Predicting Emergence of 23 Summer Annual Weed Species

Abstract

First- and second-year seedbank emergence of 23 summer annual weed species common to U.S. corn production systems was studied. Field experiments were conducted between 1996 and 1999 at the Iowa State University Johnson Farm in Story County, Iowa. In the fall of 1996 and again in 1997, 1,000 seeds for most species were planted in plastic crates. Seedling emergence was counted weekly for a 2-yr period following seed burial (starting in early spring). Soil temperature at 2 cm depth was estimated using soil temperature and moisture model software (STM2). The Weibull function was fit to cumulative emergence (%) on cumulative thermal time (TT), hydrothermal time (HTT), and day of year (DOY). To identify optimum base temperature (Tbase) and base matric potential (ψbase) for calculating TT or HTT, Tbaseand ψbasevalues ranging from 2 to 17 C and −33 to −1,500 kPa, respectively, were evaluated for each species. The search for the optimal model for each species was based on the Akaike's Information Criterion (AIC), whereas an extra penalty cost was added to HTT models. In general, fewer seedlings emerged during the first year of the first experimental run (approximately 18% across all species) than during the second experimental run (approximately 30%). However, second-year seedbank emergence was similar for both experimental runs (approximately 6%). Environmental effects may be the cause of differences in total seedling emergence among years. Based on the AIC criterion, for 17 species, the best fit of the model occurred using Tbaseranging from 2 to 15 C with four species also responding to ψbase= −750 kPa. For six species, a simple model using DOY resulted in the best fit. Adding penalty costs to AIC calculation allowed us to compare TT and HTT when both models behaved similarly. Using a constant Tbase, species were plotted and classified as early-, middle-, and late-emerging species, resulting in a practical tool for forecasting time of emergence. The results of this research provide robust information on the prediction of the time of summer annual weed emergence, which can be used to schedule weed and crop management.

Base temperatures for germination of selected weed species in Iran

“…This awareness induced increasing interest in the development of models that can simulate seedling emergence and the potential benefits, but also challenges, of their adoption were recognised and thoroughly reviewed (Forcella et al 2000;Grundy 2003). Several models have been created for seedling emergence of various weed species in the main crops such as maize (Dorado et al 2009a;Masin et al 2012), soybean Werle et al 2014) or winter cereals (Royo-Esnal et al 2010, 2015García et al 2013;Izquierdo et al 2013). These models are often based on the hydrothermal time concept (Bradford 2002) and require the estimation of biological parameters, base temperature, and base water potential for germination (t b and Y b hereinafter), to simulate seedling emergence according to weather trends.…”

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

“…Akaike weights (w i ) closer to 1 indicate stronger support for a candidate model given the data. This methodology has been used in previous giant ragweed emergence modeling studies to select the best-fitting predictive model while minimizing the number of parameters (Davis et al 2013;Werle et al 2014). Since AICc will rank models even if none perform well, it is recommended that additional performance criteria be used (Anderson 2008;Kobayashi and Salam 2000;Legates and McCabe 1999;Meek et al 2009;Tedeschi 2006).…”

“…All other published models use the Weibull function to predict giant ragweed emergence. The models from Schutte et al (2008) and Werle et al (2014) include only fixed effects:…”

“…Models 4 to 8 had the same fixed effects as arable accession Model 2 but different random effects for each site-year that were predicted from Davis et al (2013). Davis et al (2013) and Schutte et al (2008) developed giant ragweed emergence models by evaluating giant ragweed emergence with no surrounding vegetation, while Werle et al (2014) developed emergence models in an experiment planted to soybean [Glycine max (L.) Merr.] that achieved a crop canopy after the majority of giant ragweed emergence had occurred.…”

Comparison of Herbicide Programs for Season-Long Control of Glyphosate-Resistant Common Waterhemp (Amaranthus rudis) in Soybean

“…Widespread resistance in common waterhemp against ALS-inhibiting herbicides and glyphosate is compelling soybean growers to depend mostly on PPO-inhibiting herbicides such as acifluorfen, fomesafen, or lactofen (Shoup et al 2003;Shoup and Al-Khatib 2004). Hartzler et al (1999) reported that common waterhemp has an extended period of emergence compared to other summer annual weed species, and Werle et al (2014) considered this weed as a late-emerging species. The PRE (soil-applied) herbicides may lose their residual activity later in the growing season; therefore, the application of POST herbicide is necessary to control late-emerging common waterhemp flushes (Hager et al 2002a).…”