Modelling Grassland GHG Balances

Dr. Gary J. Lanigan

Outline

1) Challenges facing verification of measuring management effects

on GHG balance in grasslands

2) Use of modelling to assess co-mitigation and adaptation options

into the future

3) Farm-scale models- comparing systems and downscaling to

individual farms

4) Outreach to farmers: DSS tools- what to focus on

Pasture GHG Balance: Key Uncertainties:

• C Sequestration is decadal – makes measurement and verification

difficult

• Large variations in ecosystem C sink/source can occur- driven by

land management, soils and climate

• Unclear how long sequestration can last after management/land-use

change

• Assessment of pasture management is especially difficult-

particularly when attempting to assess future CC effects

C sequestration

N2O • Pasture, range and paddock emissions vary greatly temporally and

spatially

• Large measurement uncertainties

• N balance closure is problematic

Impacts of Land-use change

Measurement of fluxes

reveals large inter-annual

variation

Modelling land-use Change

Generate Carbon Response Function (CRF’s)

Useful for assessing LULU impacts and equilibria

Linear

Polynomial

Exponential

Poeplau et al. 2011 GCB

Temporal and spatial variation in N2O from grazed pastures

• Highly episodic in nature

• PRP emissions add a high degree of spatial variation

• Emissions vary based on N type and amount, soil type and climate

• Event driven – require high soil moisture

Range: 2 – 200 mgN ha-1 d-1

N2O- CAN YOU TRUST THE MEASUREMENTS?

Li, Humphreys, Lanigan 2011 STOTEN

Dynamic Biogeochemical Models

• Biogeochemical Models allow assessment of land

management strategies under current conditions and into

the future

• Predict future climate impacts – future-proof mitigation and

an assessment of co-mitigation/adaption

• Models allow insight into mechanisms controlling

source/sink strength

• Allow insights into permanency of stocks – partition

between labile and recalcitrant C

Advantages

Disadvantages • Parameter-hungry

• Requires constraint –equilibrium runs

Principal models: DNDC (multiple versions), Century/DailyDAYCENT

PASIM, APSIM, RothC/SUNDIAL/ECOSSE

PASIM

ECOSSE

DNDC

Models require

constraint either

a) Against long-term

SOC datasets OR

B) With flux data

Long-term effects on SOC- are the

models accurate Smith.2005 EJSS

Dondini et al. 2010 GCB-B

Flux data allow greater fine detail temporal assessment of simulated and measured

emissions

Model Accuracy and Uncertainty

• Goodness-of-fit may vary between sites

Model Accuracy and Uncertainty

Vuichard et al. 2007 Global Biogeochem. Cycl.

Gottschalk et al. 2007 AGEE

Impacts of drainage

Necpalova et al. 2013 Grass For. Sci.

0

20

40

60

80

100

120

140

160

180

Undrained Drained Undrained Drained

Measured Modelled

SO

C (

tC h

a-1

)

0

20

40

60

80

100

120

140

160

180

Undrained Drained Undrained Drained

Measured Modelled

Sand/Loam

Clay

N2O

(kg N

2O

-N h

a-1

)

Fertiliser Rate N

2O

(kgN

ha

-1 y

r-1

)

N2 (

kg

N h

a-1

yr-

1)

NH

3 (

kgN

ha

-1 y

r-1

)

Leach N

(kgN

ha

-1 y

r-1

)

a b

c d

Inclusion of legumes N

2O

(kg N

2O

-N h

a-1

yr-

1)

Legume proportion (%)

Effect of management of SOC stocks

100000

110000

120000

130000

140000

150000

160000

170000

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

INT

MOD

MOD + 90t slurry

C b

ala

nce (

kgC

ha

-1

Moderate vs Intensive grazing decreases SOC by 0.20 t C ha-1

Slurry addition increases SOC by 0.31 t C ha-1

Investigating pasture management effects on sequestration

-400

-200

0

200

400

600

800

Intensive StockingRate

plus 90 kg slurry moderate stocking+ one cut

silage only grass/clover N inhibitors

C S

EQ

UE

ST

RA

TIO

N (

kg

C h

a-1

yr-

1)

Assessing quality of C sequestration

• Can relate soil C pools in models in measured C pools

How C is allocated to labile and recalcitrant pools can affect

predictive simulations

Differences between the rate of loss of carbon predicted with the weather in the indicated individual years repeated for 18 years and that predicted with the actual weather over 1981–1999 averaged over the period. The values are averages across individual soils and grid squares for each land‐use category weighted in proportion to the total area represented. M1, M2, M3 = initialization methods. Bars indicate 95% confidence intervals.

