Management impacts on the C balance

in agricultural ecosystems

Jean-François Soussana1

Martin Wattenbach2, Pete Smith2

1. INRA, Clermont-Ferrand, France 2. Aberdeen University, Scotland, UK

CarboEurope, Poznan meeting, October 9, 2007.

Fossil fuel emissions = 1850 Mt C per yearGeographic Europe

Uncertainties in the carbon balance of European ecosystems before the start of CarboEurope (Janssens et al. Science, 2003).

SinkSource

Components of the agricultural C budget

GPP

NPP

NEP

NBP

Photosynthesis Autotrophicrespiration

Heterotrophicrespiration

Cuts Manure

GPP

NPP

NEP

NBP

Photosynthesis Autotrophicrespiration

Heterotrophicrespiration

Cuts Manure

NEE: Net Ecosystem Exchange, Atmospheric C balanceNBP: Net Biome Productivity, Soil C balance

28sites

Main Grass.

Main Wet.

Anc. Grass.

Anc. Wet.

CarboEurope IP grasslands & wetlands sites

Climate drivers of grassland and wetland annual GPP at CarboEurope

IP sites

(n=50, r2=0.705, P<0.0001)

Log(GPP) = 2.27 + 0.377. Log (Temp) + 0.614. Log (Precip)

Mean C fluxes (gC m-2 yr-1) at CarboEurope grassland and wetland

sites

NBP = K2 (K1 GPP – Cut – Digest . Intake + Manure)– K3 e LN(Q10).Tsoil/10 –FCH4-C

(n=43, R2=0.52, P<0.001)

GPP1228

NBP128

Rauto.

615Rhetero.Litter 294

Rhetero.Herbivore 46

Rhetero.SOM 89

Cut75

Intake70

Manure16

K1=0.50 K2=0.43

K3 = 83Q10 =1.21Digest.=0.65

Enteric fermentation3.4

Fate of NPP and manure (at C sink sites)

CutCut & GrazedGrazedAbandoned & Wet

Current herbage utilisation is lower than maximum

Maximal grazingMaximal cuttingGrazing and cutting at managed grassland sites

Role of cutting and grazing management for NBP

-300

-200

-100

0

100

200

300

400

500

400600

8001000

12001400

16001800

20002200

0100

200300

400500

NB

P (

g C

m y

r-1)

GPP

(g C

m-2 y

r-1 )

Cuts (gC m -2 yr -1

)

Cutting only, no manure

-300 -200 -100 0 100 200 300 400 500

GPP vs Max_cutting vs NBP_max_cutting

-300

-200

-100

0

100

200

300

400

500

400600

8001000

12001400

16001800

20002200

0100

200300

400500

NB

P (

g C

m y

r-1)

GPP

(g C

m-2 y

r-1 )

Intake (gC m -2 yr -1

)

Grazing only, no manure

-300 -200 -100 0 100 200 300 400 500

-300

-200

-100

0

100

200

300

400

500

400

600800

10001200

14001600

18002000

2200

0100

200300

400500

NB

P (

g C

m y

r-1)

GPP

(g C

m-2 y

r-1 )

Intake (gC m -2 yr -1

)

Grazing only, no manure

-300 -200 -100 0 100 200 300 400 500

GPP vs Max_grazing vs NBP0

-300

-200

-100

0

100

200

300

400

500

400

600800

10001200

14001600

18002000

2200

0100

200300

400500

NB

P (

g C

m y

r-1)

GPP

(g C

m-2 y

r-1 )

Cuts (gC m -2 yr -1

)

Cutting only, no manure

-300 -200 -100 0 100 200 300 400 500

GPP vs Max_cutting vs NBP_max_cutting

Maximalgrazing

Maximalcutting

Trade-off between C sequestration and agricultural (livestock) production

Grassland GPP over Europe

Data upscaling withannual means of temperatureand precipitation

PASIM model

(Vuichard et al., 2007 GBC)

Spatial distribution of NBP of grasslands in Europe (data

upscaling)

Assuming a management similar to mean site managementNEXT STEP: map NBP using agricultural management based on statistics

C sequestration efficiency in grasslands (data upscaling)

Assuming a management similar to mean site managementNEXT STEP: map NBP/GPP using agricultural management based on statistics

How large is the grassland C sink?

