Expert workshop on Improving activity data for Tier 2 estimates of livestock emissions: Dealing with data gaps July 17-18, 2018 Collection of practices

1. TIER 2 APPROACHES FOR LIVESTOCK:
A COLLECTION OF PRACTICES
Andreas Wilkes
Den Haag, July 2018

2. © UNIQUE forestry and land use GmbH
BACKGROUND
2
In 2017, a review of MRV of livestock emissions in developing countries
identified 92 countries that included livestock emissions in their INDCs, but
• Only 20 developing countries use a Tier 2 approach in their national GHG
inventory, of which only 5 have an inventory approach that can track change in
emissions over time.
Recommendations:
Review how Tier 2 methodological approaches have evolved and the steps
countries have used to improve their methods over time
 Document and share case studies of the approaches used by different
countries to compile and improve their national GHG inventories.
In 2018, we have been conducting a review of Tier 2 approaches (developed &
developing countries) for cattle, with a focus on enteric fermentation and
manure management methane.

3. CONTENTS
1. Some basics
2. Activity data: Livestock
characterization and population data
3. Activity data: Estimation of gross
energy/emission factors
4. Common issues faced

4. © UNIQUE forestry and land use GmbH
SOME BASICS (1) PRINCIPLES FOR MRV
4
Principle Interpretation
Transparency Assumptions and methodologies clearly explained
Consistency Same methodologies used for all years
Comparability Use agreed methodologies and reporting formats
Completeness All GHG sinks and sources are covered
Accuracy No systematic over- or under-estimatation, uncertainties are
reduced as far as practicable
Cost-effective Make cost effective use of resources
Accuracy of the
trend
Ability to describe the trend in emissions over time
Compatibility Use consistent methodologies between different data systems
Additional considerations?

5. © UNIQUE forestry and land use GmbH
SOME BASICS (2) TIER 2 APPROACHES
5
Tier 1 Tier 2
Activity data Activity data
Tier 2: both population and intake can change year to year and track
change in productivity and emissions

6. © UNIQUE forestry and land use GmbH
SOME BASICS (3) CONSISTENT TIME SERIES
6
110
115
120
125
130
135
140
Gg-CH4
Adopt
Tier 2Construct historical time
series
Regular update

7. © UNIQUE forestry and land use GmbH
LIVESTOCK CHARACTERIZATION
Mean
(range)
# of
categories
Frequencies (n=59)
age
sex/
physiological
status breed
production
system use region productivity
7.84
(1-156) 10 10 3 5 5 9 1
Dairy cattle
Mean
(range)
# of
categories
Frequencies (n=60)
age
sex/
physiological
status breed
production
system use region productivity
18
(1-416) 55 51 8 9 20 11 -
Other cattle

8. © UNIQUE forestry and land use GmbH
POPULATION DATA SOURCES
Data source
Freq.
(n=63)
Statistical Agency 40
Ministry of Agriculture 15
Other government agency 6
Producer organisations 4
Extrapolation 7
Expert judgment 3
Animal registration database 3
Publication 1
Modelled 2
FAOSTAT 1

9. © UNIQUE forestry and land use GmbH
ESTIMATING GROSS ENERGY WITH THE IPCC MODEL
Average weight Av. Weight for each animal type
Cfi Coeff. for maintenance
% pasture % of time spent on pasture
Ca Coeff. for feeding situation
Weight gain For growing animal types
C Coeff. For growth
Milk yield Annual milk yield
Fat content Average fat content
% giving birth % pregnant in the year
Cpreg Coeff. For pregnancy
% DE Digestibility of feed
Gross energy Calculated
Ym Methane conversion factor

10. © UNIQUE forestry and land use GmbH
ESTIMATING GROSS ENERGY WITH THE IPCC MODEL
Greece (mature dairy cattle)
Average weight Expert opinion
Cfi IPCC default
% pasture Official statistics
Ca IPCC default
Weight gain -
C IPCC default
Milk yield Ministry of Agriculture
Fat content Official statistics
% giving birth IPCC default
Cpreg IPCC default
% DE IPCC default
Gross energy Calculated
Ym IPCC default

11. © UNIQUE forestry and land use GmbH
ESTIMATING GROSS ENERGY WITH THE IPCC MODEL
Portugal (mature dairy cattle)
Average weight Estimated  revised by expert opinion
Cfi IPCC default
% pasture Expert opinion  official stats
Ca IPCC default
Weight gain Government data  expert opinion
C IPCC default
Milk yield Official statistics
Fat content IPCC default  official statistics
% giving birth IPCC default  government data
Cpreg IPCC default
% DE IPCC default  expert opinion
Gross energy Calculated
Ym IPCC default

12. © UNIQUE forestry and land use GmbH
ESTIMATING LIVE WEIGHT
Initial Tier 2 NIR
data sources
Latest Tier 2
NIR data
sources
n=45 n=45
Regularly reported statistics 3 4
ministry of agriculture 7 11
other government agency 2 3
Producer/industry organisation 3 1
Literature from own country 8 6
commissioned study 4 7
IPCC default 3 1
Expert judgement 12 11
Estimated by calculation 3 3
Value from other country’s inventory 1 1
Equation or model 1 2

13. © UNIQUE forestry and land use GmbH
ESTIMATING MILK YIELD
Initial NIR
data
sources
Latest NIR data
sources
n=45 n=34*
Regularly reported statistics 22 18
ministry of agriculture 3 6
other government agency 1 0
Producer/industry organisation 4 0
Literature from own country 3 0
commissioned study 0 0
Expert judgement 3 0
Estimated by calculation 1 1
Value from other country’s inventory 2 0
Interpolate 0 1

14. © UNIQUE forestry and land use GmbH
ESTIMATING FEED DIGESTIBILITY
Initial NIR data
sources
Latest NIR
data sources
n=35 n=35
Regularly reported statistics 0 0
ministry of agriculture 3 4
other government agency 1 2
Producer/industry organisation 2 0
Literature from own country 5 12
commissioned study 1 6
Literature from other country 3 1
IPCC default 16 7
Expert judgement 6 6
Equation or model 0 1

15. © UNIQUE forestry and land use GmbH
COMMON ISSUES FACED:
15
(1) Making use of available data:
Extrapolation
Interpolation
Estimation using models or equations
Application of expert judgement to available data
Using data from other countries / global databases?
...other methods? (e.g. combining data sets...)
What methods can be used, under what conditions, with what implications for
accuracy, uncertainty and inventory aims?
(2) Collecting new data:
• Dedicated data collection activities
Sampling
Data collection methods
Costs vs accuracy
• Integrating data needs into statistical data collection efforts
• Aligning research planning with inventory needs

16. © UNIQUE forestry and land use GmbH
Schnewlinstr. 10
79098 Freiburg, Germany
Tel: +49 761 208534 – 0
unique@unique-landuse.de
www.unique-landuse.de
© UNIQUE forestry and land use GmbH
With many thanks!
andreas.wilkes@unique-landuse.de