Enyew Negussie, Luke: Methane emission from Ruminants: what genetics and can do?

© Natural Resources Institute Finland© Natural Resources Institute Finland
Methane emission from
ruminants:
The role of animal genetics
1 13/11/2017
Enyew Negussie
Esa Mäntysaari
Päivi Mäntysaari
Martin Lidauer
Natural Resources Institute Finland (Luke)
Enyew.negussie@luke.fi

© Natural Resources Institute Finland2 13/11/2017Valio-Luke Dairy Methane Seminar - 13.11.2017
Developing animal genetic and nutritional
tools to mitigate the Environmental impact
of dairy production systems
GREENDAIRYObjectives
- Understand the genetic and nutritional basis
- Develop practical & innovative tools
Enyew Negussie, PhD, Luke, Biometrical Genetics,
Responsible Researcher
Anna-Elisa Liinamo, PhD, Luke Biometrical Genetics
Martin Lidauer, PhD, Luke Biometrical Genetics
Esa Mäntysaari, Prof., Luke Biometrical Genetics
Kevin Shingfield, Prof., Luke Animal Production Research
Päivi Mäntysaari, PhD, Luke Animal Production Research
Marketta Rinne, Prof., Luke Animal Production Research
Tomasz Stefanski, MSc., Luke Animal Production Research
Alireza Bayat, PhD, Luke Animal Production Research
Ilma Tapio PhD, Animal Genetics
Johanna Vilkki, Prof, Animal Genetics

© Natural Resources Institute Finland
OUTLINE
3 13/11/2017Valio-Luke Dairy Methane Seminar - 13.11.2017
1. Breeding for efficient food production
2. Does current genetic progress lower CH4 emissions ?
3. Potential breeding strategies to reduce dairy system
emissions
4. Role of new advances in genetics – Genomics
5. Take home message

1
Breeding for efficient food
production

 Selective breeding : Chicken
Time to market weight for meat chickens
decreased from 16 to 5 weeks in 30 yrs
 Selective breeding: in Fish
 Selective breeding : Plants

Genetic trends in Nord Red Cattle bulls
yield, fertility, udder health and longevity

Fertility
90
92
94
96
98
100
102
104
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Index
Year of Birth
Genetic progress: fertility index, Ayrshire cows
(J. Pösö, Faba)
Fertiliy of Finnish cows improves by 1.2 index points annually
 every year ~860 000 € additional increase in cost savings
for Finnish dairy farmers! (A.-M. Tyrisevä 2017)

Production
(J. Pösö, Faba)
90
92
94
96
98
100
102
104
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Index
Year of Birth
Genetic progress: production index, Ayrshire cows
“Genetic improvement is a cost-effective
technology producing permanent & cumulative
changes in performance”
Permanent: the performance of an animal is influenced for life.
Cumulative: Improvements made in one generation are added to those made in previous generations.
Sustainable: Improvements can continue to be made as long as there is genetic variation.

The challenge:
Now we have to move even faster
Produce more from the
same resources
whilst
minimizing the
environmental impact
Methane (CH4) – is GHG
25 X the GWP of CO2
Cows lose 2-12% energy as
eructed CH4

Figure 1. Potential reductions in methane per unit of milk
(%). Development of new strategies and technologies through
research in these areas can significantly reduce methane per
unit of fluid milk in U.S.
Estimated potential reduction in Methane via
different strategies
 Different strategies for CH4
mitigation
 Each has its own pros and cons
Many approaches so far:
 inconsistent
 compromise animal performance
 unsustainable in the long run
- One low-cost & sustainable strategy
will be to utilize the natural between-
animals variation - GeneticsKnapp et al. 2011

2
Does current genetic progress
reduce CH4 emissions?

Improvements in the current breeding goals –
improve production, health & fertility
90
92
94
96
98
100
102
104
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
IndexforProduction
Year of Birth
Genetic progress for in production and fertility traits. Finnish Ayrshire (Jukka Pösö, Faba)
90
92
94
96
98
100
102
104
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
IndexforFertility
Year of Birth

