To spray or not to spray when you scout insect pests in the field, that is always a tough question. While an economic threshold calculation can help a producer make an informed decision, the simple “X insects = spray” model only captures a small part of what’s actually happening in the field.
Now, Tyler Wist and Chrystel Olivier are aiming to develop a more integrated, fuller-picture threshold calculation to help producers make better pest management decisions.
“We’re building a Dynamic Action Threshold (DAT) based on aphids and their predators in cereal crops,” Wist says. Wist is project lead and a National Science and Engineering Research Council Visiting Fellow at Agriculture and Agri-Food Canada’s (AAFC) Saskatoon Research Centre. “If you don’t factor in a pest’s natural enemies, your population predictions for the pest could be far too high. By developing a DAT equation that accounts for aphids as well as the predators and parasitoids that keep aphid populations in check, we’re hoping to help producers only spray when they really need to, thereby saving spray inputs and time, and preserving the ‘good’ insects like ladybugs.
” Current economic threshold calculations base their pesticide recommendations on an estimate of the total number of any particular pest in a field. The problem is the calculation assumes every pest counted will survive to inflict crop damage and/or to reproduce. While the calculation might make sense in a closed scientific lab, it works much less effectively in a field setting where nature constantly seeks balance between pests and hosts, predators and prey. To make an economic threshold calculation field-relevant, it needs to calculate the total number of pests minus the number of those pests that will be destroyed by predators/parasitoids prior to the pests inflicting crop damage and/or reproducing. But how? Whereas a straight estimate of the number of pests in a given area is relatively simple, quantifying multiple and complex predator/prey relationships requires sophisticated mathematics, some educated guesswork and a willingness to add layer upon layer of complexity.
For the past two years, Wist and Olivier – entomologist with AAFC in Saskatoon – have worked to develop and validate a DAT calculation that can translate into a simple, user-friendly tool for producers. Working in fields in multiple locations across Saskatchewan and Manitoba, the researchers started by scouting for any and all insects. After identifying aphid species and their natural enemies, they tracked the populations over time, using linear models to show how the enemies were helping to balance aphid populations. After collecting sufficient data, Wist began inputting the data and a series of calculations into a mathematical model to estimate the populations’ effects on one another.
“The model works on a field-by-field basis. I input the actual numbers of aphids and their natural enemies at time t0 [the initial sample date]. Then, the model takes the natural enemies and their voracities [the number of aphids they eat per day] and turns them into one input, what we call a ‘Natural Enemy Unit,’ which is a consistent way of describing the pressure the natural enemies put on the aphid population,” Wist explains.
For example, a seven-spotted ladybug eats 100 aphids per day, whereas another predator might consume only 20. By turning the predators into consistent units (a ladybug is no longer a ladybug, it is simply calculated as -100), they fit the confines of the mathematical model.
If aphid populations spread themselves relatively evenly across a given area, the aphid versus predator ratio calculation would be enough to calculate the DAT. However, aphids randomly disperse, colonizing plants inconsistently. Depending on planting density, the sweep net sampling method the researchers used covers approximately 2400 tillers, collecting most of the aphids congregating in the heads and providing an estimate of the total number of aphids in this area of the field. A ladybug, however, would not possibly be able to visit all 2400 of these tillers in search of its optimal number of aphid meals per day. Therefore, during the early stages of an aphid infestation while aphid colonies are spread out and aphids are harder for predators to find, the DAT equation overestimates the number of aphids that actually get eaten by any given predator. For this reason, Wist plans to further refine the equation by adding a factor that describes the searching time of a predator, decreasing the number of aphids eaten when an aphid infestation is low and colonies are spread out.
Wist knows he still has much work ahead, since modelling the initial pest:enemy ratio is only step one of a complex calculation.
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