Tools for quantifying GHG emissions Tools for quantifying GHG emissions from Agroecosystemsfrom Agroecosystems

E. Pattey, R.L. Desjardins and W. Smith

Agriculture and Agri-Food Canada, Research Branch, Ottawa

CAgM Expert Team Meeting on the Contribution of Agriculture to the State of Climate

Ottawa, Canada, 27 - 30 September 2004

INTRODUCTIONINTRODUCTION

Goals:

Develop a set of reliable Models for estimating net GHG emissions from agricultural sources/sinks and for deriving emissions factors relevant of a given country situation.

Establish a series of databases of the various agricultural activities for integrating the GHG emissions over space and time domains (land use, mgt practices, animal production, climate…) .

INTRODUCTION (Cont’d)INTRODUCTION (Cont’d)

A “reliable” Model is:sensitive to input conditions such as management practices;adapted to the geographical and climatic conditions under which it will be used;based on sound scientific knowledge.…Ideally it requires a set of input descriptors easily available.

Framework:

Any national GHG emission accounting system needs to be transparent (well-documented), verifiable (pilot test sites, scaling-up experiments etc.) and consistent with the Kyoto Protocol.

OUTLINEOUTLINE

•Speaker more familiar with Canadian situation Example of Canada…

•GHG emission estimates from agricultural sources in Canada, CO2, CH4, N2O

•Tools for developing models (chamber, tower)

•Tools for verifying temporal dynamic and top-down constraints (tower, aircraft)

•Results from tower- aircraft-based measuring systems

•Modeling results from Ecosys, DNDC and Daycent

•Summary

Greenhouse Gas Emissions from Greenhouse Gas Emissions from Canada’s Agroecosystems Canada’s Agroecosystems

(100 Year Time Horizon - Tg of CO(100 Year Time Horizon - Tg of CO22 equivalents) equivalents)

CO2 8 7 5 2CH4 22 19 20 23N2O 27 30 28 38

1981 1986 1991 1996 2001

02440

Total 57 56 53 63 64

GHG flux measuring techniques only cover a limited portion GHG flux measuring techniques only cover a limited portion of the space and time domainsof the space and time domains

Aircraft

Atmospheric Inversion

Tower

Chamber

1 102 105 104 103

1

103

104

102

10

Area m2

Time h

Soil Cores

Mass Balance

10 107 106

BLS&

Tracer

109 108

Regional and sub-continental estimates using tall towers and

CBL budgets

Satellite

Au

dit

ing/

Mon

itor

i ng

Long Term Experimental Sites: Flux, Meteorological and Ancillary

MeasurementsFGHG

Regional/ National

Estimates

Regional (Spatial)databases

“Ecosystem Models”

Benchmark Sites Inventory/ Monitoring Sites

Cs

Regional Flux and Surface FeatureMeasurements

Process Studies

•climate•soils•topography•land use•land management

Data collection

Driving variables

VerificationVerification

Research Needs

Model Refinement

Scaling Up

Verification

Model Refinement

Proposed Framework for a Accounting/Verification System

How do we improve and verify models?How do we improve and verify models?

ModelingModeling ModelingModeling

Measuring Measuring chambers, chambers,

towerstowers

Measuring Measuring chambers, chambers,

towerstowers

Virtual FarmVirtual Farm(with uncertainty (with uncertainty

estimates)estimates)

Virtual FarmVirtual Farm(with uncertainty (with uncertainty

estimates)estimates)

timetimetimetime

Verifying Verifying temporal temporal dynamicdynamic

Verifying Verifying temporal temporal dynamicdynamic

Top-Down Top-Down constraintconstraintTop-Down Top-Down constraintconstraint Regional & Regional &

Nat’l GHG Nat’l GHG budgetbudget

(with uncertainty (with uncertainty

estimates)estimates)

Regional & Regional & Nat’l GHG Nat’l GHG

budgetbudget(with uncertainty (with uncertainty

estimates)estimates)

Measuring Measuring towers, blimps towers, blimps

aircraftaircraft

Measuring Measuring towers, blimps towers, blimps

aircraftaircraft

Developing new knowledge Developing new knowledge on mgt practiceson mgt practices

Developing new knowledge Developing new knowledge on mgt practiceson mgt practices

Fg = dC V Mw

dt A Mv

Non-Flow Through, Non-Steady State Non-Flow Through, Non-Steady State Chamber MeasurementsChamber Measurements

Experimental design for comparing management practices and environmental conditions

Experimental design for comparing management practices and environmental conditions

Tower-based MeasurementsTower-based Measurements

Closed-path Tunable Diode Laser

Air Intakes

zKF g

g

Sonic anemometer

Setup for quantifying NSetup for quantifying N22O fluxes for two management practicesO fluxes for two management practices

1 TDL connected to 2 micromet. towers

145 150 155 160 165 170 175 180 185 1900.0

0.5

1.0

1.5

2.0

2.5

Fig. 4

15.5 g N m -2

9.9 g N m -2

N2O

Flu

x (

mg

N m

-2 h

-1)

Day of Year

ECOSYS

Grant, R. and Pattey, E., 2003. Modelling variability in N2O emissions from fertilized agricultural fields. Soil

Biology and Biochemistry:35(2): 225-243.

