The objective of this study was to determine if the presence of two acetolactate synthase (ALS)-inhibiting herbicide residues in different Saskatchewan soils would result in additive, synergistic, or antagonistic interactions. This was determined through field trials where herbicides were applied sequentially over the course of 2 yr. The herbicides examined in these experiments were imazamethabenz, flucarbazone, sulfosulfuron, and florasulam, each in combination with imazamox and imazethapyr. The phytotoxicity and persistence of the herbicides in soil was assessed using an oriental mustard root inhibition bioassay. The determination of herbicide interaction was made through the comparison of the experimentally observed values to theoretically expected values derived from a mathematical equation. On the basis of the bioassay analysis, it was found that the herbicide residue combinations resulting from sequentially applied ALS-inhibiting herbicides in the three soils produced additive injury effects rather than synergistic or antagonistic interactions.

Empirical models of crop–weed competition are integral components of bioeconomic models, which depend on predictions of the impact of weeds on crop yields to make cost-effective weed management recommendations. Selection of the best empirical model for a specific crop–weed system is not straightforward, however. We used information–theoretic criteria to identify the model that best describes barley yield based on data from barley–wild oat competition experiments conducted at three locations in Montana over 2 yr. Each experiment consisted of a complete addition series arranged as a randomized complete block design with three replications. Barley was planted at 0, 0.5, 1, and 2 times the locally recommended seeding rate. Wild oat was planted at target infestation densities of 0, 10, 40, 160, and 400 plants m−2. Twenty-five candidate yield models were used to describe the data from each location and year using maximum likelihood estimation. Based on Akaike's Information Criterion (AIC), a second-order small-sample version of AIC (AICc), and the Bayesian Information Criterion (BIC), most data sets supported yield models with crop density (Dc), weed density (Dw), and the relative time of emergence of the two species (T) as variables, indicating that all variables affected barley yield in most locations. AIC, AICc, and BIC selected identical best models for all but one data set. In contrast, the Information Complexity criterion, ICOMP, generally selected simpler best models with fewer parameters. For data pooled over years and locations, AIC, AICc, and BIC strongly supported a single best model with variables Dc, Dw, T, and a functional form specifying both intraspecific and interspecific competition. ICOMP selected a simpler model with Dc and Dw only, and a functional form specifying interspecific, but no intraspecific, competition. The information–theoretic approach offers a rigorous, objective method for choosing crop yield and yield loss equations for bioeconomic models.