Estimating future SOC stock change is complex and uncertain. Key factors include:

1. The type and intensity of practice changes (e.g., cover cropping, no-till)
2. The initial state of soil degradation
3. The soil’s carbon sequestration potential, driven by texture, climate, and other site conditions

We strongly recommend using conservative estimates of the expected sequestration when calculating sample sizes. Overestimating expected change can lead to underestimating the sample size and hence higher uncertainty deductions, and even risk of ineligibility of crediting.

Expected SOC stock change is often informed by peer-reviewed studies or simulations from process-based models. However, these sources usually reflect ideal conditions, not real-world variability. There is limited empirical evidence confirming large-scale sequestration outcomes under typical on-farm implementation of regenerative practices.

Seqana can help. Our team specializes in helping clients set realistic assumptions based on global data, modeling and real world project context. Reach out to us to get tailored guidance.

The EONS calculator requires an input for SOC stock variance, either at a single point in time (snapshot) or over time (change). Because sample size is highly sensitive to this input, we strongly recommend using conservative estimates and seek expert advice in case you are unsure how to estimate the variance. Underestimating SOC Stock variance (of change) leads to underestimating the sample size results in higher uncertainty deductions and risk of integrity to the project.

Estimating SOC stock snapshot variance

For independent sampling, use the variance of SOC stock at a single point in time. This can be estimated from digital soil maps (DSMs), existing datasets, or peer-reviewed studies. Seqana also offers global coverage for our accurate, project-specific variance modeling. Reach out to our team to learn more.

Estimating SOC stock change variance

For paired sampling, sample size is based on the variance of SOC stock change between two time points. However, this is difficult to estimate ex-ante due to:

• Variability in land management practices across the project area
• Small-distance SOC variability, which can introduce noise in paired sampling

As a workaround, we recommend using the SOC stock snapshot variance as a proxy for the variance of change.

Important note:

Even with the same numeric variance (e.g., 500 tC²/ha²), sample size requirements differ depending on whether the variance is from a snapshot or a change. Snapshot-based designs typically have more samples but offer more flexibility. Talk to our experts in case you want to fully understand the pros and cons and implications for your project.

We’re always looking to improve our tools. You can share feedback directly through the link in the calculator or by using this contact form.

EONS identifies the economic optimum number of samples, but it’s still important to assess statistical significance and margin of error at that point. These metrics offer valuable insight into the reliability and precision of your SOC stock change estimate at the EONS.

You can find an example of the results in the table below. For a detailed table of your results, reach out to Seqana.

No. Each methodology handles uncertainty deductions differently, so EONS will vary per methodology, even with the same project specifications. The calculator lets you select the methodology you work with to generate a tailored EONS for your specific scenario.

If your methodology isn’t listed, contact the Seqana team to request its addition.

Learn more about how uncertainty deductions vary across methodologies in this blog post.

No, EONS varies significantly depending on the specific characteristics of each project. The table below outlines the key specifications that influence EONS, including their units and an explanation of how changes in these assumptions affect the economic optimum number of samples.

Buffer plans enable project developers to be more flexible in their project planning and account for changes that may occur. In practice, project areas are often dynamic, with some fields dropping out over time.

Additionally, sampling points may become inaccessible during certain measurement campaigns. It is strongly recommended to include a buffer to account for field churn and accessibility issues, especially with paired sampling. This buffer is not automatically included in the EONS calculation.

Contact Seqana for guidance on determining the appropriate buffer to plan for your project.

Paired sampling requires the variance of SOC stock change. While this often results in a smaller sample size, there are key considerations:

1. Operational Constraints: Paired sampling requires revisiting the same sampling location for every sampling campaign, which in practice often cannot be guaranteed. 
2. Variance Estimation: The variance of change is difficult to estimate ex-ante, making predictions less reliable.
3. Small-distance variability: The measured variance tends to be inflated due to small-distance variability and GPS imprecision.

