Updating agriculture: Can data analysis save farming?

Furthermore, network connectivity remains a problem in rural areas, especially in the more remote regions of the country. There are even rural locations that still do not receive mobile phone signals. For these data analysis solutions to be effective, there needs to be further investment in the rural telecommunications infrastructure.

One potential challenge to analysis-driven farming is the lack of information-sharing between different providers. There is little to no interoperability between devices of different manufacturers, meaning farmers have lots of different datasets, but no way to combine them together to see how the they all interact with each other. 

“We have dozens of pieces of software that the farm uses to input and output data, but I refer to them as the data silos,” says Ed Harris at the Data Science Agriculture Research Centre at Harper Adams University. “They don’t speak to each other and you only get information about your farm.”

Being able to combine multiple datasets together would provide a more holistic overview of a farm’s operations, rather than a series of independent data inputs. However, sharing this data while also protecting each farm’s personal and financial information would be essential for such a service to remain compliant with data protection regulations, such as the UK’s Data Protection Act 2018.

Being able to compare data with other farms would provide farmers with a greater understanding of how their farms operate. Independently, this information would be worth very little, but by being combined it would enable baseline estimates of how a typical farm operates. This would enable farmers to identify areas on their farm where they need to focus their attention.

“A pet peeve of mine is that if you get those numbers from a single farm, the farmer will have that one number,” says Harris. “What I think is needed is a business case to create benchmarking for carbon and for other farm operations. I then have a little more information in the context of the other farms.”

There are also regional elements to consider. A data analysis system developed using farms in a particular part of the country will not necessarily work in another. 

There are environmental and geographical issues that need to be adjusted for each data analysis solution to be effective. “We worked on a project from Australia and it worked perfectly, but you bring it into Scottish conditions and it’s just a disaster as it was a different environment,” says Ross.

Alongside data analysis, automation is being used to address recent labour shortages. These are typically repetitive and mundane tasks such as milking cows, which allows the workforce to focus their attention on more complex tasks. 

“Trying to get labour on a farm is a big problem, so farmers are open to using technology in all different forms,” says Ross. “A robot on a dairy farm costing £30,000 a year has the potential to save the farmer £85,000 a year in labour.”

Although agriculture is not seen as an IT-driven industry, it is one that is reliant on needing reliable and accurate data to enable informed decision-making. Data analysis could be used to identify emerging trends within the datasets and alert farmers to when intervention may be required to protect crops and increase their yields.

“Farmers are cynical buggers. It needs to be proven and there needs to be a return on their investment,” concludes Ross. “That’s not always cash, as it can be time. It needs to be relatively simple, robust and work on a messy farm and in dirty environments, because there’s just no getting around that.”