Junglerice (Echinochloa colona L.) seedling emergence model as a tool to optimize pre-emergent herbicide application

Abstract

Junglerice (Echinochloa colona), one of the worst and most problematic weeds in the world, causes large economic losses as a result of yield loss and control cost increase. Taking into account that this weed emerges in approximately five months from September to January, and considering that reducing herbicide use is key in the current intensification of agricultural production systems, the present study was carried out under the hypothesis that there should be an optimal moment for pre-emergent herbicide application to achieve maximum weed control effectiveness and efficiency. Experiments were therefore carried out from August 2016 to January 2021 in Pergamino, Buenos Aires province, Argentina, using a double-logistic emergence model of junglerice seedlings. Bicyclopyrone plus s-metolachlor, clomazone, and pyroxasulfone plus saflufenacil were applied at different times between 92 and 478 growing degree days (GDD). Single applications between 348 and 399 GDD were observed to reduce junglerice seedling emergence by 85-99%, depending on the herbicide used. Such a seedling emergence reduction could, in combination with a competitive crop and within a sustainable production system, be a convenient strategy to provide significant weed suppression in the field. The overall results from the present study lead to the conclusion that using predictive models for pre-emergent herbicide applications ensures a more effective use of herbicides and reduces the amounts of herbicides used as well as the risks of selecting herbicide-resistant junglerice populations. Highlights - Weed occurrence indirectly increases the number of herbicide applications in Argentina. - Reducing the number and volume of herbicide applications contributes to mitigating environmental impact in the short term. - There is a critical time during weed emergence in which chemical control via herbicide application is most effective. - Seedling emergence models are useful management tools to predict critical timing for weed control.