animal’s feed intake will meet this requirement. In order to predict accurately an animal’s metabolisable energy requirement, certain key parameters have to be known, most importantly production level. If assumed values describing the physiological state of an animal are used to parameterise the metabolisable energy requirements model instead of empirical observations, the error of prediction when estimating methane emissions is likely to be amplified beyond that shown in this analysis where empirical data on each animal’s state were available. However, given the known difficulties in estimating dry matter intake across a diverse range of diets, the choice of calculated metabolisable energy requirement as the basis for the model of methane output should still be considered the most practical solution.

For lactating cows, the models that predict methane as g/cow/d are generally more reliable than those that predict methane emissions as a percentage of gross energy intake. On this basis we recommend adopting such models for lactating animals in contrast to the current IPCC tier 2 method, whereby a fixed percentage of gross energy intake is assumed to be emitted as methane (IPCC, 2006). The data also show that there is a tendency for the proportion of gross energy lost as methane to decline as the level of intake rises. The current IPCC tier 2 model suggests a constant 6.5% gross energy intake is lost as methane. For lactating cows in this analysis, this represents the observed methane output from an animal eating only 13 kg dry matter per day, well below the level of intake observed for lactating animals in typical UK production systems. However, a lactating animal eating 20 kg dry matter per day (typical intake in mid lactation) would emit only 5.7% of the gross energy intake as methane according to this analysis. Whilst there is inevitably uncertainty surrounding the absolute proportion of feed energy emitted as methane, it is clearly most


important that a revised inventory system takes account of the dynamic nature of the relationship between the level of feed intake and the proportion of energy lost as methane. This analysis has shown that the model of metabolisable energy requirement and methane output conforms to this pattern.

In contrast to lactating animals, for non-lactating animals the best model requires the estimation of methane output as a percentage of gross energy intake. Within this analysis, non-lactating models using metabolisable energy requirement as the independent variable were not reliable due to lack of construction data with identifiable live-weight change data.

In contrast to the clear effect of the level of dry matter intake, most measures of dietary nutrient composition were found to be non-significant factors in determining methane emissions across the dataset. This does not mean that within certain experiments that an effect of nutrient composition (e.g. fat) could not be observed, but merely that when taken in the context of the full range of observations such effects were minimal even with direct observations of these parameters. However, including a measure of diet quality through the use of neutral detergent fibre in the models does lead to a small improvement in prediction in some cases. This is because neutral detergent fibre concentration is a measure that represents the broad balance of the types of fermentable organic matter in the diet with high levels of neutral detergent fibre being associated with relatively more lipogenic volatile fatty acids being produced at the expense of their glucogenic alternatives. A higher lipogenic volatile fatty acid to glucogenic volatile fatty acid ratio results in relatively more feed energy being partitioned to methane as anticipated for high forage diets. However, the evaluation of the models in this analysis assumes that the neutral detergent fibre level has been measured for each observation as was the case in the evaluation data set. In practice, care should be exercised when assuming that the inclusion of neutral detergent fibre as a term in the model will reduce any uncertainty. This is because the errors associated with estimating neutral detergent fibre level in any given diet in the absence of direct measurement may outweigh any improvements to the model predictions when used in the inventory scenario.

Overall, the data set is heavily biased towards lactating animals, with the result that the models for lactating cows are significantly more reliable than for non-lactating animals due to the comparative differences in the availability of data for model development. Non-lactating models contain significantly greater bias in the overall predicted mean methane output. They tend to under or over predict (depending on model used) across the whole range of intake for any given group of animals. Therefore, more non-lactating data is required for model construction to improve predictions and reduce uncertainty for this group of animals. Data arising from Defra project AC0115 will be included in any future analysis.

Non-linear models have previously been shown to demonstrate an improved ability to predict methane emissions from feed intake. However, a consistent effect of this type was not seen in this analysis. The extensive range of diet composition and experimental treatments encompassed by the dataset led to a blurring of any such subtle effects as might be observed for a more homogenous group of observations. The linear relationships studied were not only able to predict the methane output at least as well as their non-linear alternatives, but they


also tended to involve fewer terms relating to diet composition and were more straightforward to apply as a result.

Previous research has demonstrated the effect of added dietary fat on methane emissions from cattle within controlled experiments. However, in the current analysis, the broad effect of dietary ether extract concentration did not demonstrate a clear relationship with methane production or methane as a proportion of gross energy intake. Within the database, it was not possible to distinguish between the fat concentration of the basal diet and supplementary additives or between the different types of fat used. Within the database, it is not possible to distinguish between dietary fat concentration of the basal diet and supplementary additives or between the different types of fat. There was no evidence of a relationship between the balance of starch and fibre in the diet and methane production. Variation within the current dataset did not allow the development of suitable models describing the influence of dietary fat and dietary starch and fibre balance on methane production. This does not mean that no effect exists, merely that within the context of the available data, the determination of the nature of the effect was not possible.

Recommendations for revised national inventory

The construction of an emissions inventory inevitably involves a compromise between a comprehensive breakdown of enteric emissions based on the many potential determining factors and the practicality of producing estimates where data describing the characteristics of the population of animals is limited. Therefore, based on the analysis in this report and the probable data availability for the construction of the inventory we recommend the following models for calculation of enteric methane emissions from cattle.

Lactating cattle

The most suitable model for emissions from lactating cattle correlates calculated metabolisable energy requirement with enteric methane output. Metabolisable energy requirement can be estimated using the Feed into Milk feed evaluation scheme and it requires knowledge of the bodyweight and production level of the animal. For this analysis, we have assumed that energy requirements for pregnancy are zero as these data were not available during model development.

The model is described as follows:

CH4 = 0.860 × MER + 193

where CH4 is enteric methane production (g/d) and MER is metabolisable energy requirement (MJ/d) (FiM calculations net of pregnancy).


For non-lactating animals there is evidence that the proportion of feed energy emitted as enteric methane declines as feed intake increases. However, due to a lack of data describing


animals’ growth rates and therefore energy requirements in the existing database, it is not possible to derive a suitable model that relates an estimated measure of feed intake with methane output. It is unlikely that observations of dry matter intake will be available for the revised inventory and therefore we recommend that for the time being the current IPCC (2006) Tier 2 model structure be used for non-lactating animals. Subsequent to the generation of the appropriate data for non-lactating cattle in Defra project AC0115, it should be possible to produce a revised model that is appropriate for the purposes of the revised inventory.

The IPCC model is described as follows:

CH4 = 0.065 × GEI

where CH4 is enteric methane production (MJ/d) and GEI is Gross Energy Intake (MJ/d).


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