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Reducing standard errors by incorporating spatial autocorrelation into a measurement scheme for soil carbon credits


Several studies have suggested that geostatistical techniques could be employed to reduce overall transactions costs associated with contracting for soil C credits by increasing the efficacy of sampling protocols used to measure C-credits. In this paper, we show how information about the range of spatial autocorrelation can be used in a measurement scheme to reduce the size of the confidence intervals that bound estimates of the mean number of C-credits generated per hectare. A tighter confidence interval around the mean number of C-credits sequestered could increase producer payments for each hectare enrolled in a contract to supply C-credits. An empirical application to dry land cropping systems in three regions of Montana shows that information about the spatial autocorrelation exhibited by soil C could be extremely valuable for reducing transactions costs associated with contracts for C-credits but the benefits are not uniform across all regions or cropping systems. Accounting for spatial autocorrelation greatly reduced the standard errors and narrowed the confidence intervals associated with sample estimates of the mean number of C-credits produced per hectare. For the payment mechanism considered in this paper, tighter confidence intervals around the mean number of C-credits created per hectare enrolled could increase producer payments by more than 100 percent under a C-contract.

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  1. Antle JM, Capalbo SM (2001) Econometric-Process Models for Integrated Assessment of Agricultural Production Systems. Amer J Ag Econ 83(May):389–401

    Article  Google Scholar 

  2. Antle JM, Capalbo SM, Mooney S, Elliott ET, Paustian KH (2001) Economic Analysis of Agricultural Soil Carbon Sequestration: An Integrated Assessment Approach. Journal of Agricultural and Resource Economics 26(December):344–367

    Google Scholar 

  3. Antle JM, Capalbo SM, Mooney S, Elliott ET, Paustian KH (2002) A Comparative Examination of the Efficiency of Sequestering Carbon in U.S. Agricultural Soils. American Journal Alternative Agriculture 17(3):109–115

    Article  Google Scholar 

  4. Antle J, Capalbo S, Mooney S, Elliot E, Paustian K (2003) Spatial Heterogeneity and the Design of Efficient Carbon Sequestration Policies for Agriculture. Journal of Environmental Economics and Management 46(2):231–250

    Article  Google Scholar 

  5. Bailey TC, Gatrell AC (1995) Interactive Spatial Data Analysis. Pearson Education Limited, Essex. Longman Group Limited

  6. Cerri CEP, Cerri CC, Paustian K, Bernoux M, Mellilo JM (2004) Combining Soil C and N Spatial Variability and Modeling Approaches for Measuring and Monitoring Soil Carbon Sequestration. Environmental Management 33, Supplement 1:S274–S288

    Article  Google Scholar 

  7. Conant RT, Paustian K (2002) Spatial Variability of Soil Organic Carbon in Grasslands: implications for detecting change at different scales. Environmental Pollution 116:S127–S135

    Article  Google Scholar 

  8. Environmental Protection Agency (2005) Greenhouse Gas Mitigation Potential in U.S. Forestry and Agriculture. EPA report 430 R-05-006, November

  9. Feng H, Zhao J, Kling CL (2001) Carbon: The next big cash crop? Choices. Second Quarter:16–19

    Google Scholar 

  10. Kurkalova LA, Kling CL, Zhao J (2004) Value of Agricultural non-point Source Pollution Measurement Technology: Assessment from a Policy Perspective. Applied Economics 36:2287–2298

    Article  Google Scholar 

  11. Lal R, Kimble LM, Follett RF, Cole CV (1998) The Potential of U.S. Cropland to Sequester C and Mitigate the Greenhouse Effect. Chelsea MI: Ann Arbor Press

    Google Scholar 

  12. McCall Jr CH (1982) Sampling and Statistics Handbook for Research. Ames IA: The Iowa State University Press

    Google Scholar 

  13. Mooney S, Antle JM, Capalbo SM, Paustian K (2004a) Design and Costs of a Measurement Protocol for Trades in Soil Carbon Credits. Can J Agr Econ 52(3): 257–287

    Article  Google Scholar 

  14. Mooney S, Antle JM, Capalbo SM, Paustian K (2004b) Influence of Project Scale on the Costs of Measuring Soil C Sequestration. Environmental Management 33 (supplement 1): S252–S263

    Article  Google Scholar 

  15. Parton WJ, Schimel DS, Ojima DS, Cole CV (1994) A general model for soil organic matter dynamics: Sensitivity to litter chemistry, texture and management, in Quantitative Modeling of Soil Forming Processes Bryant RB, Arnold RW (Eds.), SSSA Special Publication No. 39, pp 147–167, Soil Science Society of America, Madison, WI

  16. Paustian K, Elliott ET, Peterson GA, Killian K (1996) Modelling Climate, CO2 and Management Impacts on Soil Carbon in Semi-arid Agroecosystems. Plant Soil 187:351–365

    Article  Google Scholar 

  17. Pautsch GR, Kurkalova LA, Babcock BA, Kling CL (2001) The Efficiency of Sequestering Carbon in Agricultural Soils. Contemporary Economic Policy 19(April):123–134.

    Article  Google Scholar 

  18. Pew Center on Global Climate Change (2002) Climate Change Activities in the United States. Downloaded June 14, 2002

  19. Pew Center on Global Climate Change (2004) Learning From State Action on Climate Change. In Brief, 8. Downloaded December 19, 2005.

  20. Rosenzweig R, Varilek M, Feldman B, Kuppalli R, Janssen J (2002) The Emerging International Greenhouse Gas Market. Pew Center for Global Climate Change, Arlington VA, projects/trading.pdf

  21. Smith GR (2002) Case Study of Cost versus Accuracy When Measuring Carbon Stock in a Terrestrial Ecosystem. Agricultural Practices and Policies for Carbon Sequestration in Soil. Kimble J, Lal R and Follett RF (eds.), pp 183–192. Boca Raton, FL: CRC Press LLC

    Google Scholar 

  22. Williams JR, Peterson JM, Mooney S (2005) The Value of Carbon Credits: Is There a Final Answer? Journal of Soil and Water Conservation 60(2):36A–40A

    Google Scholar 

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Correspondence to Siân Mooney.

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Mooney, S., Gerow, K., Antle, J. et al. Reducing standard errors by incorporating spatial autocorrelation into a measurement scheme for soil carbon credits. Climatic Change 80, 55–72 (2007).

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  • Kriging
  • Transaction Cost
  • Spatial Autocorrelation
  • Reduce Transaction Cost
  • Springer Climatic Change