For some time, large-scale data-collection networks have been used to help predict and control field pest pressures on farms in Ontario, Canada. Similar systems have also been employed to reduce the threat posed by livestock diseases.
However, experts see more harmonised data systems was the future of effective prevention.
Projects like the now 8-year-old Western Bean Cutworm trap network – an initiative designed to track and help support prevention efforts for the pest exemplify how pest-prevention data can work. This crowd-sourced project aggregates cutworm numbers across Ontario and beyond; data generated through the network are then applied to digital tools that can warn farmers of imminent cutworm pressures, hopefully allowing them to take pre-emptive actions.
Clare Schlegel, an Ontario pig farmer and board member for Swine Health Ontario – a collaborative group focused on improving how Ontario’s pork industry responds to serious swine health threats – says similar reporting programmes have long been employed to prevent, track, and respond to outbreaks of swine diseases such as PRRS and PED. Much of this is done through voluntary producer reporting; farmers sign up through a formalized program, and the health of their animals is tracked.
As a long-time participant, Schlegel says this information allow farmers and other industry professionals to identify where disease originates, and apply controls.
If PRRS or PED is discovered on a farm, for example, participants in the programme (known as ARC&E) are sent a notice. Those producers can then enact more stringent biosecurity protocols, talk to employees about the situation, help transporters ensure trucks vising contaminated sites are thoroughly cleaned, and more. This system, says Schlegel, has allowed the Ontario pork industry to limit the spread of diseases like PED, and stamp it out wherever possible.
Schlegel also says the programme will soon be upgraded to incorporate transportation data. More specifically, digital records of every animal will be kept as they move between farms and to the processing plant. Health data for those animals can then be layered on top.
“This is one of the few systems, where if you talk about it for an hour, virtually everyone sees the benefit,” says Schlegel. “It’s not government regulated – this is farmer driven.”
Reporting programmes like those described above are seen by many as powerful prevention tools. However, the overall on-farm impact of aggregate data is highly segmented in its usefulness, says Dr Karen Hand, researcher in applied biostatistics and director of research data strategy for the University of Guelph’s Food from Thought programme – an initiative focused on using big data to benefit food production and biodiversity.
Hand says data streams exist on a multitude of levels, and are held by a multitude of sources – from in-tractor precision application programmes to individual agronomy companies and commodity monitoring systems, such as the Western Bean Cutworm network. Currently, though, there is no one location where all the information can be combined and analysed to determine a farm’s infection risk. Looking at each data stream individually, she says, is not conducive to delivering farmers and veterinarians useful information in real-time.
If such a system did exist, farmers could then be notified concerning their quantified risk and suggested next steps.
“If we had the capability to predict risk in real-time we could get the resources needed to critical areas” says Dr Hand. “We need pipelines in between.”
Dr Hand says synthesising all agricultural data streams requires collaboration from the “3 pillars” of government, academia, and industry. Work done by agricultural groups and institutions has already produced and tested a made-in-Canada “IT architecture” that could begin absorbing data streams from all Canadian agriculture sectors.
The system was created after consulting with industry on what data streams already exist in different sectors. According to Hand, the platform was tested last year. However, long-term government support – not support on a project-to-project basis – is required for the system to be sustainable.
“This type of system has the potential to move us to a proactive as opposed to a reactive model, where farmers that are at high risk will have access to products and support and farmers at low risk do not have to purchase or apply products,” she says.
Hand also believes synthesizing multiple data streams into one accessible, Canadian system could prove useful for policymakers. She adds the next generation of farmers are tech-savvy and will need access to – and be willing to access – data more than ever.
“Can we use it for regulatory purposes to reduce on-farm paperwork? […] We’re increasingly asking farmers to do more paperwork but a lot of that data already exists,” Hand says.
Interconnected agricultural data systems could also be used to prove the sustainability of Canadian agriculture both domestically and abroad. However, a lot of farm-generated data is currently held by private companies and organisations both in and outside the country.
This raises sovereignty questions as data generated on Canadian farms only becomes truly accessible to those holding the data. Sometimes this isn’t an issue – most companies, Dr Hand says, are very open and willing to share – though there are cases where the opposite is true.
“We need data to support sustainability,” says Hand. “We know Canadian farmers follow best management practices, but how do we prove that? It’s really going to come down to proof.”
Trade aside, a more nationalised data system could have practical implications for day-to-day farm operations.
“If you’re a livestock producer you might want data off your robot that your nutritionist can use, but you can’t get it,” says Hand. “Can we assure producers they known where their data goes and that they have the right to that data?
“We like to say [farmers] need it in their own bank accounts.”