From Decision-Support Tools to Digital Twins: A Review of Digital Farming, Data Platforms, and AI for Sustainable Dairy Systems

Sustainability targets for livestock require decision support that is both scientifically credible and operationally usable on farms. This integrative narrative review synthesizes the broader peer-reviewed literature on digital farming, artificial intelligence and machine learning, simulation modeling, optimization, and digital-twin concepts as applied to sustainable dairy systems, and uses selected peer-reviewed dairy studies from one integrated research program as illustrative worked examples that show how these elements can be connected end-to-end. We organize the synthesis around a data-to-decision pipeline that links data foundations and interoperability, governance and trust, analytics, decision engines, and deployment, comparing model classes by data needs, temporal resolution, interpretability, and deployment maturity. Recurring barriers to impact—weak ground truth, data drift, fragmented identifiers, and misaligned incentives—are highlighted alongside the design principles that address them. The contribution of the review is the transferable pipeline framework, demonstrated through worked examples drawn from one integrated research program; the program’s studies appear repeatedly because they together trace decisions across all pipeline layers, not because they constitute the field. We conclude with a practical roadmap and implementation checklist for designing and scaling decision-intelligence systems with transparent tradeoffs and measurable sustainability outcomes.