Soil Carbon Measurement Costs and Protocols Using a Linked Economic and Biophysical Model

1. Soil Carbon Measurement Costs and Protocols Using a Linked Economic and Biophysical Model Forestry and Agriculture GHG Modeling Forum October 9th, 2002 Siân Mooney Dept. Agricultural and Applied Economics University of Wyoming

2. Other Collaborators • John Antle and Susan Capalbo Dept. Agricultural Economics and Economics Montana State University • Keith Paustian Natural Resource Ecology Laboratory Colorado State University

3. Motivation • C sequestration in agricultural soils • C is “invisible” • Is it possible to sell? • Need monitoring/measurement • Possibly have large number of producers • How to design monitoring/measurement • Will it be too costly?

4. Study Objectives • Develop a measurement protocol for C credits sequestered in agricultural soils • Estimate its cost for a region of the US • Examine characteristics that influence measurement costs

5. Influence of Contract Design • Contract design will determine monitoring and measurement needs • Per-hectare contract • Per-credit contract • $MM=$monitoring practice+$measuring C

6. Measurement Issues • Severalproducers – cover large area • Statistical sampling • $M=f(#samples,$/sample,frequency) • Combine field measurements and predictive models

7. Measurement - General • Predictive biophysical models – estimate C • Measure baseline – statistical sampling/field samples/lab testing • Measure C periodically over duration of contract • Measure C at end of contract

8. Measurement - Specific • Stratified random sampling • Sample population – producers with contracts to supply C-credits • Strata based on crop system change • Cost/sample • $16.37 • Frequency – 4 times • Years 1, 5, 10, 20.

9. Study Area Field level production data Climate, soil and biophysical characteristics

10. Models Econometric Models (output supply, input demand) Century Ecosystem Model (NREL) parameter estimates carbon estimates 1. #producers participating 2. #producers in each strata 3. Opportunity cost of system changes Land use simulation -stochastic output and input prices -policy designs and payment levels

11. Price /credit ($) Sub-MLRA 52-high Sub-MLRA 52-low Sub-MLRA 53-high MPC ($) M Cost (% of Price) MPC ($) M Cost (% of Price) MPC ($) M Cost (% of Price) 10 0.18 1.81 0.30 3.03 0.29 2.99 50 0.05 0.10 0.13 0.26 0.19 0.38 100 0.03 0.03 0.09 0.09 0.16 0.16 Price /credit ($) Sub-MLRA 53-low Sub-MLRA 58-high Sub-MLRA 58-low MPC ($) M Cost (% of Price) MPC ($) M Cost (% of Price) MPC ($) M Cost (% of Price) 10 1.05 10.57 0.14 1.39 0.29 2.92 50 0.51 1.03 0.07 0.14 0.18 0.37 100 0.39 0.39 0.05 0.05 0.13 0.13 Cost per credit (5% , 95% confid)

12. 0.30 0.25 0.20 0.15 0.10 0.05 0.00 3.5 3 2.5 2 1.5 1 0.5 0 C Change Variability and Cost per Credit Cost/credit Coefficient of Variation in C Changes 52H 52L 53H 53l 58H 58L Variability Decreasing

13. Conclusions • Measurement costs not large enough to prevent producers from participating in C market • Efficiency of measurement protocol depends on the price of credits • Measurement costs largest in spatially heterogeneous areas

14. Other issues • Uncertainty associated with initial C change estimates (see other paper on web) • Baseline – will change costs per credit

15. Additional Information Siân Mooney Dept. Agricultural and Applied Economics University of Wyoming Phone: 307-766-2389 E-Mail: smooney@uwyo.edu