Next-generation soil monitoring: linking metagenomics, biosensors, and ecological modeling for sustainable agriculture

Soils represent one of the most complex and dynamic biological systems on Earth, where microbial communities play a central role in regulating ecosystem functions, including nutrient cycling, carbon sequestration, and plant productivity. However, increasing pressures from land-use intensification and climate change threaten soil health and biodiversity, highlighting the need for innovative monitoring and management approaches. In this review, we synthesize current advances in soil microbial ecology, sustainable soil management, environmental sensing technologies, and metagenomics to propose an integrative framework for soil monitoring and prediction. This review integrates environmental sensing, microbiome characterization, ecological modeling, and AI-based analytics into a unified framework for next-generation predictive soil monitoring systems. We discuss how high-resolution environmental sensors enable real-time characterization of soil physicochemical dynamics, while metagenomic approaches provide unprecedented insights into the taxonomic and functional diversity of soil microbiomes. Furthermore, we explore the role of microbial network analysis and ecological modeling in uncovering interaction patterns and predicting ecosystem responses to environmental change. The integration of these tools through machine learning and data-driven approaches is transforming soil science from a descriptive to a predictive discipline. We also address key challenges, including data standardization, scalability, and the interpretation of complex biological datasets. Finally, we highlight emerging directions such as microbiome-informed precision agriculture, microbiome engineering, and the development of soil digital twins. Together, these advances pave the way toward sustainable soil management strategies that enhance ecosystem resilience and agricultural productivity in the face of global change.