Application of a Tier 3 for enteric methane in dairy catlle_Bannink LiveM_Macsur_Bilbao_2014

1. Application of a Tier 3 for enteric methane in dairy cattle
André Bannink

2. A Tier 3 for enteric CH4
Why ?
●accommodate for variation in rumen fermentation
How ?
●addressing chemical/physical aspects rumen function
●using extant process-based model
Activity data from Central Bureau of Statistics to estimate N & P excretion by dairy cows based on diets and productivity
Outputs Tier 3
●CH4 emission factor (kg CH4/cow/yr)
●CH4 as % of gross energy intake

3. Non-resistant 1, 2
Q Substrate
Q Micro-organisms
Resistant 1
Resistant 2
Microbial growth 1 Microbial growth 2
Feed 1
Feed 2
Rumen
CH4 1, CH4 2
Fixed characteristics, no variation with type of ration
Additivity for feed components assumed
Microbial growth = table value
Substrate degradation = table value
Non-Tier 3 approaches for enteric CH4

4. Substrate degr = fion ( QSub, QMi )
Q Substrate
Q Micro-organisms
Substrate outflow = fion ( QSub )
Microbial growth = fion ( QSub, QMi )
Microbial outflow = fion ( QMi )
Feed
Rumen
No fixed degradation rates, growth rates, and so on
Microbial growth = f ion ( [substrates] & [micro-organisms] )
Substrate degradation = f ion ( [micro-organisms] & [substrates] )
Microbial death/predation = fion ( QSub, QMi )
Tier 3 for enteric CH4

5. Tier 3 for inventory of effects ration on CH4 3 causal factors to quantify CH4
Organic matter
Micro- organisms
VFA
CH4
Feed intake
Rumen
1. Chemical composition & degradation characteristics
2. Microbial growth (efficiency)
3. Type VFA fion(substrate type, pH)
Small intestine
outflow
absorption
Acetic acid
H2
Propionic acid
Butyric acid
Longchain FA hydrogenation
Valeric acid
Microbial growth on ammonia
Microbial growth on AA
H2 source
H2 sink
Methane CO2 + 4H2 CH4 +2H2O
SURPLUS

6. Chemical composition affects CH4
Line 1
●Level 2
Line 2
●Level 2
●Level 2
●Level 3
●Level 3
Line 3
Line 4
0
100
200
300
400
Eiwit
NDF
Zetmeel
Suikers
Methane (mmol/mol VFA)
Meta-analysis in vivo data lactating cows
Bannink et al (2006 & 2008)
+10%
+68%
+55%
FAT Delivers no VFA hence no CH4 Negative effect of fat on CH4
Protein NDF Starch Sugars

7. Recently updated NIR data dairy enteric CH4
Update Ym IPCC Tier 2 from 6.0% to 6.5% GE intake Budget period 2013 onwards
11%
Update Bannink et al. (2011), unpublished
year
IPCC Tier 2, 6.0% GE intake IPCC Tier 2 update, 6.5% GE intake Tier 3

8. Tier 3 estimates realistic ?
↑ DMI, ↓ methane per MJ feed
IPCC Tier 2 Tier 3
Independent data-base University of Reading; Reynolds & Mills

9. Tier 3 far too complex compared to Tier 2 ? Type of data input required, ordered by colour
So, both methods use the same type of input data
Methods use data in different manner
Choice of method depends on data availability, detail en goal
Data requirement per method
Tier 2
Tier 3
Digestibility / NEL value feed
₰
Rumen degradation characteristics
₰
NEL requirement → Feed intake
₰
Feed intake
₰
Chemical composition → gross energy feed
₰
Chemical composition
₰
If goal is mitigation/adaptation on a farm,
do not apply generic numbers

10. Effect grassland management on CH4
10.0
12.0
14.0
16.0
18.0
20.0
22.0
GS-EC GS-LC
g CH4 / kg DM
6.0
8.0
10.0
12.0
14.0
16.0
18.0
GS-EC GS-LC
g CH4 / kg FPCM
GS = grass silage
= high N-fertilization = low N-fertilization
EC = early cut; LC = late cut
Bannink et al (2010)
18 kg DM/d (90% grass silage & 10% concentrates)

