Session 10 14.00_adel_prado_whole-farm models

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, DenmarkJørgen Olesen, Aarhus University, Denmark

Alan Rotz, USDA-ARS, University Park, USA

Agustin Del Prado Paul Crosson

Alan RotzJørgen Olesen

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 change4. Linking mitigation and adaptation to climate change

5. Final Recommendations

Models reviewed

LANDDAIRY

+NGAUGE

Casey & Holden

Foley et al.

Lovett et al.

O’Brien et al.SIMSDAIRY

Holos-NOR

DAIRYWISEFARMSIM FARMGHG

FASSET

Holos

DAIRYGEM

IFSM

Ecomod-suite

OVERSEER, HoofPrint

DAIRYNZ

Main components of a ruminant livestock system

grass

Enteric CH4manure CH4 manure N2O

Sources of GHG (from the cradle to farm gate)

Animal Manure

soils N2O Fuel combustion Secondary

emissions

Data from del Prado et al. (2013), Animal (this issue)

Animal Manure

Feed Production Different levels

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

How much do they excrete?

mechanistic

empiricalCO2Farm models prefer empirical

Component 2: manure handling

How much excreta?

How much and how is it mixed & collected?

How much and how is it stored?How much and how is it stored?

How much and how is applied?

Is manure treated?

Component 3: feed production Soil N2O

vs Emission Factor

mechanistic and dynamic

Bouwman (1998)

Empirical and static

Affected by soil type, weather and management

• Soil environment

• Soil inorganic N availability

• Soil Organic Matter

• Competing processes (plant, denitrification, leaching…)

Component 3: feed production

Indirect N2O: NH3, NO3-

Indirect emissions

Important 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)

Component 3: feed production grazing

Bryant and Snow (2012)

• How much herbage is produced?

• Digestibility, protein?

• How much N fixation?

Component 3: feed production grazing

Specialized models vs “Other” models

• Grazing patterns

• Spatial variability (urine,

dung patches)

e.g. ECOMOD+DairyMod

(Johnson et al., 2008)

• Semi-empirical

• More uniform

grasslands

e.g. SIMSDAIRY

(based on Brown et al., 2005

and Scholefield et al., 1996)

Soil Carbon

↓

↓

↓Fixed CO2

Runoff C

Respired CO2

↓

Radiant energy

↓

↓ Runoff C

Leached C ↓

↓

↓Saturated flow

Stored C

IFSM (Integrated Farm System Model)

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

After RAMIRAN 2013 presentation (del Prado A. and Pardo G.)

3 pools of SOC with different decomposition rate

Uncertainty-model structure

• Complex model structure

•More reliable results

•More mitigation options

• BUT – model parameterisation requirements much greater

Clarke et al., (2013)

Uncertainty-emission factors

Foley et al. (2011)

• Emission factors

•Considerable source of uncertainty

•Soil N2O and carbon cycling

Sensitivity analysis MC simulation

Interactions among farm components-key to mitigation

CATTLE

PLANT MANUREDUNG

URINE

forages

milk+meatconcentrates

N

fixation

purchased/sold

grazing housing

silage

grazed

milk+meat

CATTLE

MANURE

NH3

N2O

SOIL

URINE

roots + stubbles

fixation

CO2

purchased/sold

Manipulation 1 (Animal)-Crude protein concentration

CATTLE

PLANT MANUREDUNG

URINE

forages


N

fixation

purchased/sold

grazing housing

silage

grazed

milk+meat

CATTLE

MANURE

CH4

higher energy

lower proteinmilk+meat

CH4

∆volatile

solids (VS)

SOIL

URINE

roots + stubbles

fixation

CO2

purchased/sold


solids (VS)

B0

CATTLE

PLANT MANUREDUNG

URINE

forages


N

fixation

purchased/sold

grazing housing

silage

grazed

milk+meat

CATTLE

MANURE

SOIL

URINE

roots + stubbles

fixation

CO2

purchased/sold


NH3

SOIL

CATTLE

PLANT MANUREDUNG

URINE

forages


N

fixation

purchased/sold

grazing housing

silage

grazed

milk+meat

CATTLE

MANURE

SOIL

URINE

roots + stubbles

fixation

CO2

purchased/sold


∆inorganic N

SOIL

N2O

NO3

CATTLE

PLANT MANUREDUNG

URINE

forages


N

fixation

purchased/sold

grazing housing

silage

grazed

milk+meat

CATTLE

MANUREPLANTN

fixation

SOIL

URINE

roots + stubbles

fixation

CO2

purchased/sold


∆inorganic N

SOIL

fixation

CATTLE

PLANT MANUREDUNG

URINE

forages


N

fixation

purchased/sold

grazing housing

silage

grazed

milk+meat

CATTLE

MANUREPLANTN

fixation

CH4

∆ crude protein

concentration

∆ urine: dung ratio

…

SOIL

URINE

roots + stubbles

fixation

CO2

purchased/sold


SOIL

fixation

Confinement vs grazing

400

600

800

1000

1200Conc.

