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
Advanced MRV to capture mitigation impacts -recent analysis and tools
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
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