An inquiry into growing Australia’s agriculture sector to AU$100 billion by 2030 has highlighted that digital technology will be key to driving growth in Australia’s agriculture.
The inquiry, conducted by the House of Representatives Standing Committee on Agriculture and Water Resources and chaired by Liberal MP Rick Wilson, estimated that digital agriculture could add AU$20 billion to the value of the sector.
“A boost of this size would, by itself, cover the projected shortfall required to reach the AU$100 billion by 2030 target,” the Growing Australia report [PDF] said.
The inquiry was launched last September to investigate whether the National Farmers’ Federation’s goal to grow the country’s agriculture sector to AU$100 billion by 2030 would be attainable.
Read: CSIRO using artificial intelligence to map 1.7m Australian grain paddocks
In releasing the findings, Wilson described the target as ambitious but achievable.
“Australian farmers have consistently identified and embraced new technologies and techniques that can improve their businesses. This innovative mindset makes our producers well placed to benefit from the digital technologies that are rapidly becoming central to the process of farming,” he said.
The committee also put forward a total of 13 recommendations as part of the report. These included a suggestion for the Australian government to work with the agricultural industry, including the agricultural technology industry, to develop a framework on how to use agricultural data that is nationally consistent so datasets could be used across a variety of software and technologies.
“Remote sensing technologies can now monitor localised weather conditions; pest and disease prevalence; plant, animal, and soil health; and much more … despite the obvious benefits of these technologies, their growing use is resulting in the creation of vast datasets and there is currently little regulation regarding the management of this data,” the report stated.
“There are questions about the ownership and use of data generated by farmers, but perhaps more critically there is also a need for data standardisation. The potential benefits of sensor technologies are severely limited if they are unable to work with other technologies or management software.
“Data that is ‘locked’ to certain brands, or reliant on unique formats that are at risk of becoming obsolete, restricts producers from being able to freely use information on the functioning of their own business. There is a clear need for data to be exportable and inter-operable between systems and this will require some degree of standardisation. There is a role for government to work with industry, and if necessary research organisations, to develop such a system.”
See also: Top 5 things to know about farm tech (TechRepublic)
Additionally, the committee noted there was an opportunity for Australian farmers to improve the speed of adoption for new technologies and techniques, recommending that the Department of Agriculture, Water and Environment implement a program to push this along through activities such as targeted events or small-scale grants.
“Ultimately, technological developments and innovative farming techniques can only drive growth if they are adopted by producers,” the report said.
Similar findings were revealed in September in a report released by the Australian Council of Learned Academics (ACOLA).
The Future of Agriculture Technologies report [PDF] identified that adopting new technologies — such as sensor, robotic, artificial intelligence (AI), data, biotechnology, nanotechnology, and distributed ledger — could improve the sector’s productivity, diversity, and profitability.
The report highlighted that the deployment of technologies, such as robotics, coupled with AI and Internet of Things (IoT), has the potential to generate vast amounts of data, which could assist with complex decision-making and environmental monitoring while allowing farmers to devote time to focus on complex tasks, for instance.
It also added that data, AI, and IoT, if properly harnessed, could underpin other solutions such as asset automation and rapid testing of localised crops, resulting in cost reduction and increased investment in computational hardware, software, and algorithm development.