Your Farm Will Have an Operating System - The Question Is Who Builds It?

How AgriLLM, digital twins and agentic AI will turn Australian agriculture into an intelligent system - and why RDCs, corporates, startups and growers all have a role in designing it

There is currently a $20 billion leak in Australian agriculture - not in water, not in waste - in knowledge.

The Australian Farm Institute estimates that full adoption of digital tools across the agricultural sector could boost productivity by approximately $20.3 billion annually. That number sits there, year after year, largely unrealised. This is not because the research doesn’t exist - Australian RDCs collectively fund some of the world’s best agricultural science. It is also not because growers lack sophistication - Australian farmers are among the most technically literate and commercially capable anywhere.

The leak occurs in the space between discovery and practice, in the slow, lossy chain of translation that separates a research finding from a changed behaviour on farm.

Research is published. Extension officers interpret it. Advisers translate it for local conditions. Growers evaluate whether to change behaviour. At every handoff, fidelity degrades and latency grows. A breakthrough in disease management that could save millions takes years to become routine operational practice - if it ever does. The constraint was never the quality of the science. It was the bandwidth of the humans tasked with moving it from bench to paddock.

That constraint is about to break.

AgriLLM - large language models purpose-built for agricultural reasoning - can compress the translation chain from years to days. A validated research finding can become machine-interpretable operational logic embedded directly in farm workflows, applied across thousands of individual paddock contexts, within weeks of publication.

When you combine that with digital twins that simulate whole seasons before a seed touches soil, and agentic AI systems that coordinate irrigation, spray, harvest and market decisions autonomously, you are no longer looking at a technology upgrade. You are looking at the emergence of something new: an operating system for the farm.

Every farm will have one. The strategic question - for RDCs, corporates, startups and growers alike - is who builds it, whose knowledge it encodes, and whose interests it serves.

The global AI in agriculture market is projected to grow from approximately USD 2.8 billion in 2025 to USD 8.5 billion by 2030, at a compound annual growth rate above 25 per cent. Some forecasters project it reaching USD 61 billion by 2035 as emerging economies accelerate adoption. The capital is already flowing. The question for Australia is whether it designs the operating system or adopts someone else’s.

What Is AgriLLM? The Intelligence Layer Beneath the Operating System

AgriLLM refers to large language models purpose-built or fine-tuned for agricultural reasoning. At a technical level, these are transformer-based foundation models - architecturally similar to GPT-4, Claude or Gemini - but trained, fine-tuned or augmented with domain-specific agricultural corpora: trial data, agronomic advisory literature, crop physiological models, soil science, pest and disease diagnostics, supply-chain logistics, commodity market signals and regulatory frameworks.

What makes AgriLLM distinct from general-purpose LLMs applied to agriculture is the depth of domain grounding. A general model can summarise a research paper on integrated pest management. An AgriLLM can reason across a specific paddock’s soil moisture profile, a regional pest pressure forecast, the current growth stage of the crop, the withholding period constraints of available chemistry, and the grower’s contractual delivery window - and produce a contextualised recommendation that an agronomist would recognise as sound.

Think of it this way. The sensors, satellites and machinery telemetry are the eyes and ears. The digital twin is the simulation engine. The agentic AI framework is the coordination layer. AgriLLM is the reasoning layer - the part that converts what the system perceives into what the system decides. It is the intelligence that makes the farm operating system intelligent.

The architecture typically involves several layers working in concert. A foundation model provides language understanding and reasoning capability. Retrieval-augmented generation (RAG) pipelines connect the model to structured and unstructured agricultural knowledge bases in real time, so it reasons over current data rather than stale training snapshots. Tool-use and agentic frameworks allow the model to query APIs - weather services, satellite imagery platforms, machinery telemetry feeds, market price engines - and synthesise multi-modal inputs into coherent operational guidance. Domain-specific fine-tuning and reinforcement learning from expert feedback calibrate the model’s judgement to the norms, risk tolerances and practical constraints of real agricultural operations.