Delay in clarity: The quality of the ex-ante variance estimate is only clear after remeasurement, leaving no opportunity to adjust the sample size for the first crediting period.

Contact Seqana for expert advice on the most suitable sampling type for your project.

There are two compliant approaches to sampling for SOC quantification, independent sampling and paired sampling.

• Independent Sampling allows flexibility in selecting sampling locations for every campaign. You do not need to resample at the same point as the initial campaign. The variance of SOC stock snapshot is required to estimate the sample size.
• Paired Sampling requires sampling the same location at each campaign throughout the project duration. It uses the variance in SOC stock change between initial and remeasurement events to estimate sample size.

While paired sampling may result in lower sample size requirements, it introduces important operational complexities. Contact Seqana for advice on which sampling type is best suited for your project.

Note: This calculator automatically selects the appropriate statistical calculations based on your selected sampling type.

Variance measures the heterogeneity of the project area regarding the target variable. The higher the variance is, the more samples are required to achieve the same precision. Depending on the chosen type of sampling, the relevant variance is either the variance of SOC stock snapshot (for independent sampling), or the variance of SOC stock change (for paired sampling). 

An ex-ante variance estimate is needed for sample size calculations. While this estimate is challenging, it is critical. For example, SOC stock snapshot variances can range from less than 150 tC²/ha² to more than 1000 tC²/ha²., significantly impacting sample sizes and project economics. Conservative estimates are recommended.

For help estimating your project's variance, contact Seqana.

EONS helps determine the economically optimal number of samples, but additional factors should be considered when finalizing your sample size:

1. Statistical Power and Significance (MDD Approach): It’s important to assess the likelihood that your sampling design can detect a real change in SOC. This is typically done using power analysis, which quantifies the risk of failing to detect a true sequestration effect. The EONS tool provides estimates of power and significance levels at different sample sizes.
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2. Margin of Error (MoE): Understanding the precision of your estimated SOC sequestration is also important. Calculating the Margin of Error at a chosen confidence level gives additional insight into the reliability of the SOC stock change estimate. MoE outputs are also available through the EONS tool.
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3. Premium Pricing and Offtaker Expectations: Some credit buyers may require specific levels of statistical significance or precision. Stronger statistical claims can justify premium pricing. These scenarios can be simulated in the premium features of the EONS tool. Contact the Seqana team to explore buyer-aligned sampling strategies.
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4. Paired Sampling and Re-measurement Risk: For paired sampling designs, only locations sampled consistently at both initial sampling (t₀) and re-measurement (t₁) count as valid pairs. To ensure enough usable paired samples, build in a buffer to account for potential inaccessibility due to land turnover, weather, or other constraints. Contact the Seqana team for guidance on this.

EONS (Economic Optimum Number of Samples) helps balance two critical needs for scaling regenerative agriculture: Market integrity and project profitability.

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Integrity: EONS supports credibility by aligning sample size with uncertainty deductions which are built-in safeguards against over-crediting. These deductions are verified by Verification and Validation Bodies (VVBs) and incentivize developers to increase sample sizes, since more samples reduce uncertainty deductions and boost revenue. However, there’s a tipping point: beyond a certain number of samples, added cost outweighs the revenue gain.

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Profitability: Unlike traditional methods (e.g., Minimum Detectable Difference (MDD) or Margin of Error (MoE) approaches), EONS factors in the actual economics of the project, including sample cost, expected credit prices, and uncertainty deductions. This makes it a more realistic and scalable approach for projects operating under financial constraints.

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Intuition:

Following EONS, 

• Smaller projects, typically pilot projects before scaling, benefit from lower sample size requirements under EONS, making them financially viable (though with higher uncertainty deductions). It allows projects to pass pilot phases and to become larger projects. 
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• Larger projects, which are expected to be more common after pilot phases, are recommended to take higher sample sizes, which improves both economics and statistical soundness.

See the visual below:

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