Approaches to Activity data collection in livestock systems

Transcription

1 Approaches to Activity data collection in livestock systems Ed Charmley, CSIRO Townsville Hayley Norman, CSIRO Perth

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

3 Kg CO 2 equiv /kg protein Global emissions intensity Beef Dairy Small ruminants meat Small ruminants milk Pork 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 Replicated experiment 5 ha per animal 1500 ha ha >50 ha per animal 15 ha per animal

13 Australia s spatial distribution of methane

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

15 Measurement techniques for benchmarking

16 Methane (g/d) A strong relationship between intake and methane production (Charmley et al. unpublished) y = x DMI R² = 0.96 n = DMI (kg/d)

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

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

19 Spatial variability Tropic of Capricorn

20 Methane (g/d) Average methane emissions across 6 properties in N. Australia (equated to 450 kg beast) ? 242 g/d a 5b Property

21 Proxies for Methane: NIR tried and tested

22 Predicted_CH4_L/day FNIRS for methane (Dixon and Kennedy, unpublished) Pred_CH4/day y = x R² = Reference_CH4_L/day

23 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?

24 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 Approximate budget would be in the $40,000 to $50,000 range.

25 Thank you Agriculture Flagship Ed Charmley Group Leader t e ed.charmley@csiro.au w AGRICULTURE FLAGSHIP