Improvement and development of emission factor for methane emissions from enteric fermentation

Regional workshopBogor, Indonesia: December 7-11, 2015

By the end of this workshop, participants should be able to:

– Understand the scope of emissions from enteric fermentation source category

– Understand basic concepts and terms related to methane emissions from enteric fermentation

– Identify data needs for developing a country-specific emission factor (EF) for methane from enteric fermentation

– Understand and apply different approaches to developing country specific EF for methane from enteric fermentation

Learning objectives of the workshop

2

The workshop is targeted at:

– Compilers of methane emissions inventory for enteric fermentation for their country

– Experts and scientists who make specific measurements with ruminant animals to improve methane inventory

Participant profile

3

Methane emissions from enteric fermentation in ruminant animals like:

– cattle, buffalo, goats, sheep, deer and camelids

Course Scope

4

Lead trainer

5

Dr. Chris Johnson

Dr. Kristen A. Johnson, earned a Bachelor’s degree in Animal Industries from the University of Connecticut, an MS and Ph.D. in Animal Sciences from Michigan State University. Since arriving at Washington State University. One of her major research foci is production and mitigation of enteric methane emissions. She was integrally involved in the development of the SF6 technique to measure emissions from grazing livestock and she is currently working on a smart system to measure trace gases remotely. Dr. Johnson teaches undergraduate and graduate classes in nutrition and beef cattle management. In addition, she serves on national, and international committees and has worked on the US Greenhouse Gas Inventory (enteric emissions) as a contributor or reviewer for many years. Dr. Johnson’s major teaching responsibilities include Animal Sciences Orientation, Beef Cattle Production, and Ruminant Nutrition.

Module 1: Introduction (slides 7-36)– Basic concepts, definitions and IPCC methodology

Module 2: Measurements to develop an emission factor (slides 37-78)

– Chamber, external tracer techniques and micro-meterological methods

Module 3: An overview of SF6 Technique for Measuring Methane Emissions from Cattle (slides 79-104)

– Requirements, procedure, sampling and calculations

Module 4: Greenfeed (slides 105-125)– Introduction, Theory, and Principles and Validation

Module 5: Models and equations to predict CH4 (slides 126-131)– Different models, input data and concerns

Table of contents

6

Module 1: Introduction

Methodology for developing country-specific emission factor for methane

4 – 12% of GEI

5 – 15% of DE

7 – 21% of fermented energy

Magnitude of dietary energy loss attributable to methane production

Methane is not a source of Metabolisable Energy and its loss is a decrease in oxidisable substrate for

the ruminant

Methane production in ruminant animals

Anaerobic fermentation

Microbial rich environment

– Fungi

– Protozoa

– Bacteria

Digestion of low quality feedstuffs

Ruminal Environment

Cellulolytic

Hemicellulolytic

Pectinolytic

Amylolytic

Ureolytic

Proteolytic

Sugar-utilizing

Lipid-utilizing

Ammonia-producers

Methane-producers

Categories of Ruminal Bacteria

Chemical reactions:

– 4CHOO- +2H2 = CH4 + 3CO2 + 2H2O

Prevent:

– Excess H2 accumulation

– Subsequent decrease ruminal pH

Reduction reactions can continue:

– FAD to FADH

– NAD to NADH2

Why are Methane-producers Good?

For the animal

– C lost as CH4= loss of energy

For Humans:

– Global warming

Quest

– Find an alternative H2 sink

Why are Methane-producers Bad?

H2 sinks in the rumen

– CH4

– Microbial bodies

– Volatile fatty acids

– Acetate (C2H3O2)

– Propionate (C3H5O2)

– Butyrate (C4H7O2)

Altering the Ruminal Environment

Sources of information for animal characteristics

– Literature search

– Government information

– Publications compiled by IPCC, FAO, ILRI, ILCA

– Country-wide census data

– Augment information from professional opinion

Animal populations

Breeds: % of the population

Body weight (kg)

Average daily gain (kg/d)

Daily milk production (kg/d)

Milk fat (%)

Characterization of population

Amount of work/d (hr/d)

Reproduction rate

Culling rate

% males castrated

Categories (milk, meat, draught, hides etc.)

