New directions for integrated weed management: Modern technologies, tools and knowledge discovery

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New directions for integrated weed management: Modern technologies, tools and knowledge discovery Nicholas E. Korresa,*, Nilda R. Burgosa, Ilias Travlosb, Maurizio Vurroc, Thomas K. Gitsopoulosd, Vijaya K. Varanasie, Stephen O. Dukef, Per Kudskg, Chad Brabhama, Christopher E. Rouseh, Reiofeli Salas-Pereza a

Department of Crop, Soil and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States Faculty of Crop Science, Agricultural University of Athens, Athens, Greece c Institute of Sciences of Food Production, National Research Council, Bari, Italy d Institute of Plant Breeding and Genetic Resources, Hellenic Agricultural Organization-Demeter, Thermi, Thessaloniki, Greece e Bayer U.S. – Crop Science Division, Monsanto Legal Entity, Research & Development, Triangle Park, NC, United States f Natural Products Utilization Research Unit, USDA, ARS, University, Oxford, MS, United States g Department of Agroecology, Aarhus University, Slagelse, Denmark h FMC corporation, Newark, DE, United States *Corresponding author: e-mail addresses: [email protected]; [email protected] b

Contents 1. Introduction 2. Mechanical weed control 2.1 Automated weed control and robotics 2.2 Thermal weed control 2.3 Weed control by electrocution 2.4 Weed control by abrasive grit 3. Agronomic weed management 3.1 Row configuration 3.2 Allelopathy 3.3 Harvest weed seed control 3.4 Cover crops 4. Crop breeding and genetically modified crops 4.1 Herbicide-resistant crops 4.2 Crops resistant to parasitic weeds 5. Bio-based herbicide products 5.1 Corn gluten meal 5.2 Acetic acid 5.3 Fatty acids

Advances in Agronomy ISSN 0065-2113 https://doi.org/10.1016/bs.agron.2019.01.006

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2019 Elsevier Inc. All rights reserved.

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5.4 Essential oils 5.5 Microbial products 5.6 Brassicaceae seed meal 6. Novel technologies and tools for weed control 6.1 Nanotechnology 6.2 Image processing and remote sensing 6.3 Genomics advancing to the next generation 6.4 RNA interference (RNAi) technology 6.5 Plant genome editing 7. Knowledge discovery: Data warehouse and data mining 7.1 Data warehouse 7.2 Data mining 8. A synthesis References Further reading

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Abstract Weed science, as an integral part of agricultural production needs to evolve by moving away from its mono-disciplinary perspective at targeting weeds, sometimes a single species, through the overreliance on few single herbicide mechanisms of action. Herbicides remain a simple and cost-effective way to control weeds but they are rapidly losing their effectiveness due to evolution of herbicide resistance. Additionally, weed science has been left wanting for a strong theoretical foundation rooted in evolutionary and ecological disciplines therefore, there is a great need for a new weed management paradigm in modern agriculture based on ecological principles and nonconventional weed management approaches. The “many little hammers” concept and the “use of technological advancement” are two major integrated weed management components that are gaining momentum. Automated, robotic weed control is being rapidly developed, particularly for vegetable crops and organic agriculture. Cover crops and weed seed destruction techniques are becoming popular with growers. In the future, RNAi technology, gene editing and robotics will yield new tools for weed control. Agriculture is also moving into a new era of big data or “digital farming.” It will be interesting to see what new, unforeseen weed control solutions will be derived from this new farming approach that will allow more intelligent application and integration of weed management technologies. In an attempt to facilitate the suitability of these technologies into integrated weed management systems, this chapter reviews the strengths and weaknesses of these modern technologies and tools, and it highlights future research needs for each of these technologies.

1. Introduction The need for sustainable food production necessitates a rapprochement of agricultural practices which should holistically incorporate the

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interaction of environmental, economic, and societal dimensions of agroecosystems. Weed science, as an integral part of agricultural production needs to evolve by undertaking research on complex problems through collaborations with multiple scientific disciplines. This means that weed science needs to move away from its mono-disciplinary perspective at targeting weeds, sometimes a single species, through the overreliance on few single herbicide mechanisms of action (Lamichhane et al., 2016; Mortensen et al., 2012; Smith et al., 2006). This approach, despite its remarkable achievements in improving yield and maximizing farming efficiency, has consistently led to eventual reduced utility over time (Lewis et al., 1997; Mortensen et al., 2000). The incidence of herbicide resistance and the infestation of herbicide-resistant weeds are increasing (Heap, 2018). Acreage infested with glyphosate-resistant Palmer amaranth in the United States cotton and soybean crops, for example, continues to increase (Culpepper et al., 2010; Duke and Powles, 2009). Weed science has been left wanting for a strong theoretical foundation rooted in evolutionary and ecological disciplines (Neve et al., 2009). Therefore, there is a great need for a new weed management paradigm in modern agriculture (Bajwa, 2014) based on ecological principles and nonconventional weed management approaches. Information for this need can be harnessed already from the literature on weed demographics and population dynamics within crops and cropping systems (Korres and Norsworthy, 2017; Korres et al., 2019a) or between diverse environments (Korres et al., 2017a); weed eco-physiological aspects (Korres et al., 2017b); and weed-crop interactions (Bravo et al., 2017; Korres and Froud-Williams, 2002, 2004; Korres and Norsworthy, 2015a, b; Korres et al., 2019b) where weeds are considered as intrinsic elements of the agroecosystem in which crops and weeds coexist. These approaches may offer more durable weed management solutions to lessen problems of herbicide resistance, environmental pollution, weed diversification, weed invasion, and yield losses (Chauhan, 2013; Chauhan et al., 2010; Singh, 2007; Travlos, 2012, 2013). Even more, these approaches will facilitate the development of integrated weed management (IWM) strategies which could spearhead strengthening and broadening of the eco-physiological and evolutionary basis of weed science. The “many little hammers” concept (Liebman and Gallandt, 1997) and the “use of technological advancement” (Young et al., 2017) are two major IWM components that are gaining momentum (Harker and O’Donovan, 2013; Menalled, 2018). The former refers to a combination of interconnected control tactics with a cumulative impact on weed abundance and weed-competitive ability; the latter refers

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Fig. 1 Classification of new technologies and approaches suitable for integrated weed control management.

to the use of crop improvement, remote sensing, decision support systems, or other modern technologies (Harker and O’Donovan, 2013; Menalled, 2018). As the number of herbicide-resistant weed ecotypes increases, and the discovery of new herbicide modes of action (MOAs) declines (Strek, 2014), the need to utilize all available weed management options is crucial. This chapter presents a review of modern technologies and approaches, or new uses of old technologies and tools (Fig. 1) suitable for IWM.

2. Mechanical weed control Opportunities for automated weed control vary widely among cropping systems and locations. High-value conventional and organic horticultural crops, for example, are cropping systems best suited for automated weed control. This is due to high dependency on labor for hand weeding and the lack of approved herbicides for these crops. Fresh market vegetable crops are planted year-round in small areas, which make them an attractive target for automated weed control (Fennimore et al., 2013). Thermal weed control techniques and precision weed management may also be proven important alternatives for mechanical weed control (Heiniger, 1998; Travlos, 2013).

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2.1 Automated weed control and robotics Automated cultivators were developed as intra-row cultivators in transplanted vegetable crops such as cabbage (Brassica oleracea) and celery (Apium graveolens) (Garford, 2012; O’Dogherty et al., 2007; Tillett et al., 2008). These cultivators use a variable-speed rotating, semicircle-shaped disc blade. A camera and computer-controlled guidance system adjust the rotational speed of the disc in real time so that the opening of the disc blade passes around the transplanted crop. The rotating cultivator results in 30–54% lesser weed densities than the standard cultivator. The latter requires 16–31% more time to thin and weed a lettuce crop than the rotating cultivator (Tillett et al., 2008). Future robotic weed control systems will be able to collect data about the presence of weeds, facilitate storage and analysis of those data, support decision making about when and where to control weeds, execute weed control by deploying the robot, and then gather data about the efficacy of the treatment and thus allow evaluation of decisions. A wide range of inter- and intra-row weeders are already available including such as inter-row hoes, basket weeders, brush hoes, powered vertical axis tines, finger weeders, spring tine harrows, torsion weeders, mini-ridgers, rotating wire weeders, and pneumatic weeders (Bond et al., 2003; Bowman, 1997; Merfield, 2016). 2.1.1 Robotic weed management Robotic weed management is a four-step process involving guidance, identification, precision weed removal, and mapping of weed species (Young et al., 2014). The feasibility of a robotic weed control system depends upon accurate machine vision analyses, robotic efficiency and suitability, variable-rate-application technology, decision support system, and strength of weed-sensing tools (Bajwa, 2014; Slaughter et al., 2008). Guidance in row crops is accomplished with real-time kinematic global position system (GPS) or machine vision. The conceptualization diagram of the automatic weed removal system is shown in Fig. 2. Slaughter et al. (1999) used a real-time color-segmentation technology as guidance system in direct-seeded lettuce, cotton (Gossypium hirsutum), and tomato (Solanum lycopersicum) at different growth stages. Another guidance system utilizing near infrared (NIR) stereovision has also been developed for cereals (Kise et al., 2005). The NIR stereovision system provided satisfactory machine guidance for detecting weeds in cereals after calibrating the devise using a weed-free area.

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Fig. 2 Conceptual diagram of a robotic weed removal system.

The precision of machine vision directly depends upon climate conditions, farming practices, regional topographic differences, and cropping systems (Astrand and Baerveldt, 2002; Slaughter et al., 2008). The bases for detection are classified as morphological features, spectral features, and visual textures. Morphological features of weedy plants are potential factors for detection by machine vision, particularly for distinction from desired vegetation (Brown and Noble, 2005; Søgaard, 2005). Using active shape models enabled accurate identification of shepherd’s-purse (Capsella bursa-pastoris), scentless mayweed (Tripleurospermum perforatum), and wild mustard (Sinapis arvensis) (Søgaard, 2005). Recently, a convolutional neural network approach was applied to identify weed species and estimate weed growth stage (Teimouri et al., 2018). Light reflectance is another successful indicator for weed detection by machine vision (Scotford and Miller, 2005). Precise robotic weed removal on the basis of weed detection and computer guidance is the next step. There might be the use of mechanical (Astrand and Baerveldt, 2002), chemical (Lee et al., 1999), thermal, or electrical (Blasco et al., 2002) approaches to remove weeds through robots. Automated weed control through robotics is considered a viable option for best integrated weed management in the future, which eventually will involve robots in sensor and plant recognition technology (Young et al., 2014). 2.1.2 Constraints associated with automated weed control Weather alters soil physical properties and can significantly affect the efficacy of many weeders such as spring tine harrows (Brandsaeter et al., 2012). Only few of these weeders, e.g., brush weeders and mini-ridgers, can perform well regardless of soil moisture or weather conditions (Merfield, 2014). The morphology of crop plants and their associated harvesting requirements introduce numerous constraints, for example avoiding soil contamination

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on harvested leafy crops such as lettuce (Lactuca sativa). Non-discriminatory weeders (Merfield, 2014) require the crop to be tolerant or resistant to the weeding method. Robotic arms and grippers or precision high-voltage electrical probes (Diprose and Benson, 1984) are some examples of discriminatory weeders. For machine (and/or human) visual guidance, the crop needs to be visible, especially when weed species are in the cotyledon stage (Merfield, 2014). In addition, the control of big weeds through automated weed control is ineffective. The achievements of agricultural roboticists to date are rather profound (Merfield, 2016). However, the high complexity and demands of mechanical weeding add many challenges to robotic weeding improvements.

