Post on 02-Jun-2018
transcript
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
1/49
ModellingModelling
biochemical rumen functionsbiochemical rumen functions
with special emphasis onwith special emphasis onmethanogenesismethanogenesis
DDD
Dr Jan DijkstraDr Jan DijkstraAnimal Nutrition GroupAnimal Nutrition Group
Wageningen UniversityWageningen University
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
2/49
Overview
Principles of mathematicalPrinciples of mathematical modellingmodelling
Empirical models of methane productionEmpirical models of methane production
Mechanistic models of methane productionMechanistic models of methane production
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
3/49
Prediction ~ mathematical models
A model is anA model is an equation or set of equationsequation or set of equations
thatthatrepresent therepresent the behaviour of a systembehaviour of a system
France and Thornley (1984)France and Thornley (1984)
Prediction requires use of modelsPrediction requires use of models
A model can be viewed as an idea, hypothesis orA model can be viewed as an idea, hypothesis orrelation expressed in mathematicsrelation expressed in mathematics
Symbiosis between experimentation andSymbiosis between experimentation andmodellingmodelling
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
4/49
Model classification
dynamic OR staticdynamic OR static
deterministic OR stochasticdeterministic OR stochastic
mechanistic OR empiricalmechanistic OR empirical
To put categories into a more familiar context, a modelTo put categories into a more familiar context, a modelbased on:based on:
regression analysisregression analysis
static, stochastic, empiricalstatic, stochastic, empirical
linear programminglinear programming static, deterministic, empiricalstatic, deterministic, empirical
differentialdifferential eqnseqns
dynamic, deterministic,dynamic, deterministic,
mechanisticmechanistic J. DijkstraModelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
5/49
Levels of organization
1
1
-
-
+
i
i
i
2
Description of levelLevel
Herd / flock
Animal
Organ / tissue
Cell
i
Dijkstra & France
(2005)
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
6/49
Properties of hierarchical systems
Each level has its own concepts and languageEach level has its own concepts and language
Each level is an integration of items from lowerEach level is an integration of items from lowerlevelslevels
Successful operation of a level requires lowerSuccessful operation of a level requires lower
levels to function properly, but not necessarilylevels to function properly, but not necessarily viceviceversaversa
LevelLevel Description of levelDescription of level
i + 1i + 1
ii
i - 1i - 1
i - 2i - 2
Herd / flockHerd / flock
AnimalAnimalOrgan / tissueOrgan / tissue
CellCell
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
7/49
Contributions of modelling
Models make best use of (precious) dataModels make best use of (precious) data
Models provide a convenient data summary,Models provide a convenient data summary,
useful for interpolation and cautious extrapolationuseful for interpolation and cautious extrapolation
Models provide quantitative description andModels provide quantitative description and
understanding of biological problemsunderstanding of biological problems
ModellingModelling
provides strategic and tactical supportprovides strategic and tactical support
to researchto research programmesprogrammes
ModellingModelling
allows exploration of possibleallows exploration of possible
outcomes when data are not availableoutcomes when data are not available
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
8/49
Overview
Principles of mathematicalPrinciples of mathematical modellingmodelling
Empirical models of methane productionEmpirical models of methane production
Mechanistic models of methane productionMechanistic models of methane production
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
9/49
Livestock greenhouse gases
manure /
fertilizer
CO2
