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Summary Recent development of site-specific weed management strategies suggests patch application of herbicides to avoid their excessive use in crops. The estimation of infestation of weeds and control thresholds are important components for taking spray decisions. If weed pressure is below a certain level in some parts of the field and if late germinating weeds do not affect yield, it may not be necessary the spray such places from an economic point of view. Consequently, it makes sense to develop weed control thresholds for patch spraying, based on weed cover early in the growing season. In Danish maize field experiments conducted from 2010 to 2012, we estimated competitive ability parameters and control thresholds of naturally established weed populations in the context of decision-making for patch spraying. The most frequent weed was Chenopodium album, accompanied by Capsella bursa-pastoris, Cirsium arvense, Lamium amplexicaule, Tripleurospermum inodorum, Poa annua, Polygonum aviculare, Polygonum persicaria, Stellaria media and Veronica persica. Relative leaf cover of weeds was estimated using an image analysis method. The relation between relative weed leaf cover and yield loss was analysed by nonlinear regression models. The competitive ability parameters and economic thresholds were estimated from the regression models. The competitive ability of weed mixtures was influenced by the increasing proportion of large size weeds in the mixtures. There was no significant effect of weeds which survived or established after the first herbicide application, indicating that early image analysis was robust for use under these conditions.

Summary Small unmanned aerial systems (UAS) with cameras have not been adopted in weed research, but offer low-cost sensing with high flexibility in terms of spatial resolution. A small rotary-wing UAS was tested as part of a search for an inexpensive, user-friendly and reliable aircraft for practical applications in UAS imagery weed research. In two experiments with post-emergence weed harrowing in barley, the crop resistance parameter, which reflects the crop response to harrowing, was unaffected by image capture altitude in the range from 1 to 50 m. This corresponded to image spatial resolution in the range from 0.3 to 17.1 mm per pixel. This finding is important because spatial resolution is inversely related to sensing capacity. We captured 20 plots comprising a total of about 0.2 ha in one image at 50 m altitude without losing information about the cultivation impacts on vegetation compared with ground truth data. UAS imagery also gave excellent results in logarithmic sprayer experiments in oilseed rape, where we captured 37 m long plots in each image from an altitude of 35 m. Furthermore, perennial weeds could be mapped from UAS images. These first experiences with a small rotary-wing UAS show that it is relatively easy to integrate as a tool in weed research and offers great potential for site-specific weed management.

Summary Information on temporal and spatial variation in weed seedling populations within agricultural fields is very important for weed population assessment and management. Most of all, it allows a potential reduction in herbicide use, when post-emergence herbicides are only applied to field sections with weed infestation levels higher than the economic weed threshold; a review of such work is provided. This paper presents a system for site-specific weed control in sugarbeet (Beta vulgaris L.), maize (Zea mays L.), winter wheat (Triticum aestivum L.) and winter barley (Hordeum vulgare L.), including online weed detection using digital image analysis, computer-based decision making and global positioning systems (GPS)-controlled patch spraying. In a 4-year study, herbicide use with this map-based approach was reduced in winter cereals by 60% for herbicides against broad-leaved weeds and 90% for grass weed herbicides. In sugarbeet and maize, average savings for grass weed herbicides were 78% in maize and 36% in sugarbeet. For herbicides against broad-leaved weeds, 11% were saved in maize and 41% in sugarbeet.

Summary It has been established that weeds are spatially aggregated with a spatially varying composition of weed species within agricultural fields. Site-specific spraying therefore requires a decision method that includes the spatial variation of the weed composition and density. A computerized decision method that estimates an economic optimal herbicide dose according to site-specific weed composition and density is presented in this paper. The method was termed a ‘decision algorithm for patch spraying’ (DAPS) and was evaluated in a 5-year experiment, in Denmark. DAPS consists of a competition model, a herbicide dose–response model and an algorithm that estimates the economically optimal doses. The experiment was designed to compare herbicide treatments with DAPS recommendations and the Danish decision support system PC-Plant Protection. The results did not show any significant grain yield difference between DAPS and PC-Plant Protection; however, the recommended herbicide doses were significantly lower when using DAPS than PC-Plant Protection in all years. The main difference between the two decision models is that DAPS integrates crop–weed competition and estimates the net return as a continuous function of herbicide dose. The hypothesis tested is that the benefit of using lower herbicide doses recommended by DAPS would disappear after a few years because weed density will increase and thus require higher doses. However, the results of weed counting every year did not confirm this hypothesis.

Summary A sugarbeet field experiment was conducted in 1999 and 2000 to measure beet yield where Sinapis arvensis or Lolium perenne were growing in the crop row at 2, 4 or 8 cm from the beet plants. The weeds were removed by cutting once in the growing season in either late May, mid-June or early July. The number of neighbouring beet plants to every target beet plant was recorded. Projected leaf cover of a subset of the data with non-cut weeds was analysed by using image analysis to investigate whether this could be used to predict beet yield loss early in the growing season. Increasing the distance between beet and weed from 2 to 8 cm increased the beet yield significantly by an average of 20%, regardless of weed species. The dry weight of non-cut and re-growing weeds at harvest time decreased when cutting was postponed to the period between mid-June and early July. The number of neighbours described a sigmoidal yield decline of the single beet plants. Results from image analysis showed that approximately 33 g of beet yield was lost in October/November for each per cent relative projected leaf cover of the weeds in May, despite variation in growing conditions. The results are discussed in relation to potentials for robotic in-row weed control.

