mitigation strategies versus adaptation strategies · asggn workshop @ wcgalp 2014 – august 17,...
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ASGGN Workshop @ WCGALP 2014 – August 17, 2014 1
Mitigation Strategies versus Adaptation Strategies
N. GenglerUniversity of Liège, Gembloux Agro-Bio Tech, Belgium
ASGGN Workshop @ WCGALP 2014 – August 17, 2014 2
Mitigation Strategies versus Adaptation Strategies
(OR dairy cows and climate is more than only about methane)
N. GenglerUniversity of Liège, Gembloux Agro-Bio Tech, Belgium
ASGGN Workshop @ WCGALP 2014 – August 17, 2014 3
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Mitigation and Adaptation
IPCC TAR 2001 WG2 after Smit et al., 1999 (Mitigation and Adaptation Strategies for Global Change 4: 199-213)
CLIMATE CHANGEIncluding Variability
Policy Responses
Human interference
MITIGATIONof Climate Change via
GHG Sources and Sinks
Planned ADAPTATIONto the Impacts and
Vulnerabilities
Exposure
Initial Impactsor Effects
AutonomousAdaptations
Residual or NetImpacts
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Dairy cows Climate change
Context
• Mitigation and Adaptation– Doing management choices– But also breeding: permanent and cumulative !
e.g., heat stress
e.g., methane emissions
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Breeding for Mitigation and Adaptation
Modified from IPCC TAR 2001 WG2 after Smit et al., 1999 (Mitigation and Adaptation Strategies for Global Change 4: 199-213)
CLIMATE CHANGEIncluding Variability
Climate smart breeding objective
Human interference
MITIGATIONReducing GHG emissions of
animals by breeding
ADAPTATIONIncreasing genetic resilience of animals to climate change
Exposure
Initial Impactsor Effects
AutonomousAdaptations
Residual or NetImpacts
ASGGN Workshop @ WCGALP 2014 – August 17, 2014 6
MITIGATION
Reducing GHG emissions ofanimals by breeding
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Context
q Selecting for reduced GHG emissionq Two conditionsØ Available dataØ Exploitable genetic variation
Both conditions remain even usingGenomics
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Focus on CH4
q Currently many efforts to develop and use large-scale methane measurement toolsØ Major objective in METHAGENE COST Action
q Needed steps:1. harmonize large-scale methane measurements
using different techniques2. develop easy and inexpensive proxies for methane
emissions3. develop approaches for incorporating methane
emissions into national breeding strategies
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Easy and Inexpensive Proxies for CH4
q Several possibilities hereqWill focus in this presentation onØ Use of milk composition described by
mid-infrared (MIR) spectral data
Current research effort in Gembloux(CRA-W and ULg-GxABT) and
several external collaborations
ASGGN Workshop @ WCGALP 2014 – August 17, 2014 10
Hélène SoyeurtPurna KandelMarie-Laure VanrobaysSylvie Vanderick
Eva LewisFrank BuckleyMathew H. DeightonSinead McParlandDonagh Berry
Amélie VanlierdeFréderic DeharengEric FroidmontPierre Dardenne
Ongoing Collaborations for CH4
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Milk constituants
Ruminalfermentation of
fiber
95% CH4
PropionicAcetic Butyric
Context
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MIR milkspectra
Equation
EntericCH4
Basic Concept
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Basic Concept
MIR milkspectra
Equation
EntericCH4
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MIR milkspectra
EquationEntericCH4
Previous Work
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Milk Fatty Acids Change With Lactation Stage
q Changes due to the equilibrium: mobilization ó intakeDIM = days in milk
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Milk Fatty Acids Change With Lactation Stage
q Changes due to the equilibrium: mobilization ó intake
Hypothesis:Relationship between MIR spectra and CH4
affected by lactation stage
DIM = days in milk
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MIR milkspectra
EquationEntericCH4
Linear and QuadraticLegendre Polynomial
functions of DIM
DIM
Inclusion of Lactation Stage (DIM) in Methane Equation
DIM = days in milk
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Calibration
q SF6 data from BEL and IRLØ Breeds: HOL, JER, HOL x JERØ 446 recordsØ Feeding systems: TMR or grass-based
q Cross-validation R2: 0.67Ø Value slightly lower then previous studies
But two findings show (next slides)èpotentially appropriate strategy
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Application on Walloon Spectral Database
300
350
400
450
500
0 50 100 150 200 250 300 350
CH4(
g/d)
Days in milk
Novel lactation stage aware equation Garnworthy et al., 2012
MIR proxy for CH4 follows expectedevolution (similar to DMI) throughout the lactation
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Shows potential for other future collaborations
First Completely External Validation
q Validation data (X) of completely different origin, different:Ø Measurement method (chambers)Ø Feeding system (hay based)Ø Breed (Brown-Swiss)
q With novel lactation stage aware equation correlation of 0.48Ø When adding this new data to calibration correlation up to 0.70
New collaborationwith Qualitas AG + ETH Zurich (CHE)
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Useful for harmonization of measurements?