Recent academic work validates the trajectory. AgroLLM, a RAG-enhanced agricultural knowledge system built on curated textbook corpora, achieved 95.2 per cent accuracy on a 504-question agricultural benchmark when constrained by a Domain Knowledge Processing Layer - a structured set of agronomic rules, causal relationships and thresholds that validate model outputs before they reach the user. The key finding was not just accuracy but the substantial reduction in hallucinations and numerical violations. Domain-grounded AI does not merely answer agricultural questions - it can be trained to reason within the safety boundaries that real-world agriculture demands.

This is not a distant prospect. CGIAR, in partnership with the UAE’s AI71, is building AgriLLM as an open-source digital public good - a foundation model designed for global agriculture, grounded in CGIAR’s decades of scientific knowledge, and intended to be localised by governments and partners worldwide. A working prototype is targeted for COP30. Separately, AgriGPT has introduced Tri-RAG, a three-channel retrieval framework combining dense retrieval, sparse retrieval and multi-hop knowledge graph reasoning, to improve the reliability of agricultural AI inference. The field is moving from theory to deployable infrastructure remarkably fast.

The result is not a chatbot that answers farming questions. It is a reasoning system capable of converting fragmented biological, operational and commercial signals into actionable decisions - spray, harvest, ship, insure, blend, finance - at a speed and consistency that human advisory networks cannot match at scale. Agriculture is moving from digitisation to delegated judgement. And the organisations that embed their knowledge into that judgement layer will shape how food is produced for decades.

1. Research & Development Corporations (RDCs): The Institutions Best Placed to Close the $20 Billion Gap

If the $20 billion leak is a translation problem - world-class knowledge failing to reach operational practice at scale - then Australia’s RDCs are the institutions best positioned to fix it. Not because they need to change what they do, but because AgriLLM transforms what their existing work can become.

RDCs already sit at the intersection of science, industry and grower networks. They fund the research. They know the growers. They understand the commercial realities of each commodity sector. What they have lacked is a delivery mechanism that matches the quality of their knowledge creation. The traditional extension pathway - publish, present, hope for diffusion - was never designed for a world where the volume of actionable science exceeds the capacity of human advisers to transmit it. A CSIRO review confirmed what many already suspected: digital agriculture in Australia remains “immature and ad hoc” despite a relatively advanced technology innovation sector, with fragmentation in the innovation system and a persistent disconnect between technology development and end users.

AgriLLM dissolves that disconnect.

Instead of research findings sitting in reports, waiting to be discovered by the right adviser at the right moment, RDC-funded knowledge can become machine-interpretable operational logic embedded directly in the farm operating system. A new variety trial result, a revised integrated pest management threshold, an updated nutrient response curve - each can be encoded into a reasoning layer that applies it to thousands of individual paddock contexts within days of validation. The research doesn’t change. The delivery mechanism transforms.

This is already beginning to happen. GRDC’s GrainInnovate fund, managed through Artesian’s VCaaS platform, has invested in 24 agtech startups to date, many building the sensor, data and analytics infrastructure that forms the perception layer of the emerging farm operating system. GrainInnovate and Hort Innovation’s Venture Fund is backing companies like BioScout, whose AI-powered spore detection system delivers real-time disease intelligence to growers via online dashboards. These investments are building the eyes and ears. AgriLLM becomes the brain - the reasoning layer that converts what these systems see into what the system does. And the knowledge that brain reasons with? That’s exactly what RDCs have been creating for decades.

The CGIAR model offers a useful reference for how this can be done well. Their AgriLLM development began with “writeshops” - structured workshops where scientists, extension experts and agricultural practitioners co-created the question-and-answer datasets that form the training backbone of the model. The first two workshops produced over 860 high-quality Q&A pairs reflecting the actual concerns, language and decision contexts of real agricultural stakeholders.

Australian RDCs, with their unmatched grower networks and decades of structured trial data, are exceptionally well-positioned to lead a similar effort - and to go further, because Australia’s RDC system already embeds the principle of co-investment between public research and industry that makes this kind of collaboration natural rather than forced.