Foreid et al. 2011 EJSS

C STOCKS AND POOL DISTRIBUTION FROM

EQUILIBRIUM RUNS

C stocks measured

C stocks and pools measured

Predicting sward productivity into the future

Lolium

4 spp mix

Lolium/trifolium

Sum of Harvested Biomass over Time

0

1

2

3

4

5

6

7

8

9

Lp Pp Tp Tr Cent Lp/Pp Lp/Tp Lp/Tr Pp/Tp Pp/Tr Tp/Tr

Community

Bio

ma

ss

[t

d.m

. h

a-1

]

Net Ecosystem Productivity

0

1

1

2

2

3

3

4

4

Lp Pp Tp Tr Cent Lp/Pp Lp/Tp Lp/Tr Pp/Tp Pp/Tr Tp/Tr

Community

Bio

ma

ss

[t

d.m

. h

a-1]

Predicting future trends

Abdalla et al. 2013 STOTEN

• Future Climate Change will not make much difference in SOC assuming seasonal

temperature and moisture distribution remains unaffected

• Weather volatility induces much larger changes

0

1

2

3

4

5

6

7

8

9

10

BG Measured BG Modelled Graz Measured Graz Modelled

N2O

(kg

N h

a-1

yr-

1)

Gleysol

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

BG Measured BG Modelled Graz Measured Graz Modelled

2003/10

A2 2070

HP69 2070

HP85 2070

Sandy

Impacts of CC on N2O

NEE is a sink except during dry years.

Decrease in sink activity is due to

waterlogging

N2O increases with aridity in clay soils

N2O decreases with aridity in sandy

soils

Use of modelling to generate Emission Factors

• Development of emission factor (EF) look-up tables for calculating the

direct nitrous oxide (N2O) emissions from grazed pasture soils

• Particularly useful for systems analysis of individual farms

• Scaling up to the farm- assessment within a whole production system

• Can set the modelled emissions in the context of production efficiency

Giltrap et al. 2013 STOTEN

Use of modelling to generate Emission Factors

• Development

of emission

factor (EF)

look-up tables

for calculating

the direct

nitrous oxide

(N2O)

emissions

from grazed

pasture soils

• Particularly

useful for

systems

analysis of

individual

farms

• …or farm-type

comparisons

Giltrap et al. 2013 STOTEN

What does this all mean to the farmer?

• Policy stakeholder require absolute emissions and

accurate emissions intensity

• Farmers need to know a) Is my efficiency increasing?

b) where am I relative to my peers?

c) ease of use

Carbon navigator: Principles

Mitigation measures:

Reduce carbon footprint

Increase profitability

Practical to implement

Software program

User friendly - data

Scientifically robust

Complementary to ongoing programmes

A comparison of published analyses of GHG emissions from

beef production systems (LCA and systems analysis)

0

5

10

15

20

25

30

35

40

GH

G e

mis

sio

ns (

kg C

O2e/k

g c

arc

ass)

Crosson & Lanigan 2011 An. Feed Sci Tech.

Set current and

target performance

Review impact on GHG

emissions per kg beef

Scoring chart

Indicator of

improvement

in profitability

Review impact on GHG

emissions per kg beef

Set current and

target performance

Scoring chart

Indicator of

improvement

in profitability

Conclusions

• Modelling offers a strategy for verification of land

management impacts of C sequestration and N2O

• Viable risk assessment tool for impacts of climate

change – future-proof mitigation

• Generate downscaled EF’s for farm-scale models

• Care needs to be taken with incorporation into DSS