An estimation of the critical period of weed control is helpful in formulating appropriate weed-control strategies. A regression approach is presented to estimate the thresholds of critical period of weed control and time of equal interference (or time of onset of competition). In this approach, yields were either a linear or logistic function of the duration of weed-free and weed-infested periods. Confidence intervals of the thresholds of critical period and time of equal interference were determined for the linear model. An approximation to the standard error of critical period and associated confidence interval were given for any general form of the model. The method was applied to estimate the critical period of weed control in rainfed lentil using data from four field experiments conducted in Jordan. The relationship of yield with the duration of weed-free period was described by a linear function, whereas the relationship with the duration of weed-infested period showed a better fit with a logistic function. To maintain 90% of maximum seed yield, the maximum time allowed to let weeds grow after the crop emergence varied over locations from 4.8 to 5.8 wk. The same level could be achieved if the crop is kept free of weeds from its emergence until 12.1 to 14.1 wk; while the time when the same amount of yield would be achieved under both approaches varied from 7.7 to 9.3 wk after crop emergence. For straw yield, the time to get 90% of the maximum yield could vary over location from a maximum of 4.5 to 8.0 wk under weed-infestation and from at least 11.5 to 13.5 wk when weed-free. The time to achieve the same amount of straw under two systems of competition varied from 6.5 to 9.9 wk after crop emergence. One of the four experiments showed a longer critical period than the others for seed and straw yields.

Round-leaved mallow interference in spring wheat and flax was investigated under field conditions in southern Manitoba. Additionally, the growth of mallow alone and in a wheat crop was compared. In six of seven trials, wheat yield losses due to mallow interference were not significant. In a seventh trial, where mallow densities of up to 237 plants m−2 were recorded, wheat yields were reduced up to 15%. In contrast, significant flax yield losses occurred in each of three trials where maximum mallow densities ranged from 21 to 52 plants m−2. From the regression equations it was calculated that 20 mallow plants m−2 caused flax yield losses of 33, 9, and 10% in 1987, 1988, and 1989, respectively. Periodic sampling of mallow plants growing alone and in wheat, and regression analysis of these data using a logistic function as a biological model of plant growth indicated that mallow growth was severely suppressed by wheat By the eighth week after emergence, the lamina area and shoot dry matter of mallow plants growing in wheat was less than 3% of plants growing alone. Similarly, in wheat, mallow produced less than 1% of the seed produced by plants growing alone. This extreme suppression of mallow growth corresponded with an 80 to 90% decrease in photosynthetically active radiation penetrating the wheat canopy for approximately six continuous weeks, beginning 4 wk after crop emergence.

The relative competitive ability of common lambsquarters and giant foxtail in mixed weed–corn communities was characterized in 1998 and 1999 using empirical models that described late-season weed biomass on the basis of weed density, early-season relative leaf area, or early-season relative shoot volume. Competition coefficients estimated from weed density were inconsistent between years because they indicated that giant foxtail was more competitive than common lambsquarters in 1998 but that common lambsquarters was more competitive than giant foxtail in 1999. In contrast, the competition coefficients based on relative leaf area and relative volume were consistent between years. Competition coefficients estimated from relative leaf area indicated that giant foxtail was more competitive than common lambsquarters in each year. Competition coefficients estimated from weed relative volume indicated that the relative competitive ability of each weed species was similar in each year. Weed relative competitive abilities were characterized further by describing the mechanisms of competition related to shoot height and width growth. Giant foxtail was taller than common lambsquarters shortly after emergence each year, but plasticity of common lambsquarters growth resulted in reduced height differential between the weed species over time. Even so, giant foxtail was taller than common lambsquarters at physiological maturity each year. Coefficients that described the ability of each weed species to crowd neighbors indicated that giant foxtail shoot width was affected more by increased common lambsquarters density and proportion than was common lambsquarters shoot width by giant foxtail. The greater ability of common lambsquarters to crowd neighbors relative to giant foxtail was attributed to the greater leaf area density (LAD) of common lambsquarters compared with that of giant foxtail. Although characterization of shoot height, width, LAD, and biomass elucidated in part the mechanisms of competition between these species, models that accounted for differences in early-season relative plant size were consistent between years, indicating that giant foxtail was equally or more competitive than common lambsquarters in corn.

Lobesia botrana is the most significant pest of grape berries in Spain. Further knowledge of its phenology would enable wine growers to decide on an optimal treatment schedule. The aim of this study is, therefore, to predict the flight peaks of L. botrana in seven wine-growing regions of Spain. The main goal is to provide a prediction model based on meteorological data records. A logistic function model, based on temperature and humidity records, together with an exhaustive statistical analysis, were used to compare the wine-growing regions in which the male flight phenology of L. botrana displays similar patterns and to sort them into groups. By doing so, a joint study of the dynamics of the moth is possible in the regions within each group. A comparison of the prediction errors before and after applying the Touzeau model confirmed that the fit of the latter model is not sufficiently accurate for the regions under study. Moth flight predictions with the logistic function model are good, but accuracy may still be improved by evaluating other non-biotic and biotic factors.