N2
O
CH4
deforestatio
n
enteric
fermentation
FAO (2006)
GWP*-100 yr:
CO2 1
CH4 25
N2
O 298*Global Warming
Potential
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
10/49
Livestock greenhouse gases
manure /
fertilizer
CO2N2
O
CH4
deforestatio
n
enteric
fermentation
GWP*-20 yr:
CO2 1
CH4 72
N2
O 289*Global Warming
Potential
J. Dijkstra
Modelling methane emissions
FAO (2006)
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
11/49
Rumen methanogenesis
Fermentative microFermentative micro--organisms utilize dietaryorganisms utilize dietary
organic matter to produce VFA plus gases (e.g.,organic matter to produce VFA plus gases (e.g.,
COCO22
, H, H22
))
amount of Hamount of H22
depends on type of VFAdepends on type of VFA
MethanogensMethanogens
reduce COreduce CO22
to CHto CH44
using Husing H22
,,keeping Hkeeping H22
partial pressure in rumen lowpartial pressure in rumen low
HH22
is used up as it is producedis used up as it is produced
CHCH44
production:production:
22
12% of GE intake12% of GE intake
1010
35 g/kg DM intake35 g/kg DM intake J. DijkstraModelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
12/49
Organic
matterMicro-
organismsVF
A
feed
intake
Rumen
2. Microbial
growth
(efficiency)
3. Type VFA
f (substrate type, pH, )
Gut
outflow
absorption
Factors involved in rumen methanogenesis
1. Chemical composition & degradation characteristics
H2 CH4
CO2
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
13/49
Empirical models of methane production
> 30 empirical models available> 30 empirical models available
inventoryinventory
mitigation strategymitigation strategy
Independent variables include live weight, milkIndependent variables include live weight, milk
production, feed intake, dietary components,production, feed intake, dietary components,
digestibilitydigestibility
Applied in models of greenhouse gas emissionsApplied in models of greenhouse gas emissionsin whole farm settingin whole farm setting
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
14/49
Assessment of accuracy of empirical models
9 methane equations applied in 8 whole farm9 methane equations applied in 8 whole farm
modelsmodels
169 observations from 9 studies169 observations from 9 studies
Ellis et al. (2010) Global Change Biology
MeanMean SDSD MinMin MaxMaxFeed intake (kg DM/d)Feed intake (kg DM/d) 19.619.6 4.04.0 11.211.2 32.032.0
Milk production (kg/d)Milk production (kg/d) 30.330.3 9.059.05 8.88.8 49.449.4
Methane production (g/d)Methane production (g/d) 371371 77.177.1 117117 698698
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
15/49
Empirical models of methane production - 1
274 (Europe) or 323 (N274 (Europe) or 323 (N--America)America)
IPCC (1997) Tier IIPCC (1997) Tier I
0.06 x GE intake / 55.650.06 x GE intake / 55.65
IPCC (1997) Tier IIIPCC (1997) Tier II
10 + 4.9 x milk yield + 1.5 x LW10 + 4.9 x milk yield + 1.5 x LW0.750.75KirchgeKirchgenerner
et al. (1995)et al. (1995) -- 11
137 + 10 x milk yield137 + 10 x milk yield
CorreCorre
(2002)(2002)
CHCH44
production (g/d) estimated as:production (g/d) estimated as:
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
16/49
Empirical models of methane production - 2
(45(45
0.02 x DMI0.02 x DMI22
1.8 x C18:21.8 x C18:2
84 x C84 x C20) x20) x
DMIDMIGigerGiger--ReverdinReverdin
et al. (2003)et al. (2003)
[1.3 + 0.11 x[1.3 + 0.11 x DigDigmm
++ MnMn
x (2.4x (2.4
0.05 x0.05 x DigDigmm
)] x)] x
GEIGEIBlaxterBlaxter
&& ClappertonClapperton
(1965)(1965)
63 + 79 x CF + 10 x NFE + 26 x CP63 + 79 x CF + 10 x NFE + 26 x CP
212 x FAT212 x FATKirchgeKirchgenerner
et al. (1995)et al. (1995) -- 22
(3 + 0.5 x NSC + 1.7 x HC + 2.7 x CEL)/55.65(3 + 0.5 x NSC + 1.7 x HC + 2.7 x CEL)/55.65Moe & Tyrrell (1979)Moe & Tyrrell (1979)
CHCH44
production (g/d) estimated as:production (g/d) estimated as:
20 x concentrate + 22 x maize20 x concentrate + 22 x maize silsil
+ 27 x grass+ 27 x grass
((silsil)) SchilsSchils
et al. (2006)et al. (2006)J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
17/49
Evaluation of empirical methane models
