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Tier 2 approaches for livestock: A collection of practices

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

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Tier 2 approaches for livestock: A collection of practices

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

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