Despite efforts to eliminate weeds, they continue to thrive. Weed persistence is reliant upon the soil seedbank. Knowledge of the soil seedbank is continually expanding, but with the rising threat of herbicide-resistant weeds in agriculture, weed scientists have, in the past, focused their management tactics to more short-term solutions that tackle the aboveground problems, rather than long-term solutions. This article summarized the soil seedbank dynamics of weed seeds and derives management options, from a North American weed scientists’ perspective, that (i) favor the depletion of the seedbank, (ii) favor the germination of the seedbank, and (iii) reduce the possibilities of seed produced by the seedlings that germinated to return the soil. These options can potentially deter herbicide resistance and are successful in the short term for reducing field weed infestations, but are likely to take many years to affect recruitment to the weed seedbank, including recruitment of weed species with a high risk for resistance. The natural longevity of the seedbank suggests that alternative or additional weed management tactics are required to reduce the store of weed seeds in the active seedbank.
Concentration-response assays were conducted from 2008 through 2015 to measure the susceptibility of field populations of Lygus lineolaris (Palisot de Beauvois) from the Delta regions of Arkansas, Louisiana, and Mississippi to acephate, imidacloprid, thiamethoxam, permethrin, and sulfoxaflor. A total of 229 field populations were examined for susceptibility to acephate, 145 for susceptibility to imidacloprid, and 208 for susceptibility to thiamethoxam. Permethrin assays were conducted in 2014 and 2015 to measure levels of pyrethroid resistance in 44 field populations, and sulfoxaflor assays were conducted against 24 field populations in 2015. Resistance to acephate and permethrin is as high or higher than that previously reported, although some populations, especially those exposed to permethrin, appear to be susceptible. Variable assay responses were measured for populations exposed to imidacloprid and thiamethoxam. Average response metrics suggest that populations are generally susceptible to the neonicotinoids, but a few populations from cotton fields experiencing control problems exhibited elevated LC50s. Efforts to associate variability in LC50s with recorded use of insecticides and estimated cotton insect losses and control costs suggest that intensive use of insecticides over several decades may have elevated general detoxifying enzymes in L. lineolaris and some field populations may be exhibiting resistance to multiple classes of insecticide. These results suggest that efforts should be made to manage these pests more efficiently with a reduced use of insecticides and alternative controls.
Although dicamba-resistant crops can provide an effective weed management option, risk of dicamba off-site movement to sensitive crops is a concern. Previous research with indeterminate soybean identified 14 injury criteria associated with dicamba applied at V3/V4 or R1/R2 at 0.6 to 280 g ae ha−1. Injury criteria rated on a 0 to 5 scale (none to severe), along with percent visible injury and plant height reduction, and canopy height collected 7 and 15 d after treatment (DAT) were analyzed using multiple regression with a forward-selection procedure to develop yield prediction models. Variables included in the 15 DAT models (in order of selection) for V3/V4 were lower stem base lesions/cracking, plant height reduction, terminal leaf epinasty, leaf petiole droop, leaf petiole base swelling, and stem epinasty, whereas for R1/R2 variables were lower stem base lesions/cracking, terminal leaf chlorosis, leaf petiole base swelling, stem epinasty, terminal leaf necrosis, and terminal leaf cupping. To validate the models, experiments including the same dicamba rates and application timings used in previous research were conducted at two locations. For the variables specific to each model, data collected for the dicamba rates were used to predict yield. For the V3/V4 15 DAT model, predicted yield reduction (compared with the nontreated control for dicamba at 0.6 to 4.4 g ha−1) underestimated or overestimated observed yield reduction by an average of 1 and 3 percentage points. For 8.8 g ha−1, predicted yield reduction overestimated observed yield reduction by 8 points and for 17.5 g ha−1 by 20 points. For the R1/R2 15 DAT model, predicted yield reduction for 0.6 to 4.4 g ha−1 overestimated observed yield reduction by an average of 3 to 5 percentage points. For dicamba at 8.8 g ha−1, predicted yield reduction underestimated observed yield reduction by 8 points and for 17.5 g ha−1 overestimated by 6 points.
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