Can your farm data work harder for you?

A range of pressures, from sustainability reporting and carbon markets to land tendering and supply chain requirements are increasing the expectation on farmers to produce more information, more frequently and with more accuracy.

Farm businesses record large volumes of information each year, but often it sits unused in disconnected systems, spreadsheets or software platforms. 

Insights from data are frequently retrospective, averages are often used, and outputs can be difficult to rely on if the data collector isn't totally confident in the accuracy of their raw data. 

At the same time, finance providers are asking more detailed questions. Landowners are looking for evidence of performance. Food manufacturers and processors increasingly need to report Scope 3 emissions, which include details of return on investment on crops and varieties and demonstrate progress on their sustainability commitments.

Using a Leicestershire arable business as a case study, this article explores where structured farm data is already providing practical value and considers how emerging technologies are accelerating this shift.

Like many businesses at this scale, the farm collects large volumes of data – field records, input usage, yields and costs, equating to more than 50,000 individual data points in a year. But much of this information was not being used to inform decisions.

As Jon explains: 'We record all this information, but it just gets hidden away. What's the point of collecting it if you're not actually going to use it?'

Historically, questions such as cost of production were answered using industry benchmarks or rough estimates. While useful as a guide, these figures lacked the specificity required for confident decision-making, particularly on a farm spanning varied soil types, where averages can't account for field-by-field variation.

Structuring the data as well as cleaning, standardising and aligning it at a field level allowed the business to compare performance across crops, soil types and seasons. This created a consistent and comparable dataset, forming a baseline for more informed decisions.

'People often ask what it costs to grow a tonne of wheat, and historically it's been a ballpark figure. Now we've got an exact figure across our own farm and fields,' reports Jon.

Rather than relying on single-year results or informal observations, the farm now analyses multi-year data across individual fields. This enables them to identify patterns and supports decision-making around cropping, inputs and rotations.

One example Jon mentioned was his continued use of the wheat variety Gleam. Despite the variety falling off the industry's recommended list, the data showed that it had been one of the best performing wheat varieties on the farm for two consecutive years. Because the farm had detailed performance records, Jon was confident continuing to grow it, despite industry trends suggesting otherwise.

Having this baseline data from the farm alongside external benchmark data is particularly relevant where variability in soil type, rainfall and microclimate can influence outcomes. Structured data allows decisions to be grounded in farm-specific evidence, rather than broader averages.

At the same time, the data is not used in isolation. It is not replacing experience or judgement, but supporting it, providing a clearer understanding of risk, performance and opportunity.

'I wouldn't farm based on the data alone, but it supports the decisions you're already making and makes you more confident about what you're doing,' advises Jon.

Access to detailed cost and performance data allows the business to assess opportunities quickly and realistically. When considering new land, the farm can model expected margins based on its own historical performance, adjusted for soil type and location. 

As Jon puts it: 'In a couple of hours I can work out a realistic rent figure, because I know exactly what it's cost me to grow crops across different soil types.'

Structured data enables a more disciplined approach, grounded in evidence. It also supports better risk management. In one instance, the farm chose not to proceed with a potential opportunity.

'We looked at one opportunity and there was only about £50 a hectare margin after rent. When you factor in risk and time, it just wasn't worth doing.'

From a contractor's perspective, structured data can also enhance credibility. The farm adopts an open approach when engaging with landowners.

'We're a very open book. If someone wants to see how we've performed over the past few years, we're happy to show the good, the bad and the ugly.'

This level of transparency can help build trust, particularly in competitive situations, and demonstrates a clear understanding of performance and risk.

The farm has used its dataset to produce carbon footprint reports in support of accessing funding from the bank to transition to a lower-carbon, more resilient farming system. 

Carbon calculations rely heavily on underlying farm data, including input usage, yields and operations. Inconsistencies or gaps can significantly affect outputs. Where data is clean and structured, reporting is more straightforward and less time-consuming.

Extracting key information from a consolidated, verified dataset can save considerable time and provide an auditable carbon assessment that meets lenders' requirements.

However, variability between carbon tools remains an issue. Different methodologies can produce different results, even when the same underlying data is used. 

The analysis also highlights the complexity of carbon reduction in practice.

'If you halve your fertiliser, you halve your carbon, but you also halve your productivity. It's not as simple as just cutting inputs.'

This trade-off between emissions and output is central to many farm decisions. Metrics such as carbon per tonne versus carbon per hectare can lead to different conclusions, depending on how performance is assessed. Being able to analyse that data at the level of an individual farm, or group of farms, can highlight optimal applications and practices. 

Audited and structured data enables these nuances to be understood more clearly, supporting more balanced and realistic decision-making.

Farm management data is increasingly being integrated with additional data layers, including:

• soil characteristics
• weather and rainfall data
• crop health and satellite imagery
• pest and disease risk models
• external benchmarking datasets.

Combining these datasets creates a more comprehensive picture of farm performance.

Alongside this, artificial intelligence (AI) tools are beginning to play a role in analysing these complex datasets. In practice, this is less about automation and more about pattern recognition and scenario modelling.

AI is already being used to clean and validate datasets, to identify patterns in farm performance data and to interpret data from cameras to know where to spot spray.

The potential lies in connecting these capabilities with quality, structured data. Where data is consistent, comparable and well organised, AI can help spot insights more quickly and model potential outcomes under different scenarios.

However, the effectiveness of these tools is entirely dependent on the underlying data.

Food manufacturers, processors and retailers need to understand and report on the Scope 3 emissions generated in their supply chains. This requires visibility at a farm level, rather than industry averages.

In some cases, data is being aggregated across multiple farms to provide a broader view of performance and sustainability. For example, for a large UK grain processor, aggregating farm datasets allows a greater understanding and optimisation of nitrogen across their grower group.

However, this shift also raises questions around participation and trust. Farmers are increasingly being asked to provide detailed data, often with a financial incentive. But sharing sensitive information such as cost of production can create concern that it may be used against the farm.

In practice, many supply chain organisations are seeking the opposite outcome. Their objective is to build a more resilient supply base, supporting farms that are financially robust and progressing towards both economic and environmental sustainability targets. Access to quality data enables them to meet their own reporting requirements while identifying where support or investment can have the greatest impact.

The challenge is therefore not solely one of value but of confidence. When large volumes of detailed operational data are shared, concerns are understandable. Trust is built over time, typically through consistent, transparent use of data and, importantly, through longer-term relationships.

Multi-year commitments between supply chain partners and farms can play a key role in demonstrating that data is being used collaboratively rather than transactionally.

There is also the risk that data collection is seen as an additional burden rather than a source of value. Ensuring that benefits are shared, whether through improved pricing, access to finance or decision-making, will be critical to long-term engagement.

Trust and transparency are central to this. Clear agreements around data use, ownership and benefit-sharing will be essential as data flows increase. Industry initiatives, such as the Farm Data Principles, are emerging to support this, aiming to establish consistent standards, improve trust and ensure fair and responsible use of farm data.

'As expectations around transparency, sustainability and financial performance increase, the farms that are structuring and understanding their data will be the ones best placed to respond.

'What we're seeing is a transition: farm data is no longer just a record of what's happened. It's becoming a strategic asset that underpins decisions across land, finance and the entire food supply chain.'