Unscrambling digital agriculture

Emma Leonard


Emma Leonard is an Australian Farm Institute Research Fellow. Her extensive background in precision agriculture and the knowledge gained through her PhD studies described in this article will form an invaluable contribution to a new research project the AFI is commencing on ‘Assessing digital capability in the Australian agriculture sector’. The project will aim to define digital capability issues for Australian agriculture so that ongoing measurement will more accurately identify capability gaps for targeted investment and training.


Agriculture has entered the fourth wave of innovation, the digital wave. While some farming businesses are becoming digitally competent, surfing this wave, falling off and paddling out for another ride, many are still standing on the beach trying to understand the appeal or lack the skills to ‘go catch a wave’.

That is probably where the surfing analogy ends. Digitisation is unlikely to be a lifestyle choice but in time it will be a business necessity.

Digital agriculture refers to the use of connected systems to collect, integrate and analyse large quantities of data. To be of value, this data needs to be translated into actionable management decisions that can be shared, viewed and easily interpreted. Connectivity between cloud computing servers and mobile devices via the internet also facilitates remote farm management.

The hardware and software that collect, collate and analyse data are the enablers behind digital systems. Perhaps, digitally enabled agriculture is a more correct description of this new wave.

Like its predecessors – mechanical, chemical and precision agriculture – the digital wave provides new tools to support production and management. Each wave of innovation has enabled greater levels of sophistication. For example, today’s milking sheds and large, air-conditioned tractors fundamentally execute the same tasks as earlier models but facilitate greater work rates with increased functions and improved operator / animal comfort and safety. However rather than technology itself driving system change, the ways in which a farming business uses these digital enablers can result in the development of new production systems.

To ensure Australia’s farming businesses capitalise on the digital wave, farmers, employees and those in the allied industries all need to have both knowledge of the value of digital applications and the skills to implement them. The Accelerating precision agriculture to decision agriculture project1 reported that lack of clear value propositions and poor levels of digital literacy are two significant barriers to the uptake of digital agriculture in Australia.

Evidence to date suggests that the fourth wave of agricultural innovation will follow the classic diffusion pathway proposed by Everett Rogers in Diffusion of Innovations.2 For adoption or change to occur the diffusion pathway requires:

  1. understanding of the innovation – its perceived attributes and ability to solve a perceived problem; its compatibility and complexity and the ability to test and observe its use before adoption
  2. communication from and between all parties in the technology supply chain, from developer to end-user; this part acknowledges the importance of influencers such as agronomists in the adoption process
  3. commitments of time – to gain knowledge; be persuaded; decide to adopt; implement the change; and finally, confirm the value of change.

The adoption process is strongly influenced by the ‘technological’ factors of the innovation and the ‘human’ factors of its implementation. Working with farming businesses and their key stakeholders, as well as with suppliers of hardware, software and digital agriculture services, I am conducting research to assess if a prioritised approach to investment in digital technology can improve the uptake and use of digital enablers.

While diffusion theory provides a means to record factors that have driven or derailed adoption, it does not influence these factors. My research is testing management processes used by large corporations to identify and manage change in relation to the human factors as identified by diffusion of innovation theory. For example, change management offers a formalised approach to managing change, including technology adoption. Maturity modelling provides a structure against which to plot the progression of change both in terms of business procedures and human capacity.

By tailoring these processes to meet the needs of farming businesses with flat management structures, I hope to establish a digital maturity model for agricultural businesses. Against this I will measure the current and evolving position achieved by implementing a formalised change process.


For more information contact Emma Leonard: eleonar3@myune.edu.auwww.agriknowhow.net


Where are you on your digital journey from scrambled ag to digital ag?