How We Design Carbon Projects

By Adriel Hsu-Flanders

It goes without saying: successful agricultural carbon programs rely on rigorous, credible, and transparent measurement. At EarthOptics, we believe soil testing programs should do more than check a box—they should detect real, measurable changes over time driven by a farmer’s or rancher’s management, rather than noise caused by environmental variability.

Principles of Sampling Design

When designing soil sampling programs, two core factors guide our approach:

1. Variability – Every acre is different. Natural characteristics—such as soil type, slope, vegetation, and moisture—as well as management decisions, such as grazing intensity, crop rotations, and input use, all influence carbon sequestration potential. Our sampling plans must capture sufficient variability to represent the landscape accurately.
2. Acreage – We work on projects ranging from 100 acres to tens of thousands of acres. We make sure our sampling strategies adequately account for the size of the project so we can accurately measure carbon baselines and set ourselves up to capture change over time.

Most of our carbon customers are interested in understanding how soil carbon stocks change over time. Sampling design, therefore, is about understanding how both variability and acreage influence how many samples are enough to achieve this goal across the entire project, however big that project is.

How Variability and Acreage Influence Sampling

Most customers want to understand how soil carbon stocks shift year over year. To detect those changes, sampling density must reflect the complexity and size of the project.

• Larger or more variable projects require more samples.
• Smaller, more uniform projects can often be measured accurately with fewer samples.

For example, a 200-acre ranch with mixed soils and varied management practices may require more intensive sampling than a uniform, flat 300-acre field. On very large projects, we can maintain accuracy with lower sampling density because variability averages out across the broader landscape. On smaller projects, however, denser sampling is often necessary to meet quality targets.

Understanding Margin of Error

To illustrate sampling uncertainty, consider election polling. You can survey all 100,000 voters for near-perfect accuracy—or ask a representative sample and still get a reliable projection. Soil carbon measurement operates on the same principle.

Because we cannot measure every grain of soil, our estimates include a margin of error (MOE) that reflects natural uncertainty. More samples reduce the MOE, though the rate of improvement slows over time. Early samples dramatically sharpen accuracy, but eventually each additional sample yields diminishing returns. This dynamic allows us to measure large projects with efficient sampling densities.

You can see how this works in this illustrative graph of a small project (with example numbers).