reducing greenhouse gas contribution from ruminant · pdf filereducing greenhouse gas...
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Lethbridge Research Centre, Lethbridge, Alberta, Canada
Reducing Greenhouse Gas Contribution from
Ruminant LivestockKaren Beauchemin and Sean McGinn
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Outline• Contribution of livestock to GHG emissions• Measuring emissions• Potential for mitigation• “Holo”-istic approach – whole farm modeling• Future perspectives
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GHG emmisions - Global issue receiving a lot of attention
Animal scientists need to be aware of this issue
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http://www.fas.org/sgp/crs/row/RL34659.pdf
2% of the global GHG emissions
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http://www.fas.org/sgp/crs/row/RL34659.pdf
& Canada
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Proportion of total GHG for the country
Proportion of agricultural GHG by source
0.00.20.40.60.81.0
SWEDENCANADA
0.00.10.20.30.40.50.6
SWEDENCANADA
CDN GHG 6x higher than SW
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Livestock's Long Shadow Report, FAO 2006• “Livestock is one of the
top three most significant contributors to environmental problems.”
• “Livestock and their manure account for 18% of global GHG emissions”
• “Ruminants worst offenders because of enteric methane.”
Livestock sector is under increasing pressure to be more environmentally sustainable
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Animal production contributes to GHG emissions
(8-18% of all GHG emissions)
Manure Feed production
Feed digestion by ruminants
Land use
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Livestock contribution to GHG• FAO Long Shadow report (2006)
– 18% of global emissions– Included production of feed to animals, production of inputs,
transport and processing, deforestation, changes in land use
2.8
11.2
1.7
4.34.94.1
0
1
2
3
4
5
6
7
8
9
10
Denman etal (2007)
US EPA(2006)
FAO (2006)
Bill
ion
tonn
es o
f CO
2 eq IPCC other Agric
IPCC CO2IPCC animal
8-12% of global GHG emissions
Figure from F. O’Mara, 2010 GGAA
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Global agricultural emissions (2005)(IPCC sources)
38%
32%
12%
11%7%
N2O soilsCH4 entericBiomass burningRice mgmtManure mgmt
Source: US EPA (2006)
5-7 Billion tonnes CO2eq/year
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Methane production is a microbially driven process
CH4
CO2
Methanogens
protozoa
H2
H2
Feedbacteria
fungi
VFA
Methanogenesis4H2 + 2CO2 CH4 + 2H2O
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Production of methane in the rumen
Hydrogen
volatile fatty acids
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Methane formation in the rumen
– 2 to 12% of the gross energy consumed is converted to methane (Ym, methane conversion factor)
– 6 - 8% forage based diets– 3 - 4% feedlot finishing cattle
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Methane emission and dry matter intake
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350
400
450
500
0 5 10 15 20 25 30
CH
4-C
ham
ber,
g/d
Dry matter intake, kg/d
CanadianAustralian
dairy cows
beef cattle
Grainger et al. 2007. J. Dairy Sci.
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Enteric methane emissions by region
460
280 259218
175 160 13697 88
28 27.3
0
100
200
300
400
500
Latin
America
Africa
China
India
Rest o
f Asia
Western
Europe
North A
merica
Non E
U FSU
Aust/N
Z
Easter
n Euro
pe
Middle
East
mill
ion
t CO
2 eq
As summarized by F. O’Mara, 2010 GGAA
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A Research Approach
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Outline• Contribution of livestock to GHG emissions• Measuring emissions• Potential for mitigation• “Holo”-istic approach – whole farm modeling• Future perspectives
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Types of Emission Measurements
Chambers
Tracers
Dispersion(methane and ammonia)
pen
Farm / Feedlot
pasture
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Measuring using SF6 tracer method
gastracermethane ionConcentratEmission
ionConcentratEmission
=
Controlled release of trace gas in the rumen
(Johnson et al. 1994)
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Caution … SF6 tracer methodMeasures individual animal emissions …
• need to know within- and between-animal variability to understand treatment effects
• requires knowing feed intake for individual animals
• variability high (10-18%)
Can be used for grazing … but• measuring feed intake is difficult
Problem with gas leakage fromcannulated cattle
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Alternative tracer methodIndividual animals, Dr. David Griffith University of Wollongong, Australia
Tracer release inside a barn with downwind measurements
gastracermethane ionConcentratEmission
ionConcentratEmission
=
Concentrations measured downwind
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Methane Emissions using Animal Chambers (the gold standard)
Oils
DDG
Oil seeds
Acids
Ionophores
Grain/Forage
Essential Oils
Yeasts
Forage type
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SF6 Tracer vs Chamber Emissions
Grainger et al. (2007)
McGinn et al. (2007)
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Methane Emissions from Animal Chambers - doubling emission following feeding
0
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9:00
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hane
flux
(g/d
ay/c
ham
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Note- 2 animals in each chamber
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Lethbridge animal chambers
Fresh air intake
Recirculator fans
Exhaust Fresh Air Intake
TemperatureControl
CH4 and CO263 m3 at 9.3 m3/minCirculation ~ 90 m3/minTime constant 5.8 min
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Aarhus University (Denmark)Anne Louise Frydendahl Hellwing.
