A Framework for Sharing Livestock Event Information

The livestock industry, critical to the nation’s economy and food security, is undergoing increasing pressure to adopt digital innovations for improving traceability, efficiency, and sus-tainability. However, the sector faces pressing challenges in leveraging data-driven solutions effectively for enhanced productivity, traceability, and sustainability. First, there is a lack of standardisation in data collection and recording practices. Without a standardised approach, data quality varies widely, making it challenging to compare and integrate information across dif-ferent sources and thereby hindering efforts to gain a comprehensive understanding of livestock management practices and outcomes. Second, the industry struggles with fragmented infor-mation and limited interoperability, hindering effective data sharing and collaboration. This fragmentation and lack of interoperability between systems used by stakeholders, such as farm-ers, veterinarians, and industry regulators, creates barriers to aggregating and analysing data, leading to inefficiencies and missed opportunities for improving traceability and sustainability. Finally, constructing standardised livestock event information is challenging due to the wide range of event types, such as weight, breeding, and vaccination, which require an efficient way to ensure uniform data capture and overcome the technical barriers faced by livestock producers.
To address these challenges, this thesis proposes a comprehensive framework comprising three key components. The first, to overcome the lack of standardisation, a schema for the standard-isation of data capture is proposed ensuring that all livestock events are recorded uniformly and in compliance with International Committee for Animal Recording (ICAR) and Integrity System Company (ISC) standards. The schema supports uniform representation of 34 livestock event types and encompasses 325 properties. Validation through structural metrics analysis and a comprehensive case study demonstrates that this schema outperforms other standard schemas in terms of design, and confirms its superior ability to capture livestock event information. These findings lay the foundation for implementing the schema, unlocking the potential for data-driven advancements in livestock management.
The second, to resolve the issue of fragmented information and limited interoperability, a microservices-based data-sharing architecture designed and implemented to facilitate seamless information exchange. This architecture enables stakeholders to collaborate more effectively and make data-driven decisions that enhance the efficiency and sustainability of livestock manage-ment practices. The efficiency and effectiveness of this architecture are evaluated in terms of performance, scalability, and reliability and its applicability examined through several real-world use-case scenarios.
Finally, to reduce the burden of event standardisation for end-users, an event construction methodology and tool are developed to simplify the process of transforming event data into a standardised format, reducing the burden on livestock producers, enhancing data quality, and facilitating efficient data sharing. Extensive experimental evaluations were used to assess the effectiveness of the tool, affirming its capability to improve data management processes.
Overall, this research provides a comprehensive framework for the data-sharing challenges faced by the livestock industry, establishing the foundation for more efficient, accurate, and sustainable livestock management practices. The innovations proposed in this thesis directly support national objectives related to agricultural innovation, biosecurity, and sustainability, and pave the way for enhanced precision and effectiveness, fostering greater synergy among stakeholders and unlocking the potential for significant data-driven advancements across the livestock sector.