2.2 Thermal weed control Thermal weed control methods can be divided, based on mode of action, into two groups, namely (a) direct heating by hot water, steaming, flaming, infrared weeders, or hot air and (b) indirect heating by electrocution, microwaves, laser radiation, or UV-light. Cryogenic techniques comprise a third thermal method for weed control (Rask and Kristoffersen, 2007). 2.2.1 Weed control by hot water and hot foam Hot water treatment for weed control has been studied in many countries with positive results (Rask and Kristoffersen, 2007). Although this is not a new technology, its adoption for IWM has been minimal. In the 1990s a commercial tool was developed in the United States to apply hot water for weed control (Berling, 1992). Hot water application is effective on most annual weeds and a large number of perennial weed species. Similar devices were successfully used in New Zealand, where hot water was applied on weeds for prolonged periods (Rask and Kristoffersen, 2007). Hot water devices for weed control were also used in Denmark and the Netherlands. Hot water treatment is safe and has no side effects unlike flame weeding or radiation methods. The effectiveness of hot water treatment is greater on dense weed population because of its high ability to penetrate the plant canopy (Hansson and Ascard, 2002). Hot water is primarily applied on non-agricultural areas as it is non-selective weed control method. Because of its greater success rate, this technique is being considered in precision weed management strategies in Europe. The use of hot foam, as an alternative to hot water, is more energy efficient due to slowly foam disintegration and therefore the greater amount of heat transmittance to targeted weed plants (P. Kudsk, personal observation).

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2.2.2 Weed control by steam The use of steam, instead of hot water, has been reported as a quicker, more effective, and sustainable method of weed control, especially in cases where weeds are growing on relatively hard surfaces (Rask and Kristoffersen, 2007). Engineering research is needed to improve the efficiency and availability of equipment for various crop production systems. In particular, soil steaming kills weed seeds and soil-borne pests and pathogens (Dumas et al., 1998; Melander and Jorgensen, 2005; Peruzzi et al., 2011). Steaming has been “rediscovered” lately as an alternative to chemical fumigants such as methyl bromide, which has been banned except for special cases (European Regulation 2037/2000). Compared to the stale seedbed technique (i.e., weed seeds are allowed to germinate and then killed prior to crop planting with minimal soil disturbance), soil steaming reduces the weed seedbank more by killing weed seeds buried up to 20 cm deep (Barberi et al., 2009). Soil steaming is usually more expensive than other non-chemical preventive methods, but cheaper than chemical soil fumigation (Peruzzi et al., 2002). Steaming can also be performed as a “banded treatment” where only the intra-row area is treated before planting, hence reducing the energy used (Hansson and Svensson, 2007; Melander and Jorgensen, 2005). Soil steaming has a rapid effect, while other practices like solarization requires long periods and specific conditions to be effective. Unlike chemical fumigation, which requires a 15–30-day interval from application before planting the crop, steaming has no residual effect; hence, crops can be sown or transplanted as soon as the soil has cooled to ambient temperature (Luvisi et al., 2006). Another important application, in both agricultural and urban areas, is the use of a direct steam jet to kill emerged weeds. Several devices have been developed recently for this purpose (Kristoffersen et al., 2008; Merfield et al., 2009; Peruzzi et al., 2011). The depth of steaming would depend upon the weed flora. Steaming to a moderate soil depth of 50–60 mm seems sufficient, considering that most seeds in the soil seedbank are from weed species with small seeds that germinate from the upper 0–20 mm soil layer. A series of experiments have been conducted in the laboratory to study key biological factors that are important for the development of band-steaming technology (Melander and Kristiansen, 2011). Results showed that seedling emergence was reduced by 90% when the maximum soil temperature reached 60°C. A further rise in temperature to 70°C reduced seedling emergence 99%. Soil type, soil moisture content, and soil structure influenced the lethality of soil steaming when the maximum soil temperatures were below 70°C. Steaming

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is more effective in a sandy soil than in a loamy soil. The efficacy of soil steaming also increases with soil moisture content as soil water conducts heat, resulting in better kill of weed seeds (Melander and Kristiansen, 2011). 2.2.3 Weed control by flaming Flame weeding is performed by means of intense heat produced by a fuel-burning device, either hand-held or tractor-mounted. Flaming can be a viable option for in crop weed control. Brief exposure to intense heat causes intracellular water to expand and denatures cell membranes, leading to cellular leakage, dehydration, and cell death (Ascard, 1995; Rifai et al., 2002). In general, an exposure time of up to 130 ms is sufficient to kill leaf tissue. In related research, exposure to flame temperature of 800–1000°C for about 1 s resulted in satisfactory weed control (Ascard, 1995, 1997). Flaming is now one of the most widely used methods of thermal weed control (Cisneros and Zandstra, 2008), particularly in organic agriculture (Kang, 2001). Other practices such as stale seedbed technique also include preemergence flaming for weed control. Flame weeding has several advantages compared to other methods of weed control. It is significantly cheaper than hand weeding (Fennimore et al., 2010; Ulloa et al., 2010). The risk of injury to crop roots is low, especially in the case of heat-tolerant agronomic crops (e.g., maize, cotton, sugarcane), or when the flame is directed to the base of the crop to control intra-row weeds (Cisneros and Zandstra, 2008; Hatcher and Melander, 2003). Flaming is compatible with no-tillage systems, ideal for fields with erosion problems, and has a potential fungicide and insecticide action without any carryover effects to the next crop (Cisneros and Zandstra, 2008; Hatcher and Melander, 2003). Compared to other methods of thermal weed control, such as steaming, flaming is cheaper and more effective (Melander et al., 2005; Rifai et al., 2002; Sartorato et al., 2006). However, there is risk for crop injury, injury to the user, or ineffectiveness of the method on some species and on large weeds. Safety is a serious concern with flame weeding, especially with tractor-mounted units and with high volume of crop residues in the field. In general, plants with an upright habit and thin leaves are more susceptible to flaming than prostrate plants with protected growing points, such as grasses (Ascard, 1995). The timing of flaming is crucial, as it is most effective when plants are 1–5 cm tall or in the 3–5 leaf stage. Weeds that emerge with the crop cannot be controlled without injuring the crop (Heiniger, 1998). Additionally, some weeds have the ability to sprout from their root after flaming. Broadleaf weeds are generally more susceptible to flaming than

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grasses (Mojzis, 2002). It should be noted that preemergence flaming controls only the proportion of weed population that already emerged at planting. Therefore, preemergence flaming has to be followed by other methods or additional flaming treatments to prevent yield loss from competition of later-emerging weeds (Cisneros and Zandstra, 2008; Hatcher and Melander, 2003). 2.2.4 Cryogenic weed control Cryogenic weed control is one of the thermal treatments in which weed seeds or seedlings are exposed to a cryogenic material. Cryogens are materials that produce low temperatures ( Jitsuyama and Ichikawa, 2011). Cryogenic materials, such as carbon dioxide snow (CO2:
78°C) or liquid nitrogen (LN2:
196°C), applied to foliage flash-freeze tissues, which then are macerated by mechanical compaction pressure (Cutulle et al., 2013). Cryogenic substances that induce freezing-point depression (i.e., magnesium chloride-6-hydrate, sodium chloride, ammonium nitrate, or calcium chloride-2-hydrate), if added to snow or ice-covered ground, can be absorbed by weed seeds and cause reductions in weed germination. This affects only freezing-sensitive species. Therefore, using freezing treatment alone could not possibly achieve total weed control (Li et al., 2008). The efficacy of cryogenic materials for weed control has been variable although cryogenic weed control methods are potentially useful tools in agroecosystems. The use of cryogens consumes more energy than other thermal weed control methods. Thus, reduction in energy use, application technology, and application parameters could all significantly increase the sustainability of cryogenic weed control systems (Cutulle et al., 2013; Li et al., 2008).

2.3 Weed control by electrocution The practice of weed control via electric shock is called electrocution. Although it is not adequately studied, data show that weeds can be killed by spark discharge or electrical contact with 20 kV (Diprose and Benson, 1984; Parish, 1990). Eberius (2017) stated that the use of high-electric power and electrophysical engineering to minimize chemical herbicides and eliminate undesirable plants effectively has started gained pace (Fig. 3). The strength of electric shock, contact or exposure duration, weed species, morphological features and growth stage significantly affect the success of electrocution. The severity of damage is aggravated in drought conditions (Diprose and Benson, 1984). However, because of higher energy

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Fig. 3 Diagrammatic representation of electric flow resulting in electrocution of weed flora where electrophysical weeding through high-energy electrons, reaching from the leaves through to the roots into the soil (A). Apparatus of the electroherb™ technology for electrical weed control in the field. Courtesy of Zasso GmbH (B) https://zasso.eu/en/agri culture-en/.

requirement, high financial costs involved, and hazards to operators, this technology is not adopted in agriculture. In the future, this method may have utility, especially in organic vegetable and orchard farming systems.

2.4 Weed control by abrasive grit Additional tools, especially for organic cropping systems, are needed for efficient integrated weed control. Norremark et al. (2006) conceived the idea of using air-propelled organic grits to abrade tissue of small weeds. Forcella (2009, 2012) demonstrated the efficacy of grits derived from crop residues such as corncobs or walnut shells in controlling small weed seedlings in greenhouse and field experiments. This author reported that two on-row applications of air-propelled corncob grit combined with inter-row cultivation reduced weed density in corn and increased yield. Season-long, in row weed control exceeded 90% when two or three abrasion applications were

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accompanied with between-row cultivation. The timing of grit application is critical as the highest levels of weed control were achieved during one- and five-leaf stages or one-, three-, and five-leaf stages of corn development. Corn yields were similar to those of hand-weeded controls in which no abrasive grit was applied. In addition, Wortman (2015) reported a weed biomass reduction 69–97% in tomato and pepper (Capsicum annuum) cropping systems compared with the weedy control by the application of air-propelled granulated walnut shells, maize cobs, greensand fertilizer (i.e., pelletized poultry manure), and soybean meal. Yield was increased by 44% and 33% for tomato and pepper, respectively. Another factor that improves the functionality and economic feasibility of this method is the contribution of the abrasive grit to crop fertilization which has been estimated to be equivalent to 35–105 kg N ha
1 (Wortman, 2015). Direct N2O-N emissions savings from this amount of substituted N are equal to 0.6–2 N2O-N kg ha
1 (based on IPCC, 2006; Korres et al., 2010; West and Marland, 2002). This is equivalent to greenhouse gas emission reduction of approximately 150–600 kg CO2equivalent (CO2e) ha
1 (based on Korres et al., 2010). Reuse of crop residues and agricultural wastes for the control of weeds is the epitome of crop production systems development with improved resource use efficiencies and benign effects on the environment.

3. Agronomic weed management Non-chemical weed management approaches have gained renewed interest recently due to public awareness of sustainable agricultural production ( Jordan, 1996). Non-chemical weed management, in the context of this chapter focuses on preventive or cultural weed control methods that enhance crop competitiveness as well as preventing inputs into the soil seedbank. Cultural methods such as the use of competitive cultivars (Korres and Froud-Williams, 2002; Korres and Froud-Williams, 2004), optimum seeding density (Korres and Norsworthy, 2015a), and optimum fertilizer management (Korres, 2018) are universally recognized and are excluded from the discussion here.

3.1 Row configuration Row configuration from single-, to twin-, or paired-row pattern is yet unproven as a yield-enhancing practice for most agronomic crops, but literature indicates that crop row intensification is effective for weed control.

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Corn yield increases from a twin-row production system has been inconsistent, although research in Mississippi, USA showed consistent yield benefits from twin-row corn when using hybrid lines with fixed or determinate ear type (Benedict, 2008). Corn yields in twin rows spaced 19–25 cm apart were equal (Sorensen et al., 2006), lower (Nelson, 2007), or higher (Karlen and Camp, 1985) compared to single-row corn (Fig. 4). Twin-row soybean has yielded more than single-row soybean in several trials (Graterol et al., 1996; Grichar, 2007; Koger, 2007; Mascagni et al., 2008) through increased number of pods plant
1 (Bell, 2005). Soybean yield response in twin-row planting depends on varietal maturity group, soil type, and weather conditions. Soil moisture and air temperature, in particular, affect soybean yield regardless of row spacing configurations (Benedict, 2008; Bruns, 2011). Peanut (Arachis hypogaea) yields in the southern United States are higher in twin-row planting ( Jordan et al., 2001; Lanier et al., 2004). Twin-row planting of soybean or peanut allows earlier canopy closure, reducing successive weed emergence, and reducing the need for follow-up cultivation or herbicide application (which reduces crop stress), thereby benefitting crop yield. Cotton plants, on the other hand, have great capacity to compensate for available space by increasing branching, which increases boll load. Thus, cotton yields are usually similar across a wide range of plant and row spacing (Benedict, 2008). As such, cotton yields in twin rows spaced 25 cm were not significantly different compared to single rows spaced 102 cm apart (Reddy et al., 2009).

