inventory of ghg uk overview

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Inventory of Greenhouse Gases UK


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Background – Agriculture

• Agriculture is estimated to be responsible for:

– ca. 8% of UKs total GHG emissions

• ca. 60% of UKs total nitrous oxide emissions (IPCC)

• ca. 40% of UKs total methane emissions (IPCC)

• UK complies with Kyoto

• LCTP – contribute to 80% reduction in GHGs by 2050

– ca. 85% of UKs total ammonia emissions

• National emissions ceiling target – Gothenburg protocol

• IPPC (Pig and Poultry units)

– ca. 60% (E&W) of nitrate transfers to inland watercourses

• NVZ action plan


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Background – UK agricultural emissions

0

200

400

600

800

1000

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

kt C

H4

CH4 emissions

Enteric Manure management

0

200

400

600

800

1000

Enteric Waste Field burning

CH

4, k

t

1990 2010

Decrease -19.8%


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0

10

20

30

40

50

60

70

80

kt N

2O

N2O emissions

Animals N2O, kt Direct N2O, kt Indirect N2O, kt Field burning, kt

0

10

20

30

40

50

60

70

Animals Direct Indirect Field burning

N2O

, kt

1990 2010

Decrease -19.5%


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0% 1%1%

5%

6%

7%

9%

20%22%

28%

1%

N2O emissions from agriculture in 2010 (total 90.5 kt N2O)

Histosols

Improved grassland

N fixing crops

Atmospheric deposition

Animal waste management systems

Animal manure applied to soil

Crop residues

Grazing

Synthetic fertilisers

Leaching and runoff

Sewage sludge

LEACH

FERT GRAZ


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Background – UK emissions – method CH4

Source Category (IPCC table)

Method Emission Factors

4A Enteric fermentation IPCC T1

IPCC T2 (cattle)

CS, D

4B Manure management IPCC T1

IPCC T2

(cattle,lamb,deer)

CS, D

4C Rice cultivation NA NA

4D Agricultural soils NA NA

4E Prescribed burning of

savannas

NA NA

4F Field burning of agricultural

residues

NA NA

T1 – Tier 1; T2 – Tier 2; T3 – Tier 3; CS – Country Specific


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Background – UK emissions – method N2O

T1 – Tier 1; T2 – Tier 2; T3 – Tier 3; CS – Country Specific

Source Category (IPCC table)

Method Emission Factors

4A Enteric fermentation NA NA

4B Manure management IPCC T1 CS, D

4C Rice cultivation NA NA

4D Agricultural soils IPCC T1

CS

CS, D

4E Prescribed burning of

savannas

NA NA

4F Field burning of

agricultural residues

NA NA


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Background – UK emissions – Tier 1

• Emissions

Em(g,s) = A (corrected) EF

Where:

Em (g,s) = Emission of gas g from source s (kg g yr-1)

A = Activity data (animal numbers, fertiliser use, etc) for sources

(data corrected for volatilisation for example)

EF = Emission factor of gas g from source s (kg g/kg s or number)


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Background – N cycle


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GHG platform aims

To develop an improved inventory tool that will better represent UK (and DA) agricultural practices and conditions and be capable of monitoring progress against targets

• development of specific emission factors

• derivation of activity data

• definition and inclusion of mitigation practices

• quantification of uncertainties

• documentation and archiving


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AC0112

AC0115Methane

AC0116Nitrous Oxide

Prioritisation Prioritisation

Measurement Measurement

Proxies Proxies

Modelling Modelling

AssessmentDevelopment

Verification

Requirementsand Structure

Emissions FactorSynthesis

Farm Practice Synthesis

Agriculture, Soilsand Climate Data

UncertaintyAnalysis

AC0114Data Mining

Data Management and Archiving

Platform projects

• £12.6M on

three projects

• 5 years

• Multiple

partners


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Activities across the projects Prioritisation

Measurement

Data gathering/Proxies

Modelling

Uncertainty analysis/Verification


Knowledge Exchange

AC0112

AC0115Methane

AC0116Nitrous Oxide

Prioritisation Prioritisation

Measurement Measurement

Proxies Proxies

Modelling Modelling

AssessmentDevelopment

Verification

Requirementsand Structure

Emissions FactorSynthesis

Farm Practice Synthesis

Agriculture, Soilsand Climate Data

UncertaintyAnalysis

AC0114Data Mining


Within the

themes of


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Prioritisation

Defining inventory requirements

Prioritising the measurement programme


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Proposed Structure and Calculation Methodology

• Defining boundaries

• Specifying emission sources

• Linkages with other Inventory sectors


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Activity data

Emission factors

INPUT DATABASE

Enteric fermentation – CH4

Manure management – CH4

Manure management – N2O

Soils – N2O

MODEL ENGINE

Algorithms for:

UNFCCC IPCC report

Devolved administration

reports

Sector reports

OUTPUTS



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• Collate existing data sets

