Application of a Tier 3 for enteric methane in dairy cattle

André Bannink

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

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

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

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

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

Meth

ane (

mm

ol/

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

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 (2011), unpublished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

Tier 3 estimates realistic ?

↑ DMI, ↓ methane per MJ feed

IPCC Tier 2 Tier 3

Independent data-base University of Reading; Reynolds & Mills

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

Effect grassland management on CH4

10.0

12.0

14.0

16.0

18.0

20.0

22.0

GS-EC GS-LC

g C

H4

/

kg

DM

6.0

8.0

10.0

12.0

14.0

16.0

18.0

GS-EC GS-LCg C

H4 /

kg F

PC

M

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)

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 s ilage type

0

100

200

300

400

500

N e

xc

reti

on

(g

da

y-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

Effect N mitigating feeding measures on CH4

Dijkstra et al (2011)

10 12 14 16 18

N emission (g/kg FPCM)

10

12

14

16

18M

eth

ane e

mis

sio

n (g/k

g F

PC

M)

Mean maize silage

11.1 g N/kg FPCM

14.4 g CH4 /kg FPCM maize silage

Tier 3 for on-farm GHG budgets farm cases

EU – AnimalChange

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

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

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’

derived from

Dijkstra et al (2007)

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

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

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