Spatial variabilityFactors 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?

Cultivated field in TN

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Sampling costs

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

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

1. Increase duration between sampling 2. Aggregate3. Alter risk acceptance 4. Covariance – re-sample same plots5. Use spatial autocorrelation6. Extrapolate using additional information7. Increase # of samples analyzed

What are the costs/benefits associated w/ these?

Methods to reduce sample variability

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

Soil C pool20cm

2 yearschange = 3.7%

25 yearschange = 46.6%

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?

Cultivated field in TN

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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.

2. Aggregation

3. Alter risk acceptance

• Reduce the standard error

• Results in smaller confidence interval

xx SxSx 96.196.1

• Reducing the confidence intervals– Higher producer payments– Possible to achieve at low cost

3. Alter risk acceptance

Cultivated field in TN

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# microplots

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What is the balance between risk and sampling costs?

4. Covariance

Time 1 Time 2Has management led to changes over time?

Diff? = f(2 (t1-t2) = 2t1 + 2

t2 – 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

d (km)

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)(d

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D*r

D*r

D*r

• 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

5. Use spatial autocorrelation

SWF=spring wheat fallow GRA = grass CSW= continuous spring wheatWWF= winter wheat fallow CWW= continuous winter wheat

Payment $10 $30

0.1R 0.15R 0.2R 0.1R 0.15R 0.2R

Crop system change

SWF_GRA 1.93 4.96 6.57 2.69 6.83 8.93

SWF_CSW 4.19 5.44 6.41 4.14 5.22 5.81

SWF_CWW 15.21 20.10 23.30 10.78 13.95 15.62

WWF_GRA 4.89 11.84 15.50 7.93 19.06 24.71

WWF_CSW 12.50 16.86 20.66 8.21 10.72 12.08

WWF_CWW 19.01 24.89 29.15 18.43 23.54 26.27

CSW_GRA 21.62 32.08 38.84 39.59 57.31 66.67

5. Use spatial autocorrelation

• 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.)

6. Extrapolation

• Increase # samples analyzed– Decrease sample error– Increase confidence interval– Increase cost

CreditPrice

Sample Size

e=10%95% Confid.

e=5%95% Confid.

e=10%99% Confid.

10 1,307 5,109 2,239

20 1,242 4,871 2,133

30 1,199 4,711 2,062

40 1,156 4,543 1,984

50 1,152 4,528 1,977

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

7. Increase number of samples analyzed

If analytical costs fall dramatically (due to LIBS, NIR, EC, etc.) risk/uncertainty can be reduced and producers will be the beneficiaries.

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.