Biological Uncertainties

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Sensitivity of Carbon Sequestration Costs to Scale and to Economic and Biological Uncertainties Presented at the ASSA Annual MeetingsWashington, DCJanuary 5, 2003 Susan M. Capalbo,Montana State University with the collaboration of John M. Antle, Montana State University,Siân Mooney, University of Wyoming,Keith H. Paustian, Colorado State University

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Acknowledgements This research was funded by the USDA special grants CASMGS, the USDA National Research Initiative Competitive Grants Program, the NSF Methods and Models for Integrated Assessment Program, and the EPA STAR Program.

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In this paper, we develop methods to investigate the efficiency of alternative types of policies or contracts for C sequestration in cropland soils, taking into account: Objectives 1. The spatial heterogeneity of agricultural production systems, and Sensitivity of the marginal cost of supplying carbon to: a. carbon rates b. scale c. yield variations

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Objectives (2) We describe per-hectare contracts for soil C and use a model of farmers’ decisions to participate in soil C contracts to derive the on-farm opportunity costs. We present an integrated assessment modeling framework, based on coupled site-specific biophysical simulation models and site-specific economic data and models, that can be used to simulate farmers’ decisions to participate in both per-hectare contracts. Using this coupled modeling framework in a case study of the dryland grain production system of the Northern Plains region of the United States, we test the sensitivity of the model to show how the costs vary depending upon scale of analysis and uncertainty of input parameters.

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Related Papers Available at www.climate.montana.edu Antle, J.M., and S.M. Capalbo, “Econometric-Process Models for Integrated Assessment of Agricultural Production Systems.” American Journal of Agricultural Economics 83 (May 2001): 389-401. Antle, J.M., S.M. Capalbo, S. Mooney, E. Elliott and K.H. Paustian, “Economics of Agricultural Soil Carbon Sequestration: An Integrated Assessment Approach.” Journal of Agricultural and Resource Economics 26 (December 2001): 344-367. Mooney, S., J.M. Antle, S.M. Capalbo, and K.H. Paustian, “Contracting for Carbon Credits: Design and Costs of Measurement and Monitoring.” Staff Paper 2002-01, Dept. of Ag. Econ. & Econ., Montana State University. (forthcoming JEEM) Antle, J.M., S.M. Capalbo, S. Mooney, E.T. Elliott, and K.H. Paustian. “Sensitivity of Carbon Sequestration Costs to Soil Carbon Rates.” Environmental Pollution 116 (March 2002): 413–422. Antle, J.M., S.M. Capalbo, and S. Mooney. “Optimal Spatial Scale for Evaluating Economic and Environmental Tradeoffs.“ Selected paper. AAEA Annual Meetings, Nashville, TN, 1999.

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Designing Contracts for Soil C Per-tonne contract: pays farmer $P/tonne/yr for duration of contract Payment independent of practice Per-hectare contract: payment for use of BMP Payment independent of quantity of C Must monitor practices for compliance with contract Farmers enter contract if g > ji – js Must quantify amount of C Establish baseline Measure accumulation of C Farmers enter contract ifP > (ji - js)/cjis , i.e., if price per tonne is greater than opportunitycost per tonne

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Result from Earlier Papers For each quantity of C sequestered, the marginal opportunity cost of the per-hectare payment mechanism (MCH) is greater than or equal to the marginal opportunity cost of the per-tonne mechanism (MCT), i.e., MCH  MCT, and MCT /MCH is decreasing with spatial heterogeneity.

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Marginal Cost Functions for per-hectare and per-tonne Payments

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Integrated Assessment Paradigm Economic data  economic production models Soils & climate data  crop ecosystem models Output of crop ecosystem models  economic models andenvironmental process models Output of economic models  environmental process models

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Structure of an Econometric-Process Simulation Model

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

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Linkage between Century Model and Economic Production Model

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Simulation of Land Use Using Econometric-Process Model of Montana Dryland Grain Production 1995 MT Cropping Practices Survey Statistically representative sample of Sub-MLRAs in grain producing regions of MT Useable data from 425 commercial grain farms

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Montana Dryland Grain Study Sub-MLRAs

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Soil C 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 50 years for each cropping system for each sub-MLRA to achieve new equilibrium soil C levels Link economic simulation model to Century ecosystem model Assess the costs of inducing changes in levels of soil C (opportunity costs) Alternative policies: per hectare payments per tonne payments

