Authors: Lini Wollenberg (CCAFS, UVM) and Andreas Wilkes (Unique Forestry and Land Use) Presented at workshop: Increasing impact: How to achieve mitigation of greenhouse gas emissions in the dairy sector at large scales 30 August 2018 Wageningen University and Research

1. Lini Wollenberg, CCAFS and Andy Wilkes, UNIQUE Forestry and Land Use
30 August 2018
Advanced MRV to capture mitigation
impacts - recent analysis and tools

2. Why improve MRV of livestock emissions?
• 62 countries included mitigation of
livestock emissions in their NDCs
(March 2018 data)
• Improved livestock management
can decrease emissions
• Yet most developing countries use
methods designed for inventories
that don’t show mitigation impacts
well.

3. IPCC Tier 1 v 2 methods
MRV for mitigation requires IPCC
Tier 2 methods:
1. more detailed activity data
2. Regular updates of activity data
Acitvity data for IPCC Tier 2 methods
(enteric methane):
• animal weight
• average weight gain/day
• feeding situation (e.g. confined animals;
animals grazing good quality pasture)
• milk production/day
• average hours work/ day
• cows giving birth in a year;
• feed digestibility (%)
Source: IPCC 1996 and 2006 Guidelines, 2000 Good Practice Guidance

4. Challenges for Tier 2 Estimates
• Lack of activity data
• Lack of updated activity data
• Perception that data needs are too high, expensive
• No standardized approach will work across countries:
§ diverse production systems and policy priorities;
§ mitigation projects at varying subnational scales
§ countries want to design their own MRV;
• Base year v BAU baselines?

5. Supporting improved MRV 2016-2019
GRA, CCAFS, UNIQUE and FAO
collaboration
• Review of existing MRV
practices (2016-2017)
• “Making MRV work” workshop
(2017) with 20+ countries
• Tier 2 approaches in the
livestock sector: a collection of
inventory practices (2018)
• MRV Web Platform (2018)
• Activity data gap filling (2019)
• Developing improved MRV in
China, Indonesia (ongoing) Wilkes et al. 2017
French and Spanish versions
also available

6. Findings: Tier 2 matters
63 countries currently use Tier 2 methods for cattle (62 for dairy)
• ~45% of countries first used a Tier 2 approach in the last 10 years
• Tier 2 emission factors were higher than the IPCC default Tier 1
emission factors in 40/ 48 countries (83%)
• Where higher, average emission factor was 34% higher than the
Tier 1 default.
• Where lower, (8 countries) average emission factor was 20% lower
than Tier 1 default.
Source Wilkes 2018

7. Diverse structures for classification
Argentina
• 8 agro-ecological and climatic regions
• Breeding and fattening systems
identified/region
• Production systems modeled (activity, diet,
reproduction and production)
• Aggregate results cross-checked against
regional, census and agricultural production
data.
• Countries categorized dairy cattle into 1 -156 subcategories, with
average of ~8 sub-categories.
• 66% of countries using Tier 1 reported only one category of dairy
cattle (i.e. mature, female milking cows). Sufficient for Tier 2?
• In other countries, systems defined by geographic region (9
countries), production system (5 countries), breed (3 countries) or
productivity (1 country).
• Scale of projects v scale of classification systems?
Wilkes 2018
Wilkes et al. 2017

8. Activity data: Gaps and mixed data
sources
Wilkes et al. 2017

9. Data sources
Data source
Frequ
ency
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
Table 6: Frequency of sources of livestock population data (n=63)
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
Table 7: Data sources and methods for cattle animal weight estimates
Initial NIR
data sources
Latest NIR data
sources
n=40 n=43
IPCC default 28 29
Other government agency 1 0
Literature from own country 3 3
Commissioned study 0 2
Expert judgement 2 1
Estimated by calculation 1 1
Value from other country’s
inventory 0 1
Equation or model 4 5
Literature from other country 0 1
Table 11: Data sources for methane conversion rate (Ym) estimates
Population - statistical agency
CH4 conversion - IPCC default
Animal weight - Ministry of
agriculture, expert judgement
>20% of countries used expert
judgement for initial estimates of
animal weight and weight gain,
proportion of time spent grazing,
fat content of milk and % cows
giving birth

10. Conclusions
• Countries that seek to estimate mitigation should consider a
Tier 2 approach
• Tier 2 emissions were mostly higher than default emission
factors (34% higher)
• Activity data are the major constraint to reporting mitigation
• Bottom-up reviews of country practices shows diverse
approaches
• Yet countries still need improved data sources and linkages,
e.g. statistical systems, other livestock data systems and
MRV
• Resources and activities to support improved MRV are
increasing, but much more needed