PredPred
CHCH44(g/d)(g/d)
RMSPE(%)
CCC
IPCC (1997) Tier IIPCC (1997) Tier I 304304 27.6 0.01
IPCC (1997) Tier IIIPCC (1997) Tier II 399399 20.9 0.49
CorreCorre
(2002)(2002) 440440 34.2 0.13
KirchgeKirchgenerner
et al. (1995)et al. (1995)
11404404 29.5 0.29
GigerGiger--ReverdinReverdin
et al. (2003)et al. (2003) 230230 52.5 0.12
BlaxterBlaxter
&& ClappertonClapperton
(1965)(1965) 332332 21.2 0.27
Moe & Tyrrell (1979)Moe & Tyrrell (1979) 391391 20.2 0.46KirchgeKirchgenerner
et al. (1995)et al. (1995)
22 345345 20.9 0.22
SchilsSchils
et al. (2006)et al. (2006) 483483 39.5 0.25
Ellis et al. (2010)
Observed CH4371 g/d
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
18/49
Evaluation of empirical methane models
PredPred
CHCH44(g/d)(g/d)
RMSPERMSPE(%)(%)
CCC
IPCC (1997) Tier IIPCC (1997) Tier I 304304 27.627.6 0.01
IPCC (1997) Tier IIIPCC (1997) Tier II 399399 20.920.9 0.49
CorreCorre
(2002)(2002) 440440 34.234.2 0.13
KirchgeKirchgenerner
et al. (1995)et al. (1995)
11404404
29.529.5 0.29
GigerGiger--ReverdinReverdin
et al. (2003)et al. (2003) 230230 52.552.5 0.12
BlaxterBlaxter
&& ClappertonClapperton
(1965)(1965) 332332 21.221.2 0.27
Moe & Tyrrell (1979)Moe & Tyrrell (1979) 391391 20.220.2 0.46KirchgeKirchgenerner
et al. (1995)et al. (1995)
22 345345 20.920.9 0.22
SchilsSchils
et al. (2006)et al. (2006) 483483 39.539.5 0.25
Ellis et al. (2010)
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
19/49
Evaluation of empirical methane models
PredPred
CHCH44(g/d)(g/d)
RMSPERMSPE(%)(%)
CCCCCC
IPCC (1997) Tier IIPCC (1997) Tier I 304304 27.627.6 0.010.01
IPCC (1997) Tier IIIPCC (1997) Tier II 399399 20.920.9 0.490.49
CorreCorre
(2002)(2002) 440440 34.234.2 0.130.13
KirchgeKirchgenerner
et al. (1995)et al. (1995)
11 404404 29.529.5 0.290.29
GigerGiger--ReverdinReverdin
et al. (2003)et al. (2003) 230230 52.552.5 0.120.12
BlaxterBlaxter
&& ClappertonClapperton
(1965)(1965) 332332 21.221.2 0.270.27
Moe & Tyrrell (1979)Moe & Tyrrell (1979) 391391 20.220.2 0.460.46KirchgeKirchgenerner
et al. (1995)et al. (1995)
22 345345 20.920.9 0.220.22
SchilsSchils
et al. (2006)et al. (2006) 483483 39.539.5 0.250.25
Ellis et al. (2010)
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
20/49
Evaluation of empirical methane modelsEllis et al. (2010)
Model:
Giger-Reverdin
et al. (2003)0 100 200 300 400 500 600 700Observed CH
4(g/d)
0
100
200
300
400
500
600
700
Predic
tedCH4
(g
/d)
y = x
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
21/49
Evaluation of empirical methane modelsEllis et al. (2010)
Model:
Moe & Tyrrell
(1979)
0 100 200 300 400 500 600 700
Observed CH4(g/d)
0
100
200
300
400
500
600
700
Predic
tedCH4
(g
/d)
y = x
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
22/49
Conclusions empirical methane models
Predictions poor; significant bias and deviation ofPredictions poor; significant bias and deviation ofregression slope from unityregression slope from unity
Simple, generalized models performed worseSimple, generalized models performed worse
than models based on diet compositionthan models based on diet composition
Substantial errors into inventories of whole farmSubstantial errors into inventories of whole farmgreenhouse gas emissions are likelygreenhouse gas emissions are likely
Low prediction error may lead to incorrectLow prediction error may lead to incorrect
mitigation recommendationsmitigation recommendations
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
23/49
Overview
Principles of mathematicalPrinciples of mathematical modellingmodelling
Empirical models of methane productionEmpirical models of methane production
Mechanistic models of methane productionMechanistic models of methane production
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
24/49
Rate : state formalism
State variables:State variables: QQ11,,
QQ22,,
..,.., QQnn
Change of state variables with timeChange of state variables with time tt::
ddQQ11/d/dt = ft = f11 ((QQ11, Q, Q22,, ,, QQnn;;
PP ))
ddQQ22/d/dt = ft = f22 ((QQ11, Q, Q22,, ,, QQnn;;
PP ))
. .. .
