How data can work for your farm business

Data sharing brings innovation and insight, resilience and reward, say the experts, urging farmers to prepare for the opportunities it has to offer.

Despite a long history of data sharing or pooling in agriculture, it has not developed rapidly or kept pace with other industries – even though a huge transition has seen the industry go from being data-sparse to data-rich in a relatively short time.

That makes data the biggest challenge and the biggest opportunity in agriculture worldwide, believes Professor James Lowenburg-DeBoer of Harper Adams University. He is clear about the benefits it has to offer.

See also: Benchmarking arable performance – where to start

“It gives you new insights into management of the farm and it brings opportunities for discounts on purchases of goods and services,” he says.

Where farmers have been slow or reluctant to share their information, there are several factors involved, says Prof Lowenburg-DeBoer.

There have been technical issues with connectivity and compatibility as well as confusion on the legal status of data.

“Added to that, farmers are concerned about giving away a competitive advantage. There is also a suspicion that any benefit will be post-farmgate.”

Reciprocal rewards

Trust is a central issue, agrees Professor Richard Tiffin of Agrimetrics, which provides, connects and analyses complex data to drive productivity for agrifood businesses to help them deliver food sustainably.

Prof Tiffin highlights two requirements for successful data sharing. “It must deliver value back to the farmer, such as with benchmarking or pest- and disease-risk forecasts.

“And it must allow monetary reward, with payments being made by the people who get insights from the data.”

Confidence essential

He stresses that having confidence in any data-sharing system is essential, as is the need for transparency, with farmers knowing why their data is wanted and what it is being used for.

Once resolved, data provides a great opportunity to address the misdistribution of value created in the supply chain and build resilience into the food system, he says.

“Retailers have been allowed to collect the value because they are able to respond quickly to market needs. If farmers have the insight, they can capture some of this value and shorten the supply chain.”

Information for solutions

Matthew Smith of Microsoft suggests a mindset change.

“Stop thinking about it as data. It can be turned into information for solutions – in other words, it can help you see what’s going on.”

He describes three levels of information:

  • Descriptive analytics – identifies what’s happening, for example yellow rust outbreak
  • Diagnostic analytics – identifies what’s been done about it, for example fungicide use and timing
  • Predictive analytics – identifies the outcome, for example yield and quality results

“All of these have a place in farming. And as analysis methods become more sophisticated, data can be interrogated differently, for the benefit of everyone,” he adds. 

Challenges remain

However, he acknowledges two remaining challenges to better use of data in farming.

“The business process that ends with farmers getting rewarded is one of these. There must be reciprocal benefit,” he says.

“The other challenge is the people translating the information. The human interface, which could be your agronomist or farm adviser, has to extract value from the information and relay it to you.”

The data spectrum

The different types of data:-

  • Closed data – only known by yourself e.g. PIN number                               
  • Shared data – shared with identified partners e.g. bank account details for payments
  • Open data – available to everyone e.g. train timetables, weather information

In UK agriculture, there are good examples of successful data sharing. The sugar industry has collected data from all growers for years, giving them instant feedback on yields and sugars during the campaign, as well as market information.

An example of open data being used to generate income and mutual benefit is the Environment Agency’s flooding data, which has been sold to conveyancers, consultants and insurers, while allowing individuals to assess their susceptibility to flooding.

Smart farming pilot project

In the Netherlands, a four-year project is under way to convince farmers of the benefits of ‘smart farming’ and make better use of data.

Being run by Wageningen University, six farmers with no previous experience of precision agriculture have been selected to carry out variable rate applications of specific inputs, with the support of experts from the university.

The farms are also being used as demonstration sites, so that other growers can learn from the initiative and get help with any practical and technical issues.

Input savings potential

“Our growers are being urged to produce food in a way that is less harmful to the environment, so there is huge interest in this project from the government.”

For grower Max Sturm, whose onion crop is the test site for variable rate herbicides, there has been a €14/ha saving this year.

However, he reports that the real gain came from the reduction in crop damage – with herbicides now being targeted more precisely, according to soil scans and biomass assessments.

Another indirect benefit for the farmers involved has been the willingness of machinery and equipment manufacturers to engage and provide help with connectivity.

Case study – Farmer experience

North Yorkshire farmer Graham Potter is proud of what he has achieved with data over the past eight years.

The main benefit from the various technologies in place is a reduction in the variability on the farm, as he is now armed with the information that he needs to make important management decisions.

“I can see how parts of fields are performing, both physically and financially, and take rapid action to change things,” he says.

In 2009, he invested £12,000 in a Greenstar guidance system from John Deere. With input from variable-rate mapping service SOYL, he was able to link to his fertiliser spreader and make variable rate applications of P, K and lime – bringing immediate savings and covering the cost of the technology.

Farm information hub

From there, Mr Potter went on to buy Gatekeeper software, so that it could act as a hub for all of his farm information. The subsequent purchase of a new combine allowed the production of yield maps and a better insight into field performance – the next step in his journey.

To maintain the momentum, soil electronic conductivity scanning was done across the farm by SOYL to allow him to use variable seed rates, matching seed rate to expected establishment and soil zones. This then gave him the opportunity to use variable rate cultivations according to soil condition – saving on metal, diesel and time.

Joining the RTK network increased accuracy to within 20mm, helping him to refine his operations further still and minimise any compaction.

Today, drones are being used for mapping weed patches and helping identify where manganese deficiency is occurring.

“That information goes back into Gatekeeper, so that I can produce variable-rate treatment maps and just spray where it is needed,” he explains. “All of our inputs are targeted to crop potential.”

Margin maps help make decisions 

Margin maps are also proving very useful, Mr Potter adds. “If there are areas that aren’t performing as I would like, they can be earmarked for environmental schemes, for example.”

Robots are on the horizon and Graham is keen to put them into practice. “Whether it’s lasers to ‘zap’ particular problems or bots to take any weeds away, it’s very exciting. The challenge will be making it cost-effective for my 200ha farm.”


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All of the experts were speaking at the Farmers Weekly ‘Using Data and Tech’ conference, which was held in Birmingham on 10 October 2018.

Thanks to John Deere and Lely for their sponsorship, which enabled us to run the event.

See the Using Data and Tech website for more information about this event.

Farmers Weekly had full editorial control of this report.

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