Fig. 4 Two-row arrangement designs for sowing density equal to 60,000 maize plants ha
1. Configurations of intra- and inter-row sowing distances depend on the crop and sowing rate.

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As mentioned earlier, twin-row configuration can improve weed control (Brecke and Stephenson, 2006) through early canopy closure ( Jost and Cothren, 2000; Reddy et al., 2009; Stephenson and Brecke, 2010) with consequent increases in crop competitive ability and reduction of light transmittance to the soil surface, which reduces weed germination and growth (Fischer and Miles, 1973; Reddy and Boykin, 2010). In addition, narrow-row system maintains the advantages of raised beds (e.g., furrow irrigation; typical in-season crop inputs such as side-dress fertilizer and post-emergence herbicide application) without the risk of crop damage. Recent advances in planter technology, with precision planting of twin rows 18–25 cm apart, is a factor for the success of these systems (Zargar et al., 2017).

3.2 Allelopathy Allelopathy is the direct or indirect effects of chemicals produced by plants or microorganisms on the growth, development, and distribution of other plants and microorganisms in natural and agricultural ecosystems (Einhellig, 1995; Molisch, 1937; Rice, 1984). Use of allelopathic interactions to favor the crop and reduce weed infestation has been used for centuries without understanding the chemical basis of the phenomenon. As herbicide options dwindle in the era of herbicide-resistant weeds, there is renewed interest in utilization of allelopathy for weed management. Allelochemicals are chemically diverse, e.g., terpenes, alkaloids and nonproteinaceous amino acids, phenols, and sugars/glycosides (Lin et al., 2007; Rice, 1984). The integration of allelopathic crops as components of various cultural operations such as crop rotations, intercropping, or cover crops for weed control has been widely studied (Bajwa, 2014; Farooq et al., 2014; Mahmood et al., 2013; Silva et al., 2014; Wezel et al., 2014; Wortman et al., 2013). Plants with allelopathic potential are considered sustainable alternatives for weed control and are means to minimize reliance on herbicides (Appiah et al., 2015), hence reducing the selection pressure for herbicide resistance. The utilization of allelopathic plants can also lead to the discovery of new herbicidal compounds with new MOAs (Dayan et al., 2009; Duke, 2010). Species with strong allelopathic potential and effective weed suppression ability include velvetbean (Mucuna pruriens) (cvs Hassjo or Florida), Polish wheat (Triticum polonicum), proso millet (Panicum miliaceum), fava bean (Vicia faba), woolly pod vetch (Vicia villosa var. dasycarpa), hairy vetch

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(Vicia villosa), common flax (Linum usitatissimum), white sweetclover (Melilotus albus), garden vetch (Vicia sativa), jack bean (Canavalia ensiformis), cereal rye (Secale cereale), barley (Hordeum vulgare), sprouting broccoli (Brassica oleracea), common oat (Avena sativa), and cowpea (Vigna unguiculata) (Fujii, 2003; Fujii and Appiah, 2018; Korres and Norsworthy, 2015a, b; Sangeetha and Baskar, 2015; Singh et al., 2001). Unfortunately, some of these species are not viable crops (e.g., jack bean). Efforts to develop crop cultivars with allelopathic potential are continuing, albeit sporadically. In organic rice production for example, an allelopathic cultivar “Rondo” is grown to reduce weed pressure (Gealy and Yan, 2012). In an attempt to develop better allelopathic rice options, Gealy et al. (2013) used conventional breeding to develop new cultivars with improved weed-suppressive ability and better yield. Allelopathy is quantitatively inherited; the rice cultivar that Gealy et al. (2013) produced had almost equal weed-suppressive ability as the allelopathic parent. More research needs to be done in this area. Several authors have reported the important role of allelopathy in weed suppression in small grain cereals (Belz, 2007; Bertholdsson, 2011; Wu et al., 2007). Cereal cultivars, like any other allelopathic species, differ in allelopathic potential (Burgos et al., 1999). Bertholdsson (2010) developed a spring wheat cultivar with improved allelopathic potential relative to the less weed-suppressive parent by conventional breeding methods. The development of allelopathic crop cultivars is a major goal for integrated weed management.

3.3 Harvest weed seed control Harvest weed seed control (HWSC) methods include both cultural and mechanical practices that decrease seed inputs to the soil seedbank (Storrie, 2014). Combine harvesters have the potential to disperse weed seeds the farthest of any dispersal vectors within the field (Cousens and Mortimer, 1995), resulting in soil seedbank increases (Walsh and Powles, 2014). HWSC, which is a preventive weed control method, includes mainly four approaches, i.e., the use of a chaff cart for the collection of crop residues along with weed seeds that it contains (Norsworthy et al., 2016); narrowwindrow burning (Walsh and Newman, 2007); the Harrington seed destructor (HSD) (Walsh et al., 2013); and the bale-direct systems (Walsh and Powles, 2007). The chaff cart method consists of a chaff collection and transfer device, which is attached to the harvester and delivers the weed seed into a bulk

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collection bin. This allows for the chaff and the weed seed to be removed from the field (Shirtliffe and Entz, 2005). The narrow-windrow burning system is a simple and very effective HWSC method. This inexpensive system uses a chute mounted on the rear of the combine that delivers the bulk of the chaff into a narrow windrow. Burning these windrows as soon as possible after harvesting secures higher weed seed destruction (Fig. 5). The concentration of the chaff increases the temperature and duration of burning, which leaves less loss of residue versus traditional burning. In soybean, narrow-windrow burning reduced Amaranthus palmeri population by 73% and the soil seedbank by 62% over 3 years (Norsworthy et al., 2016). Nevertheless, wind speed, type of crop, air temperature, and soil moisture can affect the efficacy of narrow-windrow burning (N. Korres, personal observation). The amount of chaff could be another factor that can alter the efficacy of this system, especially in high yielding, i.e., 10–12 t ha
1 winter wheat crops in Northwestern European fields (P. Kudsk, personal observation). The HSD is highly effective in Australia wheat cropping systems (Walsh and Powles, 2007) resulting 93–99% seed destruction of various weed species including rigid ryegrass, wild radish, bromegrass, and wild oat (Storrie, 2014). In addition, the HSD technology particularly when additional weed control measures are simultaneously applied resulted in >97% weed seed destruction on the most common weeds in soybean (Schwartz-Lazaro et al., 2017). The bale-direct system method consists of a large baler that is directly attached to the combine and bales the chaff as it is exiting the harvester. This system captures the weed seed and as the baled reside can be used as feed for livestock.

Fig. 5 Narrow windrow burning in maize (A) and soybean (B).

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The limited market for the baled product and the risk of spreading weed seeds across long distances during the transportation of bales are major constraints of this approach. Nevertheless, it might be expected that continuous use of HWSC will select for early maturing phenotypes. Overreliance on this tool may lead to the same result as overreliance on herbicides—evolution of traits that avoid HWSC. Many of the important weed species in Europe shed seeds several weeks before harvesting (P. Kudsk, personal observation). Understanding more about the mechanisms of weed seed retention at crop harvest (Korres et al., 2018) is important in to facilitate the incorporation of HWSC tactics into integrated weed management systems.

3.4 Cover crops The inclusion of cover crops in the crop rotation, at a time when the land might otherwise lie uncropped, is an effective method for suppressing weeds and for improving soil chemical and physical properties (Korres, 2005, 2018; Korres and Norsworthy, 2015b; Norsworthy et al., 2016; Price and Norsworthy, 2013). Crops such as hairy vetch (Vicia villosa) or cereal rye (Secale cereale) can provide uniform and dense ground cover whereas crops like crownvetch (Coronilla varia) can provide long-term soil management. Other crops that could be used as cover crops are crimson clover (Trifolium incarnatum), red clover (T. pratense), white clover (T. repens), peas (Pisum spp.), bird’s-foot trefoil (Lotus corniculatus), common oat (Avena sativa), ryegrass (Lolium spp.), fescues (Festuca spp.), meadowgrass (Poa spp.), smooth brome (Bromus inermis), Timothygrass (Phleum pratense), and orchardgrass (Dactylis glomerata) (Korres, 2005). Among winter crops, winter cereals such as cereal rye offer efficient weed control due to high biomass production, hence excellent ground cover (Brown et al., 1985; Schomberg et al., 2006). Prevention of weed emergence through physical suppression and reduced light transmission due to high biomass production (Akemo et al., 2000; Saini et al., 2006; Teasdale and Mohler, 1993) (Fig. 6) or production of allelochemicals (Barnes et al., 1987; Chase et al., 1991) are the main reasons cover crops are recommended for weed control. According to Owen et al. (2015), adoption of the cover crops in the United States has been relatively of low interest. Nevertheless, partly due to the increased prevalence of glyphosate-resistant (GR) Amaranthus palmeri throughout the southern United States, the use of cover crops has been increasingly considered as a potential option for improved weed control (Riar et al., 2013). In addition, legume cover crops such as sweet clover

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Fig. 6 Weed suppression (A) without weed control management compared with (B) early termination date and (C) late termination date of cereal rye used as cover crop in combination with various herbicide programs.

(Melilotus officinalis) or red clover (Trifolium pratense) improve soil nutrient status through addition of organic nitrogen via fixed atmospheric nitrogen (Shaner and Beckie, 2014). The average N fertilization in maize and cotton is 75–250 and 120–170 kg of N fertilizer ha
1, respectively (Ali, 2015; Mueller and Vyn, 2016). Nitrogen fixation by legumes varies between 10 and 250 kg N ha
1 year
1 (Brockman and Wilkins, 2003; MollerHansen et al., 2002; Woodmansee, 1978). The incorporation of a legume as a cover crop for weed control into the cropping system can therefore reduce the application of a chemical nitrogen fertilizer on average by approximately 75 kg N ha
1, if the establishment of the legume cover crop ranges between 60% and 80% (based on Korres et al., 2010). This means, that 400 kg CO2e ha
1 (based on Korres et al., 2010) can be reduced which otherwise would be released into the atmosphere if chemical fertilizer was used in the absence of the legume cover crop.

4. Crop breeding and genetically modified crops 4.1 Herbicide-resistant crops Application of biotechnology in weed management, in the form of herbicideresistant (HR) crops, is arguably the most widely adopted modern tool for weed management in conjunction with herbicides. HR crops are generated with transgene technology (e.g., for glyphosate, glufsoinate, 2,4-D, dicamba resistance) or mutation breeding (e.g., for midazolinone resistance) (Duke, 2014). The rapid and widespread adoption of glyphosate-resistant (GR) crops was unprecedented, reaching 73%, 80%, and 93% of corn, cotton, and

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soybean planted in the United States, respectively, in 2012 (FernandezCornejo and Caswell, 2006; USDA-NASS, 2000–2014). The GR crop technology is unparalleled in its simplicity, efficacy, and affordability. In the years following its commercialization in the mid-1990s, farming of major agronomic crops inadvertently became a one-herbicide enterprise. The technology has become the antithesis of integrated weed management. This has accelerated the selection, and contributed to the increasing number, of GR weed species. In <10 years, GR Amaranthus spp. evolved and quickly became widespread (Heap, 2018). The superior qualities of GR technology were also its downfall. Herbicide-resistant crop technology is an ideal tool for integrating into weed management practices, but the simplicity of the technology and economics caused farmers to use it with little integration with other tools (Lamichhane et al., 2017). To mitigate the increasing herbicide resistance problem, trait stacking has become the norm of the latest cultivars of HR crops (Green et al., 2008; Green and Owen, 2011). In addition to the existing resistance traits to glyphosate, glufosinate, acetolactate synthase (ALS) inhibitors, and acetyl coenzyme-A carboxylase (ACCase) inhibitors; resistance to dicamba, 2,4-D, and 4-hyroxyphenylperoxidase (HPPD) inhibitors are being added to the mixture of HR crop traits. Stacks of three HR traits would be common with either glyphosate, or glufosinate, or both as the non-selective component(s). This would allow diversification of herbicide MOAs used in a cropping season, prevent weed escapes, and give producers more opportunities to integrate HR crops with other weed management tools to manage resistance. The following sections discuss the genetical background of various herbicide technologies. 4.1.1 Resistance to synthetic auxinic herbicides The evolution of resistance to synthetic auxin analogs has been slow, and several companies have developed HR crops resistant to herbicides within this MOA group for broadleaf weed control. Resistance to 2,4-D in soybean, cotton, and corn is being developed. Similar to the approach used in discovering resistance traits to bromoxynil and glufosinate, genes for resistance to dicamba and 2,4-D has been sourced from strains of soil bacteria that metabolize these compounds (Wright et al., 2010). The 2,4-D resistance trait that was eventually used for HR crop (cotton and soybean) development was the aryloxyalkanoate di-oxygenase 12 (AAD-12) gene from Delftia acidovorans, discovered and expressed in plants using biotechnology tools (Wright et al., 2010). The protein AAD-12 also degrades pyridyloxyacetate auxinic herbicides such as triclopyr and fluroxypyr but