The strategic shift is subtle but profound. RDCs are not just funding research outputs. They are funding the knowledge layer of a national farm operating system. Every trial, every dataset, every extension resource becomes more valuable when it can be machine-interpreted and applied at scale. The $20 billion gap does not close through better communication. It closes when RDC knowledge becomes default machine behaviour on every connected farm in the country.

The RDC that moves first on this does not just improve adoption. It becomes indispensable infrastructure.

2. Agrifood Corporations: From Buyers of Production to Co-Designers of the Operating System

Processors, exporters, input companies and retailers have traditionally influenced farming indirectly - through pricing signals, contracts and product supply. They sit downstream, reacting to what arrives at the receival point or packhouse.

The farm operating system changes this equation. If operational decisions - fertilisation timing, spray scheduling, harvest windows, storage conditions - are increasingly mediated through intelligent systems, then the organisation that provides, embeds or co-designs those systems gains structural alignment with how product is actually grown.

This is not control in a coercive sense. It is coordination through shared optimisation.

Agriculture suffers from persistent misalignment across the value chain. Growers optimise for yield. Processors optimise for uniformity and throughput. Retailers optimise for shelf performance and consumer specification. Exporters optimise for logistics timing and phytosanitary compliance. These objectives frequently conflict, and the conflicts are resolved through price penalties, rejection at receival, or wastage - all of which destroy value. The FAO estimates that 13 per cent of the world’s food is lost in the supply chain from post-harvest to retail, with a further 17 per cent wasted at the household, food-service and retail level.

An AgriLLM embedded in the farm operating system can optimise across multiple objectives simultaneously. It can recommend a harvest window that balances yield, dry matter content, storage behaviour and shipping logistics - producing a decision that no single participant in the chain would have reached alone. The corporate opportunity is not “AI efficiency.” It is supply-chain synchronisation at biological timescales.

The digital twin convergence. The most compelling near-term manifestation of this capability is the agricultural digital twin - a continuously updated virtual replica of a farm, field or supply chain that simulates outcomes before decisions are executed. A USDA-backed pilot is already using digital twins to simulate entire cropping seasons before seeds touch soil. Texas A&M AgriLife Research has demonstrated the concept in practice: their digital-twin system, trained on over 250,000 drone-collected data points per season, predicted optimal cotton defoliation timing six to eight weeks before harvest. When a farmer ignored the AI’s recommendation and delayed, subsequent rain events cost approximately $70 per acre in lost quality and profit.

For agrifood corporates, the implication is transformative: when your supply chain can be simulated and optimised end-to-end before biological production begins, procurement becomes design. You are no longer buying production outcomes - you are co-engineering them. And the operating system that enables this is the platform where corporate specifications, market signals and grower operational reality converge.

Strategic posture for corporates. Agrifood corporations should partner with RDCs to access validated domain knowledge safely and at scale - this is the most efficient pathway to credible, grower-trusted intelligence. They should invest in decision-support platforms rather than point software tools - systems that influence daily farm decisions, not retrospective dashboards reviewed quarterly. They should treat the farm operating system as infrastructure, comparable to logistics networks or cold-chain systems, rather than an IT project. And they should pay close attention to the emerging convergence of digital twins, knowledge graphs and AgriLLM - the combination that enables predictive supply-chain management at biological timescales.

The companies that participate early do not just digitise procurement. They become co-designers of the operating system through which production occurs - and in doing so, they reduce the variance that currently drives waste, cost and supply uncertainty across the chain.

3. Agtech Startups: Building the Apps That Run on the Farm Operating System

If the farm operating system is the platform, agtech startups are building the applications that run on it.

Many startups have historically sold features: mapping, scouting, sensor dashboards, analytics overlays. These products visualise the farm. They rarely change what happens on it. AgriLLM shifts the unit of value from information to judgement. Growers do not lack data. They lack time to interpret conflicting signals under uncertainty - weather models that disagree, pest pressure that is rising but not yet at threshold, a market window that is closing, a spray rig that is committed elsewhere for three days. The winning applications will not be those that visualise farms best, but those that answer a deceptively simple question: what should I do tomorrow?

