Is Science Taking Us Closer To Trade?

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Dr Jeff Baldock (CSIRO) exlains the research program seeking better ways of measuring soil carbon for trade at the Third Annual Carbon Farming Conference & Expo 2009 in Orange NSW Australia - the only soil carbon farming conference of its type in the world. (4-5 November 2009)

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Is Science Taking Us Closer To Trade?

  1. 1. Is science taking us closer to carbon trading? Jeff Baldock Carbon Farming Conference, Orange, NSW 4 November, 2009
  2. 2. Outline <ul><li>The role of Science in defining a carbon trading scheme </li></ul><ul><li>Requirements for a carbon trading scheme </li></ul><ul><li>S oil Ca rbon R esearch P rogram (SCaRP) </li></ul><ul><ul><li>Organisations involved </li></ul></ul><ul><ul><li>Objectives </li></ul></ul><ul><ul><li>Nature of the work being completed </li></ul></ul><ul><ul><li>How the Science being completed will support carbon trading </li></ul></ul>
  3. 3. Is science taking us closer to carbon trading? <ul><li>Science will not define whether or not carbon trading occurs </li></ul><ul><ul><li>Carbon trading will result where either: </li></ul></ul><ul><ul><ul><li>the confidence exists between buyers and sellers to complete a transaction </li></ul></ul></ul><ul><ul><ul><li>government policy decisions introduce a scheme </li></ul></ul></ul><ul><li>Science should be used to provide the information required to develop/select proposed trading systems </li></ul><ul><ul><li>What type of trading scheme should be implemented? </li></ul></ul><ul><ul><li>What is the potential rate of carbon change? </li></ul></ul><ul><ul><li>What is the level of confidence in carbon outcomes? </li></ul></ul><ul><ul><ul><li>Considerations include variances in management practice, spatial variation of soil properties, methodological considerations </li></ul></ul></ul><ul><ul><li>If baseline and verification sampling is required, what is the optimal way conduct such analyses? </li></ul></ul>
  4. 4. Requirements for entering a property into a carbon trading scheme <ul><li>An initial (baseline) estimate of the quantity of carbon present </li></ul><ul><ul><li>Model from a defined native condition </li></ul></ul><ul><ul><ul><li>Issues: Undefined initial composition of soil organic carbon. Potential lack of carbon input data. </li></ul></ul></ul><ul><ul><li>Measure </li></ul></ul><ul><ul><ul><li>Issues: What do we measure and what accuracy is required? How do we account for spatial variance. </li></ul></ul></ul><ul><ul><ul><li>Increased accuracy and variance lead to increased measurement cost. </li></ul></ul></ul><ul><li>Projected change in soil carbon in response to management </li></ul><ul><ul><li>Model changes associated with potential management scenarios </li></ul></ul><ul><ul><ul><li>Issues: accuracy of model outcomes (high dependence on model inputs) </li></ul></ul></ul><ul><li>Potential confirmation of changes in soil carbon (depending on nature of the scheme) </li></ul><ul><ul><li>Remeasure </li></ul></ul><ul><ul><ul><li>Issues: Relationship between desired accuracy and extent of remeasurement required </li></ul></ul></ul>
  5. 5. Soil Carbon Research Program (SCaRP) SCaRP (Soil carbon research program) Program coordination Inputs of carbon from perennial pastures Rapid & cost effective soil carbon analyses Automated measures for bulk density SA soils – with PIRSA CSIRO <ul><li>Sources of funding </li></ul><ul><li>DAFF </li></ul><ul><li>GRDC </li></ul><ul><li>Program members </li></ul>NSW soils – UNE/DPI Vic soils – DPI Qld cropping soils - DERM Qld & NT rangeland soils - DERM WA soils – University of WA Tasmania soils – University of Tas Other organisations Murray catchment soils - Murray CMA
  6. 6. Soil carbon research program (SCaRP) <ul><li>Objectives </li></ul><ul><ul><li>Define and use a nationally consistent methodology for quantifying soil carbon across Australia </li></ul></ul><ul><ul><li>Identify land management strategies with the potential to build soil carbon at regional levels </li></ul></ul><ul><ul><li>Quantify the inputs of carbon to soils under perennial pasture systems </li></ul></ul><ul><ul><li>Develop rapid and cost-effective means for quantifying soil carbon stocks and measuring soil bulk density. </li></ul></ul><ul><ul><li>Provide data for further development of NCAS (National Carbon Accounting System) </li></ul></ul>