Feed digestibility (%)

Representative livestock categories

Main Categories Subcategories

Mature dairy cow or mature dairy buffalo

• High producing cows that have calved at least once & are used principally for milk production

• Low-producing cows that have calved at least once & are used principally for milk production

Other mature cattle or mature non-dairy buffalo

Females• Cows used to produce offspring for meat• Cows used for more than one production purpose (milk,

meat, draft)Males • Bulls used for breeding purposes• Bullocks used for draft power

Growing cattle or growing buffalo

• Calves pre-weaning• Replacement dairy heifers• Growing/fattening cattle of buffalo post-weaning• Feedlot cattle fed diets >90% concentrates

Mature ewes • Breeding ewes for production of offspring and wool production

• Milking ewes where commercial milk production is the primary purpose

Other mature sheep (>1 yr)

Growing lambs Intact males; castrates; females

Others (for example) Camels; deer, llamas, alpacas

Population census

Average annual population

Breeds

Classification within breeds

Type of production (milk, meat, wool, other)

Days alive

Body weight

Nutritional management

Feed intake

Animal performance

Diet composition

Diet chemical composition

Needed information

Composition

Forage

Concentrates (high nutrient density)

Fat concentration (can decrease emissions)

Fiber concentration (can influence intake and digestibility)

Dry matter content

Digestibility

Diet characteristics

Forage species

Feed preservation

Form of feed

Plant maturity

These factors impact:

– Loss of intake

– Digestibility

– Solubility

– Rate of passage

– Rumination time

– Particle size

Factors influencing CH4 production

ItemA

LowA

HighOGLow

OGHigh

GEI, MJ/d 97.9 139.7 99.6 128.4

MEI, MJ/d 55.6 81.2 53.6 68.6

MEI, GEI % 56.9 56.2 53.7 53.2

CH4, % 6.0 5.6 6.3 6.3

Energy use of alfalfa (A) and orchardgrass(OG) silage

Source: Varga et al., 1985

Item

Grass Silage Hay

Early Late Early

GEI, MJ/d 13.4 14.7 14.1

DE, % GEI 72.4 66.5 71.1

CH4, % GEI 9.7 8.4 8.4

Effect of forage age on CH4 production

Source- Sundstol et al., 1979

Grain diets

– Dietary level

– Grain type

– Grain preservation

Alternatives

– High starch diets

– Resistant starches

– High quality forages

– Eliminate nutrient deficiencies

– Productivity enhancers

Alternatives

– Pellet forages

– Increase ionophore use

– Improve ionophorepersistence

– Protozoa inhibitors

– Shift site of digestion

3 – 8% fat in diet decreased CH4 yield

10 – 15% decrease in dairy cattle

Dietary fat

Reduction due to : Decrease in fermentable substrate

Dietary fat – unsaturated fatty acids

Function Metabolic H2 %

Biohydrogenation 1 – 2

CO2 to CH4 48

VFA synthesis 33

Bacterial cell synthesis 12

Digestibility, %Level of intake (X maintenance)

1.0 1.5 2.0 2.5 3.0

50 6.8 6.7 6.6 6.6 6.5

60 7.4 7.1 6.8 6.4 6.1

70 8.0 7.4 6.9 6.3 5.8

80 8.6 7.8 7.0 6.2 5.4

90 9.2 8.2 7.1 6.1 5.0

CH4 yield – digestibility and GEI

Source: Blaxter and Clapperton, 1965

CH4 production as a function of digestibility

Resources

http://vslp.org/ssafeed/

http://www.fao.org/docrep/x5738e/x5738e09.htma

Animals tethered in digestion stalls

Fitted with fecal & urine collection harnesses

Fed 2X daily: 7:00 am & 5:00 pm

Orts collected frequently & returned to bunk during day

Total collection digestion trial

IPCC method to calculate Gross Energy (GE) intake

If you have-

– Body weight

– Digestibility

– NEma from Table 10.8 in IPCC

Intake prediction—Alternative/Check

Diet typeNEma

(MJ/kg DM)

High grain diet (>90%) 7.5 – 8.5

High quality forage (vegetative stage) 6.5 – 7.5

Moderate quality forage (mid-season) 5.5 – 6.5

Low quality forage (mature) 3.5 – 5.5

NEma = REM * 18.45 * DE%/100

Ym = % Gross Energy in the diet as CH4

Need Gross Energy Intake (GEI) values

Need measurements of CH4 under “normal” conditions

Measurements can enhance the robustness of the inventory and assist in mitigation strategies

IPCC Tier 2 Conversion factor (Ym)

Enteric CH4 emission factor

Where: GEI = Gross energy intake, MJ/hd/dYm = methane conversion rate, % dietary energy resulting in CH4

55.65 is energy content of CH4

IPCC equation 10.21

Emission factor (EF) =GEI * (Ym/100) * 365 d/yr

55.65 MJ/kg CH4

Tier 1 – requires data on livestock species and categories & annual population data. Default emissions factors are used for each group identified. Some knowledge of days alive is required (if an animal is only alive for part of a year) and productivity is needed.