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Land Allocation in Montana Dryland Grain Production Systems  Winter Wheat Recrop  Barley Recrop  Spring Wheat Recrop  Winter Wheat Fallow  Barley Fallow  Spring Wheat Fallow Fallow

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Soil C Levels Predicted by Century Model for Cropping Systems in Montana

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Additions to the Share of Cropland in Continuous Cropping: Base Scenario Additional Share of CroplandIn Continuous Cropping

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Changes in Soil C over a 20-Year Time Horizon: Base Scenario

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Marginal Cost of per-hectare andper-tonne Payment Mechanisms Sub-MLRA52-high Sub-MLRA52-low — per-hectare payment — per-tonne payment

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Marginal Cost of per-hectare and per-tonne Payment Mechanisms Sub-MLRA53a-high Sub-MLRA53a-low — per-hectare payment — per-tonne payment

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Marginal Cost of per-hectare and per-tonne Payment Mechanisms Sub-MLRA58a-high Sub-MLRA58a-low — per-hectare payment — per-tonne payment

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Sensitivity of Marginal Cost Results to: Soil C rates Scale for measuring soil C rates Yield uncertainties Output price uncertainties

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Scenario Descriptions

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Changes in Soil C Rates Keep spatial heterogeneity Adjust by 50% increase in soil C rates Adjust by 50% decrease in soil C rates Show results for per hectare contracts

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Sensitivity of Marginal Costs to Carbon Rates, Sub-MLRA 52-high per-tonnecontract per-hectarecontract Base Quantity  50% Increase in C  50% Decrease in C

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Sensitivity of Marginal Costs to Carbon Rates, Sub-MLRA 58a-low per-tonnecontract per-hectarecontract Base Quantity  50% Increase in C  50% Decrease in C

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Changes in soil C rates change the quantity of soil C sequestered at various prices (shifts the MC curve) Under per-hectare policy, as soil C rates increase, the impact on soil C sequestered increases in proportion to the square of the increase in soil C rates Under per-tonne policy, we have a linear mapping of changes in soil C rate and changes in MC curve Results of Change in Soil C Rates

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Marginal Costs for Soil C:Soil C Rate Scenarios Sub-MLRA52-high Sub-MLRA53a-high Sub-MLRA58a-high Base Scenario  Scenario 1  Scenario 2 (150% of base) (50% of base)

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Sensitivity of Marginal Coststo Scale Use average rates of soil C across all Sub-MLRAs Impacts are specific to Sub-MLRA Using “mean” rates of soil C overestimates the MC for Sub-MLRA 52-high and 58a-high Using “mean” rates of soil C underestimates the MC for Sub-MLRA 53a-high

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Marginal Costs for Soil C:Scale Scenario Base Scenario  Scenario 3 Sub-MLRA52-high Sub-MLRA53a-high Sub-MLRA58a-high

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Sensitivity of Marginal Coststo Output Price Changes Scenario 4: A 10% increase in the mean of the estimated sample distributions of output prices respectively. Scenario 5: A 10% decrease in the mean of the estimated sample distributions of output prices respectively.

Marginal Costs for Soil C:Output Price Scenarios : 

Marginal Costs for Soil C:Output Price Scenarios Base Scenario  Scenario 4  Scenario 5 (10% Increase) (10% decrease) Sub-MLRA52-high Sub-MLRA53a-high Sub-MLRA58a-high

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Sensitivity of Marginal Coststo Change in Yields Scenario 6: A 10% increase in yields for fields that are in the program.

Marginal Costs for Soil C:Productivity (Yield Increase) Scenario : 

Marginal Costs for Soil C:Productivity (Yield Increase) Scenario  Base Scenario  Scenario 6 (10% yield change) Sub-MLRA52-high Sub-MLRA53a-high Sub-MLRA58a-high

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Conclusions Contracts based on BMPs (per hectare contracts) are as much as 5 times more costly than efficient contracts that pay per tonne of C, a degree of inefficiency similar to that found in studies of industrial regulation. The case study confirms that the relative inefficiency of per-hectare contracts varies spatially and increases with spatial heterogeneity. The estimates of MC are sensitive to four key parameters (variable) in the model Soil C rates Scale of analysis (biophysical scale only) Yields Output prices Uncertainty in an integrated biophysical/economic model affects both biophysical and economic measures Not always a linear mapping Policy design plays a key role in assessing impacts of uncertainty and scale