Modelling herbicide dose and weed density effects on crop:weed competition

The effects of a range of herbicide doses on crop:weed competition were investigated by measuring crop yield and weed seed production. Weed competitivity of wheat was greater in cv. Spark than in cv. Avalon, and decreased with increasing herbicide dose, being well described by the standard dose–response curve. A combined model was then developed by incorporating the standard dose–response curve into the rectangular hyperbola competition model to describe the effects of plant density of a model weed, Brassica napus L., and a herbicide, metsulfuron‐methyl, on crop yield and weed seed production. The model developed in this study was used to describe crop yield and weed seed production, and to estimate the herbicide dose required to restrict crop yield loss caused by weeds and weed seed production to an acceptable level. At the acceptable yield loss of 5% and the weed density of 200 B. napus plants m^–2, the model recommends 0.9 g a.i. metsulfuron‐methyl ha^–1 in Avalon and 2.0 g a.i. in Spark.     

Modelling the effect of crop and weed on herbicide efficacy in wheat

Recommended field application rates of herbicides have to give effective weed control in every situation and are, thus, often higher than that required for specific fields. An understanding of the interaction between crop:weed competition and herbicide dose may, in many cases, allow herbicide application rates to be reduced, important both environmentally and economically. We have developed a model of the interaction between crop:weed competition and herbicide dose, using an empirical model of the relationship between crop yield and weed biomass (related to weed density), and an empirical model of the relationship between weed biomass and herbicide dose. The combined model predicts crop yield, given herbicide dose and weed biomass at an interim assessment date. These crop yield loss predictions may be used to quantify the herbicide dose required to restrict yield loss to a given percentage. Parameters of the model were estimated and the model tested, using results from experiments, which used cultivated oats ( Avena sativa ) or oilseed rape ( Brassica napus ) as model weeds in a crop of winter wheat ( Triticum aestivum ).For the crop:weed:herbicide combinations investigated there was little increase in crop yield for herbicide dose rates above 20% of recommended field rates, in broad agreement with the model predictions. There may still be potential for further reduction below this level on economic grounds; the model could be used to estimate the `break-even' herbicide dose.     

Predicting Avena spp. control with clodinafop

Previous analyses of two independent data sets, one generated by industry and the other involving purpose-designed field experiments, showed that the factors relating to Avena spp. control with clodinafop in Australia are fairly consistent. This article details the combination of those, together with additional new industry data, into an overall set that was subject to linear mixed model and covariate analyses for the purpose of developing a predictive model. Cross-validation methods were used to assess the potential for agronomic and environmental variables at the time of spraying clodinafop to predict Avena spp. mortality. The analyses showed that clodinafop dose, available soil moisture, cumulative minimum temperatures, maximum temperature on the day of spraying, spray water volume and the spray water volume by maximum temperature interaction at spraying were useful predictors and these were subsequently incorporated into a model. This model allows growers and agronomists to use knowledge of weather conditions on the day of application to tailor clodinafop dose and water volume accordingly, or to avoid spraying if they are adverse. The model's potential to improve herbicide efficiency and be used as part of a long-term Avena spp. management programme are briefly discussed.     

Crop competitive ability contributes to herbicide performance in sweet corn

Crop variety effects on herbicide performance is not well characterised, particularly for sweet corn, a crop that varies greatly among hybrids in competitive ability with weeds. Field studies were used to determine the effects of crop competitive ability on season‐long herbicide performance in sweet corn. Two sethoxydim‐tolerant sweet corn hybrids were grown in the presence of Panicum miliaceum and plots were treated post‐emergence with a range of sethoxydim doses. Significant differences in height, leaf area index and intercepted light were observed between hybrids near anthesis. Across a range of sub‐lethal herbicide doses, the denser canopy hybrid Rocker suppressed P. miliaceum shoot biomass and fecundity to a greater extent than the hybrid Cahill. Yield of sweet corn improved to the level of the weed‐free control with increasing sethoxydim dose. The indirect effect of herbicide dose on crop yield, mediated through P. miliaceum biomass reduction, was significant for all of the Cahill’s yield traits but not Rocker. These results indicate that a less competitive hybrid requires relatively more weed suppression by the herbicide to not only reduce weed growth and seed production, but also to maintain yield. Sweet corn competitive ability consistently influences season‐long herbicide performance.     

Modelling the correlation between plant phenology and weed emergence for improving weed control

The efficiency of weed control practices could be improved if their timing is linked to weed emergence dynamics. A study was conducted in a pre‐alpine valley in northern Italy to evaluate whether the phenological phases of some perennial plants could serve as reliable indicators of time of weed emergence and thus be an alternative to bioclimatic models for supporting management decisions. Weed emergence was observed from 2003 to 2004 in five sites at different altitudes. The emergence dynamics of the main weeds were modelled with a Gompertz model. The phenology of 10 common shrubs and a tree was monitored by visual assessment. The flowering and fruit‐ripening phases of the most useful shrubs were modelled with a lognormal model. Correlation analysis between the two functions was used to study the correspondence between plant phenology and weed emergence. Flowering and fruit‐ripening phases of the shrubs were well described by the lognormal model. The correlation analysis between the lognormal model and Gompertz model showed correspondences between the phenological phases and emergence dynamics of the main weeds. The proposed method can be used to examine shrub phenology–weed emergence correlations and consequently for supporting weed management, under certain conditions.     

