Herbicides are powerful chemical agents that exert strong biological activity on plants. The release of new formulations of dicamba and 2,4-D and their use in transgenic agronomic crops will probably result in many more applications during the time of year when sensitive nontarget vegetation will be present. The use of herbicides is regulated by the U.S. Environmental Protection Agency, and there are usually no negative effects on nontarget species. One negative aspect of herbicide use occurs when the application moves away from the target area and causes unwanted plant injury on susceptible species. Interest in herbicide drift is increasing, as evidenced by the number of refereed articles that investigate the mitigation or potential for herbicide drift (Figure 1). Although the topic of herbicide drift is broad, in this manuscript I will focus on an overview of off-site movement from a historical perspective and then discuss specific research protocols to examine vapor drift.

Much of agriculture-related research today involves weed resistance to herbicides. Resistance evolution is perhaps the strongest driver for the quest for new herbicide targets, novel weed intervention technologies, and the promotion of best management practices for sustainable crop production (Burgos et al., 2006; Norsworthy et al. 2012; Vencill et al. 2012). To date, 222 weedy species collectively have evolved resistance to 150 herbicides representing 21 sites of action (Heap 2014). For decades, scientists have developed numerous protocols for resistance confirmation using seeds, different plant parts, or whole plants. These have been reviewed by Beckie et al. (2000) and Burgos et al. (2013). We draw from these and other sources to present general guidelines for resistance confirmation that students and new researchers can use in planning their experiments. The most immediate questions that stakeholders seek to answer with resistance bioassays include:

1. 1. Is the population resistant?

2. 2. What is the level of resistance?

3. 3. What alternative herbicides can be used?

The widespread adoption of glyphosate-resistant corn and soybean in cropping rotations often results in volunteer plants from the previous season becoming problem weeds that require alternative herbicides for control. Corn yield losses due to season-long volunteer soybean competition at several densities in two growing seasons were used to define a hyperbolic yield loss function. The maximum corn yield loss observed at high volunteer soybean densities was about 56%, whereas, the incremental yield loss (I) at low densities was 3.2%. Corn yield loss at low volunteer soybean densities was similar to losses reported for low densities of velvetleaf and redroot pigweed, with 10% yield loss estimated to occur at 3 to 4 volunteer soybean plants m−2. Several herbicides, including dicamba with or without diflufenzopyr applied at the V2 growth stage of volunteer soybean, provided > 90% control, demonstrating several economical options to control volunteer glyphosate-resistant soybean in glyphosate-resistant corn. Reevaluation of control recommendations may be needed with commercialization of other genetically modified herbicide-resistant soybean varieties.

Herbicide-resistant weeds are the result of evolutionary processes that make it easy to think about the problem from a purely biological perspective. Yet, the act of weed management, guided by human production of food and fiber, drives this biological process. Thus, the problem is socioeconomic as well as biological. The purpose of this article is to explain how well-known socioeconomic phenomena create barriers to herbicide-resistance management and highlight important considerations for knocking down these barriers. The key message is that the multidimensional problem requires a multifaceted approach that recognizes differences among farmers; engages the regulatory, academic, extension, seed and chemical suppliers, and farmer communities; and aligns the diverse interests of the members of these communities with a common goal that benefits all—more sustainable weed management. It also requires an adaptive approach that transitions from moreuniform and costly standards and incentives, which can be effective in the near-term but are unsustainable, to more-targeted and less-costly approaches that are sustainable in the long term.

Studies were conducted to evaluate 14C-aminocyclopyrachlor absorption and translocation in alligatorweed, waterhyacinth, and waterlettuce. Alligatorweed plants were treated at the seven-node stage, waterhyacinth was treated at the five-leaf stage, and waterlettuce was treated at the eight-leaf stage. All plants were pretreated with nonlabeled aminocyclopyrachlor at 0.14 kg ai ha−1 with 1% (v/v) methylated seed oil (MSO). 14C-aminocyclopyrachlor was then applied to a protected leaf, and plants were harvested at 1, 2, 4, 12, 24, and 96 h after treatment (HAT). Radioactivity was determined in the treated leaf, shoots above treated leaf, shoots below treated leaf, roots, and growing solution. Absorption was rapid in all species and reached a maximum of 73, 72, and 73% of applied radioactivity for alligatorweed, waterhyacinth, and waterlettuce, respectively. In alligatorweed at 96 HAT, 43% of absorbed carbon-14 (14C) was translocated to shoots above the treated leaf and 17% was translocated to lower shoot tissue. In waterhyacinth at 96 HAT, 56% of absorbed 14C remained in the treated leaf, whereas 14 and 13% were found in parts above and below the treated leaf, respectively. In waterlettuce at 96 HAT, 50 and 33% of absorbed radioactivity was located above the treated leaf and in the growing solution, respectively. The low recovery of aminocyclopyrachlor in alligatorweed roots and growing solution might explain regrowth potential after herbicide treatment. These results also indicate that the lack of waterlettuce control with aminocyclopyrachlor is not due to reduced absorption or translocation.

