Climate change impacts our food systems in serious and sometimes irreversible ways. However, agriculture — the backbone of food systems — has the power to combat climate change. Today, we can combat climate change by enabling farmers to adopt climate-friendly farming practices and rewarding them for their efforts.
Ecosystem markets are one way to incentivize farmers to adopt climate-friendly practices. These markets are still in their nascency. They allow farmers to receive payment for the emissions (like carbon)sequestered in their soil. However, these markets rely on manual data entry, extensive in-field data collection, and soil sampling to quantify payment for farmers. These market programs, as they currently operate, will not be scalable for agriculture.
Science and technology can help overcome these barriers in scalability. The new methods won’t instantly meet the accuracy of the old methods, but they will dramatically reduce the cost of market transactions and will incentivize farmers and catalyze supply and demand within the market.
What are these technologies?
- Remote sensing
- Biogeochemical analysis
- Soil carbon analysis
Let’s take a deep dive into each one.
Satellite imagery can help us map agricultural practices that significantly impact soil carbon sequestration. These practices include tillage, crop rotation, and cover cropping. By using satellite imagery instead of manual labor (people driving to check fields), markets can dramatically reduce their costs. This also allows for a standardized method of reporting, which relies on objectively verifiable imagery rather than subjective in-person data collection.
Typically, farmers measure the amount of carbon in their soil by conducting soil samples. However, sampling is a manual process that requires a lot of time and labor. Instead of sampling the soil, technology can allow us to use calibrated and validated scientific models to estimate how much carbon is in the soil. It can also estimate how much additional carbon will be sequestered if new management practices are adopted. Compared to traditional soil sampling, modeling lends itself well to scenario planning, meaning that a farmer can estimate how much profit a practice would bring before making the practice change.
It is important to acknowledge that a certain amount of soil testing is necessary to calibrate and validate suitable soil models. However, once the model is calibrated and validated for use in a specific geography and with a set of farming practices, it can be used on an ongoing basis with minimal resampling.
Another important attribute of a calibrated and validated model is the knowledge of accuracy and uncertainty of the estimates that it provides. In order for carbon markets to function efficiently, they need to have an estimate of the uncertainty associated with the carbon credits they are issuing. As more fields are enrolled in carbon programs, and as more data is collected on field practices, the uncertainty of the modeling is reduced. The lower the uncertainty, the fewer credits the market operator needs to“withhold”, and the more credits the farmer can be paid for.
Soil carbon analysis methods
There have been a number of innovations in soil carbon analysis, including proximal and enhanced in-field sensors which can analyze soil without sending it to the lab. Other innovations include novel laboratory-based techniques for analyzing soil cores, which dramatically reduce the cost and time required to perform an analysis. As these technologies mature and come into the market, we will see more soil data become available, which will further fuel the development of the models and algorithms for new geographies in agricultural systems.
The next generation of measurement, reporting, and verification (MRV) systems will combine the best of these three technologies and provide the scaffolding for scalable ecosystem markets. The MRV platform connects these technologies as follows:
The biogeochemical model is calibrated and validated using targeted soil sampling. Remote sensing data is then used to continuously map agricultural practices and initialize or update the biogeochemical model.
Once the model provides an estimate of soil carbon sequestration, a project developer or a market operator can make an initial payment to the farmer. This provides the seed capital farmers need to adopt new practices.
Once these new practices are in place, satellite imagery is used to verify those practice changes.Verifying in this method dramatically cuts costs and enables individual farmers to earn more profit for their efforts., as well as more farmers to enroll in the system.
As more data and farm records become available, the model estimates are updated to perform a so-called “true-up”. This is a correction to the final amount of carbon that the farmer needs to be paid for. This provides additional confidence to the market by capturing discrepancies in model data at the field scale. These additional measurements can be used to further validate the model, and may even be used to calibrate the model and improve it in the future. Eventually, as the model is validated, there will be less need for “true-up” measurements.
Once a credit is generated, a project developer or market operator is required to monitor the farmers’ fields where credit was generated and ensure that carbon does not get released back into the atmosphere. This ensures the carbon’s “permanence”. The permanent aspect is very important to buyers, as they want to make a lasting impact on climate change.
Finally, when the time comes for credits to be verified, the MRV platform offers verifiers the necessary information about the practices and the model parameters used to generate credits. The platform streamlines the verification process and further cuts costs for all involved.
As demonstrated above, each credit is handled multiple times within the ecosystem market system.Therefore, the scalability of each process is essential in making these markets cost-effective. Utilizing a full MRV will enable the scalability of the market as a whole.
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