Optimizing Insecticide Input Efficiency for Climate-Resilient Maize Production: A Bioassay of Novel Binary Formulations Against Spodoptera frugiperda

Fall armyworm, Spodoptera frugiperda (J.E. Smith), has become the most damaging insect pest of maize in Pakistan since its 2019 incursion into Punjab, and commercial binary premixed insecticides have reached the market faster than locally generated efficacy data to support their use. This study evaluated the comparative bioefficacy of seven binary premixed insecticide formulations against a field-collected Sahiwal strain of S. frugiperda under laboratory and field conditions. In the laboratory, third-, fourth-, and fifth-instar larvae were exposed to five serial concentrations (0.25–4× the recommended field rate) using a leaf-dip bioassay in a completely randomized design, with mortality scored at 12, 24, 48, 72, and 168 hours. A parallel field trial assessed larval population and leaf-damage reduction at 3, 7, and 14 days after treatment. Both insecticide and concentration significantly affected mortality (p < .001 in nearly all cases), with concentration the dominant factor; mean 168-hour mortality of third-instar larvae rose from 22.7% at the lowest dose to 77.7% at the highest. Probit analysis ranked Astris Gold (LC50 = 0.0495%) and Taragen (LC50 = 0.0837%) as the most potent products, though Tukey’s HSD grouped all seven as statistically equivalent on raw mortality. Contrary to the classical expectation that older larvae tolerate insecticides better, fifth-instar larvae were consistently more susceptible than third-instar larvae. Sublethal assessment showed dose-dependent suppression of growth, pupation, and adult emergence. In the field, larval population and leaf damage fell by approximately 92% and 90%, respectively, at the highest dose by 14 days after treatment. These findings identify locally effective binary formulations for Sahiwal-strain fall armyworm management and establish a baseline susceptibility reference for future resistance monitoring and evidence-based product selection in Pakistani maize production systems.