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In support of a better decision

Decision support tools are not new to agriculture. Yet despite the apparent benefits they offer their uptake has been low. Teresa Rush caught up with a researcher who has been finding out why this is so and what might be done to increase their use.

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Do you use decision support tools (DST)? And if not, what might lead you to do so? DST, from simple paper-based approaches to much more sophisticated software or app-based systems, are designed to help users make more effective decisions. In a farming context they can provide a means to record data, analyse it and generate evidence-based recommendations.


However, despite their apparent value, uptake of decision support tools by farmers and advisers has been limited, says Cambridge University researcher David Rose. This is a problem for two reasons, firstly, lack of uptake means the aim of improving the evidence base for decisions is not realised, and secondly, resources are wasted.


Now, as part of Defra’s Sustainable Intensification Platform (SIP), the University of Cambridge, alongside other partners, has led work to find out why farmers and advisers use, or do not use, decision support tools, with the aim of helping DST developers provide tools farmers will want to use.


See also: Data in agriculture: should we be sharing more?


Using focus groups, surveys, and interviews, farmers across six sectors, as well as arable and livestock advisers, were asked about whether, and why, they used tools to inform their decisions.


The researchers compiled a list (not exhaustive) of 395 different DST available to farmers. Of these, 73 were found to be in use in practice. Overall, 49% of the farmers surveyed used some kind of DST to inform decisions, with software the most useful (28% of those using DST), followed by paper-based (22%) and apps (10%).


With the study recently completed, the research team suggests 15 factors are influential in uptake and use of tools and has compiled a checklist to inform the future design of tools.

Performance expectancy

The performance of a decision support tool in terms of improving decision-making and productivity was a widely mentioned factor.


Dr Rose says: “The key question here was ‘Can you convince me it is better than how I make decisions currently?’”


Often there was a need for a tangible benefit from use of a DST. What that benefit might be depended on the tool but often it was improved productivity.

Ease of use

Simplicity, ease of use, speed of use and clarity of information produced were all-important considerations, even for paper-based systems.

Peer recommendation

Personal recommendation, between farmers or advisers, was a key determinant of uptake of DST.


Trust in a DST determined whether or not it was used in practice. Both farmers and advisers were keen to use tools from trusted sources, while advisers were particularly concerned with the evidence base behind DST development.


An important finding was that trust was easily lost – an example might be mis-identification of a weed because of the way a photograph inputted into the DST was taken – farmers were nonetheless reluctant to return the tool once this had happened.


Tools were more likely to be trialled if they were free or a grant was provided for purchase.


Habit was a significant factor affecting use. While the research team found there could be an aversion to trying out new approaches, there was also evidence of a feeling younger farmers accustomed to using computers and smartphones were likely to move easily into using software- or app-based DSTs.


However, even those farmers who had embraced software-based decision support indicated they would find it hard to move away from their current tools if new ones were developed.


“I think this is the most challenging one. Habit is probably one of the most difficult factors to overcome as it will not be affected by designing more user-friendly systems which perform better,” says Dr Rose.

Relevance to user

A decision support tool’s ability to be tweaked according to individual farm conditions, such as soil type and differences in local weather conditions, was seen as important and insufficient flexibility in this respect was likely to lead end users to consider it unsuitable for their situation.

Farmer-adviser compatability

The need for knowledge exchange between farmers and advisers was found to facilitate use of DST.


A number of further factors modified use of DST, including age, scale of farming, farming type and IT education. Among farmers age affected whether a user thought a high-tech tool could be beneficial or easy to use, with younger farmers using DST more and finding software most useful.


Farm size was significant, particularly in terms of the cost/performance benefit of using a tool. The survey results showed the probability of using DST is higher on larger farms.


“The bigger the farm the more likely they were to have the cashflow to be able to use DST or the more likely there was to be a performance benefit – if you have got so many fields, with so much going on, you need some sort of system to help you,” says Dr Rose.


Farming type was closely linked to the scale of farming as a key determinant of DST use. Arable and dairy farmers and advisers in these sectors were more likely to need DST than upland livestock farmers. Analysis showed the probability of use of DST and of finding software useful was higher among cereals farms.


Once a farmer decides they want to use a DST, the next question is whether they are able to use it. Potential problems in this respect included poor internet access and phone signal, poor compatibility with existing systems and mismatch between tool and end user workflows.


A number of ‘driving factors’ were found to be useful in encouraging use of DSTs; these included compliance (where a DST could help satisfy legislative or market requirements), as well as effective marketing.


So, as farming increasingly looks to harness the value of ‘big data’ in search of yield and quality improvements and cost reduction, what can be done to deliver decision support tools which will deliver benefits on-farm and that farmers can trust?


First and foremost designers could use the 15 factors identified in the research as a checklist alongside which to measure the quality of new tools, says Dr Rose.

DST – checklist for good design

  • Performance


  • Ease of use


  • Peer recommendation


  • Trust


  • Cost


  • Habit


  • Relevance to user


  • Farmer-adviser compatibility


  • Age


  • Scale of business


  • Farming type


  • IT education


  • Facilitating conditions


  • Compliance


  • Level of marketing


Source: Rose et al, Agricultural Systems

But instead of focusing simply on designing sophisticated, easy to use tools, some of the other important but seldom highlighted factors, such as helping users satisfy legislative requirements, should also be taken into account, he suggests. And aiming tools at younger users, on larger arable farms could well be a good starting point.

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