The fall armyworm (Spodoptera frugiperda) is a moth pest native to the Americas that causes major damage to economically important grains such as maize, rice, sorghum, sugarcane, and wheat, as well as other vegetable crops and cotton.
In its larval stage it feeds in large numbers on the leaves, stems and reproductive parts of more than 350 plant species.
In its adult stage, the moth can cover large distances in migratory flight, meaning it can spread rapidly into new areas it hasn’t occupied before.
Research scientist Dr Alex Slavenko with Cesar Australia said fall armyworm was first reported in Australia in January 2020 in the Torres Strait and subsequently discovered in Queensland in February 2020. By February 2021, fall armyworm had spread into NSW and down to northern Victoria.
It is now widely established in growing regions across northern Australia year-round, and adults fly into more southerly growing regions as temperatures increase in spring and establish summering populations.
The wind dispersal forecasting tool was developed by Dr Alex Slavenko at Cesar Australia for the
project“Rapid real-time simulation of wind-assisted long-ranged dispersal
of Fall armyworm in Australia” funded by Plant Health Australia (PHA).
Wind forecast tool
As this pest can migrate large distances over short periods of time, Dr Slavenko said regular monitoring and surveillance are vital. Knowing when the adult moths are likely to appear in an area helps to inform monitoring for larvae, and proactive management if required.
“We developed an online tool to help growers predict when fall armyworm might arrive in their crop based on wind forecasts.
“Fall armyworm, like many other moth species, uses strong, warm low-level jet winds to perform nocturnal migrations. Our tool sources wind forecast data which are then used to predict where winds are likely to carry moths from a starting point chosen by the user.”
Using this tool, growers and agronomists are now equipped with a simple way to receive early warnings of possible fall armyworm incursions. While the model cannot yet predict fall armyworm presence with certainty, it can provide a warning that conditions are suitable for an incursion of the pest.
This is an important step towards a more comprehensive surveillance and mitigation strategy for this economically important pest species.