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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 winter Triticum aestivum L. (winter wheat)–Aegilops cylindrica Host. (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 of A. cylindrica density: fall seedling, spring seedling, and reproductive tiller densities. Two parameters: i, the slope of the yield loss curve as A. cylindrica density approaches zero, and a, the maximum percentage yield loss as A. cylindrica density 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), parameter a varied significantly among years, but parameter i did 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 parameter i. Site-to-site and year-to-year variation in winter T. aestivum–A. cylindrica yield 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 winter T. 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.
Jointed goatgrass is a problem weed in winter wheat production areas of the Great Plains. Winter wheat seeding rates are easily adjusted by the growers and influence competition by some weeds. Field experiments were initiated in Kansas, Nebraska, and Wyoming using winter wheat cultivars selected from leading adapted cultivars from each region to determine the effect of wheat plant density in the fall on jointed goatgrass competitiveness. Three winter wheat seeding rates (50, 67, and 84 kg seeds/ha) were used at Hays, KS, and Sidney, NE, and four seeding rates (33, 50, 67, and 101 kg seeds/ha) were used at Torrington and Archer, WY. An analysis of covariance model was fit with winter wheat fall plant density as the covariate. In 1996, winter wheat grain contamination (dockage) was reduced at the rate of about 6% for every 10 additional wheat plants/m2 above the threshold density of 70 plants/m2 at Archer, WY, and at the rate of about 0.5% for every 10 additional wheat plants/m2 above the threshold density of 110 plants/m2 at Hays, KS. At Hays the reduction occurred only with the semidwarf cultivar ‘Vista’. Increased wheat density reduced jointed goatgrass reproductive tillers in four out of six location–year combinations and biomass in two out of four location–year combinations. Despite the lack of a consistent reduction in jointed goatgrass competitiveness as the result of increased wheat density, increased seeding rates may be a good, low-cost, long-term investment as part of an integrated jointed goatgrass control program in winter wheat.
Secale cereale is a serious weed problem in winter Triticum aestivum–producing regions. The interference relationships and economic thresholds of S. cereale in winter T. aestivum in Colorado, Kansas, Nebraska, and Wyoming were determined over 4 yr. Winter T. aestivum density was held constant at recommended planting densities for each site. Target S. cereale densities were 0, 5, 10, 25, 50, or 100 plants m−2. Secale cereale–winter T. aestivum interference relationships across locations and years were determined using a negative hyperbolic yield loss function. Two parameters—I, which represents the percent yield loss as S. cereale density approaches zero, and A, the maximum percent yield loss as S. cereale density increases—were estimated for each data set using nonlinear regression. Parameter I was more stable among years within locations than among locations within years, whereas maximum percentage yield loss was more stable across locations and years. Environmental conditions appeared to have a role in the stability of these relationships. Parameter estimates for I and A were incorporated into a second model to determine economic thresholds. On average, threshold values were between 4 and 5 S. cereale plants m−2; however, the large variation in these threshold values signifies considerable risk in making economic weed management decisions based upon these values.
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.
Field experiments were conducted at five locations in Kansas, Nebraska, and Wyoming to determine the effects of imazamox rate and application timing on winter annual grass control and crop response in imidazolinone-tolerant winter wheat. Imazamox at 35, 44, or 53 g ai/ha applied early-fall postemergence (EFP), late-fall postemergence, early-spring postemergence (ESP), or late-spring postemergence (LSP) controlled jointed goatgrass at least 95% in all experiments. Feral rye control with imazamox was 95 to 99%, regardless of rate or application timing at Hays, KS, in 2001. Feral rye control at Sidney, NE, and Torrington, WY, was highest (78 to 85%) with imazamox at 44 or 53 g/ha. At Sidney and Torrington, feral rye control was greatest when imazamox was applied EFP. Imazamox stunted wheat <10% in two experiments at Torrington, but EFP or LSP herbicide treatments in the Sidney experiment and ESP or LSP treatments in two Hays experiments caused moderate (12 to 34%) wheat injury. Wheat injury increased as imazamox rate increased. Wheat receiving imazamox LSP yielded less grain than wheat treated at other application timings in each Hays experiment and at Sidney in 2001. No yield differences occurred in one Torrington experiment. However, yields generally decreased as imazamox application timing was delayed in the other Torrington experiment. Generally, imazamox applied in the fall provided the greatest weed control, caused the least wheat injury, and maximized wheat yield.
Proso and foxtail millets are regionally important dryland crops for the semiarid portions of the Central Great Plains. However, few herbicides are registered for use in either crop. The efficacy of carfentrazone was studied in proso millet from 2003 through 2005 at the University of Nebraska High Plains Agricultural Laboratory located near Sidney, NE, and in foxtail millet in 2004 and 2005 at the University of Wyoming Sustainable Agriculture Research and Extension Center near Lingle, WY. Carfentrazone was applied POST at 9.0, 13.5, and 18.0 gai/ha with combinations of 2,4-D amine, prosulfuron, and dicamba. Although leaves of treated plants exhibited localized necrosis, leaves emerging after treatment were healthy. Grain and forage yields were not affected by the application of carfentrazone. Dicamba and 2,4-D amine provided visual control of 30% or less for buffalobur. Adding carfentrazone to one or both of these herbicides improved buffalobur control to 85% or greater. Carfentrazone applied at 18.0 g/ha improved Russian thistle, kochia, and volunteer sunflower control in 2003, when plants were drought-stressed, but did not help with these and other weeds during wetter years. Carfentrazone provides proso millet producers with a way to selectively control buffalobur, a noxious weed in several western states. In foxtail millet, carfentrazone provides POST broadleaf weed control with little risk for serious crop injury. Crop injury has been a concern with 2,4-D, which is currently the only other herbicide registered for use in foxtail millet.