Size 1.85 x 3.9 x 2.45 = 17.5 m3
Measure CO2,CH4 and O2Flow through 0.9-1.2 m3/minTime constant 14-18 min
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Wind
C (g/m3)
Modeling the gas plume dispersion
Dispersion model relates concentration C to emission rate E … measure C and the model can then infer E
E (g s-1)
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Sensors
Concentration using open-path lasers and FTIR (7 gases includes N2O)
Mixing capacity of air
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laser
laser reflector
reflector
Example at a Dairy Farm
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Dairy Farm Methane Emissions
-----------FARM -----------
Methane (g/cow d) 405 442 448--------------------------------------
Methane Emissions (g/cow/d)
IPCC Tier I 323 (lactating cow)
New Zealand 330 (lactating cow)
Canada 400 (as high as 491)
German 300 (non-lactating cow)
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Example at a Alberta Beef Feedlot • 22,500 cattle• Equipment set up in middle
laser
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Pattern of methane emissions - feedlot
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Met
hane
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issi
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d-1 d
-1)
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Beef Cattle Ammonia Emissions
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Hour
Am
mon
ia E
mis
sion
(ug
m-2
s-1
)
2591 kg N /day 63% N in ration
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Lethbridge Isolated Pens – using GPS collars
SF6 Tracer Methodwith GPS on collar
Laser Concentration
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Precision of Dispersion Model for Dietary Trts
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bLS
Trac
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ispe
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H 4 a
nim
al-1
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)
Control Diet
DDG ‘oil’ Diet
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CSIRO Australia – Methane Pasture StudyA B GRAZING CATTLE
ARE A LARGE % OF THE GLOBAL METHANE EMISSIONS
- Mitigation ?
141 ± 147 g / (animal d)
(McGinn et al. 2011)
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Dispersion of Methane from Grazing Cattle
wind
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Rangeland Study at Onefour Station
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Final point
Team approach
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Outline• Contribution of livestock to GHG emissions• Measuring emissions• Potential for mitigation• “Holo”-istic approach – whole farm modeling• Future perspectives
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How can beef and dairy industries reduce methane emissions?
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Mitigating Methane from Ruminants• What is the metric?
– g CH4 / head / day (amount)– g CH4 / kg dry matter intake (intensity)– g CH4 / L energy corrected milk (intensity)– g CH4 / ha (intensity)
efficiency
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Methane Mitigation ApproachesReduce CH4formation in the rumen
Increase animal
efficiency
Increase animal
productivity
Feed intake = ??
Animal product = ?? =
Methaneg / head / day
g / kg milk or meat
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Dietary Strategies
Rumen Manipulation
Animal Management Strategies
Animal Breeding
Technologies to Reduce Enteric Methane
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Animal Breeding1. High vs low methane emitters
• 15% variability amongst animals in CH4 / kg DMI (NZ)• Realistic given other breeding objectives?
