Large language models and the future of soil health: Bridging knowledge gaps through scalable semantic intelligence

Soil health has become a critical lens through which global challenges in sustainability, food security, and climate resilience are addressed. However, the operationalization of this concept remains hindered by fragmented knowledge systems and unstructured textual data. This perspective article argues that large language models (LLMs), exemplified by tools like GPT-4 and domain-specific models such as GeoGalactica, offer transformative potential for soil health science. We highlight emerging applications—including automated indicator extraction, synthesis of management practices, policy analysis, and knowledge democratization—that leverage LLMs’ semantic capabilities to bridge disciplinary silos and scale qualitative insight generation. These applications are synthesized in a conceptual framework that demonstrates how LLMs integrate textual data for soil health assessment. While acknowledging limitations such as hallucinations and lack of numerical reasoning, we present a conceptual framework to guide responsible integration of LLMs into soil health research workflows. We conclude that embracing LLMs not only enhances scientific synthesis but also aligns with urgent calls for more inclusive, anticipatory, and systems-based approaches in soil and ecological governance.