Blockchain and Artificial Intelligence for Transparent Animal Environmental Governance and Traceability

Abstract

Ensuring transparency, environmental accountability, and end-to-end traceability in livestock production remains a major challenge due to fragmented data management, limited stakeholder trust, and inadequate monitoring of sustainability indicators. Conventional governance frameworks often fail to provide tamper-resistant records and intelligent decision support for real-time environmental compliance. This paper proposes a Blockchain and Artificial Intelligence (AI)-enabled framework for transparent animal environmental governance and traceability. The proposed architecture integrates IoT-based environmental sensing, permissioned blockchain, smart contracts, and AI-driven predictive analytics to securely collect, validate, analyze, and share livestock environmental data across farmers, regulators, processors, and consumers. Machine learning models are employed to predict environmental risks, detect anomalies, and optimize compliance with animal welfare and sustainability standards, while blockchain ensures immutable data provenance and decentralized auditability. Experimental evaluation demonstrates 96.84% traceability accuracy, 95.73% environmental compliance prediction accuracy, 94.58% anomaly detection precision, 97.26% data integrity, and a 61.42% reduction in record verification time compared with conventional centralized systems. The proposed framework improves governance transparency, strengthens stakeholder trust, enhances regulatory compliance, and supports sustainable livestock management through secure and explainable decision-making. Its novelty lies in the seamless integration of AI intelligence with blockchain-enabled immutable traceability, providing a scalable and trustworthy digital governance ecosystem for next-generation environmentally responsible animal agriculture