1) Livestock, especially ruminants like cattle and sheep, contribute significantly to global greenhouse gas emissions, accounting for about 15% of total anthropogenic emissions. 2) There are challenges in accurately estimating livestock numbers, characteristics, and spatial distribution at different scales due to limitations in census and other data. 3) Methane emissions from livestock can be measured directly but these techniques are not practical for large-scale monitoring; proxies using techniques like laser measurement and near infrared spectroscopy of feces show promise for benchmarking emissions across properties and regions.

1. Approaches to Activity data collection in
livestock systems
Ed Charmley, CSIRO Townsville
Hayley Norman, CSIRO Perth
ED.CHARMLEY@CSIRO.AU

2. 0
500
1000
1500
2000
2500
BEEF DAIRY PIGS BUFFALO CHICKENS SMALL
RUMINANTS
OTHER
PUOLTRY
Million tonnes CO2 -equiv
Total livestock emissions
• 7.1 gigatonnes CO2 -equiv
• 14.5% of global anthropogenic emissions
Global estimates of GHG emissions
Source: Tackling Climate Change through Livestock, FAO 2013

3. Global emissions intensity
0
100
200
300
400
500
Beef Dairy Small
ruminants
meat
Small
ruminants milk
Pork
Kg CO2 –equiv /kg protein
Source: Tackling Climate Change through Livestock, FAO 2013

4. Overview
1. Estimating animal numbers, weight, physiological state
2. Temporal/spatial distribution/scale
 Seasonality
 Selective grazing
3. Measurement techniques for benchmarking
 Laser
4. Methane proxies
 F-NIRS
 Intake
5. Cost effective methods for benchmarking and mitigation

5. Estimating cattle numbers,
weight, physiological state

6. Bovine livestock units density in the year
2000 (from Herero et al 2013).

7. Problems
• How many animals?
• National and regional statistics
• Market information
• Processed feed consumption
• How large are the animals?
• Body weight
• Herd structure
• Body condition
• Physiological state
• Growing
• Mature
• Lactating
• gestating

8. Some thoughts on estimating animal numbers
• Census data is unreliable (snapshot in time)
• What are the alternatives?
• Catch and release methodology?
• Arial surveillance of animals?
• landscape condition
• Landscape condition = grazing pressure / pasture growth
• Pasture growth = land class x rainfall

9. Temporal/spatial distribution,
scale

10. Measurement across scale and uncertainty
In vitro Chamber Poly tunnel Laser Model
Methane Map for Australia after Bentley

11. Diet selection – intensity and availability

12. An issue of scale
100 ha 500 ha
1500 ha
25000 ha
Replicated experiment
>50 ha per animal
15 ha per animal
5 ha per animal

13. Spatial grazing behaviour

14. Australia’s spatial distribution of methane

15. Methane emissions by bovines in the year
2000 (from Herero et al 2013).

16. Measurement techniques for
benchmarking

17. A strong relationship between intake and
methane production (Charmley et al. unpublished)
y = 21. 6 x DMI
R² = 0.96
n = 1000
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30
Methane (g/d)
DMI (kg/d)

18. Can we predict intake?
From Herrero et al. 2013

19. Using laser to measure methane emissions at
Douglas Daly Research Station, NT
•Field based remote
measurement
•Open path laser

20. Spatial variability
1
2
3
4
5
Tropic of Capricorn

21. Average methane emissions across 6 properties in
N. Australia (equated to 450 kg beast)
0
50
100
150
200
250
300
350
400
450
1 2 3 4 5a 5b
Methane (g/d)
Property
? 242 g/d

22. Proxies for Methane:
NIR – tried and tested

23. FNIRS for methane (Dixon and Kennedy, unpublished)
y = 0.6139x + 53.038
R² = 0.631
0.0
50.0
100.0
150.0
200.0
250.0
0.0 50.0 100.0 150.0 200.0 250.0 300.0
Predicted_CH4_L/day
Reference_CH4_L/day
Pred_CH4/day

24. NIRS method for international methane inventory
• Reference open circuit respiration chambers
• South America, Africa, Australia, SE Asia
• Faecal and feed samples associated with individual animal measurement collected, stored and
processed under standard methods
• Each feed/faeces sample set associated with individual animal methane emission (g/kg DMI)
• Standardised in country NIRS capability
• Does not require high level technical competency
• Machines linked into international network
• Centralized data processing
• All data into a global correlation
• Clustring of like samples to improve predictions.
• Centralized NIRS expertise (e.g. CSIRO, INRA, other)
• Wet chemistry to help with predictions
• NIRS for plant quality simultaneously.
• Can we predict CH4 from diet?

25. A CSIRO plan for Australia – extend to
international?
• That CSIRO, either independently or in collaboration with others, should develop a program of
research to develop a robust faecal NIR method for the estimation of livestock methane emissions
for Australia
• CSIRO have the equipment and technical capability at the Floreat Lab in Perth to undertake a
broad-scale analytical/NIR study of faeces and feeds collected from cattle and sheep studies
where methane production has been measured directly using open circuit respiration chambers.
• The dataset is increased by negotiating access to all samples and data generated under:
 The Livestock Methane Research Cluster. Cluster members have already been discussing this
idea and are keen to take it further.
 Negotiation with the National Livestock Methane Program to access samples generated as part
of that research program to further expand the database.
• The main components of the work would involve:
 Collection of samples and associated data on intake and methane emission related to each
feed/faecal sample pair.
 Processing and running samples through Spectrastar NIR equipment in Perth
 Timeframe would be November 2014 to June 2015.
 Approximate budget would be in the $40,000 to $50,000 range.

26. Thank you
Agriculture Flagship
Ed Charmley
Group Leader
t +61 7 4753 8586
e ed.charmley@csiro.au
w www.csiro.au
AGRICULTURE FLAGSHIP