. .. .
ddQQnn
/d/dtt = f= fnn
((QQ11
, Q, Q22
,, ,, QQnn
;;
PP ))
Differential equations based on law of massDifferential equations based on law of mass
conservation, 1conservation, 1stst
law of thermodynamics, etclaw of thermodynamics, etc
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
25/49
Organic
matterMicro-
organismsVF
A
feed
intake
Rumen
2. Microbial
growth
(efficiency)
3. Type VFA
f (substrate type, pH, )
Gut
outflow
absorption
Factors involved in rumen methanogenesis
1. Chemical composition & degradation characteristics
H2 CH4
CO2
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
26/49
Mechanistic rumen module
smallsmall
intestineintestineNDFNDFNDF
feed intakefeed intake rumenrumen
gut wallgut wall
NDFmicrobmicrob
starchstarchstarchstarch
VFAVFA
Ac Pr Bu
starch
LCFA LCFA
protein proteinproteinprotein
NH3NHNH33
amino
acids
aminoamino
acidsacids
NH3urea
protein
sugars
F-AA +pept
glucoseglucoseglucose
NDF
glucose
amino acid
NH3urea
microb
Dijkstra et al. (1992)
Modifications:
Dijkstra (1994)
Mills et al. (2001)
Dijkstra et al. (2002)Kebreab
et al.(2004)
Bannink
et al.(2006)
In use in:
Netherlands
UK
AustraliaBrazil
Canada
USA
and many moreJ. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
27/49
Efficiency of microbial growth
Substrate is used for
non-growth purposes (maintenance)
growth purposes
Yield is related to fractional
growth rate
(Pirt, 1965)
1 / Y = M /
+ 1 / Ymax
Y = growth yield
M = maintenance requirement
Ymax
= maximum yield
= fractional growth rate 0.00 0.10 0.20 0.30Fractional growth rate (/h)
0.00
0.20
0.40
0.60
Growthyield(g/g)
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
28/49
Microbial metabolism: N source
Yield affected by availability of preformed molecules
0 500 100090
110
130
150
per unit OM
per unit CHO
Bacterialproteinyield
(gprotein/kg
degrOMor
CHO)
Trypticase (mg/l)
Russell & Sniffen (1984)
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
29/49
Energy and N synchrony: energy spillingK. aerogenes; Neijssel
& Tempest
(1976)
0.00 0.25 0.50 0.750
5
10
15
20
Dilution rate (/h)
glycerol limited
ammonia limited
Micro
bialyield
(gDM
/molATP)
J. Dijkstra
Modelling methane emissions
Uglycerol
in energy spilling
= vmax
Qmicrobes
/(1+[ammonia]/Jammonia
)
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
30/49
Significance of VFA molar proportion
CC66
HH1212
OO66
+2H+2H22
OO
2C2C22
HH44
OO22
(acetate) + 2CO(acetate) + 2CO22
++ 4H4H22
CC66
HH1212
OO66
++2H2H22
2C2C33
HH66
OO22
(propionate) + 2H(propionate) + 2H22
OO
CC66
HH1212
OO66
CC44
HH88
OO22
(butyrate) + 2CO(butyrate) + 2CO22
++ 2H2H22
COCO22
++ 4H4H22
CHCH44
+ 2H+ 2H22
OO
acetateacetate
70%70%
propionatepropionate
15%15%butyratebutyrate
15%15%
CHCH44
0.39 mol/mol VFA0.39 mol/mol VFA
60%60%
25%25%15%15%
0.31 mol/mol VFA0.31 mol/mol VFAJ. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
31/49
Mechanistic model methane production
(Dijkstra et
al. 1992; Mills et al. 2001; Bannink et al. 2006)
Acetate
H2
Propionate
Butyrate
Lipidhydrogenation
Valerate
Microbial growthwith ammonia
Microbial growthwith amino acids
H2 source H2 sink
Fermentation
Feed input
Large IntestinalModel
Small intestinaldigestion
Rumen ModelFermentation
FermentationMethane
CO2
+ 4H2
CH4
+2H2
