Why Australian Farm Research Is Ditching Linear Models for Complexity Thinking

Agriculture has long been treated as an engineering problem: identify the constraint, apply the technology, measure the yield. A new study published in Frontiers in Complex Systems argues that this reductionist mindset is failing farmers at precisely the moment the world needs agricultural systems to become more resilient, profitable and regenerative. Drawing on two large collaborative soil research projects in Australia, a team led by Hanabeth Luke of Murdoch University shows how research projects can be deliberately designed to work with complexity rather than against it, offering a practical blueprint for the future of agri-food research and development.

The stakes could hardly be higher. The authors situate their work within what the Club of Rome has called the Polycrisis, an interconnected web of global challenges in which accelerated climate change intersects with soil degradation at massive scale. In Australia, these pressures take a particularly volatile form: periods of drought and catastrophic fire have swung rapidly into sustained heavy rainfall, at times so persistent that crops have been deprived of sufficient sunlight for months. Layer on top of this the rising input costs linked to broken supply chains following the COVID-19 pandemic and geopolitical instability, alongside shifting consumer demands for ethically and environmentally responsible production, and the environment in which farmers make decisions becomes extraordinarily turbulent.

Yet, as the researchers point out, conventional agricultural research and development still rests on assumptions of continuity and linearity that rarely hold in practice. The dominant extension model, descended from Everett Rogers’ 1962 Diffusion of Innovations theory, imagines knowledge flowing one way: scientists generate it, and farmers adopt it at varying rates depending on their receptivity. This linear framing places the scientist at the centre of power and casts farmers as passive receivers, which the authors note has bred distrust in farming circles. For nearly a century, the mechanistic worldview that powered the Green Revolution, treating soil as merely a structural medium to which fertiliser and pest control are added, has reinforced the illusion of control and permanence. The authors argue that a paradigm shift is needed, one that recognises causality as multidirectional, systems as self-organising, and change as often sudden and unpredictable.

To test how such a shift might work in practice, the team reflected on two projects run through the Cooperative Research Centre for High Performance Soils, a ten-year national program uniting universities, industry groups, government departments and twenty rural farming groups across Australia. The first was a national Rural Landholder Social Benchmarking Study, a mail-out and online survey conducted across six farming regions, from the Eyre Peninsula in South Australia to the Wimmera of Victoria, designed to capture the complex social, economic and environmental factors driving farmer decisions about innovation. The second was a Knowledge-Sharing Project, bringing together researchers from four universities and four regional farming groups over two years to improve how farmers are engaged with new technologies and soil stewardship practices.

The Social Benchmarking Study demonstrates how scale and local relevance can coexist when boundaries are set from within the context of the activity. Local partnerships between universities, farming groups and natural resource management agencies determined the spatial scope of each survey, and key topics were co-designed in regional workshops to ensure maximum local relevance, while core questions fed a national dataset. Each region saw one to two thousand surveys mailed out, achieving response rates of 24 to 35 percent, with contact addresses linked to cadastral data enabling cross-referencing against soil type and rainfall layers. Crucially, the survey design itself evolved: open questions about challenges and profitability, and an analysis by generation first requested in South Australian workshops, became standard features of subsequent iterations. The findings flowed back quickly through four-page summaries and detailed reports, and in several regions the data was woven directly into strategic planning documents and used to attract substantial additional investment.

Complexity thinking also paid off in how the data was analysed. The team found that having family working on the farm was linked not only to improved succession planning, which is the expected linear relationship, but also to collective decision-making, which was in turn associated with feeling better supported, coping better, and having greater capacity for managing data and farm planning. Having a succession plan was also linked to longer timeframes for strategic planning, which connected to higher rates of sustainable farming practices. These tangled, non-linear associations, the authors suggest, have significant implications for farming families wrestling with succession and for the organisations that support them, and they only emerge when researchers are willing to explore complex relationships rather than hunt for single causes.

The Knowledge-Sharing Project, by contrast, illustrates complexity enacted through how a project is organised and governed. The funding application outlined objectives and broad stages but was deliberately not prescriptive, granting each farming group budget and freedom to identify engagement activities to trial. An empowerment evaluation process shaped the project’s Big Picture Plan, and each partner developed regional knowledge-sharing plans drawing on existing local priorities. Monitoring combined co-designed Process Diaries, event evaluation forms, monthly semi-structured online meetings and digital engagement metrics. When engagement slowed mid-project, a leader of one partner organisation proposed and facilitated a re-focus session, an act of distributed, self-organising leadership that the authors highlight as evidence of the humility and trust underpinning the whole enterprise.

Adaptability was tested hard. Three of the partner regions experienced catastrophic floods halfway through the project, and the Central West Farming Systems group seized the crisis as a moment to deepen connections between affected farmers, actively building resilience in extremely challenging times. In another region, heavy rains enabled an opportunistic field day that drew hundreds of farmers to learn how to deal with bogged machinery in waterlogged soils, an unplanned event that sparked wider discussion about how to reach the generally unengaged. Failures were discussed as openly as successes, including webinars with low attendance and Twitter feeds with poor uptake, and insights flowed in what the authors call fractal learning: a successful activity trialled by one sub-group was rapidly adopted by another, which then fed its experience back to the team.

From these two case studies, distilled through six principles of complexity, defining boundaries, self-organisation, adaptability, feedback loops, non-linearity, and emergence, the researchers identify a set of attributes for deliberative project design. Boundaries work best when set from within the context. Humility and trust are not abstract values but operational conditions that let partners’ strengths surface and alliances deepen. Willingness to identify improvements must be matched by institutional arrangements, such as funder evaluation criteria, that permit flexibility. Genuine feedback processes need to be built in from the start, because treating reflection too informally limits its usefulness. And teams must be willing to explore data and seek complex relationships even when these are not simple to address. The authors concede lessons from their own missteps, noting that initially underestimating how strongly externally imposed boundaries constrained collaboration was a significant error.

The study’s broader message is that deliberative approaches to complex socio-agri-ecosystems require more than bolting on participatory methods; they demand deliberate attention to how boundaries, relationships and learning processes are designed and maintained. The legacy of the projects suggests the payoff: Process Diaries have been embedded in the standard procedures of at least two farming groups and carried into two subsequent farmer-led research projects, while the benchmarking data continues to inform regional strategy years on. As agricultural industries face mounting and sometimes conflicting expectations to produce food and fibre while regenerating ecosystems and rural communities, the authors argue that supporting organisations have a responsibility to help farmers build adaptive capacity through complexity thinking, transforming research projects from rigid pipelines into living, self-organising systems capable of learning their way through an uncertain future.

Subject of Research: Deliberative project design applying complexity principles to collaborative agricultural research in Australia

Article Title: Deliberative project design for understanding and working within complexity in agricultural systems

Article References: Deliberative project design for understanding and working within complexity in agricultural systems. (n.d.). https://doi.org/10.3389/fcpxs.2026.1749741

Image Credits: AI Generated

DOI: 10.3389/fcpxs.2026.1749741

Keywords: agricultural systems, complexity thinking, deliberative project design, co-learning, soil health, farmer engagement, resilience, social benchmarking, knowledge sharing, self-organisation, adaptive capacity, agri-food systems