Digital tools are becoming widespread in agriculture, especially crop production, whether this is a GPS guidance system, an internet-connected weather station, or a simple soil thermometer.

Using these tools can improve agronomic, environmental and economical performance.

They also create lots of data, leading to an exponential growth in the amount of data produced in crop production systems over the past couple of decades.

However, data is not just created by digital tools. Data could be your spraying records, soil test results, or the growth stage of your crop recorded in a notebook.

A significant amount of agri-data is still recorded on paper or, in many cases, it exists solely in the head of the farmer.

Data is often not utilised efficiently. It can be difficult to integrate different sources of data together to make best use of it and the amount of data available to farmers now can feel overwhelming.

To overcome this, a three-year Horizon Europe-funded project, DIVINE, aims to explore the collection and use of this agri-data.

DIVINE aims to simplify collecting data in digital formats, provide mechanisms to improve how digital tools interact and work together, and create tools to help farmers make more informed decisions using the available agri-data.

This project brings together 15 different partners across eight European countries from many backgrounds, including computer science, software engineering and agriculture.

DIVINE is focusing on a user-centred design, with farmers from all over Europe providing their thoughts and opinions on the various aspects of the project.

DIVINE will also create a platform through which agri-data can be shared, whether that be with your adviser, other farmers, or to a third-party.

This can create new opportunities for farmers to interact with other stakeholders in the agri-food supply chain and will allow farmers to decide exactly what data they would like to share and what data they would prefer to keep to themselves.

UCD Lyons Farm

As part of this project, a pilot study is operating at UCD Lyons Farm, Celbridge, Co Kildare focusing on cereal crop production.

The main outputs of the DIVINE project will be trialled at the farm before being rolled out to farmers to ensure that there is a real benefit to using these systems.

The project has collected one growing season worth of data so far, but this work will continue over the next number of years.

Learn more

To find out more, follow DIVINE on social media or click on this link.

Decision support system

UCD’s role in this research, which forms part of my PhD, focuses on creating a complete digital model blueprint for cereal crop production to help with decision-making.

Knowledge of your fields, soils, and weather has been used for generations to make important decisions.

However, digital tools now have the potential to supplement a farmer’s wealth of experience.

A grower will make literally hundreds of crop husbandry decisions in one growing season, with many of these decisions made intuitively or even subconsciously by the farmer.


We want to challenge growers to reflect on the decisions they are making; why are they making a certain decision, and what are they considering when weighing up their options to support them in making the best decisions.

The model will guide farmers towards the next decisions that need to be considered

Our aim is to have all relevant information available to the farmer in a digital format for every decision, and to be guided towards the most optimum choices via a series of prompts, questions, and decision support tools by the digital model.

The model will also guide farmers towards the next decisions that need to be considered.

To do this, we are breaking down every aspect of the growing season into over 200 individual decisions, from deciding what crop and variety to grow in a field to whether the straw should be chopped or baled after harvest.

We then look at what data points should be considered for each decision and how these data points can be captured in a digital format.

How could the tool benefit you?

For example, if a farmer is deciding on when to plant their crop, they might look at the weather forecast and check the field to see how dry the soil is.

Our proposed solution would consider the normal sowing dates for the crop, the weather forecast, and the soil temperature and moisture levels from probes placed in the field before coming to a decision. This has the benefit of measuring how dry the soil is at depth as well as at the surface.

The model will also be able to predict if any forecasted rain will have an impact on the crop, whether that be through capping or waterlogging of the soil.

Advice can then be given to a farmer on whether they should still proceed with planting the crop or to wait until after the forecasted rain. The model can also give suggestions, such as to not roll the crop before heavy rainfall to help mitigate potential damage.

Helping to make decisions

By using all of the available information and the farmer’s own knowledge and experience, we can become more accurate and precise in our crop management.

It is by no means our intention to replace the role of a farmer, but to support farmers to make better decisions and, ultimately, to become more profitable and sustainable.

Growers with little experience in crop husbandry can also be supported through this model, which hopefully will reduce some of the barriers preventing farmers from other enterprises or new entrants from entering the tillage sector.

This digital blueprint is currently being drawn up and the resulting decision support tools will be used at UCD Lyons Farm over the coming years to make crop management decisions to verify and validate their usefulness in a real-world setting.

This work is part of a larger strategy looking at the development and application of a range of digital tools to support and enhance agricultural production.

This is through the development of decision support systems that have the capabilities of guiding growers, agronomists, agri-processors, policymakers and other engaged entities through an intelligence-led decision-making process.

This strategy will combine existing production knowledge with wider datasets to support the optimal decision at each step of the production process.

Get involved If any cereal growers would like to be involved in the development and validation processes or to provide feedback, email Conor is supervised on the project by Professor Kevin McDonnell and Dr Gary Gillespie.

  • Tillage farmers can have a lot of data on their crops.
  • This data is often not utilised efficiently.
  • It can be hard to compile data from one machine with another, or with soil samples and spraying records.
  • This project aims to bring this data together into a workable system.
  • It may help to build a decision support system for farmers.