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Spatial variability Factors of soil formation (Jenny) Climate Organisms Parent material Topography

Spatial variability Factors of soil formation (Jenny) Climate Organisms Parent material Topography Time We must live with spatial variation – it is unchangeable and irreducible. How can uncertainty of measurements be reduced? What are the implications for cost-effectiveness?.

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Spatial variability Factors of soil formation (Jenny) Climate Organisms Parent material Topography

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  1. Spatial variability • Factors of soil formation (Jenny) • Climate • Organisms • Parent material • Topography • Time • We must live with spatial variation – it is unchangeable and irreducible How can uncertainty of measurements be reduced? What are the implications for cost-effectiveness?

  2. Sampling costs

  3. Costs and benefits of reducing uncertainty in accounting for soil carbon credits R. T. Conant, Colorado State University S. Mooney, University of Wyoming K. Gerow, University of Wyoming

  4. Background: Value of C credits • Most producers will require economic incentives to change practices • Money received by producers is a function of price offered for each credit, perceived uncertainty (i.e., discounting) and transaction costs • Both uncertainty and transaction costs are related to verification and sampling

  5. Methods to reduce sample variability • Increase duration between sampling • Aggregate • Alter risk acceptance • Covariance – re-sample same plots • Use spatial autocorrelation • Extrapolate using additional information • Increase # of samples analyzed What are the costs/benefits associated w/ these?

  6. Soil C pool 1. Increase duration between sampling Average Cultivated soil C (top 20cm): 14.5 Mg C ha-1 Accumulation rate (top 20cm): 0.27 Mg C ha-1 yr-1 20cm 2 years change = 3.7% 25 years change = 46.6%

  7. # samples 1. Increase duration between sampling Two potential outcomes: • Decreases the number of samples required for a given precision • Can increase the precision for a given number of samples Either way, income potential increases Question: • Do future earnings justify reduced sampling now?

  8. 2. Aggregation Measurement Cost per Credit ($) Mooney, S., J. M. Antle, S. M. Capalbo and K. Paustian. 2004. Influence of Project Scale on the Costs of Measuring Soil C Sequestration. Environmental Management 33 (supplement 1): S252 - S263.

  9. 3. Alter risk acceptance • Reducethe standard error • Results in smaller confidence interval

  10. # samples 3. Alter risk acceptance • Reducing the confidence intervals • Higher producer payments • Possible to achieve at low cost • What is the balance between risk and sampling costs?

  11. 4. Covariance Has management led to changes over time? Time 1 Time 2 Diff? = f(2(t1-t2) = 2t1 + 2t2 – 2covt1 t2) Implication: Large covt1 t2 small 2(t1-t2) Small 2(t1-t2) likelihood of difference covt1 t2 can be maximized by: ensuring uniform treatments, texture, slope, aspect, etc. re-sampling same location

  12. 5. Use spatial autocorrelation • Reducing the confidence intervals • Higher producer payments • Possible to achieve at low cost Mooney, S., K. Gerow, J. Antle, S. Capalbo and K. Paustian. 2005.The Value of Incorporating Spatial Autocorrelation into a Measurement scheme to Implement Contracts for Carbon Credits. Working Paper 2005 – 101. Department of Agricultural and Applied Economics, University of Wyoming

  13. 5. Use spatial autocorrelation SWF=spring wheat fallow GRA = grass CSW= continuous spring wheat WWF= winter wheat fallow CWW= continuous winter wheat

  14. 6. Extrapolation • No studies that directly examine Krieging to date • Expect that information about spatial autocorrelation will: • Decrease sample size • Decreasing measurement costs • Krieging with additional information is best method of extrapolation (Doberman et al.)

  15. 7. Increase number of samples analyzed • Increase # samples analyzed • Decrease sample error • Increase confidence interval • Increase cost If analytical costs fall dramatically (due to LIBS, NIR, EC, etc.) risk/uncertainty can be reduced and producers will be the beneficiaries. Mooney, S., J. M. Antle, S. M. Capalbo and K. Paustian. 2004. Design and Costs of a Measurement Protocol for Trades in Soil Carbon Credits. Canadian Journal of Agricultural Economics. 52(3):257-287

  16. Conclusions • Soil variation is irreducible • There are several things we can do to increase statistical confidence in our measurements, thus reducing risk/uncertainty and increasing returns to producers • Improved analytical techniques could be a significant contributor in the future.

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