We conducted a previous systematic and meta‐analysis review that showed differences in results from studies that evaluated the effectiveness of cover crops for weed suppression in cropping systems; these differences were largely due to management approaches used in growing the cover crop and main crop. The current meta‐analysis provides a quantitative review on how cover crop and main crop management practices influence the impact of cover crops on weed suppression. The meta‐analysis used observations from 53 studies published from 1990 to 2018. Cover crop biomass was inversely related to the amount of weed biomass (r2 = 0.67) and weed density (r2 = 0.64). In general, the meta‐analysis shows that cover crops provided a range of weed suppression depending on management decisions such as choice of cover crop species, cover crop sowing season (fall or spring), sowing dates within seasons, seeding rate, termination date, delay in main crop planting date after cover crop termination, tillage system under which the cover crop was produced, and integrating the cover crop with other weed control inputs. For example, grass cover crop species provided greater weed suppression than broadleaf species. Fall‐sown cover crops provided greater weed suppression (weighted mean of response ratio [R*] = 0.19) than spring‐sown cover crops (R* = 0.48) by the summer. Weed suppression increased by increasing seeding rate of cover crops from 1× (R* = 0.50) to 2× (R* = 0.27) or 3× (R* = 0.10). In addition, cover crops provided greater weed suppression in reduced tillage systems (R* = 0.19) than no tillage (R* = 0.29). The differential weed suppression provided by these management approaches suggests that a cover crop management approach should be rightly selected for weed suppression benefits.

W eeds occurring during early crop growth need to be removed because these are known to be most competitive with crops (Knezevic et al., 2002; Norsworthy and Oliviera, 2004; Tursun et al., 2016; Osipitan et al., 2016). Uncontrolled weeds at this early growth stage could cause irreversible and substantial crop yield losses (Knezevic et al., 2002, Adigun et al., 2014). If weeds are controlled at this time, crops can get a head start, achieve canopy closure, and compete effectively with later emerging weeds (Rajcan and Swanton, 2001). Typical early season weed control options include pre-plant, pre-emergence, and early post emergence herbicide applications in no-till cropping systems or mechanical cultivation in tilled systems. Herbicides provide an easy and cost-effective way of controlling weeds in crops and result in increased crop vigor and yield. Conversely, they are also a potential threat to the environment (e.g., pesticides residues in surface and/or groundwater) and in some areas, the development of resistant weed biotypes has reduced the utility of herbicides. In tillage-based cropping systems, mechanical operations such as plowing, harrowing, disking, and cultivating are used. Tillage for weed control has been utilized for a long time (Abdin et al., 2000) as it reduces weed density. At the same time, weed seeds receive a brief exposure to sunlight, due to soil inversion after tillage that can trigger their germination. There are still concerns about the negative impact of tillage on soil health and topsoil erosion (Loaiza Puerta et al., 2018). Cover crops have been documented to improve soil quality and minimize environmental degradation while providing a level of weed suppression in crops

A fast-spreading weed, kochia (Kochia scoparia), has developed resistance to the widely-used herbicide, glyphosate. Understanding the relationship between the occurrence of glyphosate resistance caused by multiple EPSPS gene copies and kochia fitness may suggest a more effective way of controlling kochia. A study was conducted to assess fitness cost of glyphosate resistance compared to susceptibility in kochia populations at different life history stages, that is rate of seed germination, increase in plant height, days to flowering, biomass accumulation at maturity, and fecundity. Six kochia populations from Scott, Finney, Thomas, Phillips, Wallace, and Wichita counties in western Kansas were characterized for resistance to field-use rate of glyphosate and with an in vivo shikimate accumulation assay. Seed germination was determined in growth chambers at three constant temperatures (5, 10, and 15 C) while vegetative growth and fecundity responses were evaluated in a field study using a target-neighborhood competition design in 2014 and 2015. One target plant from each of the six kochia populations was surrounded by neighboring kochia densities equivalent to 10 (low), 35 (moderate), or 70 (high) kochia plants m−2. In 2015, neighboring corn densities equivalent to 10 and 35 plants m−2 were also evaluated. Treatments were arranged in a randomized complete block design with at least 7 replications. Three kochia populations were classified as glyphosate-resistant (GR) [Scott (SC-R), Finney (FN-R), and Thomas (TH-R)] and three populations were classified as glyphosate-susceptible (GS) [Phillips (PH-S), Wallace (WA-S) and Wichita (WI-S)]. Of the life history stages measured, fitness differences between the GR and GS kochia populations were consistently found in their germination characteristics. The GR kochia showed reduced seed longevity, slower germination rate, and less total germination than the GS kochia. In the field, increases in plant height, biomass accumulation, and fecundity were not clearly different between GR and GS kochia populations (irrespective of neighbor density). Hence, weed management plans should integrate practices that take advantage of the relatively poor germination characteristics of GR kochia. This study suggests that evaluating plant fitness at different life history stages can increase the potential of detecting fitness costs.

