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Application of a Tier 3 for enteric methane in dairy catlle_Bannink

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Application of a Tier 3 for enteric methane in dairy catlle_Bannink

LiveM_Macsur_Bilbao_2014

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Application of a Tier 3 for enteric methane in dairy catlle_Bannink

  1. 1. Application of a Tier 3 for enteric methane in dairy cattle André Bannink
  2. 2. A Tier 3 for enteric CH4 Why ? ●accommodate for variation in rumen fermentation How ? ●addressing chemical/physical aspects rumen function ●using extant process-based model Activity data from Central Bureau of Statistics to estimate N & P excretion by dairy cows based on diets and productivity Outputs Tier 3 ●CH4 emission factor (kg CH4/cow/yr) ●CH4 as % of gross energy intake
  3. 3. Non-resistant 1, 2 Q Substrate Q Micro-organisms Resistant 1 Resistant 2 Microbial growth 1 Microbial growth 2 Feed 1 Feed 2 Rumen CH4 1, CH4 2 Fixed characteristics, no variation with type of ration Additivity for feed components assumed Microbial growth = table value Substrate degradation = table value Non-Tier 3 approaches for enteric CH4
  4. 4. Substrate degr = fion ( QSub, QMi ) Q Substrate Q Micro-organisms Substrate outflow = fion ( QSub ) Microbial growth = fion ( QSub, QMi ) Microbial outflow = fion ( QMi ) Feed Rumen No fixed degradation rates, growth rates, and so on Microbial growth = f ion ( [substrates] & [micro-organisms] ) Substrate degradation = f ion ( [micro-organisms] & [substrates] ) Microbial death/predation = fion ( QSub, QMi ) Tier 3 for enteric CH4
  5. 5. Tier 3 for inventory of effects ration on CH4 3 causal factors to quantify CH4 Organic matter Micro- organisms VFA CH4 Feed intake Rumen 1. Chemical composition & degradation characteristics 2. Microbial growth (efficiency) 3. Type VFA fion(substrate type, pH) Small intestine outflow absorption Acetic acid H2 Propionic acid Butyric acid Longchain FA hydrogenation Valeric acid Microbial growth on ammonia Microbial growth on AA H2 source H2 sink Methane CO2 + 4H2 CH4 +2H2O SURPLUS
  6. 6. Chemical composition affects CH4 Line 1 ●Level 2 Line 2 ●Level 2 ●Level 2 ●Level 3 ●Level 3 Line 3 Line 4 0 100 200 300 400 Eiwit NDF Zetmeel Suikers Methane (mmol/mol VFA) Meta-analysis in vivo data lactating cows Bannink et al (2006 & 2008) +10% +68% +55% FAT Delivers no VFA hence no CH4 Negative effect of fat on CH4 Protein NDF Starch Sugars
  7. 7. Recently updated NIR data dairy enteric CH4 Update Ym IPCC Tier 2 from 6.0% to 6.5% GE intake Budget period 2013 onwards 11% Update Bannink et al. (2011), unpublished year IPCC Tier 2, 6.0% GE intake IPCC Tier 2 update, 6.5% GE intake Tier 3
  8. 8. Tier 3 estimates realistic ? ↑ DMI, ↓ methane per MJ feed IPCC Tier 2 Tier 3 Independent data-base University of Reading; Reynolds & Mills
  9. 9. Tier 3 far too complex compared to Tier 2 ? Type of data input required, ordered by colour So, both methods use the same type of input data Methods use data in different manner Choice of method depends on data availability, detail en goal Data requirement per method Tier 2 Tier 3 Digestibility / NEL value feed ₰ Rumen degradation characteristics ₰ NEL requirement → Feed intake ₰ Feed intake ₰ Chemical composition → gross energy feed ₰ Chemical composition ₰ If goal is mitigation/adaptation on a farm, do not apply generic numbers