Tier 2 – requires definitions for subcategories as well as feed intake estimates for the subcategories. Thus the calculation of the emission factor is more specific. Default values are used for populations for which there is little data. Feedstocks need to be well defined and research with local animals is required to determine a methane conversion (Ym) factor.

IPCC Tier 1 and Tier 2

Tier 3 – complex method that requires a model to predict emissions from highly defined and categorized populations. The model would take into account all of the factors associated with enteric emissions and generate emissions. IPCC suggests peer review of the model prior to adoption.

Hybrids – use parts of each tier to be most effective.

Tier 3 and Hybrids

Module 2- Measurements to develop an emission factor

Chamber methods

External tracer techniques

Micro-meterological methods

Measurements to develop EF

Measurements to develop EF

Chamber systems

– Calorimeters

– Ventilated Hoods

– Head boxes

– Tunnels

Tracer systems

– External tracers

– Internal Tracer

Greenfeed

1. Whole Animal Measurements

Construction requirements

– Sustained slight negative pressure

– Restraint – metabolism stall

– Air conditioning/dehumidification

– Air ducting

– Vacuum pump

– CH4 analyzer

Required measurements

– Air volume

– CH4

A. Chamber

CH4 emissions change over the day

Continuous Methane Emissions from a Cow Over 2 Days

Calibration of

Lower calibration point

Upper calibration point

CO2 and CH4 N2

0.1007% CH4

1.06% CO2

19.56% O2

O2

0.1007% CH4

1.06% CO2

19.56% O2

(outside air)

20.628% O2

NH3 N2 7.98 ppm NH3

Source- McLean and Tobin

Gases used for the calibration of CH4, CO2, O2

and NH3

Chamber outputCalibration of CH4, CO2, O2 and NH3

Item Cova

High concentrate All forage

Experiment I Experiment II Experiment III Experiment IV

Range RSDb Range RSD Range RSD Range RSD

----------------------------------kcal • d-1 • kg.75------------------------------------

CH4 GE 10 – 27 1.3 14 – 24 2.2 15 – 26 1.3 8 – 20 2.0

ME GE 150 – 300 7.8 145 – 310 7.6 110 – 250 10.3 75 – 130 5.7

HP GE 130 – 175 3.6 128 – 190 5.5 125 – 190 5.5 110 – 130 4.5

RE GE 30 – 120 6.5 20 – 120 9.9 -20 – 60 13.2 -40 – 5 7.1

Variation observed in calorimetry experiments with steers

aCovariate used in Analysis of Variance (AOV)bResidual standard deviation from AOV

Sealed—slight negative pressure so leaks are in

Restraint of animals

Air conditioning and dehumidification

Feed and H2O provision

Removal of waste

Calorimetry – considerations

Advantages

– Accurate

– Includes hind gut CH4

– Controlled intake

Disadvantages

– Expense

– Restriction of movement

– Training (people and animals)

– Limited numbers of animals to be measured

Intake--reduced

Calorimetry

Box – reasonably air-tight (wood or metal)

Add clear panels on sides – normal behavior & ability to check on animal

Removable panel – access to feed & H2O

Sleeve or drape around neck – minimize leakage around head/neck

Sufficiently large to allow animal to move its head in an unrestricted manner

B. Ventilated hood

Draw air past animal’s nose without a hood

Light & sturdy (fiberglass)

Inlet & sampling vents >50 mm to allow free flow of gas

Face masks

Must collect a representative sample of gas & then measure concentration

Sufficient outflow of gas to ensure lower gas pressure in hood & gas lines so leaks are inward

Must know total air flow volume

Hood and mask

Steer

Heat production (kcal)