  7. 7. Types of soil sampling to be included <ul><li>Resampling of sites previously sampled </li></ul><ul><ul><li>Objective: Provision of temporal soil carbon data for enhancing NCAS, in particular to provide testing at different sites/managements </li></ul></ul><ul><ul><li>Requirements </li></ul></ul><ul><ul><ul><li>SOC change still occurring and duration between first and current measurements >5 and preferably >10 years </li></ul></ul></ul><ul><ul><ul><li>Archived samples (0-30cm) exist with bulk density values </li></ul></ul></ul><ul><ul><ul><li>Well documented agronomic treatments and carbon inputs </li></ul></ul></ul>Multiple sites required to establish generality Poor model performance may be dealt with through recalibration A need will exist to define why a different calibration was needed
  8. 8. Types of soil sampling to be included <ul><li>Management practices within replicated designs </li></ul><ul><ul><li>Objective: define the relative influence of management on soil carbon </li></ul></ul><ul><ul><li>Requirements </li></ul></ul><ul><ul><ul><li>Adequately replicated experimental design </li></ul></ul></ul><ul><ul><ul><li>Not considering paired sites </li></ul></ul></ul>Issue: the results are only applicable to the site where the work was completed a 42.3 3 b 26.1 4 a 38.6 2 b 28.9 1 Statistical test Mean Soil carbon (tC/ha) Treatment
  9. 9. Types of soil sampling to be included <ul><li>Single point sites </li></ul><ul><ul><li>Objective: quantify the amount and variability of soil carbon under defined management practices on a region by soil type basis </li></ul></ul><ul><ul><li>Requirements </li></ul></ul><ul><ul><ul><li>Define soil types to be examined within defined agricultural regions within each state (project) </li></ul></ul></ul><ul><ul><ul><li>Collect soil samples from >25 paddocks that represent the management by soil type combination for each region </li></ul></ul></ul><ul><ul><ul><li>Collect defined background data (e.g. cropping/pasture history, yield, rainfall, etc.) </li></ul></ul></ul><ul><ul><ul><li>Quantify the carbon content and allocation to carbon fractions </li></ul></ul></ul>
  10. 10. Data format Stratification: Victoria, Wimmera Soil type: Vertisol (self mulching) Management: 1 = Rotations with <25% pasture 2 = Rotations with >25% pasture Location L1 L2 L3 L4 L5 Ln 45.1 62.3 51.2 84.2 38.1 48.9 SOC (tC/ha) % Clay 39.6 48.6 49.5 44.2 50.1 47.3 Avg Rain 352 225 528 881 265 455 Slope 3.2 6.1 1.1 2.4 1.0 0.9 1 1 2 2 1 2 Management
  11. 11. Analysis of collected soil carbon data Soil organic carbon (tC/ha) Frequency distribution (number of samples) 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 As the number of samples collected and analysed increases, the µ = average σ = standard deviation
  12. 12. Defining differences between management practices Soil organic carbon (tC/ha) Frequency distribution The classical comparison of mean values uses a 95% confidence T 2 µ 2 σ 2 n 2 T 1 µ 1 σ 1 n 1 Confidence 0.01 0.05 0.10 0.25 0.50 10.2 6.3 2.1 1.1 0.5  SOC Relationship between level of confidence and the size of the change in soil carbon
  13. 13. Additional analyses: removing variability associated with factors other than management Size of residual Frequency distribution T 2 µ 2 σ 2 T 1 µ 1 σ 1 Soil organic carbon (tC/ha) Frequency distribution T 1 µ 1 σ 1 T 2 µ 2 σ 2 Size of residual Frequency distribution T 1 µ 1 σ 1 T 2 µ 2 σ 2 Rainfall gradient Remove variability associated with other factors (rainfall)
  14. 14. Additional analyses: linear regression Soil organic carbon (tC/ha) Frequency distribution T 1 µ 1 σ 1 T 2 µ 2 σ 2 Rainfall gradient Rainfall SOC Regression approach
  15. 15. Provision of data to NCAS to initialise regionally based scenario modelling Soil organic carbon (tC/ha) Frequency distribution T 1 µ 1 σ 1 T 2 µ 2 σ 2 Use the frequency distribution to initialise NCAS Define a modelling scenario and sample the frequency distribution  SOC (tC/ha) Cumulative Probability 75% 5 10
  16. 16. Inputs from the introduction of perennial pastures <ul><li>To increase soil carbon we need to increase the amount of carbon captured and returned to the soil </li></ul><ul><li>Under appropriate circumstances the introduction of perennials can: </li></ul><ul><ul><li>Extend the growing season – increases the proportion of the year over which carbon can be captured </li></ul></ul><ul><ul><li>Alter the allocation of captured carbon to above and below ground components </li></ul></ul><ul><li>The perennial pasture component of SCaRP will: </li></ul><ul><ul><li>Use 14 C labelling to quantify the allocation of carbon to above and below ground components for kikuyu and panic/rhodes pastures </li></ul></ul><ul><ul><li>Amount of soil carbon under kikuyu </li></ul></ul>
  17. 17. Inputs of carbon from perennial pastures: 14 C labelling studies <ul><li>Process </li></ul><ul><li>Place a chamber over growing plants </li></ul><ul><li>Inject 14 C-CO 2 into the chamber </li></ul><ul><li>Plants take up 14 C-CO 2 </li></ul><ul><li>14 C is used by the plant to create the various molecules and structures </li></ul><ul><li>Measure 14 C content to define the relative allocation to above and below ground components </li></ul><ul><li>Conduct this analysis at under different environmental conditions </li></ul>14 C 14 C 14 C 14 C 14 C 14 C 14 C 14 C-CO 2 14 C 14 C 14 C