Urea applied at the following rates:0.218 0.254

0.120 0.120

Meas. model

Non-linear increase of N2O emissions with fertilizer application rate

Flux Towers are the only suitable measuring approach …Flux Towers are the only suitable measuring approach …during Snow meltduring Snow melt ( (Permanent Site, Ottawa)Permanent Site, Ottawa)

Harvested corn field - Snow meltHarvested corn field - Snow melt

The global Fluxnetglobal Fluxnet project features towers tracking the movement of carbon dioxide between various ecosystems and the air with emphasis on forest

Ameriflux

Euroflux

Japanflux

Establish a network of towers for measuring N2O fluxes to verify temporal dynamics of models and assist in scaling up from individual agricultural fields to region

Biocap

Aircraft-Based Measurements

The REA sampling system and TDL Laser

Vent (Dead band)

PTFESample Bag

DC Power supply

3-wayValve

Mass-FlowController

2-mFilter

Reliefvalve

DiaphragmPump 12 l/min

Inlet

UP

DOWN

¼” PTFEtubing

Aircraft REA system

LaboratoryTDL Laser

Canada

Casselman Flight Track

Morewood Flight Track

0 5 10 15 km

Tower Site

AC/Tower Study Sites, Spring 2001, 2003 and 2004

Casselman Flight Track

12 km

13km

Casselman

Highway 417

N

soy

cereals

pasture/grass

alfalfa

forest

corn

town

LEGEND

Morewood Flight Track

N

Mean Crop Cover in 2000 within Footprint of Aircraft Transects

0

5

10

15

20

25

30

Hay Alfalfa Corn Soybean Forest Pasture Cereals

Per

cen

tag

e (%

)

Casselman

Morewood

Aircraft Results, 2001

-10

0

10

20

30

40

50

60

70

80

90

100

15-Mar 25-Mar 4-Apr 14-Apr 24-Apr 4-May 14-May 24-May 3-Jun 13-Jun

N2O

Em

issi

on

s (n

g N

2O-N

m-2

s-1

)

Casselman

Morewood

-10

0

10

20

30

40

50

60

70

80

90

100

15-Mar 25-Mar 4-Apr 14-Apr 24-Apr 4-May 14-May 24-May 3-Jun 13-Jun

N2O

Em

issi

on

s (n

g N

2O-N

m-2

s-1

)

Casselman

Morewood

Combining Tower and Aircraft N2O Fluxes

FN2O by ACkg N2O-N

ha day

FN2O by Towerkg N2O-N

ha day

FN2O by AC (1130 to 1430)

ng N2O-N

m2 s

FN2O by Tower (1130 to 1430)

ng N2O-N

m2 s

=

Unknown

Tower and Aircraft Results, 2004

-50

0

50

100

150

200

250

300

350

400

15-Mar 25-Mar 4-Apr 14-Apr 24-Apr 4-May 14-May 24-May 3-Jun 13-Jun

N2O

Em

issi

on

s (g

N2O

-N h

a-1)

CasselmanMorewoodTower

-50

0

50

100

150

200

250

300

350

400

15-Mar 25-Mar 4-Apr 14-Apr 24-Apr 4-May 14-May 24-May 3-Jun 13-Jun

N2O

Em

issi

on

s (g

N2O

-N h

a-1)

CasselmanMorewoodTower

Modeling and Aircraft Results, 2004

0

20

40

60

80

100

120

140

160

180

200

15-Mar 22-Mar 29-Mar 5-Apr 12-Apr 19-Apr 26-Apr 3-May 10-May 17-May 24-May 31-May 7-Jun

Date

N2O

Em

issi

on

s (g

N2O

-N h

a-1)

Observed

DNDC

DayCent

Cummulative N2O-N Emissions

Interpolated Data (Mar 29 - Jun 4)