Coevolving interactions between a plant population and its microbiota can potentially yield a rhizosphere enriched in metagenomes containing the blueprints for a vast array of natural products. We describe a method of isolating those metabolites through activity-based screening of soil metagenomic libraries. The method allows for the isolation of small molecules produced in vector–host expression systems containing large-insert DNA fragments extracted from the target plant rhizospheres. Allelopathic activities derived from selected clones were screened against a series of controls. Nonmetric multidimensional scaling (NMS) showed similar effects of the set of controls on lettuce growth, whereas annual bluegrass had a broader range of growth responses. Methanol extracts from clones indicating activity showed distinct patterns in grass seedling growth from the empty vector control, but the same extracts showed no effect on lettuce. The results indicate that the metagenomics method and bioassay screen of clone extracts are tools that can be used for initial determination of allelopathic activity from noncultured soil microbiota.

This article is written to provide some directions on how to determine if a plant species might influence its neighboring plants by allelopathy. There is insufficient space for detailed instructions for individual experiments, so I will provide references for different general approaches and opinions on common pitfalls of those who have worked in this research area.

As cases of resistance to herbicides escalate worldwide, there is increasing demand from growers to test for weed resistance and learn how to manage it. Scientists have developed resistance-testing protocols for numerous herbicides and weed species. Growers need immediate answers and scientists are faced with the daunting task of testing an increasingly large number of samples across a variety of species and herbicides. Quick tests have been, and continue to be, developed to address this need, although classical tests are still the norm. Newer methods involve molecular techniques. Whereas the classical whole-plant assay tests for resistance regardless of the mechanism, many quick tests are limited by specificity to an herbicide, mode of action, or mechanism of resistance. Advancing knowledge in weed biology and genomics allows for refinements in sampling and testing protocols. Thus, approaches in resistance testing continue to diversify, which can confound the less experienced. We aim to help weed science practitioners resolve questions pertaining to the testing of herbicide resistance, starting with field surveys and sampling methods, herbicide screening methods, data analysis, and, finally, interpretation. More specifically, this article discusses approaches for sampling plants for resistance confirmation assays, provides brief overviews on the biological and statistical basis for designing and analyzing dose–response tests, and discusses alternative procedures for rapid resistance confirmation, including molecular-based assays. Resistance confirmation procedures often need to be slightly modified to suit a specific situation; thus, the general requirements as well as pros and cons of quick assays and DNA-based assays are contrasted. Ultimately, weed resistance testing research, as well as resistance management decisions arising from research, needs to be practical, feasible, and grounded in science-based methods.

There are various reasons for using statistics, but perhaps the most important is that the biological sciences are empirical sciences. There is always an element of variability that can only be dealt with by applying statistics. Essentially, statistics is a way to summarize the variability of data so that we can confidently say whether there is a difference among treatments or among regression parameters and tell others about the variability of the results. To that end, we must use the most appropriate statistics to get a “correct” picture of the experimental variability, and the best way of doing that is to report the size of the parameters or the means and their associated standard errors or confidence intervals. Simply declaring that the yields were 1 or 2 ton ha−1 does not mean anything without associated standard errors for those yields. Another driving force is that no journal will accept publications without the data having been subjected to some kind of statistical analysis.

