The RDC System and Agricultural Research in Australia

Research has been a cornerstone of productivity growth in Australian agriculture. ABARES estimates that national productivity growth in the broadacre industries averaged 1.0% per year from 1977–78 to 2022–23 and dairy industry productivity growth averaged 1.2% per year from 1978–79 to 2022–23.^[1]

 Source: ABARES farm survey data, 1977-78 to 2023-24

A significant proportion of this growth is attributable to technological and management innovations stemming from R&D activities. R&D-led improvements in crop varieties, livestock genetics, pest and disease control, water use efficiency, and soil management have all made measurable contributions to farm profitability and resilience.

Australia’s model for agricultural R&D is underpinned by the Rural Research and Development Corporation (RDC) system. This co-investment model, introduced in the 1980s, sees industry levies matched by government contributions (up to a statutory cap) to fund research projects. There are currently 15 RDCs, each focusing on a specific commodity or cross-sectoral issue, such as horticulture, grains, red meat, or fisheries.

Research is generally commissioned by RDCs in response to strategic priorities, with projects delivered by universities, CSIRO, private providers, or industry groups. This demand-driven model ensures that research is relevant and responsive to sectoral challenges. However, it also means that clear frameworks for prioritisation, impact assessment, and adoption planning are essential.

Data from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) and other reviews show that public investment in rural R&D typically generates high returns. A cross-RDC aggregation of investments from 2014 to 2019 shows a weighted average benefit to cost ratio of 4.4:1 over a 30-year horizon.^[2]  In other words, for every dollar invested in agricultural R&D, about $4.4 of benefits accrue to producers, supply chains, and the broader community. These benefits include not only higher yields and reduced costs but also improvements in environmental outcomes and animal welfare, and reduced risk of catastrophic losses.

Why Valuing Research Matters

Investing in agricultural R&D requires long-term commitment and trust. For producers contributing levies, governments allocating funding, and researchers proposing future projects, a key question is: what value does this investment deliver?

Valuing the impacts of research enables:

1. Accountability – Demonstrating to levy payers and the public that funds are being used effectively
2. Learning – Understanding what works, what doesn’t, and why
3. Strategic decision-making – Informing future research priorities and optimising portfolio mix
4. Continuous improvement – Strengthening the efficiency and relevance of R&D efforts over time.

Because the outcomes of research can be diffuse, long-term, and variable across different regions and production systems, impact assessment plays a crucial role in connecting research outputs to economic, environmental, and social outcomes.

How Impact is Typically Assessed

Cost-benefit analysis (CBA) remains the go-to tool for valuing agricultural R&D because it clearly shows how dollars invested translate into measurable returns. These assessments are typically undertaken at the project, program, or portfolio level.

To support consistency and rigour in this process, the Council of Rural RDCs (CRRDC) developed the Impact Assessment Guidelines.[3] These guidelines set out a standard methodology for conducting CBAs across the RDC system, including assumptions about discount rates, time horizons, and the valuation of non-market benefits (e.g. environmental improvements or improved animal welfare).

Common Challenges in Valuing Research Impacts

Despite the standardisation provided by the CRRDC guidelines, there are several inherent challenges in assessing the value of R&D in agriculture.

1. Adoption: A key factor in realising research impacts is whether producers actually adopt the innovations developed. Adoption is influenced by a range of variables, including the ease of implementation, compatibility with existing systems, perceived risk, cost-benefit at the farm level, and access to extension or advisory support. Because adoption may occur slowly or unevenly, assessments must make assumptions about the rate and extent of uptake, which introduces uncertainty.^[4]
2. Attribution: Agricultural outcomes are rarely the result of a single intervention. Multiple research projects, policy settings, market factors, and on-farm decisions all interact to shape outcomes. As such, attributing observed impacts,such as a reduction in disease incidence or an increase in crop yield,to a specific research project can be difficult. Many assessments rely on expert judgment, triangulation with producer surveys, and scenario analysis to estimate plausible contributions.^[5]

Valuing Outcomes and Impacts — Even When They’re Intangible

An R&D investment rarely produces impact in isolation. More commonly, there is a series of related investments in which information and evidence from one study can lead to further investigation, development, or validation.

In some cases, R&D does not lead directly to a commercialised product or a widely adopted practice. Instead, it produces information, and information itself can have value.

One useful approach in such contexts is the Bayesian Value of Information (VoI). This method quantifies the value of reducing uncertainty in decision-making. For example, a research project might investigate whether a new pest management strategy is likely to be cost-effective. If the study shows it is not, and this prevents further investment in an unpromising pathway, the research has delivered value by avoiding wasted resources.[6]

In other instances, the information generated may not be actionable immediately but may influence future research directions or inform regulatory settings. Consider a project that maps emerging risks from climate change on pasture productivity. Even if this does not lead to changes in on-farm practices right away, it could be critical for guiding future adaptation research or helping industry bodies plan their priorities.

For example, an initial research project might explore genetic markers for disease resistance in sheep. That study may yield inconclusive results, but it may also uncover a promising gene, which leads to a second project on breeding programs. That, in turn, might lead to a successful commercial deployment, resulting in improved flock health, reduced treatment costs, and animal welfare gains. In this case, even the "dead-end" paths help clarify direction, avoid duplication, and build cumulative knowledge.

Therefore, when valuing research, it’s important to consider not just the outcome, but the contribution of each piece of research to a broader trajectory. This systemic view encourages more nuanced decision-making and supports investment in foundational research, even if the impacts are delayed or mediated by subsequent work.

Conclusion

Australia’s agricultural success is closely tied to sustained investment in R&D. The RDC system plays a central role in funding and coordinating research efforts, but measuring the return on this investment remains critical. Through cost-benefit analysis guided by national frameworks, RDCs and policymakers can demonstrate value, refine strategies, and build a more robust and adaptive research ecosystem.

While challenges exist in measuring the impacts, and while not all research leads directly to tangible results that can be easily assessed, valuing the full spectrum of outcomes, from avoided costs to new knowledge, is essential. In doing so, we recognise that information itself is a form of capital, one that underpins long-term productivity, sustainability, and innovation in Australian agriculture.