Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.

Advanced MRV to capture mitigation impacts -recent analysis and tools

38 Aufrufe

Veröffentlicht am

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

Veröffentlicht in: Wissenschaft
  • Als Erste(r) kommentieren

  • Gehören Sie zu den Ersten, denen das gefällt!

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

×