MON 37500 is a sulfonylurea herbicide that selectively controls Bromus spp. in winter wheat. Field studies were conducted near Sidney, NE, and Archer, WY, to determine the sensitivity of corn, foxtail millet, grain sorghum, proso millet, and sunflower to soil residues of MON 37500. MON 37500 was applied to winter wheat at 0, 35, 69, and 139 g/ha in the autumn of 1997. Rotational crops were no-till seeded into the standing residues of the previous year's crop from 1999 through 2001. Grain yields for corn, foxtail millet, and proso millet planted 18 to 20 mo after herbicide application were not affected by soil residues of MON 37500. In contrast, average grain yields of grain sorghum were reduced from 1,760 to 30 kg/ha at Archer and from 4,480 to 390 kg/ha at Sidney as MON 37500 rates increased from 0 to 139 g/ha. Thirty to 32 mo after herbicide application, average grain yields of grain sorghum were reduced from 2,360 to 620 kg/ha at Sidney and average aboveground biomass was reduced from 4,000 to 1,800 kg/ha at Archer as MON 37500 rates increased from 0 to 139 g/ha. Nineteen to 20 mo after herbicide application, average sunflower seed yields were reduced from 1,450 to 20 kg/ha at Archer and from 1,830 to 540 kg/ha at Sidney as MON 37500 rates increased from 0 to 139 g/ha. Visual injury was observed 31 to 32 mo after herbicide application, but drought in 2000 prevented collection of seed yield data. In the High Plains, foxtail millet, proso millet, and corn may be successfully grown 18 to 20 mo after the application of MON 37500 to winter wheat. Successful production of grain sorghum and sunflower may require a minimum recrop interval between treatment and planting of >36 mo.
There is increasing demand for the implementation of effects-based monitoring and surveillance (EBMS) approaches in the Great Lakes Basin to complement traditional chemical monitoring. Herein, we describe an ongoing multiagency effort to develop and implement EBMS tools, particularly with regard to monitoring potentially toxic chemicals and assessing Areas of Concern (AOCs), as envisioned by the Great Lakes Restoration Initiative (GLRI). Our strategy includes use of both targeted and open-ended/discovery techniques, as appropriate to the amount of information available, to guide a priori end point and/or assay selection. Specifically, a combination of in vivo and in vitro tools is employed by using both wild and caged fish (in vivo), and a variety of receptor- and cell-based assays (in vitro). We employ a work flow that progressively emphasizes in vitro tools for long-term or high-intensity monitoring because of their greater practicality (e.g., lower cost, labor) and relying on in vivo assays for initial surveillance and verification. Our strategy takes advantage of the strengths of a diversity of tools, balancing the depth, breadth, and specificity of information they provide against their costs, transferability, and practicality. Finally, a series of illustrative scenarios is examined that align EBMS options with management goals to illustrate the adaptability and scaling of EBMS approaches and how they can be used in management decisions.
To determine the utility of an antibiogram in predicting the susceptibility of Pseudomonas aeruginosa isolates to targeted antimicrobial agents based on the day of hospitalization the specimen was collected.
Single-center retrospective cohort study.
A 750-bed tertiary care medical center.
Patients and Methods.
Isolates from consecutive patients with at least 1 clinical culture positive for P. aeruginosa from January 1, 2000, to June 30, 2007, were included. A study antibiogram was created by determining the overall percentages of P. aeruginosa isolates susceptible to amikacin, ceftazidime, ciprofloxacin, gentamicin, imipenem-cilastin, piperacillin-tazobactam, and tobramycin during the study period. Individual logistic regression models were created to determine the day of infection after which the study antibiogram no longer predicted susceptibility to each antibiotic.
A total of 3,393 isolates were included. The antibiogram became unreliable as a predictor of susceptibility to ceftazidime, imipenem-cilastin, piperacillin-tazobactam, and tobramycin after day 10 and ciprofloxacin after day 15 but longer for gentamicin (day 21) and amikacin (day 28). Time to unreliability of the antibiogram varied for antibiotics based on location of isolation. For example, the time to unreliability of the antibiogram for ceftazidime was 5 days (95% confidence interval [CI], <1–8) in the intensive care unit (ICU) and 12 days (95% CI, 7–21) in non-ICU hospital wards (P = .003).
The ability of the antibiogram to predict susceptibility of P. aeruginosa decreases as duration of hospitalization increases.
This chapter deals with clinical aspects of compulsive buying disorder (CBD). CBD must be distinguished from normal buying behavior, although the distinction is sometimes arbitrary. Frequent shopping does not by itself constitute evidence of the presence of CBD. A distinct clinical picture of the compulsive shopper has emerged. Four distinct phases of CBD have been described, including: anticipation; preparation; shopping; and spending. CBD behaviors occur all year but can be more problematic during the Christmas holidays and others, as well as around the birthdays of family members and friends. The Compulsive Buying Scale (CBS) was developed by Faber and O'Guinn to distinguish normal from pathological buyers. Psychiatric comorbidity is the rule in individuals with CBD. Some researchers suggest that CBD is related to obsessive-compulsive disorder (OCD) and others that it is related to the substance use disorders, the mood disorders, or the disorders of impulse control.