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Approaches to Activity data collection in 
livestock systems 
Ed Charmley, CSIRO Townsville 
Hayley Norman, CSIRO Perth 
ED.CHARMLEY@CSIRO.AU
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
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
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
Estimating cattle numbers, 
weight, physiological state
Bovine livestock units density in the year 
2000 (from Herero et al 2013).
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
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
Temporal/spatial distribution, 
scale
Measurement across scale and uncertainty 
In vitro Chamber Poly tunnel Laser Model 
Methane Map for Australia after Bentley
Diet selection – intensity and availability
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
Spatial grazing behaviour
Australia’s spatial distribution of methane
Methane emissions by bovines in the year 
2000 (from Herero et al 2013).
Measurement techniques for 
benchmarking
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 unpublished) 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)
Can we predict intake? 
From Herrero et al. 2013
Using laser to measure methane emissions at 
Douglas Daly Research Station, NT 
•Field based remote 
measurement 
•Open path laser
Spatial variability 
1 
2 
3 
4 
5 
Tropic of Capricorn
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
Proxies for Methane: 
NIR – tried and tested
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
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?
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.
Thank you 
Agriculture Flagship 
Ed Charmley 
Group Leader 
t +61 7 4753 8586 
e ed.charmley@csiro.au 
w www.csiro.au 
AGRICULTURE FLAGSHIP

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Charmley Activity data collection livestock systems Nov 10 2014

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