Influence of developmental stage and time of assessment on hot water weed control

Summary The influence of plant developmental stage in hot water weed control was studied on the test weed Sinapis alba in field experiments. The dose was measured as thermal energy in the hot water (kJ m⁻²) and the response as reduction in plant weight. The energy dose for a 90% reduction in plant weight was 340 kJ m⁻² at the two-leaf stage, which is one-third of the energy required for the same reduction at the six-leaf stage. Treatment at an early stage saves energy, increases the driving speed and lowers the costs. Hard surface areas with naturally developed weeds were used to study the required treatment interval and the influence of time of assessment on the reduction in weed cover. The required treatment interval was 25 d on average, which is similar to that of flame weeding. A longer lasting effect requires a higher energy dose. A 50% higher energy dose was needed to obtain a 90% reduction in weed cover that lasted for 15 d instead of 7 d. After 3–4 weeks, hardly any reduction could be recorded because of regrowth of perennial weeds. However, hot water weed control has a potential on urban hard surfaces and railroad embankments, especially where the use of herbicides is restricted.     

Model of crop:weed competition applied to maize:Abutilon theophrasti interactions. I. Model description and evaluation

A complex set of interactions among crops, weeds and their environment determines the impact of weed interference on crop productivity. These interactions can be simulated with dynamic crop:weed competition models, such as ALMANAC. In this study, ALMANAC was modified to simulate maize: Abutilon theophrasti competition. In the modified ALMANAC model, daily increases in leaf area index (LAI), height and rooting depth are attenuated on the basis of accumulated above-ground biomass and by environmental stress. Also, a simple, flexible method is adopted to partition radiation in a mixed canopy. A maize: A. theophrasti competition study conducted near Aurora, NY, in which a range of weed densities (0–16 plants m⁻²) were established in a maize crop, was used to evaluate the model. The modified ALMANAC proved to be a useful tool for segregating the maize response to competition in 1991 (simulated loss of 35% at the highest weed density) from those in 1992–94 (simulated losses not greater than 16%). Based on these findings, the modified ALMANAC model is judged to be capable of distinguishing between environmental conditions that facilitate large yield losses and those that allow maize to outcompete A. theophrasti .     

An on-farm approach to investigate the impact of diversified crop rotations on weed species richness and composition in winter wheat

Weed species diversity may benefit from organic farming due to enhanced temporal diversification of crop species in a rotation and omission of herbicide applications. However, in intensively managed conventional systems, little evidence exists as to what extent diversified crop rotations contribute to higher weed species richness. Using an on-farm approach, the effect of crop rotation (organic, conventional diverse (CD) and conventional simple (CS) crop rotations) and weed control (with vs. without) on weed species richness, cover, community composition and crop biomass, was analysed in 24 winter wheat fields. Weed species with beneficial functions for invertebrates and birds were analysed separately. Weed species richness was higher in the organic crop rotation, but did not differ between CD and CS crop rotations. Weed control treatment reduced species richness in both conventional rotations, but not in the organic one. Redundancy analyses revealed that crop rotation intensity accounted for the largest part of the explained variation in weed species composition. Results from the study indicate that the maintenance of weed species richness and conservation of species with important ecological functions requires not only temporal diversification of crop species in the rotation, but also an adjustment of weed control strategies.     

The relevance of morphological plasticity in the simulation of competition between maize and Datura stramonium

The INTERCOM model was calibrated for Datura stramonium and maize with data from monoculture plots, and used to simulate the competition between the weed and the crop. The model adequately simulated the growth (leaf area index, above-ground biomass, plant height, yield) of both species in monoculture. In competition, the growth of D. stramonium and the competitive effect of D. stramonium on the growth of maize were clearly underestimated. Simulation of competition was improved when the observed morphological adaptations of D. stramonium under competitive light stress, as reflected in dry-matter partitioning, height growth and specific leaf area (SLA) were considered. Model analysis demonstrated that the observed changes in SLA and height growth were more relevant for the increased competitive ability of D. stramonium . The modification of the model in order to simulate the effect of shading by an overtopping species in the SLA of the shaded species improved model simulation of competition.     

Spatial patterns,species richness and cover in weed communities of organic and conventional no‐tillage spring wheat systems

Heterogeneous field conditions are ubiquitous throughout agricultural systems and have given rise to the practice of site‐specific management, in an effort to increase sustainability and/or homogenise growing conditions and thereby increase crop yields. The spatial pattern of weeds in conventional systems is widely accepted to be aggregated, but there have been no scientific studies regarding the spatial pattern of weed distribution in organic systems. Using a combination of aggregation measures and quadrat variance techniques, this study compared the spatial pattern of weed distribution in conventionally managed no‐tillage spring wheat fields to those of organically managed spring wheat fields. Per cent weed cover data (by species) were collected in the summers of 2005 and 2006 from transects located in conventional no‐tillage and organic spring wheat fields. Weed cover was aggregated in both the conventional and the organic systems, but the patterns of aggregation were different for the two systems. Conventional no‐tillage systems showed a patch/gap pattern, while organic systems showed multiple scales of patchiness with few gaps. These results suggest that processes causing aggregation in the two systems may be different and that site‐specific management may be applicable to organic systems as well as conventional spring wheat systems.     

Roles of reactive oxygen species in interactions between plants and pathogens

The production of reactive oxygen species (ROS) by the consumption of molecular oxygen during host–pathogen interactions is termed the oxidative burst. The most important ROS are singlet oxygen (¹O₂), the hydroxyperoxyl radical (HO₂·), the superoxide anion , hydrogen peroxide (H₂O₂), the hydroxyl radical (OH^-) and the closely related reactive nitrogen species, nitric oxide (NO). These ROS are highly reactive, and therefore toxic, and participate in several important processes related to defence and infection. Furthermore, ROS also play important roles in plant biology both as toxic by-products of aerobic metabolism and as key regulators of growth, development and defence pathways. In this review, we will assess the different roles of ROS in host–pathogen interactions with special emphasis on fungal and Oomycete pathogens.