O
EXCESS
Methane
module
Methane
module
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
32/49
VFA stoichiometry
Type of VFA formed related to substrateType of VFA formed related to substratefermented, rumen pH, roughagefermented, rumen pH, roughage vsvs
concentrateconcentrate
J. Dijkstra
Modelling methane emissions
0.0
0.5
1.0
1.5
molacetate/molsubstrate
sugars
sta
rch
cellulo
se
hemi-
cellu
lose
roughage concentrate
0.0
0.5
1.0
1.5
molpropionate/molsubstrate
sugars
sta
rch
cellulo
se
hemi-
cellu
lose
roughage concentrate
Murphy et al.
(1984)
acetate propionate
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
33/49
Analysissubstrate type
roughage
vs.
concentratesefficiencymicrobialgrowth
kineticsVFA-absorption
pH
In vivo datarumendigestionlactating cows
VFA stoichiometry
and rumen pHBannink
et al. (2008)
J. Dijkstra
Modelling methane emissions
Sc: sugars
St: starch
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
34/49
Methane formation from various substrates
J. Dijkstra
Modelling methane emissions
Starch Cell wall Protein0.00
0.20
0.40
0.60
0.80
1.00
1.20
Relativeme
thaneformation
pH 5.5
pH 6.0
pH 6.5 CH4
from
soluble
carbohydrates
set at unity
Bannink
et al. (2008)
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
35/49
Mechanistic models of methane formation
Prediction based on description of the rumen inPrediction based on description of the rumen in
terms of components and associated processesterms of components and associated processes
Mechanistic models superior predictive powerMechanistic models superior predictive power
BenchaarBenchaar
et al. (1998); Mills et al. (2001);et al. (1998); Mills et al. (2001); KebreabKebreab
et al. (2006)et al. (2006)
Allows evaluation of dietary mitigation optionsAllows evaluation of dietary mitigation options
Inventories under Kyoto protocolInventories under Kyoto protocol
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
36/49
Example: Netherlands, database 1990 -
2008
Milk production and composition dataMilk production and composition data
Feed intake dataFeed intake data
feed categories: fresh grass, grass silage, maizefeed categories: fresh grass, grass silage, maizesilage, wet byproducts, concentratessilage, wet byproducts, concentrates
concentrate chemical composition from centralconcentrate chemical composition from central
databasedatabase
roughage chemical composition from Laboratory forroughage chemical composition from Laboratory for
Soil and Crop TestingSoil and Crop Testing
Dietary changes in 1990
2008
more maize silage and less fresh grass
crude protein content decreased
starch and sugar content increased J. DijkstraModelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
37/49
DM intake and fat corrected milk (FCM)
J. Dijkstra
Modelling methane emissions
1990 1995 2000 2005 2010
Year
12
14
16
18
20
22
24
FCMorDMI(kg/d)
FCM production
DM intake
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
38/49
J. Dijkstra
Modelling methane emissions
Simulated methane production
1990 1995 2000 2005 2010
Year
100
105
110
115
120
125
130
Methaneproduction(kg
/cow/yr)
15
16
17
18
19
20
Methane(g/kgDMorg
/kgFCM)
Methane (kg/cow/yr)
Methane (g/kg DM)
Methane (g/kg FCM)
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