  18. 18. Inputs of carbon from perennial pastures: 14 C labelling studies Measure 14 C after 7 days - defines initial allocation to shoots and below ground plant components 14 C 14 C 14 C 14 C 14 C 14 C 14 C 14 C 14 C 14 C 14 C 14 C 14 C 14 C 14 C 14 C 14 C 14 C 14 C 14 C Measure 14 C after 1 year - defines fate and biological stability carbon derived from perennial vegetation
  19. 19. Inputs of carbon from perennial pastures: C 3 /C 4 pasture transitions <ul><li>Temperate plants (C 3 ) capture carbon during photosynthesis using a different process than tropical grasses (C 4 ) </li></ul><ul><li>This provides a basis to differentiate carbon derived from C 3 vegetation from carbon derived from C 4 vegetation </li></ul> 13 C C 3 plants (-27) C 4 plants (-11) 0 -10 -20 -30 -40 Frequency
  20. 20. Inputs of carbon from perennial pastures: C3/C4 pasture transitions t 0 t Soil C old C 3 new C 4 C 3 -  13 C C 4 -  13 C  13 C C 3 veg = -27  13 C C 4 veg = -11  13 C C 3 ref soil = -27  13 C C 3 C 4 soil = -18 Approximately 56% of the carbon in the soil has come from the new C 4 vegetation
  21. 21. Predicting total organic carbon and its allocation to SOC fractions using MIR <ul><li>Distribution of signal intensity depends on chemical bonds present </li></ul>1 2 3 4 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 Intensity Frequency (cm -1 ) Fourier Transformed Mid-Infrared Spectrum
  22. 22. Predicting the amount of each form of soil carbon using MIR n = 177 Range: 0.8 – 62.0 g C/kg R 2 = 0.94 n = 141 Range: 0.2 – 16.8 g C/kg R 2 = 0.71 n = 121 Range: 0.0 – 11.3 g C/kg R 2 = 0.86 Total organic carbon (mg C/g soil) 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 Measured MIR predicted Particulate organic carbon (mg C/g soil) 0 2 4 6 8 10 12 14 16 18 20 0 5 10 15 20 Measured MIR predicted Resistant organic carbon (mg C/g soil) 0 2 4 6 8 10 12 0 2 4 6 8 10 12 Measured MIR predicted
  23. 23. Application of MIR to predict TOC in Tasmanian soils
  24. 24. Contributions that the SCaRP projects will make to carbon trading <ul><li>Regional estimates of the following: </li></ul><ul><ul><li>The average and extent of variation in soil carbon under defined management regimes </li></ul></ul><ul><ul><li>The confidence associated with differences in soil carbon between management practices </li></ul></ul><ul><ul><li>Quantification of the impact that other parameters beyond management may have on soil carbon </li></ul></ul><ul><ul><li>Provision of measured baseline data for assessing soil carbon status and modelling exercises </li></ul></ul><ul><ul><li>Ability to define differences in soil carbon and management impacts between agricultural regions </li></ul></ul><ul><li>Quantifying the input and biological stability of carbon derived from perennial grass pasture species </li></ul><ul><li>Rapid and cost effective soil carbon measurement </li></ul>
  25. 25. Role of Science in carbon trading <ul><li>Develop sound methodologies and deliver data that: </li></ul><ul><ul><li>Can be used to ensure that an appropriate scheme or an appropriate range of schemes are developed </li></ul></ul><ul><ul><li>Voluntary Schemes - provide confidence to both buyers and sellers of carbon in voluntary schemes </li></ul></ul><ul><ul><li>Mandatory Scheme – ensure that appropriate procedures are put in place to ensure confidence in carbon outcomes </li></ul></ul>
  26. 26. Thank you Jeff Baldock Stream Leader Sustainable Agriculture Flagship Phone: +61 8 83038537 Email: Jeff.Baldock@csiro.au Contact Us Phone: 1300 363 400 or +61 3 9545 2176 Email: Enquiries@csiro.au Web: www.csiro.au
  27. 27. MIR spectra of different soil components 4000 3500 3000 2500 2000 1500 1000 500 Quartz Kaolinite Gibbsite Smectite Organic Carbonate Gypsum Wavenumbers (cm -1 ) Soil
  28. 28. Predicting soil carbon content and allocation to fractions: the MIR/PLS process 4000 3000 2000 Wavenumbers (cm -1 ) 1000 Soil MIR spectra Analytical data Soil 1 21.4 Soil 2 11.8 Soil 3 19.5 Soil 4 21.9 Soil 5 10.1 Partial Least Squares analysis Measured value MIR predicted value 4000 3000 2000 Wavenumbers (cm -1 ) 1000 Loadings spectra
  29. 29. Additional analyses: multivariate analyses Principal Components Regression Partial Least Squares Soil organic carbon (g C/kg soil) Frequency distribution T 1 µ 1 σ 1 T 2 µ 2 σ 2 Multiple gradients Multivariate analyses Multiple regression SOC = a + b(clay) + c(rain) + ... R 2 and p-value to evaluate Principal components Loadings Rain %Pas Clay Sand Scores

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