Advances in rumen-scale enteric methane modeling

Transcription

1 Advances in rumen-scale enteric methane modeling Ermias Kebreab University of California, Davis Melbourne, Feb. 16, 2016

2 Several countries have GHG reduction targets: 1990 level by % by 2030 below 1990 levels Cattle enteric methane emission estimates...60% of anthropogenic emissions 3.8 to 7.4% (mean 5.6%) of GEI in US/Canada (Kebreab et al. 2008) 6.3% of GEI in Australia (Charmley et al. 2016) 97-98% from reticulo-rumen 2

3 Potential Mitigation Strategies Knapp et al J. Dairy Sci. 3

4 Mitigation Strategies -Meta-analysis 4

5 ModelingEnteric Methane Extant models Statistical Dynamic mechanistic Linear Simple & quick Dry Matter Intake, kg/d Cannot be extrapolated beyond data range Methane, MJ/d Non-linear May be numerically expensive Can be extrapolated beyond available data Dr Jonathan Mills 5

6 Mechanistic Models feed intake (CHO, N, fat..) outflow Organic matter Microorganisms VFA (Ac, Pr, Bu) Rumen CO 2 H 2 CH 4 absorption Gut 6

7 CH 4 in Mechanistic Models Feed input H 2 Source H 2 Sink Rumen Model Fermentation Methane module Acetate Butyrate Propionate Valerate Small intestinal digestion Methane Microbial growth with amino acids Microbial growth with ammonia H 2 Large Intestinal Model Fermentation Methane module Lipid Hydrogenation of dietary unsaturated fatty acids Methane CO 2 + 4H 2 CH 4 +2H 2 O Zero pool scheme EXCESS Baldwin, Mills et al J. Anim. Sci. Bannink et al Anim. Feed Technol. 7

8 Substrate Degradation 8

9 Substrate Degradation 9

10 Microbial Groups 10

11 VFA Production Type of VFA formed in rumen depends on: Substrate fermented Microbial population Rumen environment (Bannink et al. 2008) Proportions of individual VFA are important because they impact calculation of methane production Several models available to predict rumen fermentation stoichiometry (Dijkstra et al. 2008) Evaluation of such models shows that the prediction error remains considerable (e.g. Alemu et al., 2011; Morvay et al. 2011) 11

12 Modeling Rumen VFA Prodn Study Level Units Mechanistic Species Murphy et al Digestion Mol/mol sub Yes Cattle/sheep Argyle & Baldwin, 1988 In vitro Mol/mol sub Yes In vitro Pitt et al., 1996 In vitro Mol/mol sub Semi In vitro Friggens et al., 1997 Dietary components % VFA No Sheep Nagorcka et al., 2000 Microbial Mol/mol sub Yes Microbe Kohn & Boston, 2000 Thermodynamics Mol/mol sub Yes None/theory Bannink et al., 2006 Rumen digestion Mol/mol sub Yes Lactating cows Sveinbjörnsson et al., 2006 Rumen digestion dietary components Mol/mol sub Semi Lactatingcows (Nordic) Bannink et al., 2008 Rumen digestion Mol/mol sub Yes Lactating cows Nozière et al., 2010 Dietary components % VFA No Bovines 12

13 Acetate Production Morvay et al. (2011) 13

14 Propionate Production Morvay et al. (2011) 14

15 Challenges in VFA Modeling Study indicated all models evaluated struggled with VFA prediction For improved explanation of observed variation need to focus on developing improved model representations include further aspect (concepts, theories, parameters) more data on measured production rate vs concentration Accuracy is likely to require: data on rumen digestion & fermentation data from target animal (sheep grain/corn-fed cattle lactating cow) 15

16 Evaluation of AusBeef 16

17 AusBeefRuminant model

18 Sensitivity Analysis 18

19 Global Sensitivity Analysis 19

20 Future Developments Bayesian Mechanistic Models Stochasticity (error) Prior parameter distribution Non steady state dynamics Reed et al In review 20

21 Summary Mechanistic model of rumen function have advanced since the 70s AusBeef model has advanced representation of substrate degradation, microbial growth and VFA production Sensitivity analysis guides us where new research should be focused Further development should include nonsteady state conditions and stochastic elements within mechanistic models 21

22 Acknowledgements Holland Dougherty University California, Davis USA Mark Evered and Malcolm McPhee NSW DPI Australia Bryce Little and Aaron Ingham CSIRO Australia Roger Hegarty University of New England Australia David Pacheco AgResearch New Zealand 22