Improvement and development of emission factor for methane emissions from enteric fermentation Regional workshop Bogor, Indonesia: December 7-11, 2015
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: [email protected]
Contact Information