Livestock Related Emission

Modeling

Bogotá, 2012

Ermias Kebreab Professor of Animal Science

Sesnon Endowed Chair in Sustainable Agriculture

University of California, Davis

Outline

• Introduction

• Empirical modeling

• Mechanistic modeling

• Whole farm models

• Comparison of models

• Selection of appropriate models

2

Introduction

3

• Climate change due to GHG emissions

• ~ 4% of global anthropogenic GHG comes

from dairy sector (FAO, 2010)

• Methane is the main gas from enteric

fermentation and manure storage

• ~1-11% of dietary GEI (Moraes et al. 2011)

• Measurement involves complex and

expensive equipment

Quantification Tools

4

National Inventory

“What is”

GHG Calc./Decision Support Tool

“What if”

Offset Protocols

“What's Changed”

Carbon Footprint (LCA indices)

What’s Green and Different”

Types of Models

5

• Empirical (input and output directly) or

Mechanistic (fermentation biochemistry)

• Dynamic (time is model element) or Static

• Deterministic (no associated uncertainty

value) or Stochastic

• Models developed for prediction of methane

are mostly empirical but few mechanistic

ones exist

Empirical (Statistical) Models

6

• Describe the response of the animal to a

change in conditions (such as a change in

diet), in which scientific understanding is not

necessarily needed

• Simpler, and more easily and quickly

constructed than a mechanistic model

• Disadvantage is that the model parameters

are usually not biologically meaningful

IPCC Related Equations

7

• Dairy Cattle: 6.5% (± 1%)*gross energy

intake (GEI)

• Beef Cattle (on Feedlot): 3% (± 1%)*GEI

• FAO uses slightly different equation

[(9.75 – 0.05 × Digestibility Rate (%))× GEI]

New Equations

8

• New equations are being developed that use

extensive dataset of cattle in calorimetry

chambers over 40 years

• State-of-the-art statistical methods are used

• Bayesian linear models and the reversible

jump MCMC techniques used to develop new

equations

Data Summary

Mean Min Max SD

DMI (kg) 13.08 2.33 29.40 5.88

NDF (%) 34.69 13.99 76.14 8.32

ADF (%) 20.35 4.99 47.39 5.35

ME (Mcal/kg) 2.60 1.55 3.49 0.23

Lignin (%) 4.52 0.52 14.28 1.67

CP (%) 16.13 4.92 23.51 2.47

EE (%) 2.72 0.72 7.64 0.96

BW (kg) 620.8 304.3 890.3 94.66

Milk Yield (kg/d) 23.27 0 56.61 10.24

CH4 (Mcal/d) 3.32 0.40 7.33 1.35

Ym (% of GEI) 6.01 1.32 10.94 1.52

• 1733 indirect calorimetric records of lactating and non-

lactating Jersey and Holstein cows

RJMCMC - Results

Variable Number Marginal

Probability

BW 1 0.99

Breed 2 0.27

Year 3 0.98

GE intake 4 1.00

CP 5 0.01

NFE 6 0.06

EE 7 0.91

NDF 8 0.94

Lignin 9 0.01

Model convergence assessed through auto-correlation and chain mixing plots and Gelman-Rubin diagnostics BW, Year of study, GE intake, dietary EE and NDF have high marginal probability (P>0.9)

Two chains starting with different model dimensions

Other Empirical Models

11

• Several empirical equations published since the

1930’s and 40s

• Models use a range of animal and dietary factors as

covariates, e.g., DMI, BW, milk production,

proportion of forage and diet chemical composition

• Majority of equations use some measure of intake

(e.g., DM, OM, DE, GE, etc.). This is because

generally, 60-80% of the variation in CH4 prediction

could be attributed to a measure of intake (Mills et

al. 2003)

Factors in Methane Emission

12

Diet

Animal F:C ratio

Milk yield

Intake (GE, DE, ME,

DM)

Body Weight

Methane production

Digestibility

pH microbes

Feeding strategy

Composition

Environment

Breed

Rumen

Mechanistic Models

13

• A model constructed based on the structure

of a system, dividing it into its principal

components, and analyzing the behavior of

the whole system in terms of its components

and their interactions is mechanistic

• MOLLY (Baldwin et al) – UC Davis

• COWPOLL (Dijkstra, Mills, Kebreab, France)

Understanding Methanogenesis Feed input

Acetate

H2

Propionate

Butyrate

Lipid

Hydrogenation of dietary unsaturated fatty acids

Valerate

Microbial growth with ammonia

Methane CO2 + 4H2 CH4 +2H2O Zero pool scheme

Microbial growth with amino acids

H2 Source H2 Sink

EXCESS

Rumen Model

Large Intestinal Model

Small intestinal digestion

Methane module

Methane module

Methane

Fermentation

Fermentation

Kebreab et al. 2004. Anim Feed Sci. Technol. 112: 131

MOLLY

15

• Dynamic and mechanistic model based on

rumen digestion and metabolism of dairy cow

• Assumes continuous feeding. The digestion

element comprised of 15 state variables.

• Chemical composition - starch, cellulose,

hemicellulose, lignin, soluble carbohydrate,

VFA, CP (soluble and insoluble), non-protein

nitrogen, urea, ash, lipid, organic acid,

lactate, pectin and fat.

