Developing a Localized Emergence Model of Echinochloa crus-galli to Predict Early Post-Herbicide Effectiveness in Maize

In order to achieve integrated weed management, precision timing is just as important an aspect to consider as spatial precision: the stage of the plant at the time of action will impact its successful control or survivability and thus the selection pressure for herbicide resistance. Weed emergence models are one aspect of this precision timing, but they are yet underutilized. One critique has been that models based on bare ground emergence are not always validated with emergence in the crop, and yet also residual herbicides and their timing may also affect the model. In this work, we compare emergence of Echinochloa crus-galli on bare ground and in maize and the impact of early post-residual herbicides at several timings. Experiments on bare ground and in maize were set in Prague, Czech Republic, in 2021, 2022, and 2023. Bare-ground quadrats of 0.25 m(2) were randomly assigned in a space of 100 m(2). Maize plot treatments of four herbicides at each of five timings were assigned in a randomized complete block design (dimethenamid-P at 1008 g ai ha(-1), pethoxamid at 1200 g ai ha(-1), isoxaflutole at 96 g ai ha(-1), and mesotrione at 480 g ai ha(-1)). Three 0.25 m(2) quadrats were enumerated in each plot from first emergence to full canopy closure (May to July). Model fit to emergence from the bare-ground plots using thermal time with a base temperature of 10 degrees C resulted in an AIC of -494. The bare-ground model was validated with emergence from the nontreated control plots in maize in 2022 and 2023, which accounted for over 85% of the variability in observed emergence. At canopy closure, total emergence since herbicide application was affected by herbicide and application timing. All herbicides at all timings reduced the emergence after application except for mesotrione. When beginning thermal time from the day of application, the emergence pattern after mesotrione application at all timings could be modeled with a single equation. E. crus-galli had a reliable emergence pattern within a local population; the predictive model created using bare-ground plots adequately predicted emergence in maize. This information can be used to time herbicides to coincide with the most effective moment in the flush in areas where E. crus-galli is the driver weed species.