Bermudagrass and goosegrass are problematic weeds with limited herbicides available for POST control in creeping bentgrass. Metamifop effectively controls these weeds with greater selectivity in cool-season grasses than other ACCase inhibitors. The objectives of this research were to determine the physiological basis for metamifop selectivity in turfgrasses. In greenhouse experiments, metamifop rate required to reduce shoot biomass 50% from the nontreated (GR50) at 4 wk after treatment was > 6,400, 2,166, and 53 g ai ha−1 for creeping bentgrass, Kentucky bluegrass, and goosegrass, respectively. The GR50 for bermudagrass treated with diclofop-methyl or metamifop was 2,850 and 60 g ha−1, respectively. In laboratory experiments, peak absorption of 14C-metamifop was reached at 48, 72, and 96 h after treatment (HAT) for goosegrass, creeping bentgrass and Kentucky bluegrass, respectively. Grasses translocated < 10% of the absorbed radioactivity out of the treated leaf at 96 HAT, but creeping bentgrass translocated three times more radioactivity than goosegrass and Kentucky bluegrass. Creeping bentgrass, Kentucky bluegrass, and goosegrass metabolized 16, 14, and 25% of 14C-metamifop after 96 h, respectively. Goosegrass had around two times greater levels of a metabolite at retention factor 0.45 than creeping bentgrass and Kentucky bluegrass. The concentration of metamifop required to inhibit isolated ACCase enzymes 50% from the nontreated (I50) measured > 100, > 100, and 38 μM for creeping bentgrass, Kentucky bluegrass, and goosegrass, respectively. In other experiments, foliar absorption of 14C-metamifop in bermudagrass was similar to 14C-diclofop-methyl. Bermudagrass metabolized 23 and 60% of the absorbed 14C-diclofop-methyl to diclofop acid and a polar conjugate after 96 h, respectively, but only 14% of 14C-metamifop was metabolized. Isolated ACCase was equally susceptible to inhibition by diclofop acid and metamifop (I50 = 0.7 μM), suggesting degradation rate is associated with bermudagrass tolerance levels to these herbicides. Overall, the physiological basis for metamifop selectivity in turfgrass is differential levels of target site inhibition.

Resistance to herbicides occurs in weeds as the result of evolutionary adaptation (Jasieniuk et al. 1996). Basically, two types of mechanisms are involved in resistance (Beckie and Tardif 2012; Délye 2013). Target-site resistance (TSR) is caused by changes in the tridimensional structure of the herbicide target protein that decrease herbicide binding, or by increased activity (e.g., due to increased expression or increased intrinsic activity) of the target protein. Nontarget-site resistance (NTSR) is endowed by any mechanism not belonging to TSR, e.g., reduction in herbicide uptake or translocation in the plant, or enhanced herbicide detoxification (reviewed in Délye 2013; Yuan et al. 2007).

The effect of weed interference on corn yield and the critical period for weed control (CPWC) were determined in Germany and Benin. Treatments with weed control starting at different crop growth stages and continuously kept weed-free until harvest represented the “weed-infested interval.” Treatments that were kept weed-free from sowing until different crop growth stages represented the “weed-free interval.” Michaelis–Menten, Gompertz, logistic and log–logistic models were employed to model the weed interference on yield. Cross-validation revealed that the log–logistic model fitted the weed-infested interval data equally well as the logistic and slightly better than the Gompertz model fitted the weed-free interval. For Benin, economic calculations considered yield revenue and cost increase due to mechanical weeding operations. Weeding once at the ten-leaf stage of corn resulted already profitable in three out of four cases. One additional weeding operation may optimize and assure profit. Economic calculations for Germany determined a CPWC starting earlier than the four-leaf stage, challenging the decade-long propagated CPWC for corn. Differences between Germany and Benin are probably due to the higher yields and high costs in Germany. This study provides a straightforward method to implement economic data in the determination of the CPWC for chemical and nonchemical weed control strategies.

Metsulfuron is used for POST control of spotted spurge in many warm-season turfgrasses. A suspected resistant (R) biotype of spotted spurge was collected from turfgrass in Georgia with a history of exclusive metsulfuron use. Research was conducted to evaluate the resistance level of this biotype to metsulfuron, efficacy of other mechanisms of action for control, and the molecular basis for resistance. Compared with a susceptible (S) biotype, the R biotype required >90 and >135 times greater metsulfuron rates to reach 50% injury and reduce biomass 50% from the nontreated, respectively. The R biotype was also resistant to trifloxysulfuron but was injured equivalent to the S biotype from dicamba, glyphosate, and triclopyr. Gene sequencing of the R biotype revealed a Trp574 to Leu substitution that has conferred resistance to acetolactate synthase (ALS) inhibitors in previous research. This is the first report of ALS resistance in spotted spurge. More importantly, this is the first report of a herbicide-resistant broadleaf weed from a turfgrass system in the United States.