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it only provides commercial level resistance to 2,4-D (Griffin et al., 2013; Wright et al., 2010). The AAD-12 enzyme, which catalyzes cleavage of the oxygen bond in side chains of most auxinic herbicides, also cleaves a similar link in aryloxyphenoxypropionates, endowing multiple resistance to ACCase- and auxinic herbicides in grass crops. In conjunction with the 2,4-D resistance trait, a new 2,4-D-choline salt formulation has been developed to help reduce the volatility and drift potential of 2,4-D amine or ester formulations currently in the market. Recently the dicamba (3,6-dichloro-2-methoxybenzoic acid) resistance trait in soybean and cotton. Resistance was achieved by introducing a dmo (dicamba mono-oxygenase) gene into the crops from the soil bacterium Stenotrophomonas maltophilia. The DMO enzyme converts dicamba to the non-phytotoxic products 3,6-dichlorosalicylic acid (DCSA) and formaldehyde (Dumitru et al., 2009). In dicamba resistance crops, only dicamba formulations with reduced drift and volatility potential are labeled for over the top use. There are two dicamba formulations that contain the diglycolamine salt (DGA) of dicamba while another dicamba produce is formulated with a N,N-bis-(3-aminopropyl) methylamine salt or BAPMA salt. All of these dicamba formulations are considered less volatile and low-drift risk compared to the dicamba DGA alone or the dimethylamine salt (DMA) formulation. 4.1.2 Resistance to HPPD-inhibitors The HPPD (4-hydroxyphenylpyruvate dioxygenase) inhibitors are the newest SOA discovered almost 30 years ago with a broad weed control spectrum. HPPD herbicide-resistant soybeans are currently in the pipeline. One of the cultivars will be resistant to two HPPD herbicides, mesotrione and isoxaflutole, as well as to, glufosinate. Another soybean product will be resistance of isoxaflutole and glyphosate. The resistance to only isoxaflutole was generated using a gene from Pseudomonas fluorescens for a HPPD with a single amino acid change (APHIS, 2009; Matringe et al., 2005). Resistance to both mesotrione and isoxaflutole was generated using a gene from oat (Avena sativa) for a HPPD containing a single amino acid change (APHIS, 2012; Hawkes et al., 2010). 4.1.3 Resistance to ALS-inhibitors The acetolactate synthase (ALS)-resistance gene from shattercane (Sorghum bicolor) was introgressed into grain sorghum using traditional breeding approaches (Tuinstra and Al-Khatib, 2008). This ALS-resistance sorghum technology provides control of grass weeds, which otherwise would be

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problematic in this crop. The presence of two mutations (Val560Ile and Trp574Leu) in the ALS gene makes this sorghum resistant to a wide variety of ALS-inhibitors. Nicosulfuron was approved in 2016 for use with these ALS inhibitor-resistant sorghum hybrids. This technology is expected to be launched in 2019–20. In addition to this technology, soybeans containing both ALS-inhibitor and glyphosate-tolerance traits have been launched recently. Imidazolinone-resistant soybeans produced with a mutant ahas gene (isolated from Arabidopsis thaliana) is being developed (Aragao et al., 2000; Carlson et al., 2012; Kiihl and Arias, 2008). 4.1.4 Resistance to PPO-inhibitors Protoporphyrinogen oxidase (PPO) inhibiting herbicides are widely used in preplant burndown, PRE, and POST applications mainly for broadleaf weed control in different cropping systems. In addition to commercial available herbicides, a number of chemically diverse compounds have been found to inhibit the PPO enzyme with broad-spectrum weed control but little crop selectivity. Trifludimozain, a triazinone heterocycle compound, is one of these compounds (Parra et al., 2017). A modified version of a plant PPO enzyme (PPX2) in corn and soybean has resulted in increased resistance to the new PPO inhibiting herbicide chemistries (Aponte et al., 2017; Li and Nicholl, 2005).

4.2 Crops resistant to parasitic weeds Parasitic weeds are most difficult to control because of their physical, biochemical, physiological, and genetic connection with their hosts (Aly, 2017). The most notorious parasitic plants include numerous species of Orobanche spp. (broomrape), Striga spp. (witchweed), and Cuscuta spp. (dodder). Conventional weed control strategies, whether cultural or chemical, are mostly ineffective on these weeds because their seeds germinate only upon exposure to certain compounds (collectively known as strigolactones) exuded from roots of host plants such as strigol from various species including cotton (Gossypium hirsutum) or sorgolactone from sorghum (Yoneyama et al., 2010). The development of crops resistant to parasitic weeds is limited by the lack of information on appropriate genes that could be used for crop transformation, or markers for breeding host resistance (Rispail et al., 2007). One output of this effort is transgenic maize that exudes a minimal amount of germination stimulant by inhibiting part of the terpenoid biosynthesis pathway (Matusova et al., 2005). Advances in genomics, transcriptomics, proteomics, and metabolomics, coupled with advanced software for

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bioinformatics, will allow comprehensive understanding of the molecular basis of host-parasite interaction. Host-parasite signaling genes and genes shared by host and parasitic weeds have been identified (Kim and Westwood, 2015; Mower et al., 2004; Yoshida et al., 2010; Zhang et al., 2014a, b). This information could provide novel approaches for developing parasitic weed-resistant crops. On the chemical aspect, new analogs of strigolactones have been tested as germination stimulants for O. ramosa, with some positive results (Zwanenburg et al., 2014). The ability to stimulate parasitic weed germination in the absence of a host plant, also known as “suicidal germination,” allows for depleting the soil seedbank. This is an old concept ( Johnson et al., 1976), but progress in this area has been limited by the difficulty in finding compounds that are effective and stable across a wide range of soil environments. Germination stimulants also are effective only when applied to wet soil (also called preconditioning), which limits its utility. It has to be leached to the subsoil to stimulate germination of a large proportion of seeds in the plow layer prior to planting the crop; otherwise, efficacy is minimal. The crop cannot be planted until the weed seedlings die (for lack of host), which takes at least 2 weeks. This is akin to preplant herbicide application.

5. Bio-based herbicide products Herbicides continue to be a major tool in weed management, despite the availability of novel and usually environmentally friendly non-chemical tools. The regulation of both existing and new herbicides has required more extensive toxicology and environmental testing (e.g., Regulation, 2009), significantly increasing the cost of bringing a new herbicide to market and making the cost of keeping some existing herbicides on the market untenable. As a result, a number of non-benign compounds previously registered and commercialized have been withdrawn from the market, and the introduction of new herbicides with novel MOAs may have been hampered. Indeed, no herbicide with a new MOA has been introduced in a commercial product in >25 years (Duke, 2012). From a practical point of view, lowering the number of available herbicides has negatively impacted weed management. In many situations, farmers have had fewer MOA choices for resistance management, resulting in faster evolution of resistant weeds. Thus, the discovery of new herbicides

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with new MOAs is particularly urgent. After years of limited attention, the search for natural compounds with herbicidal activities, has received renewed attention (Dayan et al., 2012; Gerwick and Sparks, 2014). This is mainly due to the failures with GR technology and to burgeoning herbicide resistance but the improved technology for discovery of new herbicides from natural sources has made this approach to herbicide discovery more attractive. Improved extraction and purification procedures have facilitated the recovery and purification of potentially herbicidal metabolites. Highthroughput bioassays have made screening procedures faster and more accurate. More sophisticated and automated equipment have simplified analytical procedures and structure determination. The exploration of natural sources for novel bioactive compounds has been successfully exploited for drug discovery. Some of the most successful drugs and agrochemicals on the market have been developed from secondary metabolites (SMs) (Gerwick and Sparks, 2014; Newman and Cragg, 2012). Conversely, the application of an “ecological rationale” to discover novel compounds is based on the evolutionary concept that each SM provides some survival advantage. Examples of this approach include compounds produced by plants that serve as chemical defenses against herbivores or those produced by marine invertebrates that deter attacks by predators (Harper et al., 2001). Fungi commonly thrive in competitive environments, and thus some of their secondary metabolic capabilities may be influenced by the selection pressure exerted by other organisms. In the case of phytopathogenic fungi, SM can be important determinants in the virulence of the pathogen, in symptom appearance, or conversely in overcoming the resistance capability of a potential host plant. In case of plants, some of the SMs act against other plants to increase their competitiveness (allelopathy). Thus, when searching for natural compounds having herbicidal effects, the “ecological” rationale would be to look for fungal pathogens able to cause symptoms such as necrosis and chlorosis and try to identify the fungal SMs whose macroscopic effects resemble those due to the pathogen’s attack. Application of this rationale at the beginning of a screening process would limit the number of organisms needing to be investigated, reducing the costs of the screening procedures. The chemical ecological approach would be particularly well suited to academia or public research center laboratories that usually cannot afford high-throughput screening approaches of the pesticide industry.

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The structural diversity and evolved biological activity of natural compounds offer opportunities for (a) direct application as natural herbicides; (b) use as templates for chemical synthesis of novel herbicides; (c) exploitation as starting material for subsequent chemical or microbiological modification; or (d) discovery of new MOA. The use of metabolites of biotic origin also has its disadvantages and limitations, which hampers discovery. Some examples are the difficulties in scaling-up the production/fermentation process, complex chemical structures that are difficult to synthesize, low stability or persistence of bioactive compounds, and unacceptable off-target effects (e.g., mammalian toxicity). One of the main advantages of natural bioactive compounds is its rapid degradation in the environment. Although this contributes to the perception that natural compounds are more environmentally friendly, this feature may also be the “Achilles’ heel” of these compounds. Indeed, their chemical and physical properties may not be ideal for absorption and translocation in plants, so as to attain an “acceptable” dose. The rate of degradation of natural compounds may be too fast to be suitable for development as effective herbicides. While there is abundant information in the literature on the isolation and characterization of phytotoxins from many sources, and many of these compounds have been patented for potential use as herbicides, there are very few effective natural herbicides in the market. For organic farming management, where synthetic products are not allowed, non-synthetic botanical products are used for weed management. They are also used in small, private gardens, for which higher weed management costs are affordable. Generally, botanic based products have little or no selectivity and usually adequate efficacy only at very high doses compared those of synthetic herbicides. Their use therefore presents a series of limitations and problems. Examples of bioherbicides include among others.

5.1 Corn gluten meal This is a maize product used on turfgrass or high-yield crops. It does not act on the weeds already present but has only anti-germination action on seeds (Gough and Carlstrom, 1999). Moreover, it is a fairly expensive product because it must be used at very high doses (even 2 t ha
1).

5.2 Acetic acid Acetic acid has been used for centuries and now is marketed in dilute solutions (up to 20%) or in blends with other natural products. Acting by

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contact as a non-selective herbicide, acetic acid has no effects on underground plant organs, and is used in crop and non-crop environments, such as rail margins, golf courses, open spaces, or walkways.

5.3 Fatty acids The herbicidal activity of fatty acids has been known for many years. Some fatty acid salts are now marketed as non-selective herbicidal “soaps.” These are composed of aliphatic fatty acids of varying length mixed with vinegar or acetic acid and emulsifiers. They act as disinfectants in relatively short time and have no selectivity, but most of the weeds tend to recover because there is no residual activity. Pelargonic acid (the most used compound of this class), extracted for the first time from Pelargonium roseum leaves, is a broad-spectrum contact herbicide for the control of annual and, mossy weeds. It destroys cell membranes, causing a rapid loss of cellular functions. Other saturated fatty acids, C6 or C14 long, are significantly less active than medium-chain acids, or have any herbicidal activity. Pelargonic acid is considered a compound of low toxicity and low environmental impact, but has no residual activity (Dayan et al., 2009). It has only contact activity, and, thus, it is necessary to spray most of the plant for complete control. It is effective on all weeds, including moss. Being environmentally benign, it is suitable for high-traffic areas such as paths and recreational turfs, especially if these are also frequented by pets or children. The high cost of these products is a major deterrent for adoption.