Methane emission intensity in Finland reduced approx.
from 30 l/kg milk in 1990 to 19 l/kg milk in 2016
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
1985 1990 1995 2000 2005 2010 2015 2020
No.ofDairyHerds
No.Herds
Source: Statistics Finland
No. of diary cows and milk production per cow in Finland
No. of dairy herds in Finland by year
Finland USA

The main reasons for the reduction in CH4 are
Dilution of maintenance Reduction in cow numbers
Total Finnish milk production reduced rather little over
the last two decades: from 2.6 to 2.3 billion kg in 2016
(Statistics Finland)
Source: Statistics Finland
With the ongoing efforts of dairy
breeders to select for improved
economic efficiency
 Emissions are expected to reduce
further.
 Reduction of GHG by 0.6-0.9%/year
(Bell et al., 2013 and 2015).

3
Potential breeding strategies to
reduce dairy system emissions

1. Reduce wastage
(breeding for better health, fertility, life time efficiency)
1. Reduce age first calving
- Young cows start quicker
paying off
2. Selection for fitness traits
(more weight)
- Lifespan (Longevity)
- Health
- Fertility
Selection for fitness traits
(lifespan, health, fertility)
Improving longevity of cows
from 3.0 to 3.5 lactations
reduce CH4 emissions by 3%
(Wall et al. 2012)
Improving health and fertility
reduce Involuntary culling rates
& reduce emissions
Improving fertility reduce
calving interval, inseminations
and result in shorter unproductive
periods that
- reduce mang’t costs
- reduce emissions

Reduce wastage (cont’d)
Life time efficiency
e.g., CRV publishes lifetime efficiency index

Feed: largest single cost of
production in dairy operations
Efficient conversion of feed into milk
affects profitability
FE: measures how efficiently
nutrients are converted to animal
products
FE can be improved
– Better management (precision
feeding)
– Better feeding & nutrition
– Genetic selection
FE widely used in poultry, swine,
beef
In Dairy - selection for feed
efficiency has been indirect through
improvement of milk yield
• FE in dairy cattle - a complex trait,
needs DMI records
• Many different FE trait definitions
are proposed
• Genetic variation in FE traits
exists ranging (0.07 – 0.45)
– Genetic selection will
provide a long-term,
sustainable improvements
in FE
2. New traits into the breeding goal
Feed efficiency (FE)

© Luonnonvarakeskus
Economic value of feed efficiency
• Feed is the largest cost factor in milk production (about 1/3 of
total production costs)
• Annual genetic progress of 1-3 index points in feed efficiency is
feasible
 0.3% - 0.7% yearly reduction in feed requirements
 1.1 – 2.6 M€ yearly additional savings (Finnish dairy farms)
• Simulation study (T. Sipiläinen, part of ”Feed Efficiency” project)
– best alternative from economic point of view
 produce with same amount of feed and cows more milk
 5% improvement in feed efficiency increases annual
profit of Finnish dairy farmers by 38 M€
19 11/13/2017

Environmental impact of improving feed efficiency
• Finnish dairy cows are responsible for 2.5% (=1.88 million
tons CO2 eq) of the total Finnish GHG production (74.6Tg)
(Statistics Finland, 2016)
• Finnish dairy sector makes a significant positive societal
contribution and is important for food supply security.
•  this call for actions by the dairy sector to demonstrate that
GHG production is reducing and will even more reduced in
future – with the right interventions.
20 11/13/2017
Year 1920 1950 1980 2010
GHG emission (in kg CO2 eq.)
/ cow / year
4 392 5 088 9 830 10 761
/ kg energy corrected milk 1.27 0.92 1.02 0.81
GHG emission per dairy cow in Denmark (T. Kristensen, 2016)

Environmental impact of improving feed efficiency
• Cows’ CH4-production has been measured in Luke’s research farm
since 2010
– Nordic Red Cattle cows (all from ASMO nucleus breeding herd)
average CH4-production: 555 l/day (Negussie et al. Animal 2017)
 0.35 kg CO2 eq/kg energy corrected milk
• Genetic improvement of feed efficiency will reduce the amount of
CH4 produced per kg milk
• 5% improvement in feed efficiency will reduce Finnish cows’ annual
CH4 exhalation by about 45 000 000 kg CO2 eq.
21 11/13/2017
Luke & partners – working to develop genetic
evaluation for FE traits based on single-step
genomic prediction
- Potential traits - will be presented at 11th
WCGALP in NZ
- On-farm feed intake recording strategies
Negussie et al. (Manuscript)