There is an ever-larger need for designing an integrated weed management (IWM) program largely because of the increase in glyphosate-resistant weeds, not only in the United States but also worldwide. An IWM program involves a combination of various methods (cultural, mechanical, biological, genetic, and chemical) for effective and economical weed control (Swanton and Weise 1991). One of the first steps in designing an IWM program is to identify the critical period for weed control (CPWC), defined as a period in the crop growth cycle during which weeds must be controlled to prevent crop yield losses (Zimdahl 1988).

The evolution of herbicide-resistant weeds is a major concern in the corn- and soybean-producing Pampas region of Argentina, where growers predominantly plant glyphosate-resistant crop varieties and depend heavily on glyphosate for weed control. Currently, 16 weed species in Argentina are resistant to one or more of three different herbicide mechanisms of action, and resistant weed populations continue to increase, posing a serious threat to agricultural production. Implementation of integrated weed management to address herbicide resistance faces significant barriers in Argentina, especially current land ownership and rental patterns in the Pampas. More than 60% of Pampas cropland is rented to tenants for periods that rarely exceed 1 yr, resulting in crop rotation being largely abandoned, and crop export taxes and quotas have further discouraged wheat and corn production in favor of continuous soybean production. In this paper we discuss ways to facilitate new approaches to weed management in Argentina, including legal and economic reforms and the formation of a national committee of stakeholders from public and private agricultural sectors.

Although the problem of herbicide resistance is not new, the widespread evolution of glyphosate resistance in weed species such as Palmer amaranth (Amaranthus palmeri S. Wats.), common waterhemp (Amaranthus rudis Sauer), and kochia [Kochia scoparia (L.) Schrad.] raised awareness throughout the agricultural community of herbicide resistance as a problem. Glyphosate-resistant weeds resulted in the loss of a simple, single herbicide option to control a wide spectrum of weeds that gave efficacious and economical weed management in corn (Zea mays L.), soybean [Glycine max (L.) Merr.], and cotton (Gossypium hirsutum L.) crops engineered for tolerance to this herbicide and planted over widespread areas of the South and Midwest of the United States. Beyond these crops, glyphosate is used for vegetation management in other cropping systems and in noncrop areas across the United States, and resistance to this herbicide threatens its continued utility in all of these situations. This, combined with the development of multiple herbicide-resistant weeds and the lack of commercialization of herbicides with new mechanisms of action over the past years (Duke 2012), caused the weed science community to realize that stewardship of existing herbicide resources, extending their useful life as long as possible, is imperative. Further, while additional herbicide tolerance traits are being incorporated into crops, weed management in these crops will still be based upon using existing, old, herbicide chemistries.

Herbicide resistance has become a major topic of international research in weed science, with a steady stream of presentations and papers on various aspects of the biology of herbicide resistant weeds. Diverse sources—including WSSA—have generated an equally steady stream of recommendations for preventing and managing herbicide resistance. The situation in the field, however, continues to deteriorate, with new cases of herbicide resistant weeds being reported at an increasing rate. We know what to do, but why aren't we doing it?

Johnsongrass is one of the most troublesome weeds of the world and is listed as a noxious weed in Arkansas. Reduced johnsongrass control with the recommended application rate of glyphosate (840 g ae ha−1) was reported in a continuous soybean field near West Memphis, AR, in the fall of 2007. A greenhouse study was conducted (1) to confirm and characterize glyphosate resistance in the johnsongrass biotype from West Memphis and (2) to determine whether resistant and susceptible biotypes have differential glyphosate absorption or translocation. Dose–response studies revealed that the resistant biotype was five- to seven-fold less sensitive to glyphosate than the susceptible biotype. Glyphosate absorption was similar in resistant and susceptible biotypes at 72 h after treatment (HAT). However, the treated leaf of the resistant biotype retained 28 percentage points more absorbed 14C glyphosate compared to the susceptible biotype at 72 HAT. Additionally, the resistant biotype had less 14C glyphosate translocated to the aboveground tissue below the treated leaf and to roots compared to the susceptible biotype at 24 and 72 HAT. Reduced translocation and increased retention of glyphosate in treated leaves is a probable mechanism of resistance in this glyphosate-resistant johnsongrass biotype.