First Completely External Validation
q Validation data (X) of completely different origin, different:Ø Measurement method (chambers)Ø Feeding system (hay based)Ø Breed (Brown-Swiss)
q With novel lactation stage aware equation correlation of 0.48Ø When adding this new data to calibration correlation up to 0.70
New collaborationwith Qualitas AG + ETH Zurich (CHE)
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IMPA
CTS
VULN
ERAB
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IMPA
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VULN
ERAB
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Breeding for Mitigation and Adaptation
Modified from IPCC TAR 2001 WG2 after Smit et al., 1999 (Mitigation and Adaptation Strategies for Global Change 4: 199-213)
CLIMATE CHANGEIncluding Variability
Climate smart breeding objective
Human interference
MITIGATIONReducing GHG emissions of
animals by breeding
ADAPTATIONIncreasing genetic resilience of animals to climate change
Exposure
Initial Impactsor Effects
AutonomousAdaptations
Residual or NetImpacts
ASGGN Workshop @ WCGALP 2014 – August 17, 2014 23
ADAPTATION
Increasing genetic resilience of animals to climate change
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Context
q Selecting for increased resilience of animals (here focus dairy cows) to climate change
q However important question:Ø How to assess before climate change happens!
q Traditional solution:Ø Use of historical weather data and extreme
weather events as proxies of climate change
Extension of heat tolerance research
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Hedi HammamiMarie-Laure VanrobaysCatherine BastinSylvie VanderickBernard TychonJulien Minet
Contributions
q This part reports research done in the context of FACCE-JPI and supported by different people
q In collaboration with CRA-W, AWE in Belgiumq Collaboration was initiated with different other groups
Ø DLO (NLD), INIA (ESP), SRUC (SCO), UGA (USA), UNI-KS (GER), KIS (SLO), …
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Heat Tolerance Research
q Assessing variability of studied phenotypes following gradient of “heat stressors” (HS)Ø Mostly done using reaction norm approachØ In practice random regression models
q Heat stress assessed using: Ø Temperature – Humidity – Index (THI)
THI = (1.8 × Tdb + 32 ) - [(0.55 - 0.0055 × RH) × (1.8 × Tdb - 26)]
where Tbd = Dry Bulb Temperature (°C) & RH = Relative Humidity (%)
(NRC, 1981)
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Genetic Differences across THI Ranges
q Genetic differences when traits affected by head stress?
q Random regression è genetic correlationsØ High correlations è traits less affectedØ Low correlations è traits more affected
q Following slides some resultsØ Results from different studies by my coworkers
Hedi Hammami (Post-Doc) and Marie-Laure Vanrobays (PhD student)
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0,5
0,55
0,6
0,65
0,7
0,75
0,8
0,85
0,9
0,95
1
26 30 34 38 42 46 50 54 58 62 66 70 74
Cor
rela
tion
THI
SCS Fat Prot Milk Prot% Fat%
THI
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And for CH4? (results based on MIR proxy)
0.50
0.60
0.70
0.80
0.90
1.00
18 28 38 48 58 68
Corr
elat
ion
THI
Milk yield MIR methane (kg/day) MIR methane (g/kg of milk)
Indicator of HS impact on intake?