5.4 Essential oils Essential oils of plants are sold as herbicides, although often it is necessary to use surfactants which are restricted or prohibited in organic farming to help their penetration into tissues or use improved formulations (Hazrati et al., 2017) (see Section 6.1 for further information). Essential oil products are complex mixtures of mostly terpenoids and other plant SMs. The composition of essential oils from even the same plant species can vary considerably, depending on the several factors such as growth conditions of the plant and the genetics of the variety of the plant species. Producing herbicidal essential oils with optimal constituents would be attractive, but little is known of the contributions and interactions of the different components of the essential oils that are sold as herbicides. All the essential oils marketed act as nonselective herbicides that can provide a good but transient weed control.

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Table 1 Essential oils for weed control. Essential oil Description

Reference

Pine oil

Composed of terpenic alcohols and saponified fatty Young (2004) acids

Clove oil

Obtained by the steam distillation of the cloves Tworkoski (Eugenia caryophyllus) and containing mainly eugenol (2002) together with several other terpenoids

Lemongrass Obtained from Cymbopogon citratus (or C. flexuosus), Clay et al. oil better known for its use as a mosquito repellent, also (2005) marketed as a herbicide Manuka oil Currently the most interesting product. It is isolated Christoph from the leaves of Leptospermum scoparium, a shrub et al. (1999) originated in New Zealand and Australia, consisting of sesquiterpenes (up to 70%) and rich in β-trichetones

Essential oils (Table 1) are approved for control of weeds in organic farming, but all they work very quickly and their effectiveness is limited by the fact that they volatilize in relatively short time.

5.5 Microbial products With regard to microbial products directly marketed as herbicides, the only real one is bialaphos (sometimes referred to as bilanophos), a tripeptide obtained by the fermentation of the actinomycete Streptomyces hygroscopicus marketed as a herbicide in East Asia. It is a pro-herbicide, meaning that it is inactive as is. It is metabolized by plants into its herbicidal form, phosphinothricin. Phosphinothrin is the L enantiomer of glufosinate, which is a chemically synthesized racemic mixture of two enantiomers. Only the L form (phosphinothricin) is herbicidally active. Like gluphosinate, bialaphos is a broad-spectrum, post-emergence herbicide that inhibits glutamine synthetase (via phosphinothricin). Glufosinate is a model for the development of a synthetic herbicide from a natural compound. Another particularly interesting product, but not yet marketed, is based on dead cells of a strain of Streptomyces acidiscabies bacteria used together with its exhausted culture broth containing the metabolites resulting from the fermentation of the bacterium. It has selective pre- and post-emergence control of numerous dicotyledonous weed species and can be used in numerous monocot crops such as rice. Herbicidal action is not due to the development

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of a disease (the bacterium is not pathogenic, and even if it were, it is dead), but rather to the metabolites that it produces during fermentation, in particular thaxtomin A, a potent cell wall synthesis inhibitor (Bailey, 2014). Sarmentine, 1-(1-pyrrolidinyl)-(2E,4E)-2,4-decadien-1-one, is the active components of the fruits of Piper sarmentosum and Piper nigrum having several biological properties (Dayan et al., 2015). Sarmentine is a contact herbicide with broad-spectrum activity. Its herbicidal activity was discovered through a bioactivity-guided isolation (Huang et al., 2010) and is similar to herbicidal soaps such as nonanoic acid (pelargonic acid) or decanoic acid, although much more active.

5.6 Brassicaceae seed meal Oilseed meals are by-products of the oil seed extraction process from Brassicaceae plants with a relatively high nutrient content (Paul and Solaiman, 2004; Snyder et al., 2009), a property that makes these products a value-added soil amendment (Hollister et al., 2014). Brassicaceae seed meals contain 50% C, 5.3–5.9% N, and 1.3% P by weight on average (Rice et al., 2007; Snyder et al., 2009). Common field application rates are between 1 Mt and 2 Mt ha
1 of seed meal and provide 53–59 kg N ha
1, which could substitute for the application of chemical N fertilizer, in addition to weed control. This amount of substituted N compensates for greenhouse gas emission reduction equal to approximately 200–220 kg CO2e ha
1 (based on Korres et al., 2010). In addition, oilseed meals such as those from Indian mustard (Brassica juncea), rapeseed (Brassica napus), and yellow mustard (Sinapis alba) contain glucosinolates (Hansson et al., 2008; Rice et al., 2007). Glucosinolates become herbicidal upon enzymatic hydrolysis by myrosinase (Hansson et al., 2008; Mithen, 2001; Rice et al., 2007), an enzyme which is physically separated from the glucosinolates until the plant tissues are crushed (Gimsing and Kirkegaard, 2009). The type, concentration, and functionality of glucosinolate hydrolysis products vary among species (Agerbirk and Olsen, 2015) (Fig. 7). Brassicaceae seed meal provides adequate control of various weed species including wild oat (Avena fatua), Italian ryegrass (Lolium multiflorum), prickly lettuce (Lactuca serriola), and redroot pigweed (Amaranthus retroflexus) (Handiseni et al., 2011; Hoagland et al., 2008). Glucosinolates exhibit differential herbicidal activity and weed species have differential response to these compounds (Hansson et al., 2008; Rice et al., 2007). Nevertheless, the herbicidal effects of Brassicaceae seed meal could be a new weed control option for vegetable growers.

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S R

β

D

S

Glucose R

Myrosinase

C N

H

C

+

D-Glucose

N

OSO3

OSO3–

Glucosinolate Unstable intermediate

O N N H

S

Oxazolidine-thione

R

S

C S

Isothiocyanate

R

R C N

C N Thiocyanate

Nitrile

Fig. 7 Glucosinolate reaction and major autolytic products by the hydrolysis of the glucosinolate molecule. R refers to the type of organic side chain.

6. Novel technologies and tools for weed control Novel smart delivery systems, nano-sensors, and nanomaterials (e.g., nanoparticles) are receiving increasing attention for possible applications in agriculture (ObservatoryNANO, 2016). These technologies and materials could improve the application and distribution of herbicides but they could also be specifically developed to overcome some of the limiting factors hampering the practical application of SMs.

6.1 Nanotechnology Nanotechnology has emerged as a promising tool for the development of new herbicide formulations with active ingredients containing particles in the range of 1–100 nm in size. Nanoformulations allow controlled release of the compounds, both in terms of time or place, or trigger it only under certain environmental conditions. Several nanocarriers based on different basic frames such as nanoparticles, nanocapsules, nanoclays, or liposomes could be used to attach or load inside the metabolite to protect it against degradation (Perez-de-Luque and Hermosin, 2013). Additionally, specific ligands could be added to the nanocarrier aiming for specific targets and/or allowing release of the active compound under certain conditions. Controlled release formulations could minimize, if not prevent, the leaching

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of chemicals as well as reduce volatilization and degradation losses. Several materials (such as polyacetic acid, PLA) have been tested for herbicide encapsulation and their controlled release in the soil. A PLA-polyethylene glycol (PEG) chain linked copolymer (PLA-PEG-CL) was developed recently for encapsulating the herbicide metazachlor (Kucharczyk et al., 2013). For natural herbicides acting at the rhizosphere, nanoformulations could improve soil applications. For instance, the metabolite could be applied encapsulated in nanoparticles providing a slow and constant release during the crop season (Burnet et al., 2015). The nanoencapsulation may overcome the short environmental half-life limitation of most natural bioactive compounds. This would allow good weed control with just a single application, thereby reducing rates, costs, and environmental risks. Additionally, nanoencapsulation could allow the joint application of several compounds, preventing interactions between them until they are released. For example, different compounds with different modes of action could be encapsulated separately and applied at the same time, thus facilitating combined effects against the weed and reducing the risk of selection of plants resistant to the active compounds. 6.1.1 Nano-adjuvants and nano-sensors Adjuvants are chemicals that enhance the activity of herbicides by several mechanisms, including facilitating movement of the herbicides across leaf cuticles. Recently, adjuvants based on nanotechnology have been introduced. However, their efficacy in increasing the activity of herbicides on hard-to-control and HR weeds needs to be tested by impartial public sector experts. In a recent study, adding a nano-adjuvant to glyphosate did not overcome the resistance of GR species such as Palmer amaranth, kochia, and waterhemp to glyphosate (Zollinger et al., 2015). Overall weed control with nano-adjuvants was comparable to commonly used non-ionic surfactants ammonium sulfate. Nanotechnology-based sensors are being used in precision agriculture for the accurate release of herbicide spray mixtures and precision control of herbicide applications. Nano-based biosensors could enable better and more efficient use of herbicides while maintaining environmental safety (Duhan et al., 2017). A new nanobiosensor based on atomic force microscopy was developed for detecting the herbicide metsulfuron-methyl (an acetolactate synthase inhibitor) in the soil (Da Silva et al., 2013).

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6.2 Image processing and remote sensing Remote sensing is a modern technology used in agriculture to ensure the precision management of inputs as well as to probe weed presence (Thorp and Tian, 2004). Remote sensing tools can be used to detect weed patches, and consequently to map weed densities in field crops and forest areas. Remote sensing is an option for herbicide reductions, hence cost of production through increased herbicide application efficiency (Christensen et al., 2009). The effectiveness of remote sensing depends on differential spectral reflectance of vegetation (i.e., weeds and crops) and the spectral resolution of the instrument used. The pixel quality of the image should be higher than the difference in reflective indices of vegetation. The higher this difference is and the denser the pixels, the higher the quality of the picture will be, which is crucial for subsequent mapping (Zhang et al., 1998; Zhang and Chaisattapagon, 1995). Plant residues, however, may have similar reflectance as existing vegetation, which makes it difficult to discriminate the living from nonliving vegetation. This problem is greater at the early growth stage of the crop but can be overcome by using images of fallow fields as background in image processing to remove most of the “clutter” (Lamb and Weedon, 1998). Some weed species have been successfully mapped through remote sensing in cereals and legumes with higher-resolution imagery (Bajwa et al., 2015). It was demonstrated that weeds can be discriminated from the background vegetation by remote sensors. The prime considerations about weed floristic composition, canopy architecture, and leaf dimensions have made it feasible to map them against different crop backgrounds (Zhang et al., 1998; Zhang and Chaisattapagon, 1995). Differences in growth stages of weeds and crops, emergence patterns, growth habits, plant vigor, and characteristics at maturity also provide help in sensing them remotely to produce accurate maps (Lamb and Weedon, 1998; Medlin and Shaw, 2000). Some researchers use classification algorithms for weed sensing. These algorithms are based on statistical variability and the trend between the weed densities before crop emergence against bare soils and after expected populations (Lamb and Weedon, 1998; Lass et al., 2005). In this way, remote sensing is being used for statistical modeling of weed distribution patterns (Lass et al., 2005). One such technique is geostatistics, which is used for descriptive analysis of weed aggregation and spatial variation (Medlin et al., 2000). Advanced technology may facilitate intra-row weeding in the near future. This might result in significant reduction in, or even elimination

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of, the additional need for hand weeding. Optimally, such technologies should even be capable of discriminating between more or less competitive weeds (Grundy et al., 2005). However, at present, the major obstacle to the development of selective and accurate intra-row robotic weed control is the lack of automated detection and classification of crop and weeds. The crucial requirement for an automated intra-row weeder is a high level of accuracy when operating close to individual crop plants. Dedousis et al. (2007) developed a new disc that allowed hoeing in crop rows close to the crop plants. Some researchers are studying vision systems for active shape modeling of weed seedlings to distinguish them from crop plants (Søgaard and Olsen, 2003). Others have studied vision-based systems to discriminate between the crops and weeds using images of real field conditions (Gerhards and Christensen, 2003; Melander, 2004; Van Evert et al., 2006). Attempts are being made to use electronic crop seed mapping to assist subsequent computer vision for identification of crop and weed seedlings (Griepentrog et al., 2005). For this purpose, Griepentrog et al. (2005) used real-time kinematic differential global positioning system to create an electronic field map with geo-referenced seed positions for each individual crop seed. The seed map data can then be used to direct a vision camera to the approximate positions of the crop seedlings. Moreover, the application of precision agriculture technologies and sensors has increased in the recent years for mechanical weed control, with the development of high tech machines which are able to detect and discriminate crops and weeds and eliminate the weeds in the intra-row space (Fig. 8). 6.2.1 Vision systems and texture analysis Despite the variation of weed flora within agroecosystems, the differences between broad and narrow weed leaves are the most decisive factor for vision systems and texture analysis. A quick glance at various images of grasses and broadleaf plants reveals that a more robust technique for weed classification should be based on a texture property of the images. Texture features of plants have been applied in distinguishing weed species by Meyer et al. (1998). The texture analysis approach of Kubo et al. (2003) is based on gray level co-occurrence matrix. This texture feature is used for classifying tree species in the forest. Wavelet analysis has been utilized in machine vision system. Brosofske et al. (1999) used wavelets to reveal relationships between landscape features and plant diversity indices at different imagery spatial resolution. The machine vision system guided precision sprayer was developed,

