Webinar: Mapping Historical Soil Organic Carbon in the US with Machine Learning
Industries working to decarbonize by replacing fossil fuels with biofuels need accurate accounting of how biofuel feedstock production impacts soil carbon. The lack of long-term spatially and temporally continuous estimates of soil organic carbon hinders efforts to project how land-management decisions will affect future carbon stocks.
In our November 2024 webinar, presenter Dr. Hannah Rubin discusses the impact of land use, climate, and agricultural practices on carbon stocks. Learn how machine learning can help us to combine a wide variety of datasets to improve estimates and elucidate regional trends, supporting biofuel production and decarbonization.
Her work builds on prior approaches to create new maps of soil organic carbon stocks in the contiguous U.S. by comparing seven machine learning algorithms and incorporating more sources of soil measurements. Her research also focuses on evaluating regional land use and climate impacts to emphasize the scale dependence of trends and the historical agricultural practices that influence soil organic carbon.
This is a great opportunity to gain insight into the latest research driving more accurate carbon accounting for biofuel production and decarbonization efforts.
Available Resources
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For Additional Information
Contact Dr. Hannah Rubin to learn more about Stone's modeling work with soil organic carbon.