39/49
Simulated methane production
J. Dijkstra
Modelling methane emissions
1990 1995 2000 2005 2010
Year
100
105
110
115
120
125
130
Methaneproduction(kg
/cow/yr)
15
16
17
18
19
20
Methane(g/kgDMorg
/kgFCM)
Methane (kg/cow/yr)
Methane (g/kg DM)
Methane (g/kg FCM)
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
40/49
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
41/49
Methane Conversion Factor (MCF)
Trend methaneTrend methaneproductionproduction
(kg/cow/yr):(kg/cow/yr):
IPCC 1.38IPCC 1.38
model 1.05model 1.05 1990 1995 2000 2005 2010
Year
5.7
5.8
5.9
6.0
6.1
6.2
6.3
M
CF(%G
E)
Mechanistic model
IPCC Tier 2 (1997)
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
42/49
Conclusions mechanistic methane models
Continued interaction between mechanisticContinued interaction between mechanisticmodellingmodelling
and experimentation allows fasterand experimentation allows faster
progressprogress
combine expertise in traditional fermentation parameters,combine expertise in traditional fermentation parameters,
molecular microbiological tools, and mathematicsmolecular microbiological tools, and mathematics
Mechanistic methane models enable predictionMechanistic methane models enable prediction
based on understanding of the systembased on understanding of the system
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
43/49
Acknowledgements
AndreAndre BanninkBannink
LelystadLelystad, the Netherlands, the Netherlands
Jennifer EllisJennifer Ellis
UnivUniv
Guelph, CanadaGuelph, Canada
James FranceJames France
UnivUniv
GuelpGuelp, Canada, Canada
ErmiasErmias
KebreabKebreab
UnivUniv
Davis, USADavis, USA
SecundinoSecundino
LopezLopez
UnivUniv
Leon, SpainLeon, Spain
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
44/49
Acknowledgements
AndreAndre BanninkBannink
LelystadLelystad, the Netherlands, the Netherlands
Jennifer EllisJennifer Ellis
UnivUniv
Guelph, CanadaGuelph, Canada
James FranceJames France
UnivUniv
GuelpGuelp, Canada, Canada
ErmiasErmias
KebreabKebreab
UnivUniv
Davis, USADavis, USA
SecundinoSecundino
LopezLopez
UnivUniv
Leon, SpainLeon, Spain
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
45/49
Practical solutions: model results
0
510
15
20
25
mean NL doubled
maize
silage
early cut
grass
silage
reduced
fertiliser
+2% fat
Metha
ne(g/kg)
per kg feed
per kg milk
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
46/49
Practical solutions: model results
0
510
15
20
25
mean NL doubled
maize
silage
early cut
grass
silage
reduced
fertiliser
+2% fat
Metha
ne(g/kg)
per kg feed
per kg milk
-4%
-5%
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
47/49
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
48/49
Practical solutions: model results
0
510
15
20
25
mean NL doubled
maize
silage
early cut
grass
silage
reduced
fertiliser
Metha
ne(g/kg)
per kg feed
per kg milk
-3%
-6%
+8%
+11%-4%
-5%
J. Dijkstra
Modelling methane emissions
8/11/2019 Modelling Biochemical Rumen Functions With Special Emphasis on Methanogenesis
49/49
Practical solutions: model results
0
510
15
20
25
mean NL doubled
maize
silage
early cut
grass
silage
reduced
fertiliser
+2% fat
Metha
ne(g/kg)
per kg feed
per kg milk
-3%
-6%
+8%
+11%-2%
-6%
-4%
-5%
J. Dijkstra