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Fig. 8 The devise for precision herbicide application (A) where robotic nozzles (B–D) target weeds in real time (E) as the tractor passes across the field. Note the cameras (white boxes) that monitor the process of herbicide application (B, C) and the spraying pattern and spraying print in D and E, respectively (BlueRiver Technology, www. bluerivertechnology.com).

tested using discrete wavelet transformation approach by Tian et al. (1999) and obtained 75% accuracy for weed detection. With the increasing use of innovative computer technology, machine vision systems have become a possibility for weed identification. Tang et al. (1999) performed texture-based weed classification using the Gabor wavelet (GW) to classify images into broadleaf and grass categories for real-time selective herbicide application. The GW functions were used to construct spatial domain filters. The filtering output was the modulation of the average of the convolution output real and imaginary filter masks. The results showed that the method is capable of performing texture-based broadleaf and grass classification effectively with perfect classification but the sample images are limited only to 40 sample images with 20 samples from each class. The processing time on feature extraction had taken quite some time because each weed image needs to perform four frequency levels. 6.2.2 Machine vision detection The most predominant sensing modality used in automated weed control is machine vision techniques. The most powerful, and, to date, the only method capable of robust, automated in field discrimination of individual plant species, is based upon hyperspectral imaging. The most widely studied and only commercially utilized method of sensing in existing robotic weed control machines is based upon traditional 2D

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machine vision techniques. Although a number of advanced machine vision recognition techniques for plants have been documented in the literature (Hearn, 2009; Søgaard, 2005), their adoption is still limited. More commonly, 2D image processing approaches, based upon a combination of plant detection (from soil) by color or infrared to red light reflectance ratios, and crop recognition (from weeds) by apparent plant size in the 2D image and the 2D spatial planting pattern along the crop row are used (Astrand and Baerveldt, 2005; Tillett et al., 2008). These techniques are fairly effective during the early portion of the growing season, when weed control is most critical, before canopy closure has occurred. For robust performance, they require a uniform and well-established crop stand and a relatively low weed density and perform best when applied to a transplanted crop where the crop plants are larger than and more easily distinguished from weeds. The technique is better suited for the control of a robotic hoe than a sprayer, where the need for accurate recognition or mapping of weeds growing in close proximity to the crop is not necessary.

6.3 Genomics advancing to the next generation Genomics in weed science is used in two broad areas: (a) classical molecular biology and (b) functional genomics (for comprehensive review, see Slotta, 2008). 6.3.1 Classical molecular biology Classical molecular biology approaches involve sequencing and characterizing the genomic structure of an organism. Genomic sequence and structure information and parameters associated with sequence diversity are used for phylogenetic assessments, gene flow studies, species relationships and population genetic studies, species identification, evolution of weedy traits, evolution of resistance to herbicides, and others. These classical approaches have been critical in the early development of genomics tools in weed science, facilitating the identification of genes involved in herbicide MOAs. Using some of the principles developed in classical molecular biology, advances have been made into next-generation technology that expands on our knowledge and understanding of weed biology. 6.3.2 Functional genomics Functional genomics informs us of the function of genes and in the process, enables understanding what controls phenotypic traits such as weediness. Expressed sequence tag (EST) libraries (Basu and Zwenger, 2009) are among

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the earliest tools used for comparative gene expression and comparative genetic analysis of some weed species. Prior to EST, the development of DNA microarrays (Eisen et al., 1998) permitted genome-wide analysis of differential gene expression in response to certain treatments (i.e., biotic or abiotic stress). This technique allows identification of expressed genes, which are quantified relative to a reference constitutively expressed gene. While this technique was used for a variety of research in weed science, its cost and complexity limit the identification of every potential gene of interest and the broader analysis of gene networks (Lee and Tranel, 2008). Its main limitation is the lack of available gene chips for weedy species. The most recent advancements in DNA sequencing and functional genomics are facilitated by next-generation sequencing (NGS) technology. NGS technology encompasses methods by which the genome of an organism is sequenced in a high-throughput manner using a DNA template from short pieces of cDNA that are sequenced, imaged, aligned, and assembled into a contiguous genome (Metzker, 2010). Several NGS methodologies have been developed, including 454 pyrosequencing and genotyping by sequencing (GBS). NGS technology allows the assembly of plant genome, transcriptome, or proteome using bioinformatics tools. It enables the study of non-model species because it does not require complete reference genome (Brautigam and Gowik, 2010). For weed science, the transcriptome produced with RNA-sequencing (RNAseq) provides an unprecedented global view of how weedy species modify gene expression in response to agroecological conditions, including management interventions (Wang et al., 2009). Weed scientists are now using this technology to investigate genomic assembly (Lee et al., 2009; Yang et al., 2013), herbicide resistance evolution and stress adaptation, and novel herbicide target genes (Riggins et al., 2010). This method has enabled identification of target-site and non-target-site (NTS) gene groups associated with herbicide resistance. For example, transcriptome analysis of diclofop-resistant Lolium rigidum using RNAseq identified four metabolism-related transcripts associated with herbicide detoxification and identified genes associated with NTS resistance (Gaines et al., 2014). Advancing the discipline of weed science and discovery of novel tools for weed management requires critical evaluation of the biology and physiology of weedy species using genomic methods. By integrating classical and nextgeneration technologies, we can identify novel herbicide MOAs or novel herbicide targets, novel (perhaps non-chemical) means of weed control,

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and novel genes to use for improvement of crop competitive traits, weedsuppressive traits, and tolerance to stress. Molecular tools are key to discovering novel herbicide targets, means of weed resistance management, and means of developing weed-competitive and stress-resilient crops.

6.4 RNA interference (RNAi) technology RNA interference, or gene silencing, is an intrinsic mechanism in plants to tolerate biotic or abiotic stress via non-coding, small RNA molecules (Khraiwesh et al., 2012). These molecules can repress genes that may be considered harmful, mitigate plant responses to abiotic and biotic stressors, maintain genome integrity, adapt to external conditions, or regulate developmental processes. Gene silencing is attained through short interfering RNA (siRNA) or microRNAs (miRNA) (Rutz and Scheffold, 2004; Sanan-Mishra et al., 2009). This process is triggered by double-stranded RNA (dsRNA) that may originate from short hairpin RNA (shRNA), which are either native or introduced by viral infection. The presence of dsRNA or shRNA activates a series of reactions involving a Dicer enzyme, the RNA-induced silencing complex (RISC), and the delivery of a short RNA guide strand to a target mRNA site, these complexes in-turn trigger the degradation of the mRNA and stops production of the encoded protein. This process can be done artificially to manipulate the expression of genes of interest (Rutz and Scheffold, 2004); a novel application for weed management (Hollomon, 2012, Sammons et al., 2015). This was first demonstrated by the application of artificial siRNA with glyphosate to minimize the production of enolpyruvyl-shikimate-3-phosphate synthase (EPSPS) in glyphosate-resistant weeds. Resistance to glyphosate in reported populations of Amaranthus palmeri, A. spinosus, and A. tuberculatus (Chatham et al., 2015; Gaines et al., 2010; Nandula et al., 2014); Kochia scoparia (Wiersma et al., 2015); and some populations of Lolium perenne ssp. multiflorum (Salas et al., 2012) are due to increased copies of the target gene EPSPS. Increased EPSPS production compensates for the enzyme molecules inhibited by glyphosate, making the plant insensitive to the herbicide. Silencing the mRNA for EPSPS revert the plant phenotype from resistant to susceptible. The potential application of this technology for resistance management is broad, granting that the resistance mechanism is known and is amenable to gene silencing. Conversely, the utility of this technology for weed management is limited by its specificity; a similar hurdle confronting biological control agents. Weed species, populations within a species, and plants within

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a field are highly diverse. Multiple resistance mechanisms occur against oneherbicide mode of action. Therefore, this tool must be used in conjunction with other tools to be effective across locations and time. Naturally occurring miRNAs can affect complex gene regulation by binding to reverse complementary sequences, resulting in cleavage or translational inhibition of the target RNAs (Khraiwesh et al., 2012). Some miRNAs increase the expression of certain genes in response to plant developmental needs, or to stress factors (positive trait regulators) while others suppress gene expression (negative trait regulators) to turn off gene products that are not needed (Zhou and Luo, 2013). Thus, artificially modulating the expression of miRNA can alter plant traits. A promising application of this technology in weed management would be related to increasing tolerance to oxidative stress in crops. For example, overexpression of a negative regulator such as the miR398 form of CSD2 (Cu/Zn SOD gene) by RNAi increased plant tolerance to oxidative stresses (Sunkar et al., 2006). The resultant trait could lend crop tolerance to some herbicides. In the same manner, the competitive ability of crops can be improved by modifying the expression of miRNAs that control nitrogen metabolism (Fischer et al., 2013), drought tolerance (Ferdous et al., 2017), salt stress (Fan et al., 2015), or other stress factors. The critical first step governing all these is identifying the appropriate miRNA target, which can be accelerated now with NGS, bioinformatics, and functional genomics technologies.

6.5 Plant genome editing Genome editing allows precise manipulation of the genome of an organism using sequence-specific nucleases. Nucleases create specific double-strand breaks at specific locations in the genome, which are lethal, and must be repaired. The genome editing technology utilizes this DNA repair mechanism (Malzahn et al., 2017) and is enabled by genomics and biotechnology tools. The most rapidly emerging genome editing tool is the CRISPR/Cas9 system from Streptococcus pyogenes, which is based on RNA-guided engineered nucleases (Barrangou et al., 2007; Jinek et al., 2012). CRISPR (clustered regularly interspaced short palindronic repeats) consists of repeating sequences, interrupted by spacer sequences, which are footprints of past invading pathogens. Other genomic editing tools include zinc finger nucleases (ZFN) and transcription activator-like effector nucleases (TALENS), but the CRISPR/Cas9 system currently stands out as the fastest, cheapest, versatile, and most reliable system for gene editing (Abdallah et al., 2015;

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Bortesi and Fischer, 2015). The enzyme Cas9 (CRISPR-associated protein) is an RNA-guided DNA endonuclease that cuts at a specific location in the genome. The guide RNA (gRNA) is a tailored RNA sequence (20 bases long) that binds to the target sequence. The Cas9 binds to the gRNA and cuts both strands of DNA. The cell can repair the DNA damage by non-homologous end-joining, which usually causes frame shift mutations creating gene knockouts. Hence, the most successful use of this gene editing system is for gene silencing. Alternatively, DNA damage can be repaired by homologous recombination when a homologous template is available, which can be exploited to achieve precise modification or gene insertion (Bortesi and Fischer, 2015). Thus, the CRISPR/Cas9 system can be programmed to edit parts of the genome by inducing point mutations or short insertion-deletions at single or multiple loci using a specific guide RNA. In practice, the success rate of introducing a specific mutation into a specific locus is very low; random mutations are high. Nonetheless, this technology will continue to improve and is improving rapidly (Malzahn et al., 2017). Off-target activity is an important concern in genome editing. The specificity of CRISPR/Cas9 is determined by the sequence of the gRNA and the DNA target. A perfect match between the last 8–12 bases of the gRNA sequence, referred to as the “seed sequence,” is needed for target-site recognition and cleavage (Cong et al., 2013; Jiang et al., 2013a; Jinek et al., 2012). Strategies have been devised to reduce off-target genome editing such as designing highly specific guide RNA and optimizing nuclease expression (Bortesi and Fischer, 2015; Fujii et al., 2013). Bioinformatics tools have been developed online to facilitate the selection of unique target sites in well-characterized organisms. The use of truncated gRNA (17–18 bp), which are more sensitive to mismatches, improves Cas9 specificity (Bortesi and Fischer, 2015; Fu et al., 2014). Off-target effects can also be minimized with a modified version of Cas9 that acts as nickase, which reduces off-target mutation by 50–1000-fold (Ran et al., 2013; Shen et al., 2014). While off-target mutation varies among cell types and species, whole genome sequencing analysis in Arabidopsis and rice (Oryza sativa) revealed almost negligible mutations at off-target sites (Feng et al., 2014; Zhang et al., 2014a, b). Careful selection of specific gRNA sequences should minimize the risk of unwanted genomic modifications (Bortesi and Fischer, 2015). CRISPR/CAS9 has been adapted to create RNA-directed genomic editing tools in model plants, including Arabidopsis, rice, sorghum (Sorghum