The agentic frontier. The most advanced expression of this capability is agentic AI - systems that integrate perception, reasoning, action and learning in continuous closed loops. Recent research published in Frontiers in Plant Science describes agentic agricultural AI where multiple specialised agents (soil monitoring, weather sensing, disease detection, supervisory coordination) collaborate autonomously, collecting real-time data, making decisions, monitoring results and learning from outcomes. Working prototypes already coordinate irrigation adjustments, fertiliser recommendations and pest alerts across multi-agent systems using reinforcement learning algorithms like Deep Q-Networks and Proximal Policy Optimization.

Digital Green’s FarmerChat offers a glimpse of what scale looks like: an AI-powered assistant delivering real-time, locally relevant advice in farmers’ own languages through text, video and voice, now reaching over 565,000 users across Kenya, Nigeria, Ethiopia, India and Brazil, handling more than five million queries. Bayer’s E.L.Y. generative model has improved agronomic Q&A accuracy by 40 per cent. These are not pilots - they are operational applications generating behavioural data at scale.

Implications for product design. Startups should design for recommendation confidence rather than data display - the system should express not just what it suggests, but why, and how certain it is. Research at the University of Nebraska-Lincoln on explainable AI for agriculture demonstrates why this matters: their systems analyse approximately 50 different factors and reveal which influenced a decision most and to what extent. This transparency is critical because it allows growers to verify AI recommendations against their existing knowledge. A recommendation the grower cannot interrogate is a recommendation the grower will not follow.

Startups should build for workflow execution rather than reporting. They should prioritise integration across the broader operating system - weather, imagery, machinery, market data, compliance, RDC knowledge bases - rather than standalone capability. And they should invest heavily in domain knowledge processing layers - structured agronomic rules that constrain model outputs - because in agriculture, a confident but wrong recommendation is worse than no recommendation at all.

The hidden structural advantage. Large incumbents possess data assets and distribution. Bayer’s Climate FieldView platform aggregates weather, soil and yield data across 60 million hectares in 23 countries. But startups possess adaptability and workflow proximity. Because AgriLLMs improve through operational feedback loops - every decision made, every outcome observed, every correction by an agronomist - the companies closest to day-to-day farm behaviour will accumulate the strongest reasoning capability over time. The defensibility is behavioural learning, not model size. A startup processing ten thousand real spray decisions per season will build a more capable application than a platform with a petabyte of satellite imagery and no operational feedback.

In the farm operating system analogy, this is the app-store moment: the platform creates the market, but the best applications are built by those closest to the user.

4. Growers: The Operating System Should Work for You

Every analogy has its risks. Talk of “farm operating systems” could imply that growers are being managed by software, reduced to operators running someone else’s program. The opposite should be true. The operating system should work for the grower - amplifying their expertise, managing operational complexity on their behalf, and making the accumulated knowledge of the entire agricultural system available at the moment of decision.

An AgriLLM that works for a grower is one that learns from their specific operational context: their soils, their microclimate, their machinery constraints, their risk appetite, their market relationships, their labour availability. No two farming operations are identical, and the most valuable systems will be those that adapt to individual enterprise logic rather than imposing generic best practice from above.

This creates a new kind of agency. Rather than choosing between competing advisory opinions or sifting through dashboards of conflicting data, a grower working with a well-calibrated system can interrogate trade-offs directly. What happens if I delay harvest by four days? What is the cost of switching chemistry given withholding period constraints? If I allocate this paddock to the export program, what does that do to my domestic supply commitment? These are questions growers already reason through - but typically with incomplete information, under time pressure, and without the ability to model downstream consequences across the whole operation.

What the near future looks like. By 2030, three converging technologies will reshape the grower’s decision environment.

