Virtual representations of the real world used in simulation and planning have been around in some form for 50 years but not often used in agriculture. Interoperable data and AI are changing this – with implications for your business.
The first documented cases of using computers to mimic the real world and evaluate options occurred with the NASA space programmes of the 1960s and 1970s. Real world data was captured with scientific instruments and sensors and manually input into early computers to enable predictive calculations.
Fast forward to the 1990s and 2000s, and manufacturing and processing businesses were making use of a similar approach; capturing process line parameters and machine operations from plant computers and bringing it into numerical models to analyse and explore optimisations. In the early 2000s, the term “digital twin” was coined to explain the maintenance of real-world data in a virtual model to support decision making.
Until recently, digital twins in agriculture have only been the subject of research papers and haven’t been feasible for widespread use. The convergence of connected data, interoperable systems, and AI is changing this.
What has changed?
Agriculture has long made use of mathematical models: crop growing day and maturity models, or the metabolic energy models used for livestock feed budgets. These were simplified to run on the laptops of agronomists and advisors, with relatively few and simplistic inputs. There was still substantial power in these models, but in addition to manual data entry, they required experts to interpret the results and provide insight.
As agricultural systems have advanced, machines and devices have started to capture detailed data about the farm. Farm management software records policy decisions and actions. Dairy platforms record milk production and a range of animal diagnostics. In-field weigh stations record growth rates. Machinery tracks usage, fertiliser and agrichemical rates, planting and yields. Satellite analysis reveals patterns related to moisture, vegetation, yields and potential disease.
Slowly, and at times painfully, the application silos that hold that data have opened to allow farmers and growers to connect and combine data from their various devices and systems. I like to think that our work on data standards, informed farmer consent, and connectivity platforms like the Map of Ag data platform have accelerated this improvement in data connectivity.
But without a structure, connected data is only so many streams of transactions, identifiers, and sensor readings. A digital twin turns that disparate connected data into a meaningful representation of a farm, ranch, orchard or vineyard. The representation of that data in the digital twin then supports both predictive maths and the reasoning of large language models to gain insights and support decisions.
Turning connected data into predictive wins
Among the use cases for digital twins are the early warning and machine control examples that previously ran off simple manual inputs or single-device data, and which can now combine multiple inputs using machine learning:
- Animal health and welfare warning systems fusing animal, yard or paddock, and weather sensor data to prompt early and targeted action,
- Irrigation scheduling using crop, soil, and climate information to optimise water use while maintaining quality, or
- Spray timing and disease management using crop or animal data, weather and farm system data to identify risk periods and precise treatment windows.
Many tools do some of this now, with just one or two monitored inputs. Combining the inputs into the digital twin provides context that allows the user experience to move from manual scanning of charts and model outputs to targeted alerts and automated irrigation or livestock rotation changes.
Supporting more significant conversations
A digital twin can also support larger-scale analysis and decisions.
- Moving the needle in nitrogen use or methane intensity by spotlighting outliers and potential farm systems changes,
- Undertaking scenario analysis for crop rotation changes and companion crops, or for changes to livestock policies and rotations,
- Sequencing harvest and packhouse activities to maximise ripeness, size, yields, and taste profiles,
- Analysing feasibility of farm changes proposed in emissions reduction plans.
These decisions have larger potential impacts that can be “make or break” for a farming business for financial, regulatory, or staffing reasons. In these cases, digital twins and the analytics they support need to operate in an augmentation role, rather than an automation role. Augmentation keeps humans in the loop essential for trust, interpretation, and local know-how.
A digital twin can derisk these significant decisions by allowing producers and advisors to run multiple scenarios and compare the results.
It is here that large language models (LLM AI) can reduce the overhead work and dependencies for advisors. When a large language model can “understand” the output of a digital twin’s analysis, and relate it to other concepts and advisory documents, farmers, growers, and advisors can have a richer conversation with the digital twin. An LLM does not replace an agronomic model built on a digital twin but could interact with it to model various options and visualise the results.
The examples above are “farm oriented” but digital twins can deliver wider benefits:
- Reducing advisory overhead and making it easier for advisors to deliver outcomes,
- Derisking decisions for all participants,
- Producing and verifying evidential data for supply chains, and
- Enabling scalable service delivery models (whether that is in extension, sustainability, or procurement).
Leveraging an agricultural digital twin in your business
If you’re in an organisation that provides services or advice to producers, or you need to make sense of agricultural data for traceability or supply chain purposes, you will likely benefit from connected data, a digital twin, and the ability to leverage AI on that digital twin.
Consider these steps to get started:
- Identify the decision or decisions that would benefit from augmentation or automation. Where are the current or future time and money opportunities?
- Map the source data that you need to connect and find the gaps. What gaps can be filled with remote sensing or connectivity to avoid manual data entry? Fortunately, data doesn’t need to be perfect or complete initially – you can start with partial data.
- Where will consent be needed? Producer consent and trust are foundational to successful data projects. Communicate how the data will be used and the benefits for the producer.
- Find and experiment with a digital twin that could use that data. There are a few existing tools and models out there that fit this description to a greater or lesser extent. Its likely you may need to start by feeding data from files – greater process automation can wait.
- Close the loop by starting with one or two KPIs or decision points, and testing whether the digital twin, perhaps with a visualisation interface or integrated with a LLM, can provide useful outputs. Have an expert review the results to see how they align with reality.
- Look for ways to “layer up” – integrating your digital twin with a LLM, other models, or even an extension team to deliver greater benefits and adoption.
Digital twins will increasingly become the connective tissue between supply-chain requirements, advisory workflows, and farm‑system decisions. If you’re looking for advice, suggestions about models and approaches that fit your opportunity, the Map of Ag team would welcome a conversation about what is right for you.
