Harvesting smarter decisions: overcoming biases in agronomic management by: Jeremy Boychyn MSc P.Ag, Director, Agronomy Research Extension | Alberta Grains

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If all has gone well this harvest, your grain is in the bin and plans for next year are swirling in your mind. This year you may have tried a new technology, product, variety, or management practice on your farm and are determining if that new item has a permanent place in your farm management toolbox.

With a wide variety of new products on the market, assessing their value is a complex task that requires consideration. Some of these products may bring value to your farm, while many may not.

To make decisions about new adoptions more challenging, farmers, like all humans, are susceptible to cognitive biases that can influence the perceived value of a new technology or product.

What is a cognitive bias?

A cognitive bias is a systematic error in thinking where humans simplify information to make decision-making easier. With cognitive bias, humans are heavily influenced by emotion and personal opinions and are less likely to make a decision on adopting a new product on their farm using sound data. The best way to avoid being caught by cognitive biases is to be aware of them.

Types of cognitive biases

Confirmation bias

Confirmation bias is the tendency to search for, interpret, favour, and recall information that confirms a pre-existing belief while giving less con- sideration to alternative possibilities. If someone already believes a product or tool will work or has benefits before being used, they are at higher risk of falling victim to confirmation bias.

Example:
A salesperson arrives on farm and hands the farmer a sales sheet on a particular product. The farmer saw this product at a tradeshow before. The farmer talked to their neighbour who tried it and their neighbour said it benefitted their crop. The farmer then makes the decision to give the product a try and applies 80 acres of it by splitting a field in half.

If the farmer is impacted by a confirmation bias, they might subconsciously expect to find a benefit because of their neighbour’s experience. The farmer might look for colour, height, growth, or maturity differences in the crop to indicate the product works.

Selective perception bias

Selective perception bias is the tendency to cherry-pick information that supports a belief while ignoring other information that challenges that belief. Take the same example referenced under confirmation bias. If impacted by selective perception bias, the farmer may, subconsciously, walk the field for differences and pull plants from more productive parts of the treated area and compare them to less productive parts of the field area that were untreated.

Scientists and agronomists build methodology into their research to avoid selective perception bias. When collecting plant stand counts, an agronomist will ‘blindly’ throw tools behind their backs and collect data from where the tool fell to help avoid inadvertently selecting from areas influenced by their own bias.

Post-purchase rationalization

Trying a new product is an investment of time, effort, and dollars. Often, people will look for reasons to justify the purchase. This is called post-purchase rationalization. Naturally, people desire to have their efforts pay back. No one wants to spend hard-earned dollars, time and effort for new product not to work.

Ensure unbiased decision-making

So now that you know some of the bias risks involved with testing new products, how else can you ensure unbiased decision-making?

First, encourage skepticism when no replicated, randomized, third-party data exists. Always search for validated research and avoid relying on anecdotal evidence. To assess the value of a product on your farm, implement replicable and random strip trials like those implemented in the Alberta Grains Plot2Farm on-farm research program. These methods help develop strong data that can be used to make informed decisions. Avoid splitting fields or comparing treatments between two fields.

This generates unreliable data that should not be used for decision-making. To read more on how replicable field scale plots are implemented, visit Plot2Farm.com.

If you find yourself in a position where you feel bias may be impacting your perception of a new farm product, reach out to an unbiased expert. Many potential farm technologies and products have inherent risks of adoption. However, knowledge of cognitive bias and how they affect your decision-making can help guide you in the right direction.