ASGGN Workshop @ WCGALP 2014 – August 17, 2014 301164nd EAAP Annual Meeting , Nantes, France, August 26th - 30sh 2013
Indicator of More Mobilization Under HS?
q Is it possible to catch milk indicators of more mobilization (to cover intake reduction)?
q Nowadays possible to study!Ø Because large scale studies of MIR predicted
fatty acids content in milk feasible
q Following slide give some first results,please notice C18:1 cis-9 (oleic acid)Ø Indicator of body fat mobilization
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0,5
0,55
0,6
0,65
0,7
0,75
0,8
0,85
0,9
0,95
1
26 30 34 38 42 46 50 54 58 62 66 70 74
Cor
rela
tion
THI
C18:1 cis-9 C4:0 C18:0 C10:0 C12:0 C8:0 C6:0 C14:0 C16:0 C17:0
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Detection of Heat Stress in Milk?
q Results obtained until now showed:Ø Milk components affected by heat stressØ BUT differences in reaction of some key components Ø Some directly linked to the status of the cows
q Therefore:Ø Potential early detection of heat stress in milk
q Next steps:Ø Linking MIR spectral data to heat toleranceØ First indicators promising Ø More work needed
Hope to find indirect indicators of heat stress
ASGGN Workshop @ WCGALP 2014 – August 17, 2014 33
IMPA
CTS
VULN
ERAB
ILIT
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IMPA
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VULN
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Climate Smart Breeding Objective
Modified from IPCC TAR 2001 WG2 after Smit et al., 1999 (Mitigation and Adaptation Strategies for Global Change 4: 199-213)
CLIMATE CHANGEIncluding Variability
Climate smart breeding objective
Human interference
MITIGATIONReducing GHG emissions of
animals by breeding
ADAPTATIONIncreasing genetic resilience of animals to climate change
Exposure
Initial Impactsor Effects
AutonomousAdaptations
Residual or NetImpacts
ASGGN Workshop @ WCGALP 2014 – August 17, 2014 34
Climate Smart Breeding Objective (Index)
q Clear idea about breeding objective traitsØ + economic weights
q Ability to measure the required index traitsØ Cf. first parts of this talk
q Good knowledge of correlationsØ Among involved new traitsØ With existing and currently selected traits
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Consequences of Selection
q Following slides some results fromPurna B. Kandel (PhD Student)Ø Used CH4 intensity (g/kg milk)Ø Normally not optimal trait (ratio trait)Ø But here OK for testing è close to breeding goal
(less methane / unit produced)Ø Estimated correlations between EBV of sires as proxy to
genetic correlationsq If you want to know more details
Ø Please go to his talk on Tuesday 2:30 PM
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Selection Scenarios (relative weights in index)
Production Functionality Type EnvironmentMilk Fat
yieldProtein
yieldSCS Longevity Total
typeCH4
intensityCurrent Walloonscenario -10 9 29 -5 23 24 0
CH4 reductionscenario -7.50 6.75 21.75 -3.75 17.25 18 -25
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Expected Genetic Changes
-25
-15
-5
5
15
25
35
CH4intensity
Milk yield Fat yield Proteinyield
SCS Longevity Fertility BCS Type
Perc
enta
ge C
hang
e in
trai
ts
Base Scenario New ScenarioCurrent Walloon scenario
CH4 reductionscenario
ASGGN Workshop @ WCGALP 2014 – August 17, 2014 38
Conclusions
q Complex relationships betweenmitigation and adaptation traitsØ Clearly more research necessary
q Clear indications functional traits more affected by heat stressØ Presented research + some results by others in
particular I. Misztal’s group q Recent presented research indicateØ Selecting for less CH4 è reduces “robustness”Ø Affecting resilience to climate change?
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Conclusions
q Here presented research showedØ Importance of equilibrium: mobilization ó intake
and its link to Mitigation and Adaptation
q If you want to know more details about the link between fertility and milk composition Ø Please go to the talk of Catherine Bastin on
Thursday 11:00 AM
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Conclusions
q Results show again need to consider getting intake (affected by HS)Ø Context of relation RFI ómethaneØ Trait definition methane yield (g / kg DMI)
q Also indication link intake óMIR spectraØ More on this topic in the talk by Sinead McParland
on Tuesday 11:30 AM
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Acknowledgments
q Travel to ASGGN Workshop supported by
the Walloon “Agriculture – Climate Change” Network inside the European Joint Programming Initiative FACCE-JPI
q Finally again thanks toØ Many, internal and external, collaborators andØ Many involved funding bodies, in particular
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Thank you!
ASGGN Workshop @ WCGALP 2014 – August 17, 2014 43