First, the agricultural digital twin. A wheat grower on the Liverpool Plains will run a virtual season before committing inputs. What happens to my gross margin if the autumn break doesn't come until June? Do I switch 200 hectares from canola to chickpeas, and what does that do to my nitrogen budget for next year's wheat rotation? The Iowa State AI Institute for Resilient Agriculture is already demonstrating that for every year of biological data, digital-twin-based AI systems can create hundreds of reality-based simulations - compressing years of experiential learning into days of computational modelling.

Second, agentic AI systems operating at the field edge. Multiple specialised agents - one monitoring soil moisture, another tracking pest pressure, a third watching market signals, a fourth coordinating machinery scheduling - will collaborate to produce integrated operational plans, learning from every decision and outcome.

Third, voice-based natural language interfaces in the cab, the shed and the paddock. The interaction will not be a dashboard or a report. It will be a conversation - in plain language, at the point of decision - with a system that knows the farm’s history, understands the current conditions, and can explain its reasoning in terms the grower recognises.

That is the farm operating system working for the grower. Not replacing their judgement, but extending it across every dimension of a complex operation.

What growers should look for. Systems that explain their reasoning, incorporate local operational knowledge rather than overriding it, improve with use, and respect that the grower - not the algorithm - makes the final call. Growers should favour platforms built on RDC-validated science and open data standards, and should be sceptical of black-box recommendations that cannot be interrogated.

What growers should be wary of. AgriLLM will attract vendor hype. Not every chatbot bolted onto a farm management platform constitutes meaningful agricultural intelligence. Some major platforms have been observed embedding recommendations that favour the company’s own seed and chemical products, illustrating how operating systems can be designed to serve the vendor rather than the user. Growers should look for evidence of genuine domain grounding - validated agronomic logic, integration with real-time environmental and market data, transparent reasoning chains - rather than marketing claims about AI capability.

The data sovereignty question. As the farm operating system becomes central to operations, the question of who owns the data - and who profits from the reasoning built on it - becomes urgent. Australian growers, through their RDC levy system, already participate in a co-investment model for research. An analogous model for agricultural AI - where grower data contributes to shared reasoning infrastructure and the benefits flow back to the contributing community - would be more equitable than a model where data flows to corporate platforms and insights are sold back as a service.

The Cotton Research and Development Corporation’s agricultural data governance framework and the broader Digital Foundations for Agriculture Strategy represent early steps in this direction. The principle is straightforward: the operating system should be built on knowledge that growers co-funded, governed by standards that growers co-designed, and deployed in ways that growers control.

Five Futures: What the Farm Operating System Makes Possible

The convergence of AgriLLM, digital twins, agentic AI and structured knowledge systems creates the possibility of outcomes that would have seemed implausible five years ago. Not all will materialise, and none will materialise without deliberate effort. But the directional logic is clear.

1. The $20 billion leak finally closes. A validated research finding - say, a new fungicide resistance threshold in wheat stripe rust - propagates from publication to operational behaviour on every connected farm in the country within weeks, not years. RDC science becomes embedded intelligence in the farm operating system, applied at paddock scale, automatically. The traditional extension chain doesn’t disappear - it is augmented by a delivery mechanism that finally matches the quality of the knowledge being delivered. Adoption ceases to be a communication problem and becomes a software deployment.

2. Supply chains that begin before planting. Agrifood corporations use digital twins to simulate the coming season’s production across their supplier base before a single seed is sown. The farm operating system accounts for projected market demand, logistics capacity, storage availability and processor specifications simultaneously. The persistent misalignment between what growers produce and what the chain needs begins to resolve because the system optimises across the whole value chain - and every participant benefits.

3. Climate adaptation at the speed of weather, not policy. Rather than responding to drought, flood or heat events after the damage is done, the farm operating system pre-positions operations for climate variability in real time. A grower receives a recommendation to shift spray timing or adjust irrigation allocation based not on a seasonal outlook published months earlier, but on a multi-model ensemble forecast updated hourly and interpreted through the lens of their specific crop stage, soil profile and financial position. Climate adaptation becomes operational, continuous and personalised rather than strategic, periodic and generic.

