Some farmers are skeptical about the relevance of small plot tests to full-sized farming fields.
That skepticism makes sense, thinks Murray Hartman, because lots about small plot testing is questionable and results dubious.
“There are a lot of shortcomings of making sense of rotations,” Hartman, a recently retired Alberta Agriculture oilseeds specialist, said at the Canola Discovery Forum.
Researchers use small plots to show how different inputs, management and rotations affect different crops. Farmers can see how differing approaches produce different results.
But how realistic are those results, wondered Hartman, when there are many grounds on which they could be skewed.
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“Do we really know what a good separation distance is?” said Hartman, noting that bugs, weed seeds and diseases don’t necessarily respect the boundaries between adjacent plots.
“Small plots probably underestimate the rotation effect for many pests because they can trespass between the plots.”
That can make it hard to observe the varying impact of pests when plots are compared. Rotational differences might be minimized.
Are plots being managed in a way that prevents pests being mechanically moved from one plot to the next? Equipment that moves “plot to plot to plot” could be a medium for transferring pests and obscuring the positive impact of rotation.
When yield differences are noted, are researchers able to break out all the components for the difference? Hartman said apart from a major focus, such as blackleg, most of the components aren’t broken down, leaving the rotational impact’s causes a mystery.
Are plots being seeded and managed at the same rates and in the same ways as farmers treat their crops? If not, that’s another wild card that affects the applicability of results.
“Does this represent what the farmers are doing? If it doesn’t, all of a sudden the economics become a little more questionable,” said Hartman.
There is great potential for improving test plot relevance. New methods are coming and some researchers are examining different-sized plots and bigger separation distances, as well as different analytical tools.
Getting test plots to come as close as possible to situations crops would face in farmers’ fields will make them become more relevant and help counter farmer skepticism.