Chamber Face maskChamber/Face mask

1 9284 8838 1.05

2 9493a 8167b 1.16

3 10266 9041 1.14

4 7774 8262 0.94

5 8728a 7258b 1.20

6 7854a 7441b 1.06

Mean 8900 8168 1.09

Comparison of heat production measured in chamber & by face mask

abMeans within a row with different letters are significantly different P < 0.05

Atmospheric tracer used to measure trace gas fluxes

SF6 Use

– In a room

– Pasture measurements

– Individual animals

Requirements

– Known constant release rate

– Complete mixing of tracer and CH4 prior to collection

– Ambient collection of exhaled breath

– Sensitive detection of tracer and CH4

II. Tracer methodology- Use of Sulfur hexafluoride (SF6)

Room measurements

Dairy Room

8.8

m

6.1 m

Fee

dB

un

k

Black plastic

= sampling canister

SF6

Flowmeter

Animals accustomed to sampling area. Sampling apparatus constructed.

30 d: diets assigned to animals and changed to allow ruminal adaptation

10 d: release rate needed in room determined without animals

Procedure: Room measurements

[SF6]

Time

Individual animalsHalter constructed with filter, calibrated capillary tube

Room measurementsCalibrated capillary tube

Pasture measurementsCalibrated capillary tube

Sample collection

Gas sampling options

SF6 flow meter set-up

Release rate of SF6 = 50.2 µg/min

4.1 hr period to reach steady state

Room measurements

Room measurements

Objective: To measure methane production from dairy cows fed three levels of dietary oilseeds

– Dietary fat levels: 2.3, 4.0, 5.6%

– Cows: 108, 4hd/trt CH4 measurements

– Measurements:

– Feed intake

– Milk production

– Milk composition

– Methane production

Room measurements

19:00 room closed, SF6 release begins

02:00 cows milked, moved into room

07:00 sampling begins

13:00 sampling ends, cows milked

Gas Chromatography: Flame Ionization and Electroconductivity detectors

GC injection loop

CH4 detection

– detector – FID

– column – Porapak N 80/100, 1.8 m x 0.3 cm

– carrier gas – nitrogen

SF6 detection

– detector – ECD

– column – Porapak Q 80/100, 1.8 m x 0.3 cm

– carrier gas – 5% CH4/95% argon best, but can use ultrapure nitrogen

Gas Chromatography: Flame Ionization and Electroconductivity detectors

Time

Dietary fat levels

2.3% 4.0% 5.6%

1 21.1/20.5 22.5 22.8

2 18.0 16.3 18.0

3 11.6 16.8 18.3

4 12.6 15.8 17.0

5 14.9 17.4

6 16.1

Room measurements- CH4 emissions (g/h/d)

Room measurements

Item

Dietary fat levels

2.3% 4.0% 5.6%

Days in milk 275.3 294.0 291.7

DMI, kg • hd-1 • d-1 25.0 27.3* 26.9*

CH4, g hd-1 • d-1 16.6 16.3 20.5

CH4, % GEI 4.7 4.8 4.9

Milk, kg • hd-1 • d-1 32.4 38.7* 38.8*

Fat corr. milk, kg • d-1 42.1 43.5 42.8

kg CH4 / kg Fat corr. milk 105.0 112.9 92.4

*P < 0.05

Pasture measurements

Requirements

– Tractable cows

– Small area

– Low background methane

– Reasonably constant wind or breeze

Pasture measurements

Relatively docile animals

Accustomed to people normal behavior

Adequate paddock size - intake limitations

Area chosen without large upwind CH4 or SF6 sources

Wind direction carefully monitored – sampling plume

Sampling sites – downwind in plume for mixing

Constraints for grazing cattle

Animals accustomed to sampling area and people around during sampling.