Estimates of European grassland C flux during the 1990s

-160

-140

-120

-100

-80

-60

-40

-20

0

Janssens et al.(2003)

Janssens et al.(2005)

Smith et al.(2005)

CarboEurope(data upscaling)

Study and year

Sin

k o

f C

fro

m g

ras

sla

nd

s

(Mt

C y

r-1)

First estimate, which needs to be refined:i) DOC/DIC losses up to 8 % of NBPii) On site N2O and CH4 emissions reach ca. 30 % of NBPiii) Indirect N2O and CH4 emissions, reach ca 15 % of NBP

http://www.abdn.ac.uk/modelling/cropwebpage/cropwebpage4IE.htm

CarboEurope-IP Cropland sites

(Gruenwald, pers. comm. to Christine Moureaux)

Variability between years and under different crops

Using models to interpret EC data from croplands

0

2

4

6

8

10

12

01 Ja

nuar

y 200

4

21 Ja

nuar

y 200

4

10 F

ebru

ary 2

004

01 M

arch

200

4

21 M

arch

200

4

10 A

pril 2

004

30 A

pril 2

004

20 M

ay 2

004

09 Ju

ne 2

004

29 Ju

ne 2

004

19 Ju

ly 20

04

08 A

ugus

t 200

4

28 A

ugus

t 200

4

17 S

epte

mbe

r 200

4

07 O

ctobe

r 200

4

27 O

ctobe

r 200

4

16 N

ovem

ber 2

004

06 D

ecem

ber 2

004

26 D

ecem

ber 2

004

[re

sp

ira

tio

n g

C m

-2 d

ay

-1]

-15

-10

-5

0

5

10

15

20

25

[te

mp

era

ture

in

De

gre

e C

els

ius

]

Reco_st gC m-2day-1

Reco DNDC gC m-2 day-1

pesticide

planting/harvest

Ta

Fungizide: Alegro Plus / Bravo @ 1.0L + 1.0L

kernels at milk kernels soft dough to maturity

Using models to interpret EC data from croplands

Analysis by Mike Williams

0

1

2

3

4

5

6

7

8

Nov-03 Jan-04 Mar-04 Apr-04 Jun-04 Aug-04 Sep-04 Nov-04 Dec-04 Feb-05

Rs

(mic

rom

ole

s C

O2

m-2

s-1

)

Measured

Modelled Multiple Regression

Modelled Lloyd and Taylor Function

What are the uncertainties associated with the simulation of cropland ecosystems at site level ?Input parameters

and variablesUncertainty

site scale

Fertilization (Nitrogen)

+/- 10% each application

Temperature +/- 1°C

Precipitation +/- 5%

Global radiation +/- 5%

Clay content +/- 10%

Initial soil carbon +/- 10%

kgC ha-1 yr-1

Oensingen NEE

6000 6200 6400 6600 6800 7000 7200

Cou

nt

0

20

40

60

80

site NEE

measured kgC ha-1

best estimate

run kgC ha-1

Mean value of the Monte Carlo simulation kgC ha-1

Oensingen 2004

5851 6735 6675

Comparison of cumulative Eddy Covarience NEE measurements with DNDC NEE predictions

-2000

0

2000

4000

6000

8000

10000

1 51 101 151 201 251 301 351

day of year

NE

E/E

C k

g C

/ha

EC NEE

DNDC NEE

95% confidence interval for DNDC simulation of cumulative NEE.

The discrepancy between simulated mean value from the Monte Carlo runs and the annual value obtained from a single run using the best estimates. suggest that using the best estimate may not lead to the most probable model result.

Input distribution

Monte Carlo – multi model run

DNDC*model

* DeNitrification-DeComposition model

Output distribution

Are croplands as big a source of C as we thought?

Estimates of European cropland C flux during the 1990s

-500

50100150200250300350

Janssens et al.(2003)

Janssens et al.(2005)

Smith et al.(2005)

Gervois et al.(2007)

Study and year

So

urc

e o

f C

fro

m

cro

pla

nd

s (M

t C

yr-1

)

-20

0

20

40

60

80

100

120

140

160

Cro

plan

d -

agro

nom

y

Cro

plan

d -

nutr

ient

s

Cro

plan

d -

tilla

ge&

resi

due

Cro

plan

d -

wat

er

Cro

plan

d -

seta

side

&L

UC

Cro

plan

d -

agro

fore

stry

Gra

zing

land

-nu

trie

nt&

graz

ing&

spec

ies

Deg

rade

d la

nd r

esto

ratio

n

Man

ure

appl

icat

ion

Seq

uest

ratio

n un

der

ener

gy c

rops

Org

anic

soi

l res

tora

tion

Practice

CO

2 si

nk (

t CO

2 ha

-1 y

r-1)

Data from: Smith et al. (2007a)

Looking to the future…more work on organic soilsOrganic soil restoration vs. mineral soil sequestration

Change in cropland SOC – climate&NPP&technology

Climate only

J.U. Smith et al. (2005)

Change in cropland SOC – climate&NPP&technology

Climate&NPP&technology

J.U. Smith et al. (2005)

Management impacts on cropland C balance – climate mitigation

Smith et al. (2007a)

Some potential for agricultural GHG mitigation in Europe

Conclusions• Synthesis papers are being written for each

landuse type• Plans for whole agricultural sector. Same

methodology for both land uses.• Need to account better for actual agricultural

management over Europe.• Major challenges

– Interannual variability and climate change– Continental upscaling– Organic soils

– Non CO2 GHG emissions

– Interactions with mitigation and adaptation