MOLLY

16

• After microbial attachment and substrate

hydrolysis, the rumen model uses

stoichiometric coefficients to convert starch,

soluble carbohydrates and amino acids into

volatile fatty acids (VFA)

• The VFA stoichiometry is based on the

equation developed by Murphy et al. (1982),

which relates the amount of VFA produced to

the type of substrate fermented in the rumen

COWPOLL

17

• Polluting cow (COWPOLL) is based on Dijkstra et

al. (1992) which is dynamic and mechanistic

• Assumes continuous feeding. The digestion

element comprised of 17 state variables.

• Simulates the digestion, absorption and outflow of

nutrients in the rumen.

• Chemical composition of the diet is presented as

starch (soluble and insoluble), fiber (degradable

and undegradable), crude protein (soluble and

undegradable), water soluble carbohydrate,

COWPOLL

18

• …ether extract, VFA, ammonia, ethanol and lactate.

• Mills et al. (2001) added CH4 production in the

rumen and hindgut.

• Kebreab et al. (2004) extended the model to N

transactions

• As VFA molar proportions are important

determinants of CH4 formation, COWPOLL uses a

VFA stoichiometry developed by Bannink et al.

(2006) based on data collected from digestion trials

with dairy cows.

VFA Stoichiometry

19

Acetate Butyrate

Propionate

Valerate

Microb. Growth (AA)

Microb. Growth (NH3)

FA Hydrogenation

Methane

CO2 + 4H2 CH4 +2H2O H2

Input requirements

20

Mechanistic IPPC Tier 2

Dry matter intake Dry matter intake

Detailed chemical composition such as N, CP, NDF, starch, ADF etc

ME in diet

Digestibility Protein

Fractional rate of degradation for protein, fibre and starch

Average daily gain

Whole Farm Models

21

• Simulate the impact of various management

or nutritional strategies on GHG emissions

• Usually a combination of empirical models is

used due to their relative simplicity

• Differ in level of complexity and structure

• Agricultural and Land Use National

Greenhouse Gas Inventory (ALU)

• Integrated Farm Systems Model (IFSM)

22

ALU

23

• Mostly based on IPCC (2006) equations

• Simulates emissions from various types of

animals, manure storage and soil using a

factorial approach i.e., nutrient flows from

one section (e.g. animals) does not

necessarily follow to next phase in the farm

• ALU has been developed with the help of US

EPA for inventory calculations in the US

24

IFSM (Dairy GEM)

25

• Simulates whole-farm emissions of GHG and

evaluates the overall impact of management

strategies used to reduce CH4 emissions.

• Process based model with soil processes

crop growth, tillage, planting and harvest

operations, feed storage, feeding, manure

storage, and economics (Rotz et al., 2011)

• Uses Mills et al. (2003) equation to predict

CH4 emissions from enteric fermentation

Dairy Gas Emission Model

26

• Software tool for estimating GHG emissions,

H2S and ammonia

• Mechanistic model through daily simulations

of feed use and manure handling and then

summed to obtain annual values

• Model predicts: CH4 (enteric, barn floor,

manure storage, feces in pasture), N2O (crop

and pasture), CO2 (feed production,

respiration), H2S, NH3

Dairy Gas Emission Model

27

• Total GHG emission determined as sum of

net emissions converted to carbon dioxide

equivalent units (CO2e)

• Net emission determined through partial LCA

• Primary sources - farm system and

• Secondary sources – manufacture or

production resources e.g. machinery, fuel

Dairy Gas Emission Model

28

Comparison of Models

29

• Linear equations explained 42 to 57% of the

variation. Mechanistic models explained over

70% of the variation (Benchaar et al. 1998)

• Kebreab et al. (2006a) chose six models,

including two linear models, a non-linear

model, IPCC Tier 1 and 2 models and a

mechanistic model (COWPOLL)

• Mechanistic model was superior but in

cases with limited data not recommended

Comparison of Models

30

• Kebreab et al. (2008) evaluated 2 empirical and 2

mechanistic models (COWPOLL and MOLLY) for

their prediction ability

• In dairy cattle, COWPOLL had the lowest prediction

error. However, in feedlot cattle, MOLLY had the

lowest predictive error. The average Ym in dairy

cows was 5.63% of GE (range 3.78 to 7.43%) and

3.88% (range 3.36 to 4.56%) in feedlot cattle.

• IPCC values can result in an overestimate of about

12.5% and underestimate of emissions by about

9.8% for dairy and feedlot cattle, respectively.

Critical Gaps

31

• Empirical models

• Not developed using extensive dataset

• Does not include key covariates (e.g. fat)

• Inappropriate statistical methodology

• Mechanistic models

• Fermentation stoichiometry is not well

understood. Alemu et al. (2011) showed

more work needed on this aspect

Model Selection

32

• Selection of the appropriate model depends on

• the objective of the user (e.g. national inventory)

• data availability

• relevance for various situations and management

systems.

• Selection also depends on

• the scale of operation such as viability for accounting

reductions in GHG emission projects

• known limitations

• associated uncertainty inherent in models.

Summary

• Empirical models such as IPPC require

minimum input but may be less precise

• Mechanistic models require detailed inputs

• Whole farm models currently use empirical

models to estimate GHG emission

• Software such as ALU and DairyGEM offer

better prediction at national level than IPCC

• Model comparison shows more precise

estimation from mechanistic models. 33

Acknowledgement

Organizers of MAPS