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Charmley Activity data collection livestock systems Nov 10 2014

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Presentation at workshop: Reducing the costs of GHG estimates in agriculture to inform low emissions development
November 10-12, 2014
Sponsored by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the Food and Agriculture Organization of the United Nations (FAO)

Published in: Science
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Charmley Activity data collection livestock systems Nov 10 2014

  1. 1. Approaches to Activity data collection in livestock systems Ed Charmley, CSIRO Townsville Hayley Norman, CSIRO Perth ED.CHARMLEY@CSIRO.AU
  2. 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. 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. 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. 5. Estimating cattle numbers, weight, physiological state
  6. 6. Bovine livestock units density in the year 2000 (from Herero et al 2013).
  7. 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. 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. 9. Temporal/spatial distribution, scale
  10. 10. Measurement across scale and uncertainty In vitro Chamber Poly tunnel Laser Model Methane Map for Australia after Bentley
  11. 11. Diet selection – intensity and availability
  12. 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. 13. Spatial grazing behaviour
  14. 14. Australia’s spatial distribution of methane
  15. 15. Methane emissions by bovines in the year 2000 (from Herero et al 2013).
  16. 16. Measurement techniques for benchmarking
  17. 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. 18. Can we predict intake? From Herrero et al. 2013
  19. 19. Using laser to measure methane emissions at Douglas Daly Research Station, NT •Field based remote measurement •Open path laser
  20. 20. Spatial variability 1 2 3 4 5 Tropic of Capricorn
  21. 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. 22. Proxies for Methane: NIR – tried and tested
  23. 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. 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. 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. 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

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