2. Increased feed conversion efficiency• Animals that eat less (low RFI)• Approach used for beef cattle (CA, AU)
– Higher efficiency cattle produced 24% less methane/kg gain than less efficient cattle (Hegarty et al. 2007. JAS 85:1479)
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Rumen Manipulation• Alter rumen microbial ecology to reduce methane production in the
rumen– Vaccine: animals develop antibodies to methanogens (AU, NZ)
• Experimental, success unknown (15% reduction?)– Acetogens (bacteria that use H2 to form acetate)
• Exist in the rumen at low numbers• Can we promote them in the rumen? • Exploratory, success unknown
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Animal Management Strategies • Reduce inefficiencies• Increase reproductive performance
– Reduce number of open (non-pregnant) cows– Reduce number of replacement heifers (heifers contribute to
emissions (20-30%), but not to product)– Increase longevity of mature cows
• Reduce number of days to market• Others
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Dietary Strategies1. Increase animal productivity (higher inputs)
lowers CH4/kg meat or milk
0.0
5.0
10.0
15.0
20.0
25.0
Africa
Latin
America
North A
merica
Non E
U FSU
Easter
n Euro
pe
Western
Europe
India
China
Aust/N
ZAsia
Middle
East
perc
enta
ge
meat + milk energy
Enteric methane
% of Global
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Dietary Strategies
2. Decrease methane formation in the rumen- Diet manipulation and additives- Decreases CH4/cow/day
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I. Adding fat to the diet
% crude fatWhole cottonseedWhole sunflower seedsCrushed canola seedsCrushed flaxseedTallow and animal fatsSunflower oilCorn and wheat distillers dried grains
23414040
10010015
Fats used in studies at Lethbridge
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Adding ground oilseeds (3% added fat) to the diet of lactating dairy cows (Beauchemin et al., 2009. J. Dairy Sci. 92: 2118)
Methane Contol Sunflower Linseed CanolaDMI, kg/d 18.7 c 19.5 ab 19.0 bc 20.1 a
OM digestibility, % 63.5 a 52.0 c 58.1b 64.3 a
3.5% FCM 24.2 23.3 23.5 23.7CH4, g/d 293 a 264 b 241 b 265 b
CH4, g/kg DMI 16.3 a 14.6 ab 13.4 b 13.7 b
CH4, % GEI 4.9 a 4.3 ab 3.9 b 4.0 b
6% reduction/1% added fat
Control was a rumen inert Ca-soap
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Meta-analysis – diets containing up to 80 g fat/kg DM (n=76)
Grainger and Beauchemin, AFST 2011
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Meta-analysis – adding lipids to dietData for cattle (n = 59)
• Increase fat in diet – From 3 to 4% DM (4.7% ↓ in CH4 yield)– From 3 to 6% DM (14% ↓ in CH4 yield)
• No effect of – Form of fat (oil vs oilseeds)– Type of fatty acid(C12+C14, C18:1, C18:2, C18:3)– No effect of ingredient source (canola, coconut, linseed,
soybean, sunflower)– Not enough data to conclude
• Future research
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II. Feeding more grain (starch vs fiber)
• Feeding grain (starch) decreases methane ( /d and /kg product)
• Need to conduct a life cycle assessment to examine entire farm GHG budget• Very much depends of the local agriculture
• Not consistent with niche role of ruminants of converting high fiber feeds to meat/milk
grain dietforage diet
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III. Forages
• Grain-based forages vs grasses– e.g., corn silage; small grain silage– Higher starch content decreases g CH4 / kg DMI– Often improves animal productivity, decreases CH4 / kg meat (milk)– Higher cropping inputs (need LCA)
• Legumes vs grasses– Forage legumes produce less methane – Legumes have lower fibre content, faster rate of passage from the rumen
• Improving forage quality– Complicated!!!! – Harvesting at early maturity
• Increases CH4 g/d because animals eat more• Higher intake decreases CH4 g/ kg DMI • Higher digestibility increases CH4 g/ kg DMI
– Improves animal productivity, decreases CH4/kg meat (milk)
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• Tannins (bind protein)• Tannin-containing forages (e.g., sainfoin)• Extracts
• quebracho trees, black wattle trees (A. mearnsii)
• Saponins (defaunation agents)• Yucca extracts (CA)
• Essential oils (anti-microbials)• Garlic
• Bacterial direct fed microbials• Promote propionate
• Feed enzymes • Yeast
IV. Feed Additives (experimental)
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Enteric Methane Mitigation: What is feasible?
