Global livestock production represents a primary driver of planetary change, yet its full ecological and public health impacts remain poorly quantified potentially due to a deep-seated data divide. While biodiversity platforms like the Global Biodiversity Information Facility (GBIF) have excelled at aggregating data on wild organisms, crucial data on livestock systems are usually siloed within disparate agricultural and public health sectors. This Forum paper posits that this division is a primary barrier to implementing a true One Health approach, obscuring our understanding of zoonotic diseases and antimicrobial resistance (AMR). We propose a practical solution: a federated data network. This framework leverages modern data science and Application Programming Interfaces (APIs) to link existing databases, rendering large-scale integrated analysis technically feasible. This Forum paper addresses the fragmentation of data regarding livestock's environmental impact. In this Forum paper, I propose a shift from centralised data collection to a federated data network. By utilizing API-based parallel integration and AI-driven harmonisation, I argue that we can achieve real-time, predictive insights while keeping data within its source systems. I argue that historical barriers to integrating heterogeneous data are rapidly diminishing due to advances in AI, which can harmonise formats, extract features and model complex interactions. This enables a system that supports both holistic analysis and targeted filtering, offering a path towards a more comprehensive, evidence-based approach to managing our planet's interconnected agricultural and natural ecosystems.
Hideyuki Doi (2026) studied this question.