Sampling economics · Field variability · Project viability
How much field variability
can your project absorb?
v0.3
accruedcarbon.com
High CV means more samples needed and larger uncertainty deductions on every credit issued. The two effects compound. This tool shows you the full CV landscape — where the break-even cliff is, and how far you are from it.
Break-even spatial CV
max viable raw field CV
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Max profit at current CV
(at EONS)
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EONS
(economic opt. n)
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Years to credibility
(at n=30 samples)
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n within project horizon
(samples to hit threshold)
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σ / annual rate
(noise-to-signal, tC basis)
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Claim uncertainty now
(at project duration, n=30)
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Maximum profit vs. SOC stock CV
At each CV, profit shown is the best achievable (i.e. at EONS). The break-even CV is where this curve crosses zero — your project's tolerance limit for field variability.
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Break-even CV vs. credit price
Higher credit prices raise the break-even CV — your project can tolerate more variable fields. Shown for three cost/sample levels.
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EONS vs. CV — optimal sample count
EONS grows with CV. This is how many samples would maximize profit at each variability level — shown for reference.
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Break-even CV — sensitivity to credit price × sample cost
Each cell shows the maximum SOC stock CV at which the project remains profitable (at EONS). Green = generous tolerance. Red = project only viable at very low field variability. Current inputs highlighted.
How this differs from EONS tools
Tools like Seqana's EONS calculator answer: "Given your scenario, what is the optimal sample count?" — a single number for a specific set of inputs.
This tool answers: "Across the range of plausible field variabilities, where does your project stop being viable?" — a landscape that exposes sensitivity to the most uncertain input in project planning.
Both use the same underlying model. The uncertainty deduction follows VM0042 Eq. 65:
unc_deduction (%) = (σ · f / √n / δ) × 100 × M
EONS = [G · p · M · σ · f / (2c · δ)]^(2/3)
Break-even CV = 2δ/(M·f·3√3) · √(G·p/c) / mean_stock × 100%
where σ = std dev of SOC stocks (tC/ha), δ = expected SOC change (tC/ha), f = √2 (independent) or 1 (paired), M = methodology multiplier (0.4307 for VM0042), G = gross tCO₂e, p = credit price, c = cost per sample.
The break-even CV formula is derived analytically from setting profit at EONS equal to zero. It shows that break-even CV scales with √(G·p/c) — doubling gross revenue or halving sample cost increases the break-even CV by √2 ≈ 41%.
This tool models sampling cost only. Verification fees, buffer pool deductions, and methodology registration costs are not included.
Time to credibility — the core concept
In agricultural soils, spatial variability (σ) typically dwarfs the annual sequestration rate. A field sequestering 0.5 tC/ha/yr with a stock CV of 40% and mean SOC of 30 tC/ha has σ = 12 tC/ha — 24 years of signal in a single standard deviation. The removal claim only becomes statistically defensible once cumulative δ rises above the noise.
The relative uncertainty of a removal claim after Y years with n samples is:
uncertainty (%) = z · σ_total · f / (rate · Y · √n) × 100
years to credibility = z · σ_total · f / (T · rate · √n)
n to detect in Y years = ⌈ (z · σ_total · f / (T · rate · Y))² ⌉
where T = credibility threshold (fraction), σ_total = √(σ²_spatial + σ²_technical), rate = annual SOC sequestration rate (tC/ha/yr), f = √2 (independent sampling) or 1 (paired), z = confidence quantile.
Years to credibility scales with σ/(T · rate · √n). The three levers available to a corporate or project developer are: (1) increase n — more samples per campaign, diminishing returns above ~50; (2) choose lower-variability fields — halving CV halves the monitoring period; (3) increase rate or extend the horizon — longer projects accumulate more signal.
The sensitivity table sweeps n × sequestration rate to show which combinations reach credibility within a 5- or 10-year window. Fields with σ/rate > ~15 years are unlikely to produce defensible measure-remeasure claims within typical corporate reporting cycles regardless of sample count.
This tool models direct sampling uncertainty only. Additionality, permanence risk, and leakage are not included.