Apparatus constructed and validated

Release rate calculated and flow meters calibrated

Air flow patterns measured: direction, wind speed, duration

Procedure: Pasture measurements

Ambient conditions measured

SF6 release lines set up on upwind side

Collection canisters set up downwind

Ambient canister set upwind

Cows and calves moved into sampling area

SF6 release begun

After 10 min, sampling canisters opened

Pasture measurements

Diet: Mixed grass pasture

Animals: 12 cow-calf pairs

Area: 56 m2

Release rate of SF6: 6 g/min

Sampling duration: 2 - 3 hr

Procedure: Pasture measurements

Sampling canister [SF6] ppt [CH4] ppm Ratio

5 163.7 1.81 199.6

6 282.2 1.82 128.3

18 418.5 1.85 174.8

23 353.6 2.04 768.9

9 76.4 1.80 355.5

Ambient 15.5 1.78

8.2 g/hd/d

Pasture sampling results

Sampling canister [SF6] ppt [CH4] ppm Ratio

5 45.1 1.84 -310.6

6 31.4 1.79 -38.2

9 7.5 1.72 339.5

18 12.2 2.17 -1975.5

24 15.7 1.76 121.1

11 14.5 1.77 61.8

23 18.7 1.82 -168.2

Ambient 207.1 1.79

4.1 g/hd/d

Pasture measurements: Change in wind direction and speed

Technique Accuracy Cost for set-up*

Chamber (2) +++ $34,000

Hood/head box ++ $3000 – 5000

Face mask + $3500

Tracer ++ $8000

Evaluation of techniques for individual animals

*USD without gas analyzers @ $20,000 – 30,000, plus labor

Train animals to the chambers—must stand and lie down normally; must eat and drink normally

Train people to operate

Standardization: recovery of CO2 and CH4 to evaluate the chambers leakiness

Standardization of the analyzers

Diet/feed intake planning and sampling

Fecal and urine removal and sampling

Design of Experiments

If there are 24h measurements and dietary intake data and composition then you have CH4 emissions/unit GEI

If you have short term measurements, extrapolation should be done carefully

Results from calorimetry

Requires CH4 concentration measurements

– Gas chromatography, infrared spectroscopy, Fourier transform infrared radiation, tunable laser diodes

Can measure a flux (gm/min/area or gm/area)

Can measure a concentration & use models to calculate a flux

Many different types are available

– Mass flow meters and sonic anemometers

– Markers

Micrometerological Methods

Advantages

– Can measure normal conditions

– Highly precise and accurate

Disadvantages

– Require atmospheric conditions to be appropriate

– Expensive instrumentation

– Need high level of training

– Some assumptions of surface roughness

Micrometerological Methods

Field experiment data

Module 3- An overview of SF6

Technique for Measuring Methane Emissions from Cattle

Individual Animals/Room/Pasture measurements

To develop an accurate technique by which CH4 emissions can be measured while the animal is in a production environment

Objective

Known, constant release rate

Complete mixing of tracer and CH4 prior to collection

Ambient collection of exhaled breath

Sensitive detection of tracer and CH4

No impact on fermentation

Requirements of tracer methodology

MW: 146.07

Solubility in water: 0.004% (by wt)

Characteristics: colorless, odorless, non-toxic inert gas

Detectable at 1 ppt

Uses: electrical insulation, lung ventilation rates, atmospheric tracer to measure trace gas fluxes

Sulfur hexafluoride (SF6)

SF6 bolus with known release rate inserted into rumen

Permeation tubes

No effect on:

– pH

– Dilution rate

– Microbial activity

– VFA concentrations

Effect of SF6 on ruminal fermentation

Methane emissions with and without SF6

Collection canister evacuated with vacuum pump

Halter fitted to cow

Canister placed on cow; connected to halter

Canister valve opened; collection begins

Procedure (cont.)

At end of time, valve closed, canister removed, new one added

Canister final pressure read on pressure gauge (@ ½ ATM)

Canister pressurized with N2 to 1.5 X ATM

Procedure (cont.)

Gases analyzed on GC [CH4] and [SF6]

Emissions calculated:

Procedure (cont.)

Example Methane Standard and Sample Chromatograph

Example SF6 chromatograph

Calculation

QCH4 = QSF6 X [CH4]

[SF6]

Where:QCH4 = methane emission rateQSF6 = known tracer release rate[CH4] = methane concentration in collection canister[SF6] = tracer gas concentration in collection canister

Individual animal

– Halter constructed with filter, calibrated capillary tube

Sample collection

CH4 Emission Rate (L/hr)

