The annual global economic loss caused by weeds has been estimated at more than $100 billion U.S. dollars (Appleby et al. 2000). Additionally, worldwide annual herbicide sales are in the range of U.S. $25 billion (Agrow 2003). In light of these large dollar figures, it becomes clear that a greater understanding of crop—weed interactions is essential in order to develop cost-effective and sustainable weed management practices.

Herbicide resistance is an exceptional marker to quantify gene flow. Quantification of pollen-, seed-, and vegetative propagule-mediated gene flow provides key weed biology information. Pollen-mediated gene flow influences the genetic variance within a population, the frequency of multiple or polygenic herbicide resistance, and the evolutionary dynamics of a species. Seed-mediated gene flow predominates in self-pollinating species. Gene flow quantification may enable the estimation of herbicide resistance epicenter, the comparison of the relative importance of gene flow pathways, and prediction of future distribution of resistance traits. Gene flow studies using herbicide resistance also can provide insight into the rates and importance of hybridization.

Herbicides inhibit biochemical and physiological processes or both with lethal consequences. The target sites of these small molecules are usually enzymes involved in primary metabolic pathways or proteins carrying out essential physiological functions. Herbicides tend to be highly specific for their respective target sites and have served as tools to study these physiological and biochemical processes in plants (Dayan et al. 2010b).

Understanding abundance and distribution of weed species within the landscape of an agroecosystem is an important goal for weed science. Abundance is a measure of the number or frequency of individuals in an area. Distribution is a measure of the geographical range of a weed species. The study of weed population's abundance and distribution is helpful in determining how a population changes over time in response to selective pressures applied by our agronomic practices. Accurate estimates, however, of these two key variables are very important if we are to manage agricultural land both for productivity and for biodiversity.

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).

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.

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.

Herbicide degradation in soil is a major research issue, as evidenced by the number of refereed articles on the subject (Figure 1). The approximate number of total citations per year has increased from about 70 in the mid-1990s to approximately 170 per year in the last few years (Figure 1a). From a weed science perspective, publications in the journals Weed Science and Weed Technology have tended to decline from an average of eight per year in the 1995 to 2005 interval to about three per year in the last 10 yr (Figure 1b). This discrepancy of total citations vs. Weed Science Society of America journals may be due to funding availability and other more immediate research needs in weed science. Other reasons might be a lack of reader interest (indicating low potential impact in this specific topic area) or the perception that Weed Science and Weed Technology are light venues for such papers and therefore not the first choice for publication. The authors believe this research topic to be important and relevant to the discipline of weed science, even more so as herbicide use patterns become more complicated because of glyphosate-resistant (GR) weeds.

2,4-D, discovered independently in the United States and Europe in the mid-1940s, was one of the first synthetic herbicides to be used selectively for weed control (Cobb and Reade 2010). Since then, several herbicides belonging to different chemical classes and possessing diverse mechanisms of action have been synthesized and marketed globally. Herbicides have vastly contributed to increasing world food, fiber, fuel, and feed production in an efficient, economic, and environmentally sustainable manner. Before receiving regulatory approval, all herbicides (pesticides) undergo rigorous testing for their toxicological, residual, physicochemical, and biological properties. Additionally, herbicides are suitably formulated to reach their target site and maximize their efficacy on target weeds while being safe on crops.

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?

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.

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).

Since the beginning of agriculture, crops have been exposed to recurrent invasion by weeds that can impose severe reductions in crop quality and yield. There have been continuing efforts to reduce the impacts of weeds on production. More than 40 yr ago, overreliance on herbicide technology to reduce weed infestations resulted in the selection of adaptive traits that enabled weed survival and reproduction under herbicide treatments (Délye et al. 2007; Powles and Yu 2010; Vila-Aiub et al. 2008). As a result, herbicide resistance in > 200 weed species has evolved worldwide (Heap 2013; Powles 2008).

Germination and emergence assays represent the most notable examples of time-to-event data in agriculture and related disciplines. In spite of the peculiar characteristics of this type of data, there has been little effort to establish a specific and comprehensive framework for their analyses. Indeed, a brief survey of the literature shows that germination and emergence data, along with other phenological measurements such as flowering time, have been analyzed through myriad approaches, giving rise to confusion and uncertainty among scientists and practitioners as to what may represent the best statistical practice. This lack of coherence in statistical approach may reduce the efficiency of research, while making the communication of results and the cross-study comparisons extremely challenging. Here, we attempt to provide a coherent framework and protocol for the analyses of germination/emergence and other time-to-event data in weed science and related disciplines, together with a software implementation in the form of a new R package. We propose a similar approach to biological assays in ecotoxicology, based on: (1) fitting a time-to-event model to describe the whole time course of events; (2) comparing time-to-event curves across experimental treatments, and (3) deriving further information from the fitted model to better focus on some traits of interest. The most appropriate methods to accomplish this procedure were carefully selected from the framework of survival analysis and related sources and were modified to comply with the specific needs of weed, seed, and plant sciences. Finally, they were implemented in the new R package drcte. In this article, we describe the procedure and its limitations by way of providing examples of several types of germination/emergence assays. We highlight that our proposed procedure can also serve as the first step of data analyses, with its output subsequently submitted to traditional or meta-analytic approaches.