New research, led by Budiman Minasny and Alex McBratney, professors from the University of Sydney Institute of Agriculture, has detailed how AI (artificial intelligence) tools can help us adapt soils – and the systems they nurture – to a changing climate.

Published by Frontiers in Science, the paper outlines how AI tools can accelerate soil science by speeding up early-stage work; improving predictions to support decisions on land-use, carbon and climate adaptation; handling complex data; and freeing scientists to focus on questions that require expert judgment.

Senior author McBratney said: “In partnership with experts, AI could help us better match the complexity and ever-changing nature of soil ecosystems. Unlike current machine learning tools that focus on isolated tasks, these systems can mimic scientific collaboration to a highly sophisticated degree – combining reasoning, planning and interdisciplinary insight to support researchers and drive significant progress. Perception of the vital importance of soil in planetary functioning is increasing, and soil science will continue to grow and flourish under scientist-led AI.”

Soil science currently uses machine learning approaches such as digital soil mapping and spectroscopy. AI systems could enhance this by creating digital soil twins with data from sensors, enhancing soil microbiome monitoring and trialing climate adaptation strategies in computer models before testing them in the field for faster results.

AI agents’ hypothesis

To illustrate such a tool, the research team tasked a multi-agent AI system with reviewing relevant scientific literature and generating ideas about how soils store carbon and what controls their storage limits.

The paper said the AI agents successfully generated five hypotheses, including climate influence, saturation thresholds, biological and chemical controls, interdisciplinary feedback and management strategies.

Each hypothesis was then evaluated through expert opinion and simulated peer review. The system successfully mimicked key parts of the scientific process with outputs beyond what’s currently being used that strongly align with expert research.

Lead author Minasny said: “Our findings indicate the opportunity for AI to accelerate soil research – the understanding of which can benefit our food and climate systems. Improving our understanding of soils could support more sustainable agriculture, better soil management, and stronger climate adaptation by helping land managers detect nutrient loss, water stress, compaction and erosion earlier.

“We assessed the system’s ability to perform perceptual processing, strategic planning, and scientific reasoning. Our findings highlight the promise that multi-agent AI systems hold, with important global implications for soil -- a precious but perhaps undervalued resource.”

‘AI can’t replace scientists’

Co-author Mercedes Román Dobarco from the Basque Institute for Agricultural Research and Development, Spain, said: “While the use cases are clearly persuasive, and though AI can emulate some aspects of expert reasoning, it cannot replace the contextual judgement, creativity and critical interpretation scientists bring to research. AI agents also pose challenges around data quality, interpretability, creativity and dataset bias, particularly without human oversight and domain expertise. Given these limitations, we should treat AI as an augmentative tool that enhances, not replaces, human scientific work.”

Published on May 21, 2026