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Advanced MRV to capture mitigation impacts -recent analysis and tools

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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

Publicado en: Ciencias
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Advanced MRV to capture mitigation impacts -recent analysis and tools

  1. 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. 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. 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. 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. 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. 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. 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. 8. Activity data: Gaps and mixed data sources Wilkes et al. 2017
  9. 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. 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

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