Summary Stems of Chenopodium album. and Sinapis arvensis. and leaves of Lolium perenne. were cut with a CO2 laser or with a pair of scissors. Treatments were carried out on greenhouse-grown pot plants at three diAerent growth stages and at two heights. Plant dry matter was measured 2 to 5 weeks after treatment. The relationship between dry weight and laser energy was analysed using a nonlinear dose‐response regression model. The regression parameters diAered significantly between the weed species. At all growth stages and heights S. arvensis was more diAcult to cut with a CO2 laser than C. album. When stems were cut below the meristems, 0.9 and 2.3 J mm )1 of CO2 laser energy dose was suAcient to reduce by 90% the biomass of C. album and S. arvensis respectively. Regrowth appeared when dicotyledonous plant stems were cut above meristems, indicating that it is important to cut close to the soil surface to obtain a significant eAect. When cutting L. perenne plants with 2-true leaves at a height of 2 cm from the soil surface with a laser, the biomass decreased significantly compared with plants cut by scissors, indicating a delay in regrowth. This delay was not observed for the dicotyledonous plants nor for the other growth stages of L. perenne.

Importance of the paper: This paper is important because it offers examples of how simulation models can be used to develop understanding and predict how crops respond to environmental factors. It also presents and utilizes a new concept, the Kaya-Porter identity, as a means of deconstructing biological use efficiencies into component parts as a stimulus to new thinking, models and experiments in crop physiology.

Summary For implementation of simple yield loss models into threshold-based weed management systems, a thorough validation is needed over a great diversity of sites. Yield losses by competition wsth Sinapis alba L. (white mustard) as a model weed, were studied in 12 experiments in sugar beet (Beta vulgaris L.) and in 11 experiments in spring wheat (Triticum aestivum L.). Most data sets were heller described by a model based on the relative leaf area of the weed than by a hyperbolic model based on weed density. This leaf area model accounted for (part of) the effect of different emerging times of the S. alba whereas the density model did not. A parameter that allows the maximum yield loss to be smaller than 100% was mostly not needed to describe the effects of weed competition. The parameter that denotes the competitiveness of the weed species with respect to the crop decreased the later the relative leaf area of the mustard was determined. This decrease could be estimated from the differences in relative growth rate of the leaf area of crop and S. alba. However, the accuracy of this estimation was poor. The parameter value of the leaf area model varied considerably between sites and years. The results strongly suggest that the predictive ability of the leaf area model needs to be improved before it can be applied in weed management systems. Such improvement would require additional information about effects of abiotic factors on plant development and morphology and the definition of a time window for predictions with an acceptable level of error.

Transdisciplinary weed research (TWR) is a promising path to more effective management of challenging weed problems. We define TWR as an integrated process of inquiry and action that addresses complex weed problems in the context of broader efforts to improve economic, environmental and social aspects of ecosystem sustainability. TWR seeks to integrate scholarly and practical knowledge across many stakeholder groups (e.g. scientists, private sector, farmers and extension officers) and levels (e.g. local, regional and landscape). Furthermore, TWR features democratic and iterative processes of decision-making and collective action that aims to align the interests, viewpoints and agendas of a wide range of stakeholders. The fundamental rationale for TWR is that many challenging weed problems (e.g. herbicide resistance or extensive plant invasions in natural areas) are better addressed systemically, as a part of broad-based efforts to advance ecosystem sustainability, rather than as isolated problems. Addressing challenging weed problems systemically can offer important new leverage on such problems, by creating new opportunities to manage their root causes and by improving complementarity between weed management and other activities. While promising, this approach is complicated by the multidimensional, multilevel, diversely defined and unpredictable nature of ecosystem sustainability. In practice, TWR can be undertaken as a cyclic process of (i) initial problem formulation, (ii) 'broadening' of the problem formulation and recruitment of stakeholder participants, (iii) deliberation, negotiation and design of an action agenda for systemic change, (iv) implementation action, (v) monitoring and assessment of outcomes and (vi) reformulation of the problem situation and renegotiation of further actions. Notably, 'purposive' disciplines (design, humanities and arts) have central, critical and recurrent roles in this process, as do integrative analyses of relevant multidimensional and multilevel factors, via multiple natural and social science disciplines. We exemplify this process in prospect and retrospect. Importantly TWR is not a replacement for current weed research; rather, the intent is to powerfully leverage current efforts. Public domain – authored by a U.S. government employee

Seed-attacking microorganisms have an undefined potential for management of the weed seedbank, either directly through inundative inoculation of soils with effective pathogenic strains, or indirectly by managing soils in a manner that promotes native seed-decaying microorganisms. However, research in this area is still limited and not consistently successful because of technological limitations in identifying the pathogens involved and their efficacy. We suggest that these limitations can now be overcome through application of new molecular techniques to identify the microorganisms interacting with weed seeds and to decipher their functionality. However, an interdisciplinary weed management approach that includes weed scientists, microbiologists, soil ecologists and molecular biologists is required to provide new insights into physical and chemical interactions between different seed species and microorganisms. Such insight is a prerequisite to identify the best candidate organisms to consider for seedbank management and to find ways to increase weed seed suppressive soil communities.