  10. 10. Effect grassland management on CH4 10.0 12.0 14.0 16.0 18.0 20.0 22.0 GS-EC GS-LC g CH4 / kg DM 6.0 8.0 10.0 12.0 14.0 16.0 18.0 GS-EC GS-LC g CH4 / kg FPCM GS = grass silage = high N-fertilization = low N-fertilization EC = early cut; LC = late cut Bannink et al (2010) 18 kg DM/d (90% grass silage & 10% concentrates)
  11. 11. HF = high N fertilization; LF = low N fertilization EC = early cutting; LC = late cutting Reijs, 2007 Dijkstra et al (2012) HFEC HFLC LFEC LFLC Grass si lage type 0 100 200 300 400 500 N excretion (g day- 1) Immediately av ai lable N Eas i ly dec ompos able N Res is tant N C:N 0 .5 C:N 3 .4 C:N 3 3 C:N 0 .5 C:N 3 .5 C:N 2 8 C:N 0 .5 C:N 3 .3 C:N 2 9 C:N 0 .5 C:N 3 .3 C:N 3 7 A non-CH4 application, excreta composition
  12. 12. Effect N mitigating feeding measures on CH4 Dijkstra et al (2011) 1012141618N emission (g/kg FPCM) 1012141618 Methane emission (g/kg FPCM) Mean maize silage11.1 g N/kg FPCM14.4 g CH4 /kg FPCMmaize silage
  13. 13. Tier 3 for on-farm GHG budgets farm cases EU – AnimalChange
  14. 14. Tier 3 for Brazilian beef production systems De Lima et al (2014, submitted) EU – AnimalChange average Ym of 5.2% ≈ 20% lower than IPCC Tier 2 default Ym of 6.5% Without supplementation 55-60% DM digestibility Low Ym of 5.2% counter-intuitive Often, higher Ym than 6.5% adopted for poor quality diets (e.g. FAO) Huge implication for global enteric CH4 assessments
  15. 15. roselinde.goselink@wur.nl #669 - “Dry period length and rumen adaptation” Extensions, developments, other use Graphical user-interface for Tier 3 Delivering estimates of (variation in) Ym values for farm models & CFP models Applications Tier 3 model ●GHG budgets farm cases (EU-AnimalChange) ●enteric CH4 in Brazilian beef (EU-AnimalChange) ●research questions excreta (composition & volumes) ●nutritional aspects, e.g. N limitation rumen function ●etc. Further modelling efforts on ●rumen acidity model (adaptation rumen wall) ●rumen fat metabolism ●effects ionophore monensin; other additives envisaged ●intestinal (enzymatic) digestion ●extension hindgut fermentation ●feed intake patterns
  16. 16. roselinde.goselink@wur.nl #669 - “Dry period length and rumen adaptation” Conclusions on Tier 3 for enteric CH4 Advantages ●possibility to simulate wide range of conditions ●introducing ‘logic’ in outcomes ●additional outputs available not directly related to CH4 ●composition excreta ●nutrients for maintenance and production ●diet digestibility and milk production Proves to be useful ●predicts Ym ≈ 6%, comparable to empirical evidence ●example Brazilian beef production systems ●example GHG budgets with varying ‘feeding intensity’ Disadvantages ●activity data on diet composition ●different (non-practical) data on ‘digestibility’
  17. 17. for research & experimentation for inventory (Tier 3) for practice (on farm) Tier 3 Inventory / program Low Emission Animal Feed financed by Ministry Economic Affairs & Dutch Product Boards andre.bannink@wur.nl jan.dijkstra@wur.nl
  18. 18. Representing underlying mechanisms Dynamics instead of static approaches & fixed values Interactions & effect rumen conditions ●pH, volume, passage rate ●Interaction micro-organisms / substrates ●Different microbial classes: ●sugars/starch utilizers ●NDF utilizers ●protozoa (predation on bacteria / death) Production of volatile fatty acids (VFA) Concentrations of substrate & microbial mass Tier 3 approach for enteric CH4
  19. 19. Tier 3, schematic  Model structure  Schematic representation mass flows  Changes in masses and flows described by differential equations in simulation model ● parameters & equations based on useful (in vivo) studies reported in literature

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