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bicolor), wheat (Triticum aestivum), and maize (Zea mays) (Feng et al., 2014; Jiang et al., 2013b; Liang et al., 2014; Upadhyay et al., 2013; Zhang et al., 2014a, b). Targeted genomic modifications have been used to develop non-transgenic herbicide-resistant crops. This tool also allows stacking or multiplexing desired traits in a crop such as herbicide resistance (Li et al., 2013) plus tolerance to other stresses. Concerning public aversion to transgenic crops, CRISPR/Cas9 can be used to excise marker genes from plant genomes in order to generate marker-free transgenic plants. The presence of selectable marker genes, such as antibiotic or herbicide resistance, in commercialized transgenic crops raises public and regulatory concerns. There is a perceived risk that horizontal gene transfer of antibiotic resistance genes to pathogenic organisms or the transfer of herbicide resistance genes to weedy relatives might affect human health and the environment (Woo et al., 2011). Recent work by Srivastava et al. (2017) demonstrated that dual targeting by CRISPR/Cas9 achieved precise excision of transgenes from rice genome. Complete removal of the marker genes by genomic editing tool would alleviate the regulatory burden associated with transgenic plants.

7. Knowledge discovery: Data warehouse and data mining The uncertainty in weed management programs, owed mainly to existing biotic and abiotic factors that act within and upon it, has sporadically and unsystematically been discussed. Associations among plant species, although true in nature, are controversial in some aspects because they depend greatly on the effect of biotic and abiotic factors which act on the community (Greig-Smith, 1980) as an entity and this is what challenges the research in weed science. Additionally, Wiles and Brodahl (2004) and Gonzalez-Andujar et al. (2006) stated that lack of knowledge to analyze observational data is one of the greatest obstacles in comparative studies on spatial dynamics of weeds. This results in neglecting the amount of data that advanced technologies have enabled to be collected, the analysis of which could release the potential to discover useful information and knowledge (Ye, 2003). Data reflect the behavior of the system, therefore the theoretical potential to obtain useful information and knowledge through knowledge discovery approaches in databases (KDD) (Han and Kamber, 2001) is possible. KDD

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consists of various interrelated stages including data warehouse (DWH) and data mining (DMN) techniques (Abonyi and Feil, 2007; Shapiro-Piatetsky et al., 1996; Velickov and Solomatine, 2000) (Fig. 9). Applications of DWH and DMN in weed research are lacking, except for a few case studies (Ferraro et al., 2012; Korres et al., 2017a; Wiles and Brodahl, 2004) that could be possibly used within DWH and DMN operational platforms. The aim of this section is to establish a conceptual approach of DWH and DMN in relation to weed science and to provide examples, where applicable, that highlight the potential of this approach.

7.1 Data warehouse Monitoring and recording the effectiveness of a weed management program along with that of other operational applications and transactions throughout the production process, form a complex procedure in terms of information requirements. This is becoming even more complicated when spatiotemporal, climatic, and topographical data are to be incorporated into the system. Each of these systems uses different techniques and multiple methods of storing data. For example, recording systems using sensors could archive measurements such as temperature and pH in a simple text file, while a transactional application would use a relational database to store transactions and inventory like, for example, quantities of herbicides used during the production period. Heterogeneous database systems with different structures, data types, etc., provide multiple sources of information, but the goal of exploiting this information in an integrated way, in order to make intelligent decisions, is difficult to achieve. Thus, an integrated DWH system will be required in order to consolidate the appropriate data and support a decision support system (DSS). A process known as extraction, transformation, and loading (ETL) (Kimball and Caserta, 2004) (Fig. 10) can be employed along with data cubes. The use of conceptual models that facilitate the database design and query engines by representing a multidimensional view of data are known as data cubes (Fig. 11) and are individually designed to solve specific problems (Chaudhuri and Dayal, 1997; Vasiliev, 2011). The cells of a data cube contain data measures (i.e., facts), whereas the edges of a data cube represent the data dimensions. Weed population dynamics, for example, can be studied through recruits, predation, and mortality rates (population attributes) which were measured at various time periods (time dimension) and weed management schemes (management system dimension) (Fig. 11).

Selection

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Fig. 9 Data warehouse and data mining in the knowledge discovery process. Based on Velickov, S., Solomatine, D., 2000. Predictive data mining: practical examples. Proceedings of the 2nd Joint Workshop “Artificial Intelligence in Civil Engineering”, March 2000, Cottbus, Germany; Abonyi, J., Feil, B., 2007. Cluster Analysis for Data Mining and System Identification. Birkhauser Verlag AG, Basel, Boston, Berlin, 301 p.

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Databases

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Fig. 10 Schematic representation of data gathering from various sources and transforming them into useful information available for decision-making procedures.

Fig. 11 A multidimensional representation of a weed population quantified by its attributes that affected by different management systems in time.

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Cubes allow navigating through the data and viewing it from different perspectives, i.e., roll-up, drill-down/up, slice-and-dice, and pivot queries (Hand, 1998; Jackson, 2002) by allying the data content with conceptual models.

7.2 Data mining It is commonly accepted that only a fraction of available information is incorporated into decision-making processes (Berthold et al., 2008). DMN techniques are analytical processes designed to explore large amounts of data for the discovery of patterns and systematic relationships between variables along with the validation of the findings through the application of the detected patterns to new subsets of data (Statsoft, 2002). DMN consists of the following stages: (a) problem definition in terms of understanding the project objectives and requirements, (b) data gathering, (c) model building and evaluation, and (d) knowledge deployment. 7.2.1 Categorization of DMN techniques DMN techniques are used for the extraction of patterns from DWH through data mining tasks known as descriptive and predictive data mining tasks (Gupta et al., 2011). DMN, as an interdisciplinary field, incorporates statistics, database systems, machine learning, pattern recognition, neural networks, fuzzy systems, and other soft computing techniques to achieve the desirable outcome (Velickov and Solomatine, 2000). While there are only few basic data mining operations (i.e., classification, regression, clustering, link analysis, and summarization), there is a plethora of techniques (Fig. 12) corresponding to these operations (Velickov and Solomatine, 2000). In this section some case studies will be discussed. 7.2.2 Classification Classification is a process of finding a model (or function) that describes and distinguishes data classes or concepts of an object based on its attributes or properties (Han and Kamber, 2006; Ravichandra Rao, 2003). DMN techniques create classification models via the examination of existing classified data (cases) through of which they detect intelligently a predictive pattern. For example, a set of representative herbicide groups, which serve as a training set, can be used for the development of a classification model which can be represented in various forms, such as “if-then-else” conditional statements usually integrated in decision tree, neural network, and other DMN approaches.

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Hierarchical clustering Sequence discovery Model-based clustering

Decision trees Neural networks Fuzzy Logic Systems Genetic Algorithms

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Association rules Change & deviation detection Statistical techniques

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Fig. 12 Categorization of DMN operations and techniques.

7.2.3 Decision trees Decision tree is a widely used method for inductive inference (Quinlan, 1993). Decision trees represent sets of decisions which generate rules for the classification of a dataset. A rule is a conditional statement in the form “if-then-else” (Anonymous, 2005). Korres et al. (2017a) in an effort to describe the relationships between soil physical, chemical, and extractable soil nutrients properties and weed occurrence (i.e., presence or absence) used a partition analysis taking soil characteristics (e.g., macro- and micronutrients in case of extractable soil nutrients) for each soil property mentioned previously as explanatory variables. Partitioning of the data was conducted by the application of a decision tree method for categorical variables, i.e., presence or absence of weed species resulting in the production of a decision rules series that describe the relationship between soil properties and weed occurrence (Fig. 13). 7.2.4 Regression analysis Regression, in its simplest form, involves building a model to relate a predictor (i.e., an independent variable x) to a response (i.e., dependent variable y) through a linear model in which an error (noise) is allowed (Eq. 1): y ¼ gðxÞ + e

(1)

where g(x) ¼ αx + b, e ¼ error, a ¼ steepness or slope of the curve, and b ¼ intercept (Hand et al., 2001). When the x variable is multiple, it is known as multiple linear regression.

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Fig. 13 See legend on opposite page.

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Fig. 14 Relationship between Palmer amaranth population and fractional light interception by crop canopy. Based on Korres, N.E., Norsworthy, J.K., 2017. Palmer amaranth (Amaranthus palmeri) demographic and biological characteristics in wide-row soybean. Weed Sci. 65, 491–503.

A representative example concerning the application of the simple linear regression model between Palmer amaranth population and fractional light interception is shown in Fig. 14. 7.2.5 Non-linear regression analysis Many real-life problems are not simply linear projections of measured observation; hence it is difficult to predict because they may depend on complex interactions of multiple predictor variables. Non-linear regression is a technique that is used when the effect of a stimulus x on the response y is not Fig. 13 A decision tree is a visualization of a partition model that describes the relationship between Digitaria sanguinalis (large crabgrass) occurrence and extractable soil nutrients. The number of sampling sites where the weed was present or absent within a range of a soil nutrient content, e.g., 13 out of 19 large crabgrass plants were recorded at Na  17.89 mg/kg soil, along with other statistics (e.g., G2, LogWorth, rate and probability of occurrence) are shown, were appropriate, within each leaf of the tree. Based on Korres, N.E., Norsworthy, J.K., Brye, K.R., Vaughn, S.J. Jr., Mauromoustakos, A., 2017a. Relationships between soil properties and the occurrence of the most agronomically important weed species in the field margins of eastern Arkansas. Implications on weed management. Weed Res. 57, 159–171.

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additive but multiplicative (Korres, 2005). Non-linear regression is a more general technique than linear regression since it can fit hyperbolic, logarithmic, and exponential equations that define y as a function of x (or x’s) by estimating parameters of the non-linear equation that minimizes the residuals (Korres, 2005). The sigmoidal model, which is expressed by Eq. (1), is an example of non-linear regression analysis. h x
xo i y ¼ α= 1 + e
ð b Þ

(2)

The graph of y versus time is an elongated S-shaped and the curve is usually symmetrical about its point of inflection. This model is extensively applied in weed research either in herbicide research, i.e., determination of dose response curves or in weed population dynamics or in weed biology and physiology as in the example shown in Fig. 15.

Fig. 15 A three-parameter sigmoidal model describing the cumulative number of Palmer amaranth flowering plants in time under three light intensities as averaged across Palmer amaranth gender and nutrient deficient status. Based on Korres N.E., Norsworthy J.K., FitzSimons T., Roberts T.L., & Oosterhuis D.M. (2017) Differential response of Palmer amaranth (Amaranthus palmeri) gender to abiotic stress. Weed Sci 65, 213–227.

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The Gompertz model is very similar to the logistic model although unlike the logistic the curve is asymmetrical about the inflection point. This model along with the logistic model has been used extensively for the determination of critical period of weed control. Korres and Norsworthy (2015a,b), for example, used a three-parameter Gompertz model to describe the effect of increasing weed-free period on seed cotton yield and the logistic model to express the critical time of weed removal on cotton yield. On the contrary, polynomial equations provide mostly irrelevant biological information. 7.2.6 Clustering Clustering, an unsupervised learning data mining technique (Poncelet et al., 2008; Sembiring et al., 2010), is the identification of classes, also called clusters or groups, for a set of objects (or attributes, measurements, or cases) without any prior knowledge of the relationships between them. The purpose of clustering is the maximization of the intra-class similarities or maximization of the inter-class dissimilarities based on some criteria defined by the characteristics of the objects under examination (Knight, 2004; Witten and Eibe, 2005). The main outcome of a cluster analysis is known as a dendrogram or tree diagram. Fig. 16 represents a dendrogram which was developed by the application of a hierarchical cluster analysis for the characterization of various Palmer amaranth population densities counted in a wide-row soybean crop.

