1. Whole-farm models to quantify greenhouse gas emissions
and their potential use for linking climate change mitigation
and adaptation in temperate grassland ruminant-based
farming systems
Agustin del Prado, Basque Centre for Climate Change (BC3), Spain
Paul Crosson, Teagasc, Ireland
Jørgen Olesen, Aarhus University, Denmark
Alan Rotz, USDA-ARS, University Park, USA
3. Outline
1. General overview
2. Basic principles of farm models
3. Whole-farm models for quantification and mitigation of
GHG
4. Linking mitigation and adaptation to climate change
5. Final Recommendations
4. Models reviewed
LANDDAIRY
+NGAUGE
Casey & Holden
Foley et al.
Lovett et al.
O’Brien et al.
SIMSDAIRY
FARMSIM DAIRYWISE
FARMGHG
FASSET
Holos-NOR
Holos
DAIRYGEM
IFSM
OVERSEER, HoofPrint
Ecomod-suite DAIRYNZ
6. Sources of GHG (from the cradle to farm gate)
Enteric CH4
manure CH4
Animal
soils N2O
Feed Production
manure N2O
Manure
Fuel combustion
Secondary
emissions
Different levels
Data from del Prado et al. (2013), Animal (this issue)
7. Component 1: the animal
How much do they eat or meat/milk produce?
•
•
•
•
Energy and nutrient requirements (e.g. protein)
Feed on offer (e.g. fiber, energy, protein)
Genetics
Structure of the herd
mechanistic
CH4
CO2
empirical
Farm models prefer empirical
How much do they excrete?
8. Component 2: manure handling
How much excreta?
How much and how is it mixed & collected?
How much and how is it stored?
Is manure treated?
How much and how is applied?
9. Component 3: feed production
vs
Soil N2O
Emission Factor
Bouwman (1998)
mechanistic and dynamic
Empirical and static
Affected by soil type, weather and management
• Soil environment
• Soil inorganic N availability
• Soil Organic Matter
• Competing processes (plant, denitrification, leaching…)
10. Component 3: feed production Indirect emissions
Indirect N2O: NH3, NO3Important to account for pollution swapping or synergetic effects
of measurements targeting reduction of GHG emissions
• Many models use Emission Factors (but not all)
• Mechanistic NH3 requires wind, pH, etc…info
• Mechanistic NO3- soil water transport modelling (complex)
11. Component 3: feed production grazing
Bryant and Snow (2012)
• How much herbage is produced?
• Digestibility, protein?
• How much N fixation?
12. Component 3: feed production grazing
Specialized models
e.g. ECOMOD+DairyMod
(Johnson et al., 2008)
• Grazing patterns
• Spatial variability (urine,
dung patches)
vs
“Other” models
e.g. SIMSDAIRY
(based on Brown et al., 2005
and Scholefield et al., 1996)
• Semi-empirical
• More uniform
grasslands
14. But C field-scale modelling and experiments…
Modelled with LANDDAIRY farm model+RothC
Long-term effect on soil C stocks of applying slurry vs digestate vs compost
3 pools of SOC with different decomposition rate
After RAMIRAN 2013 presentation (del Prado A. and Pardo G.)
15. Uncertainty-model structure
• Complex model structure
•More reliable results
•More mitigation options
• BUT – model parameterisation requirements much greater
16. Uncertainty-emission factors
Foley et al. (2011)
Clarke et al., (2013)
Sensitivity analysis
MC simulation
• Emission factors
•Considerable source of uncertainty
•Soil N2O and carbon cycling
23. CH4
milk+meat
concentrates
∆ crude protein
concentration
∆ urine: dung ratio
…
CATTLE
CATTLE
purchased/sold
forages
silage
grazed
grazing
N
fixation
DUNG
URINE
PLANT
housing
MANURE
MANURE
CO2
purchased/sold
roots + stubbles
SOIL
SOIL
Manipulation 1 (Animal)-Crude protein concentration
24. Confinement vs grazing
1200
Conc.
Purchased
1000
ton DM / yr
800
Grain
produced
600
Grazed forage
400
Hay & silage
produced
200
0
Confined,
High
Confined,
Moderate
Confined
with pasture
Outdoors,
all grass
Rotz et al. (2009)
25. Confinement vs grazing
1.0
C-footprint: kg CO2e / kg ECM
Secondary emissions
Engine emissions
Manure handling
Net animal/feed
0.8
0.6
1000
800
Carbon dioxide
Methane
Nitrous oxide
600
400
200
0
0.4
0.2
0.0
Confined,
High
Confined,
Moderate
Confined
with pasture
Outdoors,
all grass
Rotz et al. (2009)
26. Confinement vs grazing
C-footprint: kg CO2e / kg ECM
1.0
0.8
Secondary emissions
Manure handling
Engine emissions
Net animal/feed
0.6
0.4
0.2
0.0
Confined,
High
Confined,
Moderate
Confined
with pasture
Rotz et al. (2009) (US)
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
Outdoors,
all grass
DEFRA-AC0209
(Anon, 2010) (UK)
O’Brien et al.
