The economic threshold is a concept strongly embedded within the weed management literature. There are some theoretical concerns with applying a static approach such as the economic threshold to weed management decision making. An improvement is to adopt a population management approach where the intertemporal effects of decisions are taken into account. The focus should be on managing weed populations through time rather than minimizing the yield effect of weeds in a single season or year. Rather than viewing weeds as an annual production problem, the weed seed bank can be considered a renewable resource stock, and the management goal is to deplete this resource stock through time. The principles of natural resource economics illustrate that including the intertemporal effects of weed control will, for a given size of a seed bank, result in a greater level of weed control and a higher economic benefit than if control decisions were based solely on the current period effects. A dynamic economic model was developed of an extensive Australian spring wheat (Triticum aestivum) cropping system to test these principles using wild oat (Avena fatua and A. ludoviciana) as an example. The model was solved for a 20-yr time horizon for a population management approach and the traditional static economic threshold. The economic benefits from a population management approach were significantly greater than those generated by the economic threshold, and the final seed bank was considerably lower. This result suggests that a paradigm shift from thresholds to longer term population management is warranted.

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The influence of wheat (Triticum aestivum L.) planting arrangement and density on the competitive effect of the weed, annual ryegrass (Lolium rigidum Gaudin), was examined in field experiments over three climatically contrasting years on the central western slopes of New South Wales. Results for three experiments conformed to a common trend. Geometrical arrangement of the crop (rectangularities of 1 to 6.4) at any one of a range of crop densities had no significant effect (P > 0.05) on ryegrass competition, expressed as relative wheat grain yield reduction. However, the effect of ryegrass was substantially reduced by increasing wheat sowing density from 40 or 75 to 200 plants m-2. In analysing models of weed competition a reciprocal yield model (I/ Y = 0.0092 + 0.0037X, r2= 0.89) predicted yield reduction (Y, as per cent of weed-free controls), especially when used with the ratio of weed density to crop density (X), with residual sums of squares lower than for other models.

Summary The effectiveness of crop competition for better weed control and reducing herbicide rates was determined forAvena ludoviciana and Phalaris paradoxa. Four experiments, previously broadcast with seeds of the two weeds in separate plots, were sown with three wheat densities, and emerged weeds were treated with four herbicide doses (0–100% of recommended rate). The measured crop and weed traits were first analysed across experiments for treatment effects. Grain yield and weed seed production data were then analysed using cubic smoothing splines to model the response surfaces. Although herbicide rate for both weeds and crop density for P. paradoxa had significant linear effects on yield, there was a significant non‐linearity of the response surface. Similarly, herbicide rate and crop density had significant linear effects on weed seed production, and there was significant non‐linearity of the response surface that differed for the weed species. Maximum crop yield and reduction in seed production of P. paradoxa was achieved with approximately 80 wheat plants m−2 and weeds treated with 100% herbicide rate. For A. ludoviciana, this was 130 wheat plants m−2 applied with 75% herbicide rate. Alternatively, these benefits were achieved by increasing crop density to 150 plants m−2 applied with 50% herbicide rate. At high crop density, application of the 100% herbicide rate tended to reduce yield, particularly with the A. ludoviciana herbicide, and this impacted adversely on the suppression of weed seed production. Thus, more competitive wheat crops have the potential for improving weed control and reducing herbicide rates.

A range of plant and environmental variables is known to influence the efficacy of herbicides. This paper explores whether environmental factors influencing efficacy of a herbicide can be quantified by analysing a set of industry data involving 59 experiments conducted throughout Australia in the years 1986–1995 for clodinafop‐propargyl on Avena spp. A spline method was used to analyse the combined data set of observed and interpolated covariates. In addition to dose, it was found that efficacy was significantly influenced by maximum temperature on the day of application, spray water volume, the interaction of maximum temperature and spray volume, the sum of minimum temperatures experienced in the 7 days prior to application, and the soil moisture deficit estimated for day 10 prior to application. The findings are discussed in relation to testing of new products for providing commercial factor‐adjustment information as an additional, marketable outcome of existing product testing procedures. Advantages of the spline model over the commonly used log–logistic model for evaluating dose–response and factor‐adjustment relationships are presented.