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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 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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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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Activities across the projects
Prioritisation
Measurement
Data gathering/Proxies
Modelling
Uncertainty analysis/Verification
Data Management and Archiving
Knowledge Exchange
AC0112
AC0115
Methane
AC0116
Nitrous Oxide
Prioritisation Prioritisation
Measurement Measurement
Proxies Proxies
Modelling Modelling
Assessment
Development
Verification
Requirements
and Structure
Emissions Factor
Synthesis
Farm Practice
Synthesis
Agriculture, Soils
and Climate Data
Uncertainty
Analysis
AC0114
Data Mining
Data Management and Archiving
Within the
themes of
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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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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)
Country
Emission
factor
Unit Error on EF
Error type
recorded
Statistical 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) till sept 08 (harvest). Fan used to facilitate mixing
in chamber headspace. Deployment time usually 1.5hrs, measurements with chamber inter-sections
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 plastic septum could be sealed. Gas
linearity in chamber was tested. After lids in place an initial 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 flushing 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 time.
Cumulative N2O then calc by averaging N2O emission rate of the 3/4 soil covers per plot, followed
by linear interpolation of the weekly msmts over time. 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 differences 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
Statistica 8 software. Significance of treatment effects was tested using one-
way or two-way analysis of variance (ANOVA) and post hoc Tukey test.
Pearson correlation coefficient calc for testing relationship 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 time 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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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
Methane(MJ/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
gN/ha/day
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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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
Data Management and Archiving
