Crop yield loss–weed density relationships critically influence calculation of economic thresholds and the resulting management recommendations made by a bioeconomic model. To examine site-to-site and year-to-year variation in winterTriticum aestivumL. (winter wheat)–Aegilops cylindricaHost. (jointed goatgrass) interference relationships, the rectangular hyperbolic yield loss function was fit to data sets from multiyear field experiments conducted at Colorado, Idaho, Kansas, Montana, Nebraska, Utah, Washington, and Wyoming. The model was fit to three measures ofA. cylindricadensity: fall seedling, spring seedling, and reproductive tiller densities. Two parameters:i, the slope of the yield loss curve asA. cylindricadensity approaches zero, anda, the maximum percentage yield loss asA. cylindricadensity becomes very large, were estimated for each data set using nonlinear regression. Fit of the model to the data was better using spring seedling densities than fall seedling densities, but it was similar for spring seedling and reproductive tiller densities based on the residual mean square (RMS) values. Yield loss functions were less variable among years within a site than among sites for all measures of weed density. For the one site where year-to-year variation was observed (Archer, WY), parameteravaried significantly among years, but parameteridid not. Yield loss functions differed significantly among sites for 7 of 10 comparisons. Site-to-site statistical differences were generally due to variation in estimates of parameteri.Site-to-site and year-to-year variation in winterT. aestivum–A. cylindricayield loss parameter estimates indicated that management recommendations made by a bioeconomic model cannot be based on a single yield loss function with the same parameter values for the winterT. aestivum-producing region. The predictive ability of a bioeconomic model is likely to be improved when yield loss functions incorporating time of emergence and crop density are built into the model's structure.

Phytotoxicity of glyphosate (N-(phosphonomethyl) glycine), applied at 0, 0.56, 1.12, 1.68, 2.24 and 4.49 kg ai/ha to uniform, naturally growing quackgrass, [Agropyron repens (L.) Beauv.] plants, was studied with the electron microscope. Visible damage (yellowing of the leaves) to the plants was observed at the 2.24 and 4.49 kg ai/ha dosage rates within 72 hr. Similar damage became evident 120 hr after treatment at the 0.56 to 1.68 dosages. Leaf discs (1 mm in diameter) were harvested at 24, 48, 96, and 192 hr and prepared for electron microscopy by standard techniques. Cellular damage could be detected at all dosage rates as early as 24 hr after treatment. The type of damage observed was partial to complete disruption of the chloroplast envelope, and swelling of the rough endoplasmic reticulum (RER) with a subsequent formation of vesicles. With loss of integrity of the envelope, the chloroplast became completely disrupted with increased time. Other organelles within the cell were also destroyed.

We compared photosynthesis and growth of Zea mays L (corn) and four weed species, Setaria viridis (L) Beauv (green foxtail), Echinochloa crus-galli (L) Beauv (barnyardgrass), Abutilon theophrasti Medic (velvetleaf), and Amaranthus retroflexus L (redroot pigweed), following foliar applications with atrazine, mesotrione, or a combination of atrazine and mesotrione in two greenhouse experiments. Plant responses to the three herbicide treatments were compared with responses of untreated plants (control). Photosynthesis on day 14 and dry mass of Z mays was not reduced by any of the herbicide treatments. Photosynthesis and dry mass of E crus-galli, A retroflexus and A theophrasti were significantly reduced by mesotrione and atrazine alone and in combination. Photosynthesis on day 14 and dry mass of large Sviridis plants were not suppressed by either herbicide applied alone. The mesotrione plus atrazine treatment was the most effective treatment for grass weed control because plants did not regain photosynthetic capacity and had significantly lower dry mass. Shoot dry mass of broadleaf weeds was significantly reduced by all three herbicide treatments, except for A retroflexus treated with mesotrione alone.

Glyphosate-resistant crop species have increased in number over the past decade as growers eagerly adopt this simple and effective weed management technology. Glyphosate-resistant wheat cultivars are being developed and may soon be available to growers. The objective of this paper is to discuss the pest management implications of glyphosate-resistant wheat in the western United States, a region stretching from the Great Plains to the Pacific Ocean that produces more than 80% of the nation's wheat crop. The benefits of glyphosate-resistant wheat include: (1) improved weed control, particularly of difficult-to-control weeds, such as winter annual grasses belonging to the Aegilops, Avena, Bromus, Lolium, Poa, Secale, and Setaria genera; (2) an ability to control weeds resistant to currently available wheat herbicides; (3) an extended application window for control of late-emerging weeds; and (4) improved crop safety. Although these benefits are not to be minimized, they need to be considered in the light of the concerns surrounding this new technology in wheat. These concerns are about (1) the lack of an equally effective and affordable herbicide to control glyphosate-resistant volunteer wheat, which may increase wheat diseases such as wheat streak mosaic and Rhizoctonia root rot; (2) the possibility that overreliance on glyphosate will lead to species shifts, with unknown consequences for weed management in wheat; and (3) the use of multiple glyphosate-resistant crops in rotation with glyphosate-resistant wheat, which could rapidly increase glyphosate-resistant weeds, thereby limiting the future utility of glyphosate. If, or when, glyphosate-resistant wheat becomes commercially available, it will require careful management to sustain its usefulness. We have proposed several areas of research that we feel are critical to help develop sound management guidelines for deployment and use of this new weed management technology in wheat. These include (1) developing effective “green bridge” management strategies, i.e., using cultural and chemical approaches to control plants that sustain insect vector populations between wheat crop periods; (2) predicting potential weed species shifts resulting from the use of glyphosate-resistant wheat; and (3) developing management systems that include herbicide-resistant wheat on a rotational basis and rotating the use of glyphosate with other weed management strategies in the fallow period to minimize the potential development of glyphosate-resistant weeds or weed communities.

Three models that empirically predict crop yield from crop and weed density were evaluated for their fit to 30 data sets from multistate, multiyear winter wheat–jointed goatgrass interference experiments. The purpose of the evaluation was to identify which model would generally perform best for the prediction of yield (damage function) in a bioeconomic model and which model would best fulfill criteria for hypothesis testing with limited amounts of data. Seven criteria were used to assess the fit of the models to the data. Overall, Model 2 provided the best statistical description of the data. Model 2 regressions were most often statistically significant, as indicated by approximate F tests, explained the largest proportion of total variation about the mean, gave the smallest residual sum of squares, and returned residuals with random distribution more often than Models 1 and 3. Model 2 performed less well based on the remaining criteria. Model 3 outperformed Models 1 and 2 in the number of parameters estimated that were statistically significant. Model 1 outperformed Models 2 and 3 in the proportion of regressions that converged on a solution and more readily exhibited an asymptotic relationship between winter wheat yield and both winter wheat and jointed goatgrass density under the constraint of limited data. In contrast, Model 2 exhibited a relatively linear relationship between yield and crop density and little effect of increasing jointed goatgrass density on yield, thus overpredicting yield at high weed densities when data were scarce. Model 2 had statistical properties that made it superior for hypothesis testing; however, Model 1's properties were determined superior for the damage function in the winter wheat–jointed goatgrass bioeconomic model because it was less likely to cause bias in yield predictions based on data sets of minimum size.