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How does soil carbon get measured?
Accurately measuring carbon sequestration is important because it ensures buyers have confidence in the carbon credits they purchase and farmers receive premium prices for providing carbon sequestration. When it comes to quantifying soil carbon sequestration, two options exist: soil sampling and modeling. Soil sampling for carbon can be fairly accurate (assuming similar weather and time of season in which testing occurs). But it also imposes significant limitations for providing an effective strategy for a functioning carbon credit market.
First, soil carbon sequestration is not as easily measured and sampling is prohibitively expensive and would absorb almost all the revenue from the sale of carbon credits. Second, sampling requires years to pass between the initial baseline measurement and subsequent measurements to quantify carbon sequestration. Any carbon credit program that relies on soil sampling as the primary measurement would have to have farmers participate for several years before the program could quantify and pay those farmers.
On the other hand, modeling has much lower operational costs and can allow farmers to generate carbon credits and get paid for those credits on an annual basis.
So, how do soil carbon models work?
Soil carbon models combine thousands of data points from past academic and governmental research to calculate soil carbon sequestration. These models look for patterns and relationships that consider the climate, soil type, variations in weather, and the hundreds of different combinations of practices growers can implement to generate estimates on soil carbon, and then uses the results from the data to verify, amend, and verify again until the model has figured out how the measured soil carbon sequestration actually occurred. Once the model has figured out the formula, it can now apply new numbers such as the field location and historic cropping to quantify estimates, allowing soil carbon quantification to occur yearly. There are a number of models available that consider or emphasize different components. Indigo uses a robust model that considers hundreds of relationships between biological, physical, and chemical relationships occurring in the soil.
Indigo takes this process one step further by using random sampling to continuously inform and update the model. This calibration component is novel to soil carbon quantification and provides a long-term strategic advantage. This learning approach means that the model will improve in soil carbon quantification accuracy over time and reduce the uncertainty with results from the model. The more growers who partner with Indigo to generate carbon credits, the stronger the model becomes through increased data points and sampling verification. This improvement in quantification accuracy occurs for all fields, not just the small portion that are randomly selected for sampling. Essentially, stakeholders can have higher confidence that the quantification generated by the model is accurate. This means that buyers can have more confidence in the carbon credit attached to that sequestration and farmers receive higher profits.
Soil carbon quantification continues to improve with more data research, yet recent advancements led by Indigo have reached a critical point of accuracy allowing carbon credit programs to begin rewarding farmers for implementing practices that increase carbon sequestration.