• Identify key uncertainties and knowledge gaps

• Review proposed work programme

Prioritising measurement programme

N2O field measurement sites


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Data gathering/Proxies

Farm Practice Data

Developing proxies for estimating emissions


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Farm practice data

• Great challenges in obtaining required data

• Lot of activity engaging with national statistics and industry data sources

• Farm practice review report


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Proxies

• Not everything can be measured! – how else to monitor change

• Assess proxies for N2O from soils and CH4 from enteric fermentation

• Link with modelling activities

N2O

National/regional scale – e.g. OECD N balance, N use efficiency

Farm scale – e.g. farm gate N balance, uptake of mitigations,

Field scale – e.g. soil characteristics, crop yields

CH4

e.g. diet characteristics, fed conversion efficiency


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Experimentation

Collating existing data

Initiating new experiments

Methane emission factors

Nitrous oxide emission factors


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Collating/reviewing existing experimental data

• Database compilation

- Completed and on-going UK experiments

- Assess quality and compliance of data

• Literature review

- Structured, standard protocol

- UK and international

- Emission factors, Mitigation practices

- Assess quality and compliance of data

Authors Year Title Journal Volume Pages

IPPC

COMPLIANT

(√/X)

CountryEmission

factorUnit Error on EF

Error type

recordedStatistical methods used Collection method notes from publication Calculation method notes from publication

Chirinda et al 2010

Emissions of nitrous oxide from arable organic and conventional

cropping systems on two soil types

Agri, Ecosyst and

Env 136 199-208 √ Denmark 0.56

kgN2O

100/kgN

multiple linear regression - PROC MIXED of SAS-

96. Prior to analysis they were log transformed

when needed in order to obtain variance

homogeneity and normal distribution

monitored 365 days Sept 07 (following sowing) t ill sept 08 (harvest). Fan used to facilitate mixing

in chamber headspace. Deployment t ime usually 1.5hrs, measurements with chamber inter-sect ions

extended to max 3hrs. Foulum chambers - 0.75x0.75m; Flakkebjerg - 0.6x0.6m base. Gas samples

taken through septum, evac vials, 09.00-14.00 each sampling day. 1st sample taken immediately

after installing cover. GC and ECD (GC-14B Shimadzu - Flakkebjerg; Chrompack CP9001 -

Foulum)

Fluxes calc by linear regression taking air temp into account; all

data were checked for linearity by visual inspection during data

analysis

Abdalla et al 2010

Emissions of nitrous oxide from Irish arable soils: effects of tillage

and reduced N input Nut.Cycl. Agroeco 86 53-65 x Ireland 0.42 % 0.41 SE

Checked for normal dist. Log transformed where

applic. 1 and 2 way analysis of variance applied to

flux. Multiple regression

N2O measured using Smith et al (95). Chambers - 52x52x15cm high square collar inserted

permanently into soil over which 50x50x30cm high lid with plast ic septum could be sealed. Gas

linearity in chamber was tested. After lids in place an init ial gas sample taken, 2nd and 3rd at

30/60min. sampled every weekand more intensively during fert periods. Samples taken in morning

between 9-11am. Samples taken with syringe after f lushing syringe and mix air within chamber; then

injected to pre-evac vials. GC 14B Shimadzu with ECD.

EFs calc according to equation with assumes 10% of applied N is

lost from soil through ammonia volatilisation (IPCC 2001b)

de Klein et al 2006

Restricted autumn grazing to reduce nitrous oxide emissions from

dairy pastures in Southland NZ

Agriculture,

Ecosystems and

Environment 112 192-199 x NZ

Air tight lid, 30min centre bung fitted, gas sample taken through

a septum in lid, 10mL headspace flushed 4 times through a

6mL septum-sealed glass tube. 2nd gas sample taken 30min

later. N2O - gas chromatograph. 2003 samples stored for

7months - no sign of leakage

Weekly N2O rates calc for each soil cover from the increase in N2O conc of the headspace over t ime.

Cumulat ive N2O then calc by averaging N2O emission rate of the 3/4 soil covers per plot, followed

by linear interpolat ion of the weekly msmts over t ime. Average N2O emis for each treatment was calc

from the geometric means of the intergrated emis, then log-transformed and ANOVA of randomised

blocks to determine dif ferences btw treatments for each yr as well as for the 3yr period

Brucek, P., Simek,

M., Hynst, J 2009

Long-term animal impacts modifies potential production of N2O from

pasture soil Biol.Fert Soils 46 27-36 x

Czech

Republic

Stat ist ica 8 software. Signif icance of treatment effects was tested using one-

way or two-way analysis of variance (ANOVA) and post hoc Tukey test.

Pearson correlat ion coeff icient calc for test ing relat ionship between N2O and

CO3

M edium sized, non-vented, manually closed chambers (each = 0.076m2 basal area, 15dm3 volume).