Tracer Chamber

11.53 ± 0.41 12.36 ± 0.33

SF6 Tracer vs. Chamber Measurements

CH4 production measured by two sampling techniques

Trait Calorimetry SF6 tracer gas P-value

CH4 (L d-1) 130 ± 4.0 137 ± 4.0 0.24

CO2 (L d-1) 1892 ± 74.0a 2354 ± 74.0b < 0.01

CH4 (%GEI) 6.3 ± 0.2 6.6 ± 0.2 0.23

abP < .05

Source- Boadi et al., 2001

CH4 production of heifers

Comparison of mass balance to SF6 tracer

Animals – diets Among Cows Within Cows

Dairy cows – corn silage 31.0 7.2

Dairy cows – alfalfa silage 16.2 8.5

Beef cows – hay 7.2 4.3

Beef heifers – hay 10.0 6.4

Beef cows – 90% hay 9.8 10.7

Cows – pasture 17.9 6.5

Cows – pasture 5.5 5.2

Steers – 85% grain 24.6 21.3

Steers – 40% grain 31.8 6.8

Variability in individual animal studies

Animals measured in “normal conditions”, not restrained

Less expensive than chamber measurements

Simple to use

Tracer is inert and not active

Tracer method- Advantages

Some CH4 that escapes absorption in colon not measured

Withholding time and milk restrictions – bolus removal

Some training required

Some animals will not adapt

Tracer method-Disadvantages

60 d: Permeation tube constructed; halter and canisters constructed

30 d: Diets assigned to animals and diets changed for rumen adaptation

3 d: Permeation tube inserted into rumen

0 d: Experiment begins

Procedure: Individual animals

Individual animals

Time Activity

0600 Canisters evacuated

0700 Cows haltered

1900 Canisters removed, new canisters added

Final pressure measured

Canisters pressurized with N2

SF6/CH4 analysis

Method ParameterCompleteness

%Precision

%Accuracy

%

FID/GC CH4 concentration 100 ± 2 ± 10

EC/GC SF6 concentration 100 ± 2 ± 10

Gravimetric SF6 release rate 100 ± 0.2 ± 1

Makkar & Vercoe, 2007

QA data objectives

Prevent or decrease ruminal CH4 production

No adaptation by methanogens

Increase productivity

Cost effective

Feeding program to reduce CH4 production

Module 4- Greenfeed

A portable “baiting” station that measures real-time carbon dioxide (CO2), and methane (CH4) mass fluxes from a herd/flock of animals, several times per day.

It communicates real-time over the internet, anywhere in the world to authorized users, allowing them:

To review system performance

To control operation parameters

To review results

What is greenfeed?

GreenFeed systems– Have logged about 500,000 hours of run time (60 years).

– 14 countries, on 5 continents

– Are run continuously

– Have been used with almost every type of cattle, pasture and confinement

– 70 by the end of 2014

GreenFeed is being used in production environments– GreenFeed is measuring about 300-500 animals per day

Current Research– Diet and emissions (TMR, pasture quality, etc.)

– Evaluation of animal genetics and emissions

– Animal efficiency studies

– Evaluation of environmental stresses on methane production (heat stress, lactation cycle, animal health issues)

GreenFeed Applications

CH4 emissions, for a specific cow, increase and decrease over the day according to food intake…

Importance of CH4 Emissions from Cattle

Continuous Methane Emissions from a Cow Over 2 Days

General layout of the greenfeed system

CO2 is used as the key indicator to determine if a local animal is influencing the feeder concentration measurements

– If CO2 levels are not changing fast = background measurement

– If CO2 levels are changing fast = not a background

– All other constituents (i.e. CH4) start and stop background/not background are determined from the CO2 sensor feedback.

The key assumptions:– Background concentrations don’t change unpredictably during a visit

– Background during a visit is determined by using the “just before” and “just after” visit concentrations

Concentration measurements

Calculated baseline examples

Why?

– Animals are allowed to freely move in and out of GreenFeed

– Capture rates of gas into GreenFeed change as the animal’s nose moves in and out of GreenFeed

– Consider the periods when the animal’s nose is close to the manifold for flux quantification.