4. A continuously learning national production system. Every spray decision, every harvest outcome, every yield result feeds back into the reasoning models that inform the next season’s decisions across the network. RDC research informs the models. Grower experience refines them. The system does not just record what happened - it updates what should happen next. Over time, Australia develops a form of collective agricultural intelligence that no single institution, company or grower could build alone. The $20.3 billion productivity dividend begins to compound.

5. The grower as strategic director. Individual farm management tools - weather, imagery, agronomy, finance, compliance, market access - converge into the integrated farm operating system. The grower interacts with a single intelligent interface that coordinates across all operational domains. The autonomous tractor market alone is projected to reach USD 7.86 billion by 2030; when the machinery, the sensors and the reasoning layer converge, the fully autonomous farm operation becomes technically feasible within a decade. The grower’s role shifts from tactical operator to strategic director - setting objectives, defining constraints and making the calls that matter, while the operating system handles the operational complexity.

Who Builds It: Why Collaboration Is Not Optional

No single participant can build the farm operating system alone.

RDCs hold the validated scientific knowledge and the grower networks that give the system credibility and relevance. Corporates hold the system incentives, market signals and scale pathways that make it commercially viable. Startups hold the workflow proximity, product velocity and operational feedback loops that make it usable. Growers hold the ground truth - the irreplaceable knowledge of what actually works in specific landscapes, seasons and enterprise contexts.

Separated, each creates partial intelligence. Combined, they create a functioning production nervous system - one where research findings update operational recommendations in near real time, market signals propagate upstream to influence production decisions before harvest, and on-farm outcomes feed back to improve the underlying models continuously.

Australia’s agricultural structure is unusually suited to building this. Strong publicly funded research institutions. Concentrated but relationship-driven supply chains. A vibrant agtech startup ecosystem. A grower population that is technically literate and commercially sophisticated. And a unique co-investment model - the RDC system - that already embeds the principle of shared infrastructure for collective benefit.

The VCaaS model is extending this principle into the startup ecosystem. GRDC, Hort Innovation, and GrainCorp are already operating specialised VC funds through this structure, connecting early-stage agtech companies with the domain expertise, grower networks and commercialisation pathways they need to succeed. No other agricultural nation has replicated this model at comparable scale. It is a structural advantage that becomes dramatically more valuable in a world where the farm operating system requires exactly this kind of cross-ecosystem collaboration.

But collaboration also requires shared standards. Knowledge graphs - structured, machine-readable representations of agricultural knowledge - will become essential infrastructure. CGIAR’s approach to building AgriLLM as a global open-source digital public good, with standardised evaluation benchmarks and shared data governance frameworks, offers a model. Australia should be contributing to these global standards, not waiting to adopt them.

The Real Shift

Digital agriculture recorded what happened. The farm operating system determines what happens next.

That distinction reframes everything. We are not automating tasks. We are operationalising collective expertise - the accumulated knowledge of researchers, agronomists, supply-chain operators and growers - and making it available as decision infrastructure at the point and moment it matters most.

The $20 billion leak - the gap between what Australian agricultural science knows and what Australian farms do - is not a failure of research or a failure of growers. It is a failure of the delivery mechanism. AgriLLM is the fix. Not because it replaces anyone in the chain, but because it gives every participant’s knowledge a way to reach the paddock at scale.

The organisations that treat this as another software feature will see incremental gains. Those that treat it as shared decision infrastructure - co-designed across the research, corporate, startup and grower ecosystem - will reshape productivity, resilience and adoption simultaneously.

Australia has spent decades building world-class agricultural knowledge. It has the RDC system to validate and curate it. It has the startup ecosystem to deliver it. It has the grower networks to ground-truth it. And it has a co-investment model that already proves cross-sector collaboration can work.

The farm operating system is coming. Australia has every advantage it needs to build the one that works for its own farmers, its own supply chains and its own national interest.

The window to lead is open. It will not stay open indefinitely.