jrc/al – uncertainty workshop, helsinki 06/09/2005 uncertainty in agriculture adrian leip joint...

JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

Uncertainty in agriculture

Adrian Leip

Joint Research Centre, Institute for Environment

and Sustainability, Climate Change Unit


Quantitative Tier 1 uncertainty estimates

CATEGORYCOUNTRY Year TOTAL

4ACH4

4B CH4

4BN2O

4DN2O

4D1N2O

4D2N2O

4D3N2O

Austria 2003 5.5 0.3 0.7 0.6 0.7 0 0 0

Belgium 2003 8.1 1.2 0 0 7.2 0 0 0

Denmark 2003 6.8 0.5 1.3 0.8 1.6 0 0 0

Finland 2003 15.9 0.7 0.1 0.6 0 8.8 0 2.9

France 2002 22.1 2.3 1.4 0.3 20.9 0 0 0

Greece 2003 10.8 0.6 0.2 0.2 0 5.1 2.9 1.2

Ireland 2003 12.2 2.8 1.2 1 11.5 0 0 0

Italy 2001 2.5 0.7 0.4 0.8 0 0.5 0.4 0.7

Spain 2002 15.8 0.8 4.4 0.8 0 8 0.9 11.8

Sweden 2003 7.2 1.2 0.3 0.5 6.1 0 0 0

Netherlands 2003 6 0.5 0 0.3 0 1.3 0 3.1

UK 2002 17.9 0.5 0.1 0.9 17.6 0 0 0

% of total emissions


TOTAL 4A-CH4 4B-CH4 4B-N2O 4D-N2O

United Kingdom 17.9 103% 100% 100% 19%

France 22.1 105% 102% 101% 36%

Ireland 12.2 113% 102% 101% 38%

Spain 15.8 104% 98% 100% 38%

Sweden 6.9 103% 101% 101% 56%

Belgium 8.1 102% 101% 100% 59%

Netherlands 6 103% 100% 100% 86%

Greece 10.8 102% 100% 100% 89%

Finland 15.9 102% 100% 101% 94%

Italy 2.5 98% 99% 95% 96%

Austria 5.5 103% 100% 100% 102%

Denmark 6.8 104% 99% 100% 105%

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EC uncertainty in agriculture

Overall uncertainty estimates 5 % to 8 %.

Source category

Gas Emissions 2003

Share of EC emissions used

EC uncertainty

4A CH4 130748 99% 12%

4B CH4 61967 98% 17%

4C CH4 2205 75% 38%

4D CH4 -521 102% 127%

4F CH4 107 39% 54%

4B N2O 21873 78% 93%

4D N2O 197455 98% 84% - 195%

4F N2O 369 4% 54%

Total agriculture

all 414427 97% 44 – 83%


Contribution of emission estimated with country-specific information to the total EC emission estimates for key sources in the agriculture sector.

CategoryEmissions

[Gg CO2-eq]Percentage

estimated with country-specific

approach

4.A.1: Cattle (CH4) 109814 58%

4.A.3: Sheep (CH4) 14665 73%

4.B.1: Cattle (CH4) 28982 19%

4.B.8: Swine (CH4) 30066 42%

4.B.12: Solid storage and dry lot (N2O) 20364 13%

4.D.1: Direct soil emissions (N2O) 100401 8%

4.D.2: Animal production (N2O) 28566 1%

4.D.3: Indirect emissions (N2O) 64473 53%

TOTAL 397332 34%


EU-uncertainty for agricultural soils[% of emissions, combined AD-EF]

total direct animal indirect

Austria 24

Belgium 252

Denmark 21 25-54 32 51-54

Finland 227 334

France 200

Germany 16 77 16

Greece 401 112 54

Ireland 105

Italy 32 102 54

Netherlands 61 206

Spain 380 112 906

Sweden 87

United Kingdom 424


Uncertainty for N2O emissions from soils

→ How can bias be estimated (representativeness)

→ How does temporal variability translate into uncertainty?

→ How large is the impact of correlations

AD uncertainty

small compared with EF uncertainty

EF uncertainty: Spatial variability

high, driven by

climate, soil and morphological variations

cropping patterns, fertilizer mixEF uncertainty: Temporal variability

high, driven by

weather conditions

fluctuations in management, fertilizer mix, cropping changes

bias


Jungkunst 2005

Reducing uncertainty:

STRATIFICATION

• Climate regions (freeze-thaw events/rewetting of dry soils)

• Effect of soil type (organic carbon, wetness)• Effect of type of N applied (mineral / organic)• Effect of crop type (classes)


