Giant Ragweed (Ambrosia trifida) Emergence Model Performance Evaluated in Diverse Cropping Systems

Abstract

Accurate weed emergence models are valuable tools for scheduling planting, cultivation, and herbicide applications. Multiple models predicting giant ragweed emergence have been developed, but none have been validated in diverse crop rotation and tillage systems, which have the potential to influence weed emergence patterns. This study evaluated the performance of published giant ragweed emergence models across various crop rotations and spring tillage dates in southern Minnesota. Across experiments, the most robust model was a mixed-effects Weibull (flexible sigmoidal function) model predicting emergence in relation to hydrothermal time accumulation with a base temperature of 4.4 C, a base soil matric potential of −2.5 MPa, and two random effects determined by overwinter growing degree days (GDD) (10 C) and precipitation accumulated during seedling recruitment. The deviations in emergence between individual plots and the fixed-effects model were distinguished by the positive association between the lower horizontal asymptote (Drop) and maximum daily soil temperature during seedling recruitment. This finding indicates that crops and management practices that increase soil temperature will have a shorter lag phase at the start of giant ragweed emergence compared with practices promoting cool soil temperatures. Thus, crops with earlyseason crop canopies such as perennial crops and crops planted in early spring and in narrow rows will likely have a slower progression of giant ragweed emergence. This research provides a valuable assessment of published giant ragweed emergence models and illustrates that accurate emergence models can be used to time field operations and improve giant ragweed control across diverse cropping systems. Nomenclature: Giant ragweed, Ambrosia trifida L. AMBTR.

Incorporating environmental factors to describe wild radish (Raphanus raphanistrum) seedling emergence and plant phenology

“…Predictive weed emergence models have often been used by weed scientists to understand weed biology and to encourage more effective and timelier weed control. Weed emergence models have been implemented to optimize control of summer annual weeds (Forcella 1993;Goplen et al 2018;Masin et al 2005;Werle et al 2014b). However, creating an accurate model for R. raphanistrum emergence has been challenging (Walsh et al 2002).…”

Giant Ragweed Emergence Pattern Influenced by Spring Tillage Timing in Minnesota

“…Field experiments were conducted near Rochester, MN (43.91°N, 92.56°W) on a Port Byron silt loam (Fine‐silty, mixed, superactive, mesic Typic Hapludolls) and Rosemount, MN (44.70°N, −93.08°W) on a Waukegan silt loam (Fine‐silty over sandy, mixed, superactive, mesic Typic Hapludolls) in 2015. Site details are outlined in Goplen et al (2018b). Both sites had a history of corn ( Zea mays L.) and soybean [ Glycine max (L.) Merr.]…”

“…Since giant ragweed emergence is influenced by the accumulation of thermal time, calendar date of emergence may vary from year to year and by latitude. However, the onset of giant ragweed emergence has been relatively consistent from year to year in Minnesota (Goplen et al, 2018b). Total giant ragweed emergence did not differ among tillage timing treatments, confirming the findings of Kaur et al (2016) (Table 2).…”

Response of invasive perennial western ragweed (Ambrosia psilostachya) to chemical and mechanical control

“…Invasive plants often have strong ecological adaptability, that by producing allelopathy, and changing soil properties can threaten biodiversity (Castro‐Díez et al, 2019). The rapid growth and ability to survive in adverse habitats are among the competitive advantages of these plants over crops (Goplen et al, 2018). Alien species are on the rise around the world, and this trend is projected to increase to 36% in the next three decades (Seebens et al, 2021).…”