Improving Weed Management Based on the Timing of Emergence Peaks: A Case Study of Problematic Weeds in Northeast USA - Brown, Bryan; Gallandt, Eric R.; DiTommaso, Antonio; Salon, Paul; Smith, Richard G.; Ryan, Matthew R.; Cordeau, Stéphane (Frontiers in Agronomy, 2022)
We reviewed the timing of the peak rate of emergence for 15 problematic weed species as well as ways to use this knowledge to improve their control. Much of the previous literature modeled emergence based on growing-degree-days. For these models, we input average temperature data from several zones of Northeast USA. Within species, model-predicted peak emergence in the warmest and coolest zones differed by an average of 39 days. Also within species, there was some variation between models, likely reflecting different conditions in study locations and population-level differences that will need to be addressed in future modelling efforts. Summarizing both observed and modelled results, emergence typically peaked early-season for barnyardgrass, Canada thistle, common lambsquarters, common ragweed, giant foxtail, large crabgrass, perennial sowthistle, and smooth crabgrass. Emergence typically peaked mid-season for hairy galinsoga, mouseear chickweed, and red sorrel. Emergence typically peaked late-season for annual bluegrass. Several species emerged in a protracted manner, including common chickweed, quackgrass, and redroot pigweed. With this improved knowledge, farmers may target key problematic species of a particular field in several ways. Weed seedling control efforts can be timed at the highest densities or most vulnerable phenological stage. Residual herbicides and suppressive mulches can be timed to maximize effectiveness prior to their breakdown. And if management flexibility allows, crop selection and associated planting dates may be adjusted to improve crop competition or facilitate seedbank depletion through timely bare fallow periods. Such improvements to weed management based on timing of emergence will likely become even more impactful as predictive model reliability continues to improve.