Mulligan, 2005: DNDC-Italy

… mean emission factor for mineral N fertiliser derived from the linear regression of the emission estimates plotted against non-volatilised N fertiliser: 0.83%

applied manure emission factor: 1%

Butterbach- Bahl and Werner, 2005: DNDC-Germany

… Fertilizer induced emissions, were only approx. 50% of total N2O emissions. If the latter figure is used, our estimates are approx. 1/3 lower than estimates based on the IPCC approach for Germany

Brown et al., 2002, 2005: DNDC-UK

DNDC-UK IPCC

Fertiliser 1 1.25

FYM 0.6 1.25

Slurry 1.7 1.25

Grazing 0.5 2

PROCESS-BASED MODELS


ADDITIVITY of fertilizers

→ in 40% of the cases the synchronous addition of

synthetic fertilizer and animal wastes lead to higher N2O

emissions by >10% than the sum of single EFs would

suggest

→ in 10% of the cases

the effect is >40%

→ only 12% of the cases

lead to an over-

estimation of N2O

emissions by >10%

CALCULATION OF N2O EMISSIONS FROM SEPARATE EFs FOR SYNTHETIC

FERTILIZER AND ANIMAL WASTES

CALCULATION OF N2O EMISSIONS FROM SEPARATE EFs FOR SYNTHETIC

FERTILIZER AND ANIMAL WASTES


FAO / IFA, 2001; Bouwman et al., 2002:

Global estimates of gaseous emissions of

NH3, NO and N2O from agricultural land

Factor class value for fertilizer type

• Ammonium bicarbonate, ammonium chloride, ammonium sulphate 0.6

• Calcium nitrate, potassium nitrate, sodium nitrate 2.6

• Calcium ammonium nitrate and combinations of AN and CaCO3 2.3

• Ammonium nitrate 3.0

• Urea and urine 1.9

• Urea-ammonium phosphate 3.2

• Mix of various fertilizers 3.4

• Ammonium phosphate and other NP fertilizers 3.8

• Anhydrous ammonia including aqueous ammonia 4.4

• Organic fertilizers 4.7

• Combinations of organic and synthetic fertilizers 5.9


Correlations

dependencies, even if they exist, may not be important to

the assessment of uncertainties

When dependencies among inputs are judged to be of importance

(i) modelling the dependence explicitly;

(ii) stratifying or aggregating the source categories;

(iii) simulating correlation using restricted pairing methods;

(iv) use of resampling techniques in cases where multivariate datasets are

available;

(v) considering bounding or sensitivity cases.

ADs are regarded as generally uncorrelated in

time

EFs are regarded as generally correlated in

time


Correlation: Disaggregation

→ The compensation effect reduces uncertainty when

adding source categories / countries of similar magnitude

SUM OF CATEGORIES

EF1 assumed correlated - lack of evidence to provide different default

values for various forms of N input does not imply that the error is the same for

all nitrogen input!


Correlation in time

→ In time uncorrelated

sources result in highly

uncertain trends

If higher-Tier approaches (models) are used:- How should temporal

variability be treated?- Use response to ‘climate’

rather than ‘weather’ for process-based models


Spatial variability

FAO / IFA, 2001; Bouwman et al., 2002:

“… emissions induced by fertilizers amount to 0.9 Mt or approximately 0.8 % of current nitrogen fertilizer input.”

Reported total emissions relative to

fertilizer input. Organic soils are adjusted by 8 kg

N2O-N ha-1


lognormalmean

# of estimates

included

Range* ratio high/low

Global 0.9% 643 0.04% .... 15.43% 379

Regions of the world

America 1.2% 186 0.07% ... 15.93% 239

Europe 0.9% 352 0.04% ... 17.85% 505

Asia 0.3% 69 0.02% ... 3.16% 130

Other 1.3% 34 0.26% ... 7.38% 28

Individual countries

Germany 1.5% 147 0.05% ... 26.03% 537

UK 0.6% 130 0.02% ... 14.64% 844

Canada 2.1% 27 0.29% ... 19.08% 66

* based on log-normal distribution; ±2 SD


HYPOTHESIS 1:

Is the compensation effect in the uncertainty assessment

appropriately considered?


HYPOTHESIS 2:

As measurements programs alone will not

suffice to obtain stratified emission factors, future

N2O inventories must rely on models to reduce level

uncertainty.


HYPOTHESIS 3:

Temporal variability of N2O emissions from soils

leads to high trend uncertainty.

Care must be taken how to treat it.


HYPOTHESIS 4:

Direct EFs for emissions from synthetic fertilizer, manure, crop residues should be treated as uncorrelated in the

uncertainty assessment.


THANK YOU FOR YOUR

ATTENTION !

jrc/al – uncertainty workshop, helsinki 06/09/2005 uncertainty in agriculture adrian leip joint...

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