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Fig. 16 A dendrogram representing Palmer amaranth population densities as grouped into three distinguished clusters for the investigation of Palmer amaranth demographics in wide-row soybean. Based on Korres, N.E., Norsworthy, J.K., 2017. Palmer amaranth (Amaranthus palmeri) demographic and biological characteristics in wide-row soybean. Weed Sci. 65, 491–503.

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Data warehouse and data mining techniques, as significant parts of knowledge discovery in databases, have been shown to be important tools in different business domains. Further investigation concerning the integration of these techniques in weed research for optimization procedures and maximization of weed management efficacy merits further investigation. This is particularly true since both techniques are considered two of the most important domains in database and information systems. Modern weed management strategies and decision-making procedures will require a variety of inputs in the form of multimodal, multi-scale, spatiotemporal data. Data warehouse and data mining techniques can be used to filter the vast amount of research information in weed control and, in conjunction with the progress in software engineering and control systems, can be proved invaluable tools toward the sustainability of food production.

8. A synthesis Herbicides remain a simple and cost-effective way to control weeds, they are rapidly losing their effectiveness due to evolution of resistance. Non-chemical weed control techniques become increasingly important in the future. Automated, robotic weed control is being rapidly developed, particularly for vegetable crops and organic agriculture. Cover crops and weed seed destruction techniques along with other agronomic weed control methods not mentioned in this review, e.g., the use of competitive crops, crop density manipulation or tillage, (Korres, 2018), are becoming increasingly popular with growers. In the future, RNAi technology, gene editing, robotics, and drones, while still in their research infancy, will yield new tools for weed control. Agriculture is also moving into a new era of big data or “digital farming.” It will be interesting to see what new, unforeseen weed control solutions will be derived from this new farming approach that will allow more intelligent application and integration of weed management technologies. In an attempt to facilitate the suitability of the methods mentioned earlier into IWM systems. Table 2 summarizes the strengths and weaknesses of the new technologies and tools and highlights future research needs for each of these technologies. Recommendations are made based on the feasibility and practicality of these technologies.

Table 2 A synthesis of new technologies and tools for integrated weed management. Technology Strengths Weaknesses Research needs

Recommendations

Mechanical weed control

Promising for integrated weed management

Automated weed control and robotics

Capable of collecting weed infestation data

Affected by climate conditions

Improve machine vision analyses (shape model, plant reflectance)

Facilitate storage and analysis of weed infestation data

Affected by farming Improve robotic practices and cropping efficiency and systems suitability

Supports decision making (when and where to control weeds)

Affected by regional topography

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Improve rate of application technology

Execute weed control Affected by crop plant Improve decisionthrough robot morphology making support system deployment

Hot water and hot foam

Permits gathering of efficacy data hence allows evaluation of decisions

Improve strength of weed-sensing tools

Reduces hand weeding

Improve sensor and plant recognition technology

Safe with no side effects Hot foam exhibits greater residual activity

Greater efficiency in dense weed population

Effective in precision weed management Integrated weed management Continued

Table 2 A synthesis of new technologies and tools for integrated weed management.—cont’d Technology Strengths Weaknesses Research needs

Soil steaming

Recommendations

Affects weed seedbank Efficacy issues in larger soil volume

Improve the efficiency Alternative to chemical of available devices fumigants

Can be performed as a More expensive than “band treatment” with other non-chemical preventive methods consequent energy savings

Improve bandEffective in precision weed steaming and steaming management down options

High fossil energy consumption

No residual effects, hence no delays in planting operations

Work rate for one treatment is very low

Mobile soil steaming is Lethal effect on noncommercially used on target soil organisms raised beds especially in short-term vegetables

Integrated weed management

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Rapid effect, while practices like solarization require long application periods and specific conditions to be effective

Flaming

Soil type, soil moisture, soil structure influences the efficacy at low soil temperatures

Cheaper than other thermal methods or hand weeding

Risks for the user

Low risk of crop damage

Weeds with erect growth habit and thin leaves more susceptible than these with prostrate

Compatible with no-tillage systems

Broadleaf weeds more susceptible than grass species

Ideal for fields with erosion problems

timing of flame control when

Potential fungicide and insecticide action without any carryover effects on the next crop

Weeds that emerge with the crop are difficult to control without injuring the crop

Improve weed control Effective in precision weed efficacy management

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Reduces hand weeding

Integrated weed management

Reduces hand weeding Continued

Table 2 A synthesis of new technologies and tools for integrated weed management.—cont’d Technology Strengths Weaknesses Research needs

Electrocution

Recommendations

Suitable in silviculture, High energy forestry, row crops, consumption urban areas and hard surfaces

Improve strength of electric shock

Organic, vegetable and orchard farming systems

Rapid effect

Extend exposure duration

Integrated weed management

High financial cost

Efficacy and environmental studies Abrasive grit

Significant weed control

Timing of grit application is critical

Test over wide range Integrated weed of crops and cropping management systems

Improves functionality Availability of residues Test the effects of and economic (grit) residue characteristics feasibility to crop on method efficacy fertilization Improves soil fertility and conditions Reduces greenhouse gas emissions through reductions of N2O emissions Reuse of crop residues and agricultural wastes

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Improve efficacy (severity and soil conditions)

Cryogenic weed control

High energy consumption

Improve energy consumption

Affects only freezingsensitive species

Improve application technology and parameters

Integrated weed management if sustainability of the method can be achieved

Agronomic weed management

Row configuration

Enhances crop competitiveness

Unproven as yield Test over wide range enhancement practice of agronomic crops for some agronomic crops

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Improve cooler technology Integrated weed management

Increases harvesting Seed cost efficiency due to closer spacing Availability of machinery secures easy application of the method Maintains field operational advantages Continued

Table 2 A synthesis of new technologies and tools for integrated weed management.—cont’d Technology Strengths Weaknesses Research needs

Allelopathy

Herbicidal properties

Difficulty in isolating allelochemical compounds

Breed cultivars with allelopathic potential

Recommendations

Integrated weed management

Enhances crop competitiveness

Mean to reduce the selection pressure for herbicide resistance Harvest weed seed control

Depletion of weed soil Removal of chaff seedbank interferes with soil fertility Long-term weed control method

Efficacy of narrowwindrow burning depends on climate, crop and amount of chaff

Understanding weed Integrated weed seed retention at crop management harvest

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Incorporation into various husbandry operations, e.g., crop rotations, intercropping, or cover crops

Easineness in application and manipulation by the farmer

Limited market for the baled product

Transport of bales can spread weed seeds across long distances

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Continuous use of HWSC will select for early maturing phenotypes Cover crops

Weed suppression

Crop establishment issues

Life cycle assessment of Integrated weed cover crops and management various cropping systems

Allelopathic potential

Termination issues

Economics of cover crops

Wide range of potential cover crops

Increases of production cost

Improvements of soil fertility

Suitability issues with tillage cropping systems Continued

Table 2 A synthesis of new technologies and tools for integrated weed management.—cont’d Technology Strengths Weaknesses Research needs

Reduction of N fertilizer inputs if legumes

Recommendations

Reduce soil water

Reduction of N2O emissions if legumes

Crop breeding Herbicideand genetically resistant crops modified crops

The technology (e.g., Crops became a oneGR) is simple, herbicide enterprise, efficacious, and the antithesis of affordable integrated weed management Stacks of herbicide resistance traits allow diversification of herbicide modes of action

Monopoly in food production issues

Stacks of herbicide Accelerate the resistance traits prevent selection, and weed escapes contributes to the increasing number, of resistant weed species

Integrated weed management

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Yield increases of the succeeding crop

Stacks of herbicide resistance traits offer the producer more opportunities to integrate other weed management tools Development of crops Research on Integrated weed resistant to parasitic appropriate genes that management weeds is limited could be used for crop transformation, or markers for breeding host-crops resistance Lack of parasitic weeds germination stimulants in the absence of hostcrop Bio-based herbicide products

General

Environmentally benign

Understanding of the molecular basis of host-parasite interaction

Difficulties in scaling- Starting material for up the production subsequent chemical process or microbiological modification

Consumer acceptance Complex chemical structures that are difficult to synthesize

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Crops resistant to parasitic weeds

Use as templates for chemical synthesis of novel herbicides

Integrated weed management

Organic farming

Continued

Table 2 A synthesis of new technologies and tools for integrated weed management.—cont’d Technology Strengths Weaknesses Research needs

Low stability or persistence

Recommendations

Discovery of new modes of action

Off-target effects Corn gluten meal

Anti-germination action on seeds

Low efficiency on weeds already present

Acetic acid

High use rates

Use in non-crop environments

Fatty acids

Lack of residual activity affects efficiency

Conditional use in integrated weed management

Essential oils

Provide a good but Relatively expensive transient weed control

Brassicaceae seed Adequate weed meal control High nutrient content suitable for valueadded soil amendments Indirect reductions in N2O emissions

Organic farming

Differential herbicidal Factors affect activity herbicidal efficacy Application rates, crops

New option for weed control in vegetables Organic farming

Integrated crop/weed management

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Expensive due to high application rate

Novel technologies

Nanotechnology Can increase the precision and effectiveness of the natural herbicidal compounds delivery

Nanotechnology and Nano-sensors are being delivery precision and used in precision agriculture effectiveness of natural herbicidal compounds

Nanoencapsulation can allow the joint application of several compounds

Image processing and remote sensing

Detect weed patches, Detection problems and consequently due to plant residues weed density mapping

Evaluate weed floristic Integrated weed control composition, canopy architecture, leaf dimensions, differences in growth stages, emergence patterns, growing habits, vigor, and characteristics at maturity

Good option for reducing herbicide application and cost of production by enhancing the herbicide application efficiency

Integrated geostatistics Remote sensing may and remote sensing facilitate intra-row weeding in the near future

Lack of automated detection and classification of crop and weeds

Continued

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Improve the soil application of natural herbicides

Table 2 A synthesis of new technologies and tools for integrated weed management.—cont’d Technology Strengths Weaknesses Research needs

Recommendations

Genomics Facilitate the Lack of available advancing to the identification of genes GeneChips for weedy next generation involved in herbicide species modes of action

Provides an unprecedented global view of how weedy species modify gene expression in response to biotic and abiotic stress Identification of target-site and nontarget-site (NTS) gene groups associated with herbicide resistance RNAi

Reverts the plant The utility of this phenotype from technology for weed resistant to susceptible management is limited by its specificity

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Next-generation sequencing (NGS) technology allows the assembly of plant genome, transcriptome, or proteome

Knowledge discovery

Data warehouse Knowledge acquisition

Lack of expertise (limited no. of pubs related to weed science)

Information based on multidisciplinary research Multitask projects

Development of indices for strategic weed control

Spatiotemporal dynamic simulations Data warehouse development Local, national and international collaborations

Data mining

Same as data warehouse

Same as data warehouse

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Strategic weed control

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Further reading Babineau, M., Mahmood, K., Mathiassen, S.K., Kudsk, P., Kristensen, M., 2017. De novo transcriptome assembly analysis of weed Apera spica-venti from seven tissues and growth stages. BMC Genomics 18, 128. Chauhan, B.S., Johnson, D.E., 2010. The role of seed ecology in improving weed management strategies in the tropics. Adv. Agron. 105, 221–262. Kamthan, A., Chaudhuri, A., Kamthan, M., Datta, A., 2015. Small RNAs in plants: recent development and application for crop improvement. Front. Plant Sci. 6, 208. https://doi. org/10.3389/fpls.2015.00208. Kong, C.H., Chen, X.H., Hu, F., Zhang, S.Z., 2011. Breeding of commercially acceptable allelopathic rice cultivars in China. Pest Manag. Sci. 67, 1100–1106. Van Evert, F.K., Fountas, S., Jakotevic, D., Crnojevic, V., Travlos, I., Kempenaar, C., 2017. Big data for weed control and crop protection. Weed Res. 57, 218–233.