Agriculture in Australia

Agriculture has enormous social and monetary value in Australia. Farmers grow over 90% of Australia’s domestic food supply at home, employing more than 2% of the national workforce. Australia’s population is growing steadily —at about 1.4 % per year— driving demand for Aussie-grown produce. However, a recent report raises concerns about the uncertainty surrounding vital agricultural resources that ensure food security. This resource pressure has forced the agricultural industry to innovate solutions to produce more with less. As a result, nutrient and water application efficiency has become increasingly important. This need for efficiency has caused a growth in demand for tools to help combat this challenge. This demand leads us to the topic of this blog, agtech in Australia.

Precision agriculture

Aussie farmers have been using precision agriculture technologies (e.g., variable rate technology and yield monitors) since the 90s when Global Navigation Satellite systems were first commercialised. The collection and analysis of agricultural data has become data-driven due to artificial intelligence (AI) and big data advancements. On-farm use of digitally-captured information and geospatial tools has led to a new phase in Aussie agriculture.

Researchers predict that the transition to data-driven agriculture will lead to an increase in production value. AgTech is any innovation used across the value chain to improve efficiency, profitability and/or sustainability. It includes hardware and software, business models, new technologies and new applications. The new frontiers of AgTech are in the digital space. Agribusinesses use data, tools and decision-support to meet emerging consumer demands or enter new markets. Although still in its infancy, AgTech in Australia has received increased interest, even among those not traditionally involved in agriculture. Government agencies and private investors have shown uptake in developing digital tools and systems to collect and analyse agriculturally relevant data.

Most digital agricultural data are collected using a combination of in-field, machine-based, and remote sensors. Improvements in sensor technology have enabled real-time monitoring of production conditions (e.g., soil moisture and nutrient levels). Real-time data can then be analysed to facilitate an early management response. While AgTech can benefit production and yield, farmers have expressed concern about the privacy and security of these devices and the use of the data they collect.

Remote sensing

Mounting imaging sensors on satellites and drones enables the collection of data that support farmers. After dedicated processing these data are transformed into information for determining water and nutrient requirements, land use and cover changes, and soil-moisture and health monitoring. Remote sensing makes large-scale and systematic measurements across space and time. These measurements can then be put into Geographic Information Systems (GIS) and predictive models to monitor and manage change.

Satellite imagery is the most widespread application of remote sensing in agriculture. And, growers have widely adopted using satellites as a tool in cropping. Many satellites provide imagery data, the most popular of which are the Landsat satellite from NASA and the Sentinel-2 from ESA (used in the COALA project). These two satellites provide fine spatial resolution necessary for applications related to agricultur. They are also open access (i.e. users do not pay for access to the images collected by the satellites).

Other satellites, such as the Sentinel-1 (also from the ESA), are more suitable for regions with frequent cloud cover due to its reliance on synthetic aperture radar sensors. However, these sensors lack the historical data and spatial detail that make the Landsat satellites preferable to farmers and practitioners interested in understanding long-term trends of changes. Landsat has been operational since the 1972s while Sentinel satellites date back to 2015.

Advancements in remote sensing

Proximal-sensor advancements in the private sector have led to the emergence of miniaturised satellites and sensors mounted on drones. These sensors capture a broad range of real-time data on-demand. This range of data provides a more relevant depiction of on-farm conditions. In turn providing the farmers with tailored information allowing better-informed decisions. In the public sector, projects such as the ESA’s Copernicus programme produce sensors that deliver data at greater spatial resolution (10 meters pixel size) and repeat frequencies (5 days). These projects enable temporal change and dynamics analyses at the paddock and sub-paddock scales. The emergence of sensors and platforms with higher temporal, spatial, and spectral resolutions will increase data volumes. The increased data presents challenges for storage and timely processing for farm management decisions and issues relating to integrating datasets of different scales, quality, and formats.

Agricultural decision support systems and tools provide farmers with information and analytics to make meaning of the collected data and inform their decisions. Some of these tools rely on collating significant, consistent datasets containing information on outcomes (e.g. yield) and prediction variables. However, operational and consistent access to this information is still variable within Australia. Many reported good practices and experiences remain at a project level.

Challenges in Australian Agtech

Over the past ten years, decision support systems have transitioned from spreadsheets to web and apps. As a result, producers have become increasingly familiar with using accessible mobile and tablet technologies. However, these new tools and systems haven’t seen widespread adoption. Producers say that there are barriers to using these tools like digital literacy, time constraints, restricted internet access, and perceived lack of relevance or value for money. Growers report feeling overwhelmed because there are too many data and too many competing products on the market.

Decision support tool developers face high development costs associated with diverse alpha users and the user base’s unique business objectives and requirements. These challenges, alongside the siloed nature of data within and across sectors also present barriers to the utility of products. Context-specificity is a requirement for utility in these cases. On top of this, the government and other service providers use a range of different data formats. It is time-consuming to alter and take consistent data from different places, negating the time-saving benefits of AgTech adoption.

There are also data rights and legal issues regarding digital information and data sharing and the sheer volume of data and implications for timely analysis and on-farm utility slow the uptake of digital agriculture. Additionally difficulties relating to integrating datasets of different scale, quality, and format remain.  Limitations associated with telecommunications connectivity and product interoperability and perceived lack of value to potential users means this technology is not utilised to its fullest extent.

Non-uniform investment and development of industry-specific AgTech in Australia is another important consideration for Australia’s widespread advancement of digital agriculture. Product funding and design tend to be concentrated in internationally significant sectors (e.g. the grain industry) or sectors with a larger market and greater product ‘spillover’ from more mature international digital agriculture markets with direct applicability in Australia (e.g. pork and dairy).

The future of Agtech in Australia

Untapping the potential of digital technologies to improve decision-making is far from simple. User confidence in AgTech in Australia is essential. Farm managers are accustomed to visually and physically inspecting assets and relying on ‘rule-of-thumb’ to make decisions. As such, they may be hesitant to offload critical management tasks to technology.

A lot of political, scientific, community, and individual efforts have been directed toward efficiently managing agricultural resources to maximise productivity and profit, resulting in a vast array of digital technology and data. Farm managers mainly utilise these technologies to improve production and business decisions (e.g. increase yield performance, manage inputs, and reduce costs). But, regulators, agribusinesses, governments, and financial institutions use them, although this is less frequently discussed. New technologies and data streams present significant opportunities for decision-making within the Australian agricultural sector, particularly about scarce resource management.