John Antle, Susan Capalbo and Sian Mooney Montana State University

1. An Integrated Assessment Approach to Modeling Soil C Sequestration:What Have We Learned and Future Directions? John Antle, Susan Capalbo and Sian Mooney Montana State University Prepared for USDA/EPA GHG Modeling Forum Sheperdstown, WV October 2, 2001

2. Research reported is collaboration among John Antle, Susan Capalbo and Sian Mooney Montana State University Edward Elliott, University of Nebraska and Keith Paustian, Colorado State University www.climate.montana.edu

3. Acknowledgements This research was funded wholly or in part by the National Institute for Global Environmental Change (NIGEC) through the U.S. Department of Energy (Cooperative Agreement No. DE-FC03-90ER61010). Any opinions, finding, and conclusions or recommendations expressed herein are those of the authors and do not necessarily reflect the views of the DOE.

4. Objectives • Framework for assessing the costs of Carbon sequestration and other CC research– farm/field scale approach “The Devil is in the Details” Approach • Empirical results: spatial heterogeneity, policy design, monitoring/measurement costs • Implications for Linking Economic and Biophysical Models • Future Research Directions

5. Integrated Assessment Paradigm • Economic data  economic production models • Soils & climate data  crop ecosystem models • Output of crop ecosystem models economic models and environmental process models • Outputs of economic models  env process models

6. Integrated Assessment Paradigm Degree of coupling of economic and biophysical models: loosely coupled ( existing research) closely coupled (new directions) fully integrated

8. Design of Econometric –Process Simulation Model • Estimate econometric production models (system of supply and input demand fcns) for each activity. • Simulate econometric models with site-specific data to obtain expected returns for each possible production practice • Use structure of decision making process to make optimal land use and management decisions.

10. Advantages of Econometric Process Approach • Ideally suited to study spatial heterogeneity: embeds econometric model within a simulation model, and use site specific data to simulate LU and management choices for each field • Represents production process explicitly and thus can be directly linked to Century

11. Simulation of Land Use Using Econometric-Process Model of Montana Dryland Grain Production • 1995 MT Cropping Practices Survey • statistically representative sample of MLRAs in grain producing regions of MT • useable data from 425 commercial grain farms, over 1500 fields

12. Figure 3. Montana Dryland Grain Study Sub-MLRAs

13. Econometric Production Model • winter wheat, spring wheat, barley • log-linear supply, machinery cost, and variable cost functions • hedonic procedure used to quality adjust herbicide input data

14. Econometric Production Model Estimates • NLTSLS with linear homogeneity of cost function • zero-degree homogeneity of supply fcn • quantity supplied and machinery cost are proportional to field size • supply (yield) and Machinery cost vary by sub-MLRA • SR supply elasticities 0.14 to 0.36

15. Econometric Production Model Estimates (cont) • Yields higher when crop is grown on land fallowed in previous period (31, 23, and 9 % higher) • Production costs are 40% lower for crops grown after fallow • Conclude: fields previously fallowed more productive than continuously cropped • When fallow costs are considered, similar net returns on crop/fallow and cc systems

16. Simulation Model Calibration and Validation • Calibrated to predict observed mean frequencies of crops using three parameters – • yield variability (90% of observed variance) • discount rate(7%) • expected future crop price (10% below 1995 average) • Validation: • within-sample: observed proportion of each land use in each zone compared to simulated proportion • out-of-sample: compare simulated % land in CRP to actual

17. Figure 2. Observed vs. Simulated Land Use by Sub-MLRA

18. Figure 3. Mean Net Returns by Sub-MLRAs for Spring Wheat Price Scenarios

19. Soil Carbon Simulations Performed with the Century Model for each Sub-MLRA • model parameterized for each sub-MLRA using various sources of data for soils, climate, and cropping practices • model executed over 30 years for each cropping system for each sub-MLRA to achieve new equilibrium soil C levels

20. Link economic simulation model to Century ecosystem model (loosely coupled) • Assess the costs of inducing changes in levels of soil C (opportunity costs) • Three policies: • CRP- style policy -- convert crop-land to grass (PG policy) • per acre payments for continuous cropping (CC policy) • per tonne payments

21. Spatial Heterogeneity and Scale --Biophysical heterogeneity --Economic heterogeneity

23. PG policy (payments per acre of cropland converted to permanent grass – CRP style policy)

24. Changes in Land Use Shares under PG Policy

25. Changes in Soil C under PG Policy

26. Marginal Cost for Soil C under PG Policy

27. Marginal Costs per MT C for Paymentsper Acre for PG Policy

28. Average Costs per MT C for Paymentsper Acre for PG Policy

29. Economics of PG policy • payments per acre induce conversion of both crop/fallow and continuously cropped land •  net increase in soil C • resulting average cost range from $34-$280 per MT of C • Marginal costs range from $34-$500 per MT of C • Spatial heterogeneity

30. CC Policy:Payments for Conversion to Continuous Cropping • only fields previously in crop/fallow rotation are eligible for payments for conversion to continuous cropping (targeted)

31. Changes in Land Use Shares under CC Policy

32. Changes in Soil C under CC Policy

33. Marginal Cost for Soil C under CC Policy

34. Marginal Costs per MT C for CC Policy:

35. Average Costs per MT C for CC Policy:

36. Economic Analysis of CC Policy • conversion from crop/fallow systems to continuously cropped systems increases soil C depending on payment level • average cost per ton C ranges from $12 to $50 per MT • marginal costs range from $12 to $130 per MT • Spatial heterogeneity

37. Over the 6 sub-MLRAs the total C sequestered ranged from PG policy: 1.75 to 6.50 MMT CC policy: 4.8 to 17.7 MMT

38. Marginal Costs, PG policy and CC policy, sub MLRA 52h

39. Marginal Costs, PG policy and CC policy, sub MLRA 58a-low

40. Distributional Impacts

41. Change in Land Use for Sub-MLRAs under the PG Policy

42. Costs of C Sequestration for Sub-MLRAs under the PG Policy

43. Government and Producer Costs for Sub-MLRAs under the PG Policy

44. Per Tonne Payment Policy Comparison of Marginal Costs for Per Hectare and Per Tonne payment mechanisms

46. Implications • Per tonne policy always more efficient then per acre or per hectare policy • Inefficiency is a function of spatial heterogeneity in economic and biophysical dimensions (up to 4 times)

47. Implications cont • Measurement costs to implement per tonne policy are fcn of sample size: • --one order of magnitude smaller than efficiency loss of per hectare contract • for stratified random sampling • -- approach efficiency loss for population sample • What is optimal size of spatial unit used to define soil C rates for policy analysis?

48. Future Directions: • Optimal scale, using loosely-coupled framework (MT soil C); tradeoff between accuracy and cost • Monitoring and measurement costs • Sensitivity of marginal costs to key economic/biophysical parameters, (carbon rates etc); uncertainty

49. Future Directions (cont) • Increased biophysical (spatial) heterogeneity of soil carbon rates • Extensions of econometric-process framework to larger regional analysis • Data availability issues

50. CONTACT:Susan M. CapalboDepartment of Agricultural Economicsand EconomicsMontana State UniversityBozeman, MT 59717 www.climate.montana.edu E-mail: uaesc@montana.edu