Core Ideas The impact of simulated dicamba drift on growth and yield of glyphosate‐resistant soybean was similar among dicamba formulations. The impact of dicamba drift on soybean could be influenced by moisture condition of the environmental field. Late vegetative or early flowering stage of soybean was the most sensitive growth stage to dicamba drift. New dicamba‐based herbicides such as Engenia (N,N‐bis‐(3‐aminopropyl) methylamine salt) and XtendiMax (diglycolamine salt) with VaporGrip technology were developed to reduce dicamba volatility and drift; however, there are claims that these products can still volatilize or drift. Field studies were conducted to evaluate glyphosate (N‐(phosphonomethyl)glycine))–resistant (GR) soybean response to micro‐rates (0, 1/1000, 1/500, 1/100, 1/50, and 1/10 of the label rate, 560 g a.e. ha−1) of the two new dicamba products compared with Clarity (diglycolamine salt) applied at three growth stages. The GR soybean [Glycine max (L.) Merr.] was equally impacted by the micro‐rates of all three products as measured by visual injury, height reduction, delayed physiological maturity, and yield reduction. The greatest visual injury (80%), plant height reduction (65%), maturity delay (22 d), and soybean yield loss (96%) was caused by 1/10 of the dicamba label rate when applied at V7/R1 soybean growth stage. In addition, estimation of effective dose for 5, 10, or 20% yield reduction suggested that V7/R1 was the most sensitive soybean growth stage to the three dicamba products. For example, 10% yield reduction occurred when 1.83 to 1.85 g a.e. ha−1 (∼1/300 of the label rate) of Engenia was applied at V2 or R2, whereas, a lower dose of 0.32 g a.e. ha−1 (1/1750 of the label rate) of Engenia caused the same level of yield reduction when applied at V7/R1. Similar doses were estimated for Clarity and XtendiMax; therefore dicamba drift should be avoided at all costs, because GR soybean was equally sensitive to low rates of all three tested products with different formulations or technologies.

Widespread and repeated use of glyphosate resulted in an increase in glyphosate-resistant (GR) weeds. This led to an urgent need for diversification of weed control programs and use of PRE herbicides with alternative sites of action. Field experiments were conducted over a 4-yr period (2015 to 2018) across three locations in Nebraska to evaluate the effects of PRE-applied herbicides on critical time for weed removal (CTWR) in GR soybean. The studies were laid out in a split-plot arrangement with herbicide regime as the main plot and weed removal timing as the subplot. The herbicide regimes used were either no PRE or premix of either sulfentrazone plus imazethapyr (350 + 70 g ai ha−1) or saflufenacil plus imazethapyr plus pyroxasulfone (26 + 70 + 120 g ai ha−1). The weed removal timings were at V1, V3, V6, R2, and R5 soybean stages, with weed-free and weedy season-long checks. Weeds were removed by application of glyphosate (1,400 g ae ha−1) or by hoeing. The results across all years and locations suggested that the use of PRE herbicides delayed CTWR in soybean. In particular, the CTWR without PRE herbicides was determined to be around the V1 to V2 (14 to 21 d after emergence [DAE]) growth stage, depending on the location and weed pressure. The use of PRE-applied herbicides delayed CTWR from about the V4 (28 DAE) stage up to the R5 (66 DAE) stage. These results suggest that the use of PRE herbicides in GR soybean could delay the need for POST application of glyphosate by 2 to 5 wk, thereby reducing the need for multiple applications of glyphosate during the growing season. Additionally, the use of PRE herbicides could provide additional modes of action needed to manage GR weeds in GR soybean.

Tolpyralate, an HPPD (4-hydroxyphenyl-pyruvate dioxygenase) inhibitor, is a relatively new herbicide for weed control in corn. Field studies were conducted in 2015 and 2016 to evaluate the effective dose of tolpyralate applied alone or mixed with atrazine for weed control in corn. The treatments included seven rates (0, 5, 20, 29, 40, 50 and 100 g ai ha-1) of tolpyralate applied alone or mixed with a constant rate (560 g ai ha-1) of atrazine. The evaluated weed species were common waterhemp (Amaranthus rudis Sauer), common lambsquarters (Chenopodium album L.), velvetleaf (Abutilon theophrasti Medik), henbit (Lamium amplexicaule L.) and green foxtail (Setaria viridis L.). Overall, POST-application of tolpyralate resulted in 58-94% visual weed control when applied alone; whereas, addition of atrazine provided 71-100% control of same species. Calculated dose of 19-31 g ai ha-1 (ED90) of tolpyralate applied alone provided 90% visual control of waterhemp, lambsquaters, henbit, and velvetleaf. Whereas, addition of atrazine resulted in significantly lower dose of 11-17 g ai ha-1 for the same level of control, suggesting synergy between the two herbicides.

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