– Use sensor data to determine the proper periods

Data Filtering

One feeding period and low head movement

Head position and emissions

Head position and emissions

One feeding period and significant head movement

Filtering, feed period example

Beef cows on pasture

Comparison of GF with SF6

Zimmerman et al., 2013

Chamber/GreenFeed hourly emissions herd comparison, restricted intake

Source- Waghorn et al. 2011

Black is chambers and white is greenfeed

Dairy Heifers- forage based diet

Source: Hammond et al., 2011

Source- Huhtanen et al., 2013

ItemDMI1

(g/d)Milk

(kg/d)CH4

(g/d)CH4/DMI (g/kg)

CH4/ECM2 (g/kg)

CO2

(g/d)CO2/DMI (g/kg)

CO2/ECM(g/kg)

CH4/CO2

(g/kg)

Exp. 1

Mean 20.0 26.3 455 23.0 16.7 11381 575 418 40.0

CV3 (%) 12.1 19.0 10.8 9.8 15.0 8.1 9.4 16.1 5.7

Repeatability 0.78 0.91 0.64 0.78 0.84 0.80 0.77 0.84 0.34

Exp.2

Mean 21.3 27.6 453 21.4 15.4 12337 585 418 36.7

CV (%) 14.9 18.6 12.4 14.2 26.1 11.1 14.3 24.2 6.6

Repeatability 0.87 0.88 0.81 0.77 0.80 0.83 0.77 0.77 0.90

1DMI = dry matter intake;2ECM = energy corrected milk; 3CV = coefficient of variation

Greenfeed emissions- variability

Feed samples analyzed for gross energy (GE)

– Bomb calorimetry

Total GE intake/day

CH4 g/visit determined from CH4 flux

– C-LockTM algorithm

Ym = CH4/GE intake %

Calculating Ym

Ym results

Source: www.guwsmedical.info

Source: www.dicyt.com

In-vitro studies- Rusitec

Buffer

Ruminal fluid from fistulated cow

Feedstuffs

CO2/N2

Gas pressure meter—determine total gas pressure

Gas chromatograph—FID or TCD detector

Requirements for in vitro studies

Control of fermentation and sampling

Lots of evaluations can take place

Relatively simple

Sampling over time is possible

Digestibility (in vitro) is possible

Screening of feedstuffs

Rate or total is given /gm feedstuff

In vitro studies - Advantages

Not “real” conditions with chewing

Rate of passage (biased high)

No feedback on gas head pressure

Handling of ruminal fluid critical

VFA production, etc. not “normal”

Can be difficult to scale up

In vitro studies - Disadvantages

Module 5- Models and equations to predict CH4

Reference Inputs

IPCC (2006) # hd, species, Ym

Kriss (1930) DMI

Axelsson (1949) DMI

Bratzler & Forbes (1940) Digested CHO

Mills et al. (2003) MEI, Starch, ADFI

Blaxter & Clapperton (1965) %DE, GEI, X MEm

Moe & Tyrrell (1979) Dig soluble CHO, Cellulose, Hemicellulose

Holter & Young (1992) Dig soluble CHO, Cellulose, Hemicellulose

Yan et al. (2009) Silage

Ellis et al. (2007) MEI, ADF, lignin

Models/Equations to predict CH4

Reference Inputs

Ellis et al. (2009) MEI, Cellulose, Hemicellulose, Fat, NDF,DMI

Mills et al. (2001) DMI

Holos, Little et al., 2008 Based on IPCC 2006

CNCPS (2010) Mills et al., 2003

IFSM (Rotz et al., 2011) Mits3

Phetteplace et al. (2001) Various

Kebreab et al. (2004, 2009) DMI, NDF, starch, N etc

COWPOLL DMI, NDF, starch, N etc

MOLLY (Baldwin, 1995) Similar to COWPOLL

Models/Prediction equations cont.

Some larger scale models contain prediction equations that may make them more or less credible

Models are validated for the diets used

Care in use of them across other diets

Models associated with tropical plants and diets are a critically needed area of research

Models: Concerns

Tier 1 – requires data on livestock species and categories & annual population data. Default emissions factors are used for each group identified. Some knowledge of days alive is required (if an animal is only alive for part of a year) and productivity is needed.

Tier 2 – requires definitions for subcategories as well as feed intake estimates for the subcategories. Thus the calculation of the emission factor is more specific. Default values are used for populations for which there is little data. Feedstocks need to be well defined and research with local animals is required to determine a methane conversion (Ym) factor.

IPCC Tier 1 and Tier 2

Tier 3 – complex method that requires a model to predict emissions from highly defined and categorized populations. The model would take into account all of the factors associated with enteric emissions and generate emissions. IPCC suggests peer review of the model prior to adoption.

Hybrids – use parts of each tier to be most effective.

Tier 3 and Hybrids

Kristen Johnson – Professor, Washington State University

Email: johnsoka@wsu.edu

Contact Information