Chamber in 2 parts, bottom - collar of galvanised steel 14cm high and internal diameter 31cm, it was

inserted 5cm into soil. Headspace gas samples collected at the t ime of deployment and after

60min. Gas samples analysed HP 5890 gas chromatograph equiped with Porapak Q column and

electron capture detector

Reference N2O


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Methane

Method development

• Novel techniques for enteric methane estimation

• Large-scale online monitoring (online measurement from dairy cows in milking parlours and beef cattle in over-feeder hoods

Protocols

• Common animal breeds and common diets across sites

• Calibration of measuring equipment by National Physical Laboratory

• Engagement with GRA

Training and knowledge exchange

• SF6 workshop

• Intake measurement workshop

• Technical support knowledge exchange meetings


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Methane - sources

Enteric fermentation

Sheep

Beef

Dairy

Body size, breed, genotype, diet, physiological stage

Chamber studies

SF6 at grazing


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Methane - sources

Manure management

Characterisation of manures from UK livestock:

- volatile solids content, VS

- methane producing potential, Bo 0

1

2

3

4

5

0 10 20 30 40 50 60

Met

han

e (

Litr

es)

Time (Days)

Blank

Cellulose standard

Cattle 1Cattle 2

Cattle 3

Estimation of methane conversion factor at storage, MCF:

- slurries, temperature effect

- FYM


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Methane – measurements - Bo

IPCC Tier 2 approach:

MCFx67.0xBx365x oVSEFMS

Predicted max values: 2.46

2.37

Preliminary data


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Nitrous oxide

Protocols

• Standard operating practices across all sites

• Ring-tests for analytical techniques

• Engagement with GRA

Training and knowledge exchange

• Autochamber training day


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Nitrous oxide - measurements

Emissions from soils

N2O field measurement sites

Grassland

Arable

Fertiliser - Type, grassland/tillage, rates, timings, nitrification inhibitor

Grazing returns - Dung & urine, 3 times of year

Manure - Slurry/FYM/Poultry, application method, timing


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Nitrous oxide - measurements

Laboratory study

The effect of soil properties, moisture and temperature on the efficacy of the nitrification inhibitor dicyandiamide

Leached N

Nitrous oxide emissions from leached nitrate


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Nitrous oxide - methods

Static chamber

Fast chamber

Autochamber


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Nitrous oxide - urine

Treatments:

Urine / Artificial urine / Urine + DCD / Control


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• Results-spring (application 15/5/2011, kg N/ha):

Treatment kg N/ha

Preliminary data


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• Results-summer (application 3/7/2011, kg N/ha):

Treatment kg N/ha

Preliminary data


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Modelling

Methane

Nitrous oxide

Farming systems


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Modelling

Methane

Statistical approach to estimating EF from diet/production parameters

Nitrous oxide

Mechanistic approaches using DNDC and DayCent

Not everything can be measured!

Interpolation, extrapolation, accounting for bias

Dry matter intake (kg/d)

0 10 20 30

Me

tha

ne

(M

J/d

)

0

5

10

15

20

25

30

35

Col 11 vs Col 15 - BELTSVILLE

Col 11 vs Col 15 - CEDAR

Col 11 vs Col 15 - LELYSTAD

Col 11 vs Col 15 - WAGENINGEN

-50

0

50

100

150

200

250

300

350

400

450

500

1 31 61 91 121 151 181 211 241 271 301 331 361

g N

/ha

/da

y

J day

N2O fluxes; Rowden 2006 Plot 3

UKdndc Observed

Farming systems

Disaggregation of national statistics to populate inventory structures - livestock numbers and linkages (e.g. dairy, beef systems) - soils, land use, drainage, N to crops


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Uncertainties/Verification

Quantifying uncertainties

Verifying inventory outputs


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Uncertainty Analysis

Quantify uncertainties in the existing and improved inventories

Quantify uncertainties for specific source/sector uncertainties

• Protocol developed – Monte Carlo analysis

Probability distribution functions required for all input data


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Verification

White dots are static chambers

Measurement scale Comparison between chambers and micro-met for nitrous oxide

Long-term

Long-term continuous measurements at Easter Bush

National scale verification

Tall towers at Mace Head and Angus

Inverse modelling using NAMES


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Producing a technical specification for a Greenhouse Gas Agricultural Emissions Inventory Data Model – Report about to be delivered

- data types - key properties - Relationships

Development of data archive



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Knowledge Exchange

Stakeholder workshop

GRA Technology workshop, Reading


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Knowledge Exchange Press releases Project flyers


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Knowledge Exchange Website


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Organisations involved:

ADAS, AFBI, CEH, Keith Smith, Met Office, RRes, SRUC,

University of Aberdeen, UEA

This work was funded by:

Defra, the Scottish Government, DARD, and the Welsh Government

as part of the UK's Agricultural GHG Research Platform project

(www.ghgplatform.org.uk).