2012 (IRL)
• Results will be biased on specific definition of farm system and model
• In US and UK cases GHG results are better for medium-grazing scenario
27. Modelling mitigation measures
•
•
•
•
•
•
•
•
Ba: baseline
Man: manure changes
FQ: frequency of reseeding
N-Fert: optimisation of mineral fertiliser N
Fertilit: improving animal fertility
Diet: optimising N intake
LIP: adding lipid supplements
DCD: applying nitrification inhibitors
-Measures
applied
in
combination may have
interactions amongst each
other
-The reduction of GHG
when
we
combine
measures is not equal to
adding
the
reduction
effects
from
single
measures.
Del Prado et al.(2010)
28. Farm economics is an important factor for mitigation
• Level of adoption will largely depend on economics
• A number of models permit economic evaluation of mitigation strategies
• A few models evaluate economics and GHG impacts together e.g. MACCs
Foley et al. (2011)
29. Should we try to account for non-market values?
PROVISION
Milk Q
quality
Mil
PROVISION
4.0
BAseline
1-Baseline
MARKET VALUE
3.5
2
€/L leche
3.0
£/ milk
2.5
Biodiversity
2.0
4
Biodiv
1.5
3
(SOIL, FARM)
1.0
0.5
5
0.0
6
7
N2O/ha
8
Anim. Welfare
9
10
11
SUSTAINABLE
Sustainable
Animal
Welfare
+health
Landscape
LANDSCAPE
Soil Q
CULTURAL/
Soil quality
ETICS
Soil protection
(structure, fertility)
Ecosystem Services
11 farm scenarios showing results for different Ecosystem services
Example taken from Del Prado et al. (2009) using the SIMSDAIRY model
30. Farm models should be able to be used for mitigation
+adaptation to climate change impacts
Start day of grass
growing season
SW
YH
WA
18
SC
110
100
90
80
70
YH
WA
SC
14
12
10
8
6
4
60
2
50
a
SW
16
120
annual grass growth (t DM ha-1yr-1)
average start day (Since 1st Jan) of grazing season
130
Grass productivity
0
baseline
2020
2050
2080
b
baseline
2020
2050
2080
framework
-Farm-models may be integrated in frameworks.
-For most regions in the UK grass productivity and growing season
will increase (about a month in 2020) but grass digestibility will
decrease.
-Adaptation may be increasing grazing for one month.
Del Prado et al.(in prep.)
31. Farm models should be able to be used for mitigation
+adaptation to climate change impacts
South West England (example)
C-footprint
NH3
8.0
1700
g CO2-eq/l milk
7.5
gNH3/Lmilk
1600
1500
1400
1300
1200
7.0
6.5
6.0
5.5
1100
baseline
scenario
2020
2020 (ADAPT)
25
20
15
10
5
5.0
1000
NO3-
30
g NO3-N/L milk
NO3
1800
0
baseline
2020
scenario
2020 (ADAPT)
baseline
2020
scenario
2020
(ADAPT)
-More variable results for C-footprint and N leachate in 2020.
-C-footprint decreases and NH3 and NO3- increase.
-One month extra grazing (adaptation) has no effect on C-footprint
but positive for NH3 and negative for NO3-.
Del Prado et al.(in prep.)
32. Recommendations to improve farm modelling for
quantification of GHG, mitigation and adaptation
• We need to balance complexity in farm models
• Quantifying uncertainties is essential (linkage of
components and in relation to parameterisation).
• We need better datasets against which to test farm scale
models.
• We need to improve simulation of soil C fluxes and N2O
emissions.
33. Recommendations to improve farm modelling for
quantification of GHG, mitigation and adaptation
• Future farm models for mitigation and adaptation must
be sufficiently sensitive to weather conditions and
incorporate economics.
• We need to test and compare farm scale simulation
models for their sensitivity to climate change
(temperature, precipitation and CO2).
• Wider environmental and socio-economic impacts need
to be considered when developing tailored
recommendations.
• Farm modelers should collaborate together.