Purchased

Grain

produced

Grazed forage

DM

/ y

r

Rotz et al. (2009)

0

200

400

Hay & silage

produced

Confined, Confined, Confined Outdoors,High Moderate with pasture all grass

ton

DM

/ y

r

0.6

0.8

1.0

Secondary emissions

Engine emissions

Manure handling

Net animal/feed

C-footprint: kg CO2e / kg ECM


200

400

600

800

1000

Carbon dioxide

Methane

Nitrous oxide

0.0

0.2

0.4


0

Rotz et al. (2009)

0.2

0.4

0.6

0.8

1.0Secondary emissions Engine emissions

Manure handling Net animal/feed

C-footprint: kg CO2e / kg ECM


2.0

1.0

0.4

1.4

0.8

1.2

1.6

1.8

0.6

0.0

0.2


DEFRA-AC0209

(Anon, 2010) (UK)Rotz et al. (2009) (US)

0.4

0.2

• 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

O’Brien et al.

2012 (IRL)

Modelling mitigation measures

-Measures applied in

combination may have

interactions amongst each

other

-The reduction of GHG

Del Prado et al.(2010)

-The reduction of GHG

when we combine

measures is not equal to

adding the reduction

effects from single

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

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)

4.0

Mil QMilk quality

PROVISION

BAselinePROVISION

Should we try to account for non-market values?

1-Baseline

2

3

4

5

6

7

8

9

10

11

Sustainable

N2O/ha

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Biodiv

Landscape

Soil Q

Anim. Welfare

£/ milk

SUSTAINABLE

€/L leche

LANDSCAPE

Soil quality

Animal

Welfare

+health

Soil protection

(structure, fertility)

BAseline

Biodiversity

CULTURAL/

ETICS

MARKET VALUE

(SOIL, FARM)

Example taken from Del Prado et al. (2009) using the SIMSDAIRY model

Ecosystem Services

11 farm scenarios showing results for different Ecosystem services

Farm models should be able to be used for mitigation

+adaptation to climate change impacts

70

80

90

100

110

120

130

ave

rage

sta

rt d

ay

(Sin

ce 1

st J

an

) o

f gr

azi

ng

sea

son

SW YH WA SC

4

6

8

10

12

14

16

18

an

nu

al g

rass

gro

wth

(t

DM

ha

-1yr

-1)

SW YH WA SC

Start day of grass

growing seasonGrass productivity

50

60

baseline 2020 2050 2080

ave

rage

sta

rt d

ay

(Sin

ce 1

st J

an

) o

f gr

azi

ng

sea

son

0

2

baseline 2020 2050 2080

an

nu

al g

rass

gro

wth

(t

DM

ha

ba

-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.)

framework

1300

1400

1500

1600

1700

1800

g CO

2-eq/l milk

6.0

6.5

7.0

7.5

8.0

gNH

3/Lmilk

10

15

20

25

30

g NO3-N/L milk

Farm models should be able to be used for mitigation

+adaptation to climate change impactsSouth West England (example)

C-footprint NH3 NO3-

1000

1100

1200

1300

baseline 2020 2020 (ADAPT)

g CO

scenario

5.0

5.5

6.0

baseline 2020 2020 (ADAPT)

gNH

scenario

0

5

10

baseline 2020 2020

(ADAPT)

g NO3

scenario

Del Prado et al.(in prep.)

-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-.

• 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 N O

Recommendations to improve farm modelling for

quantification of GHG, mitigation and adaptation

• We need to improve simulation of soil C fluxes and N2O

emissions.

• 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).

Recommendations to improve farm modelling for

quantification of GHG, mitigation and adaptation

(temperature, precipitation and CO2).

• Wider environmental and socio-economic impacts need

to be considered when developing tailored

recommendations.

• Farm modelers should collaborate together.

Acknowledgements

grant no. CGL2009-10176

grant no. PC2010-33A

grant no. 266018

Also thanks to the Guest Editor (Nick Holden) and 2 anonymous reviewers

Session 10 14.00_adel_prado_whole-farm models

Technology

Transcript of Session 10 14.00_adel_prado_whole-farm models