Revolutionizing soil carbon estimates with machine learning
USAgNet - 04/04/2024
Soil plays a crucial role in our ecosystem, holding twice as much carbon as the atmosphere and plants combined, making it a vital component in climate change mitigation. However, measuring soil carbon accurately has always been a challenge due to the labor-intensive and costly process involved.
Now, a team of environmental scientists is changing the game by combining decades of soil data with advanced machine-learning techniques to produce a more accurate estimate of soil organic carbon across the United States.
Published in the Journal of Geophysical Research — Biogeosciences, their findings not only offer a better benchmark for Earth system models but also highlight the importance of sustainable land management practices in enhancing soil health and agricultural productivity.
By analyzing nearly 50,000 soil samples with machine learning, the researchers have provided new insights into how environmental factors like climate, vegetation, and topography affect soil carbon levels.
This groundbreaking approach could help policymakers and land managers adopt more effective climate-smart practices and potentially turn soil into a more effective carbon sink. Supported by the National Science Foundation and the U.S. Department of Energy, this research emphasizes the need for accurate baseline data to understand and combat climate change.
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