Direct selection – utilize variation between animals
Successful mitigation strategy
via selection require:
 Definition of the
phenotype
 Measurement techniques –
Data
 Quantify available genetic
variation / associations
3. Direct selection for environmental impact (CH4)

Possible Methane phenotypes (traits)
 Methane production (MeP)
= l/day or g/day,
e.g. 555 l/d or 370 g/day
 Methane intensity (MeI)
= l/kg milk or g/kg milk
e.g. 18.5 l/kg milk 12.3 g/kg milk
 Methane yield (MeY)
= 26.4 l/kg DMI or 17.6 g/kg DMI
 Residual methane production
= Difference between observed and predicted
methane
CH4= milk + maintenance + intake + body tissue change + e
Any genetic
variation??
De Haas et al. JDS 2016, Negussie et al. JDS 2017

Data & measurement techniques
 So far only on-station measurements are possible
 On-farms large scale measurements – still limited & needs more work
 At Luke 3 different techniques established and data are being generated
 Individual animal enteric CH4 measurements on a large-
scale is a requisite for estimation of genetic parameters and
prediction of breeding values.
 Direct measurement of individual CH4 emissions is,
however logistically demanding and expensive
Cattle respiration chamber F10 Multi-gas analyzer NG Guardian
Negussie et al. Animal 2017

Genetic parameters
TARGET POPULATION
STUDY POPULATION
SAMPLE
 Genetic variation for CH4
exists
 Accurate estimates from dairy
system emissions are very rare
 Luke – first h2 estimates from
direct CH4 measurements
~0.11 (0.07)
 Progress depends largely on h2

 Accurate estimates of genetic
parameters lacking
 Estimates of genetic
associations with other
breeding goal traits are not
available
 Currently no economic value
for methane – but in the future
 Concern - selection for CH4 &
its impact on utilization of low
quality forage addressed
 Which phenotype (trait) to
use?
Opportunities and challenges of direct selection
Of course progress can be
faster BUT – watch out
this
De Haas et al. JDS 2016, Negussie et al. JDS 2017

4
Role of new advances in genetics
Genomics !

Genomic selection
• Allows prediction of genetic merit from
genome-wide SNP markers
• animals can be selected accurately early in
life, based on their genomic predictions
• Allows inclusion of traits that are difficult or
expensive to measure:
– fertility,
– disease resistance,
– feed efficiency
– methane emissions
Relationship between genomic prediction accuracy and no.
animals in the reference population for the different methane
emission traits with h2 ranging from 0.1 to 0.4

Take home message !
 In the short to medium term improving efficiency of production per
unit of output (e.g. litre of milk) is key to increased production with
limited negative environmental impact. This involves increased
genetic progress in:
- Production
- reducing wastage (breeding for better health, fertility, life time
efficiency)
- genetic evaluation for feed efficiency will reduce dairy system
emissions
 Genetics is a cost-effective, sustainable mitigation option &
harnessing advances in genetics – genomics selection is a key for
quick progress
 Genomic evaluation for Feed efficiency should be an urgent
task
 Dairy system emission multidisciplinary issue – needs
multidisciplinary approach & all round collaboration is essential

Discussion points
1. How can we optimally combine mitigation options
- Breeding
- Feeding and nutrition
- Management

Thank you!

Enyew Negussie, Luke: Methane emission from Ruminants: what genetics and can do?

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (6)

Similaire à Enyew Negussie, Luke: Methane emission from Ruminants: what genetics and can do?

Similaire à Enyew Negussie, Luke: Methane emission from Ruminants: what genetics and can do? (20)

Plus de Valio

Plus de Valio (16)

Dernier

Dernier (20)

Enyew Negussie, Luke: Methane emission from Ruminants: what genetics and can do?