Making better pest spraying decisions
By Madeleine Baerg
English grain aphid family on barley head. Photo by Tyler Wist.
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.
For example, there are two families of wasps that parasitize aphids. Within seven days of an adult wasp stinging an aphid and laying its egg inside the aphid’s body, the aphid dies, then hardens and turns into a swollen mummy. Inside the mummy, the wasp larva grows until, around seven days later, it chews its way out of its aphid host.
“Aphid parasitism is wonderful when you see it in action,” Wist says. “That said, we don’t know a lot about the parasitoids’ natural history in Canada or what they do when they emerge from their aphid hosts. The challenge for our mathematical model is their biologies have to be accounted for. For example, will they parasitize another aphid in the same season? Several species of parasitoid wasp (including one that unfortunately kills ladybugs) can re-parasitize within one hour of emergence, but others won’t parasitize until the next year. So, for the purposes of our mathematical model, it’s hard to know exactly how much ‘killing ability’ to give to these parasitoids.”
But wait – it gets more challenging still to mathematically model. About 50 per cent of aphid parasitizing wasp larvae found in Wist’s and Olivier’s survey were killed before they emerged by secondary (or “hyper-”) parasitoids.
“It gets really complicated,” Wist notes. “You can just keep adding layers and layers to the calculation, though hopefully each layer brings you closer to a relevant threshold calculation.”
For this reason, Wist and Olivier have applied for further research funding in order to more completely develop their model. Further, they plan to take their research and translate it into a user-friendly tool for producers.
“Our goal is to make this applicable to producers. Right now it’s an excel spreadsheet with a whole series of equations, but how do we make it relevant?” Wist says, adding the short answer to that question may reside in your pocket.
“What we’re trying to do is make a smartphone application that incorporates these calculations. Basically, we want to provide a simple tool to help producers identify pests and predators in their field using a user-friendly ID and sampling system. We’d like to make it as easy as producers punching in the counts they find for each insect, and then the app instantly calculates the dynamic action threshold,” Wist says. “After all, there’s no point in doing this work if producers aren’t going to benefit from it.”
Whether this model will be translatable to other insect pests is uncertain at this point, though the theory behind the model certainly applies to any pest. Despite the fact aphids might not always be producers’ most challenging adversary, Wist is confident his research and development work will pay dividends for producers.
“Aphids are a weird bunch because they are very hard to predict. In most cases, they blow up from the U.S. on the wind. Though they are not a big issue in cereals every year, if they come in at the right time (or rather, the wrong time for the crops) they can quickly surpass the economic thresholds. That’s why projects like this are important even if aphids don’t cost growers significantly every year.”
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