ITHACA, N.Y. – A new computer model from Cornell University researchers is one of the first artificial intelligence tools to advance scientific discovery in agriculture and biogeochemistry and is 50 times more efficient than its predecessors.
In a paper published in the journal Geoscientific Model Development , the researchers demonstrated the AI on processes behind the important issue of soil organic carbon, as the Earth's soils hold roughly three-quarters of the world's terrestrial carbon and more carbon than the atmosphere and all the world's plants combined.
Scientists have been exploring ways to use AI for research purposes, but most common AI tools, such as ChatGPT, mainly repurpose existing information. Researchers have also used AI to extract patterns from data. But the new model, called the Biogeochemistry-Informed Neural Network (BINN) goes a step further by predicting biological processes that are not yet well understood and suggesting factors that control them.
"BINN is very easy to use and can be democratized among the scientific community in various disciplines," said Yiqi Luo , the senior author of the study. "This is one of the first tools of this type that can promote scientific research with AI."
Soil scientists know the mechanisms by which soils acquire organic carbon – plants extract and sequester carbon from carbon dioxide to grow, and when those plants die, organic matter from stems, leaves and roots decompose into smaller and smaller bits to become part of the earth. But what is not well known are the speed of these processes and how many such processes are required to break down the litter.
"We use AI and data to tell us quantitatively how fast and how many of these kinds of processes are required," said Haodi Xu, a doctoral student in Luo's lab, and co-first author of the study.
When compared to previous models, BINN computed 50 times faster. The accuracy of predictions of quantities of soil organic carbon was found to be very similar to the previous models. But previous models contained spatial biases, meaning that when making predictions across the contiguous U.S., it might favor the data from one area versus another. The researchers found less spatial bias with BINN.
