Factoring Agricultural SOC flux into LCA-based Approaches for GHGP / SBTi Reporting

Blog Author: Grant Ivison-Lane, Founder/Managing Principal at Terranewt

Soil organic carbon (SOC) flux can contribute a significant proportion of the total carbon balance of agricultural commodities depending on the location and farm management practices. That being said, modeling and integrating SOC flux into an LCA can be difficult. Using IPCC Tier 1 or 2 factors can be a simple and straightforward way to capture this, but these are often coarse estimates of annual flux and we may want to be more precise or accurate depending on the use-case, in particular when we are trying to be sensitive to annual fluctuations in weather or farm management, or report removals in alignment with Greenhouse Gas Protocol (GHGP) or Science Based Target Initiative (SBTi) standards. When we choose to proceed with more complex IPCC Tier 3 approaches we should evaluate the considerations below.

Selecting an approach to measure/model and report SOC flux

There are multiple approaches available to measure, estimate, and report on changes of SOC at the field scale that can be standards aligned. Physical sampling of SOC between two points of time may seem like the most straightforward approach, but this often requires significant costs and labor to get a sample large enough to be representative or precise enough to detect change. This can be very challenging at large scales to implement.

Process-based models can reduce this sampling burden by modeling the full-scale SOC flux of a given geography (like a project area) based on practice and environmental inputs (like soil details, temperature, precipitation, and other factors). These models are in use in many areas today to estimate the impact of practices on SOC flux. There are also emerging technologies like digital soil mapping, which can complement sampling and “fill gaps” with remote sensing and machine learning to build a “digital map” of SOC. Regardless of the method (or combination of these methods) used we should be mindful of the uncertainty of our approach, and that our technological approach needs to be accurate enough to estimate a small change in total SOC over a very large area. We should consider that uncertainty can be significant and is not uniform. It will vary by environmental factors (weather, soil), crop type, the sampling approach or models used, the availability of soil sampling infrastructure, data availability, and our general scientific understanding of processes and measuring long term impact in a given region. There may also be other factors or parameters that contribute to uncertainty, such as the accuracy of our collected inputs into the model.

Collecting data and estimating SOC flux

Managing farm level data and entering it into a model can be complex, and at a large scale often requires a sophisticated data managed approach. There may be errors in collected data that need to be corrected or they will influence results. We should consider that often not all of the data we need is collected, and that public data sources like weather, soil, or agronomic data surveys or inferred practice data via remote sensing may be used. If the uncertainty of these parameters is significant that can carry through to the results.

When interpreting results it can be easy to attribute the current year SOC flux to practice changes made on the field, but we should be aware that results in a given year are not independent to actions taken in just that year - how the field was managed in previous years will impact the change in SOC that is observed or estimated in the present year. In addition to this the natural system (weather, soil) contributes to year-to-year variation that may explain some of the change in SOC (rather than the practice), in particular over short periods of time.

As a side note, given the amount of data required, the variety of approaches available to collect that data, the number of measurement/modeling approaches we should take caution in assuming LCAs that include SOC flux (or other emissions sources) in agriculture are “apples to apples” unless they follow the same method.

Interpreting results and Considering the GHG Reporting Standards

When integrating SOC flux into an LCA that will be used for GHG accounting in a corporate inventory we need to be in alignment with the appropriate standards, such as the GHGP Land Sector and Removals Standard (LSRS), and/or the SBTi’s Forest, Land, and Agriculture Guidance (FLAG).

The GHGP LSRS requires the separate reporting of emissions and removals (sequestration). This means we should not “net” emissions and sequestration to generate an emissions factor from an LCA result; rather, following the separate reporting of emissions and removals we have the option to also provide a “net” value. The GHGP LSRS also has general requirements that must be followed. Some of these include reporting emissions broken down by category, and meeting traceability requirements for reporting emissions at a certain inventory boundary scales. When reporting removals, the LSRS also requires removals safeguards to be followed, which includes reporting uncertainty, monitoring reported removals for potential reversals, following specific supply chain traceability requirements and other factors. Double counting must also be avoided when reporting removals. Note these requirements are not exhaustive, and reviewing the full LSRS (and the recently released Land Sector and Removals Guidance) is recommended. 

In Conclusion

While SOC can be a significant portion of the carbon footprint of a crop and is worth estimating depending on the scale and goals of the LCA, we should be mindful of selecting the best approach and consider tradeoffs. Once we select an approach we should keep track of the uncertainty associated with the parameters and quantification solution. We should expect to see variance due to natural factors, in particular over short time periods. When the LCA is intended to be used for corporate GHG accounting, we should be mindful of GHGP LSRS and SBTi standards and follow their requirements when formatting our results.

About the Author:

Grant Ivison-Lane, Founder/Managing Principal at Terranewt

Grant Ivison-Lane is the Founder and Managing Principal of Terranewt, an advisory and consulting services company for nature-based products and programs. Grant’s background is in corporate GHG accounting and ecosystem services assessment. At CIBO Technologies Grant led the development of one of the first products to use process-based modeling for inventory and intervention GHG accounting. Grant has been engaged with standards development as a member of the GHGP Land Sector and Removals Guidance and the Actions and Market Instruments working groups. He is also a part-time faculty member at Colorado State University where he teaches Scope 3 GHG accounting.