Deli Chen1, Yong Li1, Bob Farquharson1, Richard Eckard1, Kevin Kelly2, Louise Barton3 , Peter Grace4

1Melbourne School of Land and Environment, The University of Melbourne 2 DPI Victoria, 3UWA, 4QUT

Modeling N2O emissions from agricultural soils

The CCRSPI Conference, 15-17th February 2011, Melbourne

NO3-

NH4+

N2

N2O

NO2-

NH3

Den

itrificatio

n (5

-80

%)

(NH2OH)

Soil organic matter Fertilizer Animal Waste

Ammonia volatilization (10-70%)

Water is one of the key drivers

for all these processes

Nitrate leaching (5-90%)

Nit

rifi

cati

on

Processes contributing to/interacting with N2O production in soil

Measurement of N2O

Close path FTIR

GC

/c cTF K c z wcFlux

TDL

4

High spatial variability: N2O fluxes varying 40 folds within one ha (Turner et al, Plant and Soil, 2008)

5

Annual N2O Emissions (kg N ha-1 year-1)

at Treatment: DD+RET+N

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1968 1972 1976 1980 1984 1988 1992 1996 2000 2004

High temporal variability: N2O fluxes between 1968 and 2004 from rain-fed wheat at Rutherglen, simulated by WNMM

(Li et al, Plant and Soil, 2008)

Expensive to measure continuously

Impossible to rely on the field measurement alone to quantify regional N2O emissions

Mitigation of N2O emissions requires a whole system approach

N2O loss accounts for ~1%, compared with >50% total loss of applied N

Process (system) based model/DSS is a useful tool

Measurement or modelling?

7

Since the first N2O simulation model, zero-order kinetics by Focht (1974), models of varying complexity have been developed

Based on the utilisation purpose, N2O emissions models can be divided into three levels:

Laboratory Field (process based, DCDC, DAYCENT,

ecosys, WNMM ) Regional/Global

N2O simulation models

8

WNMM—spatially referenced water and nutrients management model , it simulates:

Soil water dynamicsPlant growthComprehensive C and N cycling,

including N2O emissions

(Li et al, 2005, 2007, 2008, 2009; Chen et al 2010)

9

……

ArcView interface

N2O emissions from irrigated maize, Yuci, Shanxi

11Conventional Tillage and Continuous Corn in ARDEC, Fort Collins, CO, USA. The dataset is provided by Arvin Mosier, USA.

CT-CC-224

0

5

10

15

20

25

30

1-Jan-02 30-Jun-02 27-Dec-02 25-Jun-03 22-Dec-03 19-Jun-04 16-Dec-04

So

il T

em

pe

ratu

re a

t 5

cm

(o

C)

CT-CC-224

0.15

0.20

0.25

0.30

0.35

0.40

0.45

1-Jan-02 30-Jun-02 27-Dec-02 25-Jun-03 22-Dec-03 19-Jun-04 16-Dec-04

So

il V

olu

me

tric

Wa

ter

Co

nte

nt

of

0-1

5c

m (

v/v

)

CT-CC-224

0

25

50

75

100

1-Jan-02 30-Jun-02 27-Dec-02 25-Jun-03 22-Dec-03 19-Jun-04 16-Dec-04

CO

2 F

lux

es

(k

g C

/ha

/d)

CT-CC-224

0

30

60

90

120

1-Jan-02 30-Jun-02 27-Dec-02 25-Jun-03 22-Dec-03 19-Jun-04 16-Dec-04

N2O

Flu

xe

s (

g N

/ha

/d)

N2O Emissions in USA

12WNMM simulations, Yaqui Valley, Mexico, Stanford University

N2O Emissions in Mexico

13

Validation: three key outputs should be validated before validation of N2O, example of WA Rain-fed wheat

Plant growth

Soil mineral NSoil moisture & Temp

Measured and simulated N2O fluxes

14

Irrigated pasture at Kyabram, VIC

15

Validation: three key outputs should be validated before validation of N2O, example of WA Rain-fed wheat

Plant growth

Soil mineral NSoil moisture & Temp

Measured and simulated N2O fluxes

Regional N2O emissions, WA wheat -belt using WMM (with RS,

soil database and climate data)

EFIPCC(1.0%)

WNMM(0.3-0.64%)

N2O (t N/year) 5309 1681

17

Challenges-sugarcane studies

• N2O:– South, extraordinarily large and long-lived; emission factor 20%– North, very much smaller and short-lived; emission factor 2.8%

• IPCC:– N2O emission factor 1% (Denmead and Wang et al, 2008)

Cumulative N2Oemissions, both sites

0

10

20

30

40

50

0 100 200 300 400Days after fertilising

kgN

ha

-1

South fertilised

South unfertilised

North fertilised

North unfertilised

18

Murwillumbah: OCT 2005-SEP 2006Treatment: 160 N kg/ha UREA on 19 OCT 2005

TR fertilized

5

10

15

20

25

30

01-Oct-05 15-Nov-05 30-Dec-05 13-Feb-06 30-Mar-06 14-May-06 28-Jun-06 12-Aug-06 26-Sep-06

TSOIL@5cm (OBS)

TSOIL@5cm (PRE)

TR fertilized

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

01-Oct-05 15-Nov-05 30-Dec-05 13-Feb-06 30-Mar-06 14-May-06 28-Jun-06 12-Aug-06 26-Sep-06

