Developing Population-Based Threshold Models for Predicting Weed Emergence in Time and Space to be Used in Site-Specific Weed Management

Effective weed management is crucial for optimizing agricultural productivity and minimizing environmental impacts. Weeds are most effectively managed during their seedling or early growth stages, something that could be efficiently achieved with the aid of tools for predicting seedling emergence. However, many persistent weed species exhibit dormant seedbanks thus complicating prediction attempts. The number of emerged seedlings in these species is closely tied to seedbank dormancy levels, which are influenced by seasonal variations. Thus, predictive population-based threshold models incorporate seedbank dormancy regulation to accurately forecast seedling “window” emergence. These models use the functional relationship between environmental cues (i.e. temperature, light, alternating temperatures, and soil water content) and seed dormancy behavior. Considering that these environmental signals vary among microsites in the field, these tools can be adapted to predict weed emergence in both temporal and spatial dimensions, thus making them suitable for site-specific weed management. The aim of this paper is to provide a framework for dynamic, site-specific weed emergence predictions, enabling targeted weed management practices. This kind of approach can help to improve the efficiency of herbicide applications and other control measures, reducing costs and environmental impact while enhancing crop yields. This work underscores the potential of integrating environmental cues into sophisticated modeling approaches to address the complexities of weed emergence in diverse agricultural landscapes.