Reduction Strategy
10% Improved feed conversion efficiency through animal breeding
10% Management: reduced replacements, better reproductive performance
10% Increased animal productivity through nutrition
20% Reduced methane formation in the rumen through diet (ex. oils, grain, forage source, additives)
50% Additivity ?
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Outline• Contribution of livestock to GHG emissions• Measuring emissions• Potential for mitigation• “Holo”-istic approach – whole farm modeling• Future perspectives
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Country and Production Specific Mitigation Strategies
Tailored approach for each farm
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Importance of GHG models
Process/component
GlobalNational/regional
Whole-farm
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Farm Based GHG Emissions
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NRC Nutrient Requirements
Canadian methodology for N2O
estimation
Canadian methodology for soil C estimationBasic structure
(IPCC Tier 2)
Total CO2eq
Yearly time-step
Holos – A tool to estimate and reduce GHGs from farms
Canadian methodology for enteric CH4 estimation
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Life Cycle Assessment of Beef Production in Western Canada (Beauchemin et al. 2010. Agric. Syst.)
100 Beef cow herd
Progeny finished in feedlot
•8 year cycle
•All feed produced on the farm
•GHG emissions / kg of beef produced
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Mitigation of GHG for Beef Prodiction (LCA approach)
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Outline• Contribution of livestock to GHG emissions• Measuring emissions• Potential for mitigation• “Holo”-istic approach – whole farm modeling• Future perspectives
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Projected growth of global milk and meat production
100
120
140
160
180
200
220
2000
2006
2012
2018
2024
2030
2036
2042
2048
prod
uctio
n (2
000
= 10
0)
Bovine meat
Ovine meat
Milk
FAO (2006) as presented by F. O’Mara, 2010 GGAA
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Future Perspectives for Livestock
“The Earth’s vital signs are in a danger zone: human population and consumption stretch the capacity of the planet …”
Bernard 2010 p25
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GHG Emissions Will Increase with Increasing Demand for Food
• Decreasing total GHG emissions from agriculture not likely
• Need to consider GHG efficiency • “GHG viewed as investment” (H. Janzen, GGAA2010)
efficiency = kg meat/milktonne of CO2e
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Do we have GHG models and technology for the developing world?
Livestock production by region 2007
0
50
100
150
200
250
300
350
400
Pigmeat
Poultrymeat
Cattlemeat
Sheep& Goatmeat
Milk Eggs
Mill
ion
tonn
es
DevelopedcountriesDevelopingcountries
FAO 2009,Livestock in Balance
The total demand for animal products in developing countries is expected to more than double by 2030.
Bruinsma 2003
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GHGs are only one measure of environmental impact
• Need to expand our models to include other ecosystem services.
Ecosystem services:• Food• Fuel• Nutrient cycling• Livelihood• Habitat• ‘Wild places’• Water/air filtering• Aesthetics• Draught • Etc…
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Ruminant Livestock:Stressors or benefactors?
Facet Detrimental Favorable
Greenhouse gases
Source of CH4, N2O
Grassland C sink
Food Compete for grain
Food from inedible grass
Nutrients Pollutant source
Recycling agent
Biodiversity Land use change
Grassland preservation
Social aspects Nuisance Aesthetic appeal
From H. Janzen, GGAA2010
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•Food from fiber
Connecting Livestock to their Habitat
photo: Walter Willms
“Grasslands comprise approximately 40% of the earth's land area (excluding areas of permanent ice cover)”
Wang and Fang 2009
•Maintain biodiversity
•Carbon sequestration in grasslands
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Take Home Message
• Agriculture is responsible for 8 – 12% of GHG emissions
• Regionally, livestock emissions broadly related to numbers and production practices
• Population growth will drive up GHGs from agriculture
• Need to consider efficiency• Need to look beyond GHGs (otherwise we can
make the wrong decisions in terms of our grasslands)
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Take Home Message
• Need integrated teams (livestock, crop, soils, measurement, ecologists, economists, etc) to make a difference
• Need to involve developing countries• Need local data - solutions are place dependant• Better use of models to integrate the information and
allow testing of “what-if scenarios”
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Questions?