Soil carbon effectively measured by new, efficient AI model
A new computer model 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, according to a study that offers a proof-of-principle for how AI might be used to shed light on obscure biological processes.
In a paper published in the journal Geoscientific Model Development, the researchers demonstrated the AI on processes behind the issue of soil organic carbon, as 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 Liberty Hyde Bailey Professor in the School of Integrative Plant Science, Soil and Crop Sciences Section, in the College of Agriculture and Life Sciences (CALS) and a senior author of the study. "This is one of the first tools of this type that can promote scientific research with AI."
Haodi Xu, a doctoral student in Luo's lab, is co-first author of the study. The research was a collaboration with the lab of Carla Gomes, the Ronald C. and Antonia V. Nielsen professor of computer science in Cornell Bowers Computing and Information Science, and professor in the Cornell SC Johnson College of Business.
Accurately understanding soil carbon processes could make a big difference in both predicting climate change and developing practical solutions, and even small adjustments to these processes can have oversized downstream effects. For example, in 2015, French soil scientists proposed the "4 per 1,000" initiative, which claimed, in theory, that if humans adopted practices to increase global soil organic carbon in agricultural lands by 0.4% annually, it would not only improve soil health but also offset all human-caused carbon emissions.
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 decomposes into smaller and smaller bits to become part of the earth. But scientists do not know well the speed of these processes or how many 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," Xu said.
Compared with previous models, BINN computed 50 times faster. The accuracy of predictions of quantities of soil organic carbon was found to be very similar to that of previous models. But previous models contained spatial biases, meaning that when making predictions across the contiguous U.S., they might favor data from one area over another. The researchers found less spatial bias with BINN.
The new model can be adapted to reveal other little-known agricultural and biogeochemical processes, such as those relating to soil respiration or carbon accumulation in forest systems, researchers say.
More information
Haodi Xu et al, Biogeochemistry-Informed Neural Network (BINN v1.0) for improving accuracy of model prediction and scientific understanding of soil organic carbon storage, Geoscientific Model Development (2026). DOI: 10.5194/gmd-19-6777-2026
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Citation: Soil carbon effectively measured by new, efficient AI model (2026, July 27) retrieved 27 July 2026 from https://phys.org/news/2026-07-soil-carbon-effectively-efficient-ai.html
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