11. HF = high N fertilization; LF = low N fertilization
EC = early cutting; LC = late cutting
Reijs, 2007
Dijkstra et al (2012)
HFEC HFLC LFEC LFLC
Grass si lage type
0
100
200
300
400
500
N excretion (g day- 1)
Immediately av ai lable N
Eas i ly dec ompos able N
Res is tant N
C:N 0 .5
C:N 3 .4
C:N 3 3
C:N 0 .5
C:N 3 .5
C:N 2 8
C:N 0 .5
C:N 3 .3
C:N 2 9
C:N 0 .5
C:N 3 .3
C:N 3 7
A non-CH4 application, excreta composition

12. Effect N mitigating feeding measures on CH4
Dijkstra et al (2011)
1012141618N emission (g/kg FPCM) 1012141618 Methane emission (g/kg FPCM) Mean maize silage11.1 g N/kg FPCM14.4 g CH4 /kg FPCMmaize silage

13. Tier 3 for on-farm GHG budgets farm cases
EU – AnimalChange

14. Tier 3 for Brazilian beef production systems
De Lima et al (2014, submitted)
EU – AnimalChange
average Ym of 5.2% ≈ 20% lower than IPCC Tier 2 default Ym of 6.5%
Without supplementation 55-60% DM digestibility
Low Ym of 5.2% counter-intuitive
Often, higher Ym than 6.5% adopted for poor quality diets (e.g. FAO)
Huge implication for global enteric CH4 assessments

15. roselinde.goselink@wur.nl #669 - “Dry period length and rumen adaptation”
Extensions, developments, other use
Graphical user-interface for Tier 3
Delivering estimates of (variation in) Ym values for farm models & CFP models
Applications Tier 3 model
●GHG budgets farm cases (EU-AnimalChange)
●enteric CH4 in Brazilian beef (EU-AnimalChange)
●research questions excreta (composition & volumes)
●nutritional aspects, e.g. N limitation rumen function
●etc.
Further modelling efforts on
●rumen acidity model (adaptation rumen wall)
●rumen fat metabolism
●effects ionophore monensin; other additives envisaged
●intestinal (enzymatic) digestion
●extension hindgut fermentation
●feed intake patterns

16. roselinde.goselink@wur.nl #669 - “Dry period length and rumen adaptation”
Conclusions on Tier 3 for enteric CH4
Advantages
●possibility to simulate wide range of conditions
●introducing ‘logic’ in outcomes
●additional outputs available not directly related to CH4
●composition excreta
●nutrients for maintenance and production
●diet digestibility and milk production
Proves to be useful
●predicts Ym ≈ 6%, comparable to empirical evidence
●example Brazilian beef production systems
●example GHG budgets with varying ‘feeding intensity’
Disadvantages
●activity data on diet composition
●different (non-practical) data on ‘digestibility’

17. for research & experimentation
for inventory (Tier 3)
for practice
(on farm)
Tier 3 Inventory / program Low Emission Animal Feed
financed by Ministry Economic Affairs & Dutch Product Boards
andre.bannink@wur.nl jan.dijkstra@wur.nl

18. Representing underlying mechanisms
Dynamics instead of static approaches & fixed values
Interactions & effect rumen conditions
●pH, volume, passage rate
●Interaction micro-organisms / substrates
●Different microbial classes:
●sugars/starch utilizers
●NDF utilizers
●protozoa (predation on bacteria / death)
Production of volatile fatty acids (VFA)
Concentrations of substrate & microbial mass
Tier 3 approach for enteric CH4

19. Tier 3, schematic
 Model structure
 Schematic representation mass
flows
 Changes in masses and flows
described by differential
equations in simulation model
● parameters & equations
based on useful (in vivo)
studies reported in
literature