SWC@2-8cm (OBS)

SWC@2-8cm (PRE)

TR fertilized

0

1

2

3

4

5

6

7

01-Oct-05 15-Nov-05 30-Dec-05 13-Feb-06 30-Mar-06 14-May-06 28-Jun-06 12-Aug-06 26-Sep-06

ET (OBS)

ET (PRE)

TR fertilized

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

01-Oct-05 15-Nov-05 30-Dec-05 13-Feb-06 30-Mar-06 14-May-06 28-Jun-06 12-Aug-06 26-Sep-06

N2O (OBS)

N2O (PRE)

19

Challenges on modelling

Separate N2O emission sources, very limited information about N2O emission in nitrification process

Partition of N2O and N2 in denitrification

Lack of system approaches (need to quantify all pathways of water and N and C dynamics)

Very little information about indirect GHG emissions

Scale up (catchment scale)

Shading area indicates

nitrification contribution to N2O emissions

(irrigated pasture)

More effective than controlling loss processes in soil after N addition

Options to increase N efficiency and mitigate N2O emission

Use right amount, right type, apply at right time with right method

Need a practical tool to identify BMPs and incorporate land use, soil and climate variables and economic and environmental interests

GIS based Agricultural Decision Support System

GIS-Based Agricultural Decision Support System

Crop/pastureCrop yield

Above- and below-

ground biomass

WaterSoil water

contentSoil water fluxSoil drainage

Soil evaporationCrop

transpiration

Nutrients (N&P)Soil mineral-N content

Ammonia volatilisationNitrous oxide

emissionNitrate leachingCrop N uptake

Climate Soil Landuse

Agricultural Practices

CropsCrop harvestN fertiliser applicationIrrigation

Tillage

Agricultural SurveyInformation about

agricultural management

practices (soil, climate and land use)

Scenario DevelopmentFertiliser (nitrogen)

application and irrigation

Scenario EvaluationThe outputs of various

management scenarios are assessed against the set criteria, considering crop yield, water and

fertiliser use efficiency, and environmental impacts

Outcomes in The North China Plain

While maintaining/increasing crop production:

1. Up to 30% irrigation water saving

2. Up to 25% nitrogen fertiliser saving

3. Up to 70% less ammonia N losses

4. Up to 25% less N2O (a greenhouse gas)

5. Up to 50% less nitrate leaching

Best Management PracticesFor local agricultural extension officers and individual farmers

0

2

4

6

8

10

12

14

16

18

27-Jun-98 29-Jun-98 1-Jul-98 3-Jul-98 5-Jul-98 7-Jul-98

NH

3 Fl

ux (k

g N

/ha/

day)

SBSB (predicted)SB+ISB+I (predicted)

Example: Reduced ammonia emission

Irrigating immediately after fertiliser

application was predicted to reduce NH3

loss, as confirmed through field

measurements

Development of policy optionsby integrating biophysical and economic models

Driving forces

Input data

ClimateSoil

Crop rotationPolicies

Pressures

Resources and environmental

problems

Groundwater extraction

Groundwater pollution

N2O emission

Biophysical model

Farm economic model

GIS

State

Farm decision and biophysical processes

simulationFarmers’ input

behaviourCrop growth

Water dynamicsNitrogen dynamics

Impacts

Policy evaluation

EnvironmentalSocial

Economic

Reponses

Policy optionWater

managementNitrogen

management

y = -4.7x + 22.72

R2 = 0.87

0

5

10

15

20

25

0 0.5 1 1.5 2 2.5

Water price (Yuan/m3)

Nit

rog

en f

erti

lise

r u

se

effi

cien

cy (

kg/h

a)

y = -0.01x + 0.73

R2 = 0.997

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20 25 30 35 40

Nitrogen price (Yuan/kg)

N2O

em

issi

on

(kg

N/h

a)

26

Conclusion remarks

Require regional/industry specific model or parameters for N2O estimation

To mitigation of N2O emissions, require system approaches

Spatially referenced processes based model and DSS are useful tool for quantification and mitigation of N2O emissions

Incorporate impact of EEF (inhibitors and controlled release fertilisers) into models

0 5 10 15 20 25 300

2

4

6

8

10

12

14

Cumulative NH3 loss

Urea

Green urea

Days after fertilisation

NH

3 lo

ss (k

g/ha

)

9% of applied N

29% of applied N

Effect of urease inhibitor on NH3 loss

Effect of nitrification inhibitor on N2O emission

0 10 20 30 40 50 60 70 80 90 100 110 1200.0

0.5

1.0

1.5

2.0

2.5

DMPP Urea

Days

N2O

(g/

ha.

hr)

fertiliser applied fertiliser applied fertiliser applied

44% reduction of N2O emission

Treatment N2O (kg N∙ha-1) Yield (kg∙ha-1)Urea 1.20±0.05b 10,700±170c

Urea+NI 0.90±0.03c 11,160±290b

Sulfur coated urea 0.44±0.07e 13,270±130a

Effect of NI and SCU on N2O emission and yield N2O and yield (2007-2009)

0

5

10

15

20

25 N

2O flu

xes (m

g∙m

2 ∙d-1

)

Date

Urea NI SCU CK

Most effective ways to mitigate N2O emission

Use less N fertilizer

Less Consumption (diet)

Less People

Population Control

Without population control, China would have 300-400

million more people today

What will the emissions be when we have another 3 billion people in 2050?