MLA: Advancing early on-farm detection and real time surveillance

Opportunity For

• Applications for MDC-funded projects that include RD&A and enabling activities that improve traceability, surveillance and early detection through better data quality, system integration and compliance across the domestic red meat supply chain. 

Opportunity Description

Industry Challenge

Effective biosecurity response and market assurance depend on accurate, timely and complete traceability data. However, the domestic red meat supply chain is currently constrained by inconsistent data quality, fragmented systems, variable compliance, and limited integration across platforms. These issues are not purely technical. They reflect a combination of behavioural, operational and system-level challenges, including inconsistent data entry practices, low perceived value of compliance, and limited alignment between systems and real-world workflows. As a result, traceability systems often fail at the point where they are most needed, during biosecurity events, where incomplete or inaccurate data leads to delays, increased investigation burden, and reduced confidence in decision-making. 

While the National Livestock Identification System (NLIS) and the ongoing Database Uplift Project provide a critical foundation, further RD&A investment is required to ensure that traceability systems deliver practical value. This includes addressing root causes of poor data quality, improving system usability and integration, and enabling data to be used effectively for early detection and real-time surveillance. Importantly, improvements in surveillance capability must be understood as part of a broader system. Enhanced detection and data visibility will only deliver value if they are linked to improved decision making, workforce capability, and adoption of appropriate responses. This creates a direct linkage with earlier investment areas focused on adoption (Opportunity 1) and workforce capability (Opportunity 2).

The initiative will build on existing investments, including the NLIS Database Uplift Project, and focus on complementary actions that improve system performance rather than duplicating existing system upgrades. Project proposals that clearly demonstrate meaningful engagement and consultation with producers, a clear understanding ISC and its role as well as the NLIS Database Uplift Project, will be favourably assessed.

Current Opportunity

All proposals must clearly define and quantify the impact they intend to deliver. This includes demonstrating how improvements in data quality, integration or compliance will translate into faster detection, improved surveillance capability, or more effective responses to biosecurity threats. Biosecurity threats may include potential exotic disease outbreaks (such as FMD or LSD) as well as endemic or current threats (e.g. weeds, feral pests etc). Proposals must articulate how impact will be measured, including baseline performance, expected improvement in key metrics such as data completeness, timeliness, traceability accuracy, or response time, and the extent to which improvements can be attributed to the intervention. Proposals that do not demonstrate a clear pathway from data improvement to surveillance and decision-making outcomes will not be considered competitive.

Opportunity Background

The scope will encompass: 

• On‑farm, saleyard, transport, processing and private transaction stages of the supply chain. 

• Digital systems, databases and platforms that support livestock identification, movement recording and traceability. 

• Data quality, governance and transfer processes across jurisdictions and system users. 

• Education and capability‑building activities that improve compliance and data integrity.

Applicants should clearly define the outputs (e.g. tools, knowledge products, technologies, systems, recommendations) created by the project and describe the attributable outcomes and impacts for producers and the broader red meat industry. 

Projects should address one or more of the following: 

1. Understanding Traceability Inefficiencies and Risks

 • Quantify and characterise data quality issues across the domestic supply chain, including unlinked or “ghost” eID tags, inaccurate PIC attribution, and missing movement records. 

• Identify high risk points, species, production systems or transaction types where traceability is weakest. 

   2. Improving Data Quality and Integration 

• Investigate methods to improve consistency and accuracy of data capture, entry and validation across systems.

 • Identify opportunities to better integrate NLIS with on farm production systems, saleyard, transport and processing platforms.

 • Support improved alignment between PIC registration, identifiers and animal movement records. 

   3.  Advancing Early Detection and Real-Time Surveillance 

• Explore how improved traceability and data flows can support near‑real‑time surveillance and early detection of abnormal events.

 • Identify mechanisms to improve data accessibility, sharing and analysis for biosecurity decision‑making during routine operations and emergencies. 

   4. Compliance and Capability Building (Opp 1 linkage) 

• Design and deliver (via pilot programs) targeted education and extension initiatives to address noncompliance and data quality issues, with a focus on smaller producers, private transactions and complex production systems on a limited scale.

 • Improve understanding of traceability obligations, shared responsibility and the role of accurate data in biosecurity response. 

   5. Governance, Assurance and Continuous Improvement

 • Identify system governance or process improvements that support consistent data management across jurisdictions.

 • Establish feedback loops to inform ongoing system improvement and investment prioritisation.

    6. Monitoring and Evaluation 

• Measure improvements in data quality, system usability and traceability performance.

 • Capture lessons to inform future investment in surveillance, traceability and biosecurity systems. 

More information can be found on the MLA Tenders Terms of Reference document here.