Observed 1.17 kg N2O-N ha-1

DNDC 1.80 kg N2O-N ha-1

DayCent 1.32 kg N2O-N ha-1

Exact Comparison

Observed 0.35 kg N2O-N ha-1

DNDC 0.65 kg N2O-N ha-1

DayCent 0.36 kg N2O-N ha-1

Estimated Nitrous Oxide emissions for two process based models (DNDC, DayCent) at Casselman Ontario for the year 2004

0

20

40

60

80

100

120

140

160

180

200

15-Mar 22-Mar 29-Mar 5-Apr 12-Apr 19-Apr 26-Apr 3-May 10-May 17-May 24-May 31-May 7-Jun

Date

N2O

Em

issi

on

s (g

N2O

-N h

a-1)

Observed

DNDC

DayCent

Cummulative N2O-N Emissions

Interpolated Data (Mar 29 - Jun 4)

Observed 1.17 kg N2O-N ha-1

DNDC 1.80 kg N2O-N ha-1

DayCent 1.32 kg N2O-N ha-1

Exact Comparison

Observed 0.35 kg N2O-N ha-1

DNDC 0.65 kg N2O-N ha-1

DayCent 0.36 kg N2O-N ha-1

Estimated Nitrous Oxide emissions for two process based models (DNDC, DayCent) at Casselman Ontario for the year 2004

Ecological drivers

Climate Soil VegetationAnthropogenic

activity

DecompositionCrop Growth

Soil Climate

Soil environmental factors

Temperature Moisture pHAnaerobic

balloonSubstrates

(NH4+, NO3- and DOC)

Denitrification Nitrification

N Gas Emissions Fluxes of NO, N2O, N2 and NH3

Exchange of NO and N2O

Effect of temperature and moisture on decomposition

Schematic of the major components of the DNDC model

Using models for obtaining regional and national estimates

ModelingModeling ModelingModeling

timetimetimetime

Regional & Regional & Nat’l GHG Nat’l GHG

budgetbudget(with uncertainty (with uncertainty

estimates)estimates)

Regional & Regional & Nat’l GHG Nat’l GHG

budgetbudget(with uncertainty (with uncertainty

estimates)estimates)

MeasuringMeasuringMeasuringMeasuring

0 20 40 60 80 1000

1

2

3

4

5

Time (years)

Cu

mu

lati

ve C

(T h

a-1)

Cumulative CO2-C from N fertilizer (50 kg N ha-1)

Soil C gain

Net gain

Challenge: The net impact of management practices changes with time

timetime

Cu

mu

lati

ve n

et

Cu

mu

lati

ve n

et

GH

G e

mis

sio

ns

GH

G e

mis

sio

ns

00

Option AOption A

Option BOption B

Option COption C

Option AOption A

0

10

20

30

40

50

60

70

80

90

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998

Year

Gg

N2O

-N

Estimated direct annual N2O-N emissions

Estimated direct spring N2O-N emissions

Estimated Direct N2O-N Emissions from Agriculture Soils in Canada Using DNDC

(1970-1999)

“Model”

province

region

SLC polygon

“Situations” defined by:

• Soil• Climate• Land use• Management

National C and GHG Accounting and National C and GHG Accounting and Verification SystemVerification System

country

SOC & GHGEmissions for each“situation”

Verification by direct measurement of national GHG Verification by direct measurement of national GHG estimates best done through holistic top-down estimates best done through holistic top-down national, continental, or global scale GHG budgetsnational, continental, or global scale GHG budgets

N2O emissions?

Scientific uncertaintyScientific uncertainty

CH4 N2O

GH

G e

mis

sio

n (

Mt

CO

2 e

qu

iv.

per

year)

-40

-20

0

20

40

60

80

CO2

Relativeuncertainty(estimated)

Scientific uncertaintyScientific uncertaintyUncertainty

Uncertainty

UnderstandingUnderstanding00

Tools to quantify uncertainties

•Sensitivity tests of models

•Monte-Carlo approach for evaluating uncertainty

•Sensitivity tests of models

•Monte-Carlo approach for evaluating uncertainty

SummarySummary

•Tools for measuring GHG fluxes only cover a limited

portion of the space and time domains

•The combination of tower and aircraft-based GHG

flux measurements provide valuable information to

estimate regional fluxes on a daily basis

•Models are essential for deriving national estimates

of GHG emissions

•Models still require lots of verification and

improvement to provide more accurate estimates

•Tools for measuring GHG fluxes only cover a limited

portion of the space and time domains

•The combination of tower and aircraft-based GHG

flux measurements provide valuable information to

estimate regional fluxes on a daily basis

•Models are essential for deriving national estimates

of GHG emissions

•Models still require lots of verification and

improvement to provide more accurate estimates