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.

A greenhouse study was conducted to determine the effectiveness of adjuvants on postemergence large crabgrass control with dithiopyr. Using barriers to isolate foliage or soil, the primary site of dithiopyr uptake was found to be via foliage. Laboratory and greenhouse studies elucidated the factors involved in enhancement of crabgrass control by dithiopyr with nine superior adjuvants. Two adjuvants increased dithiopyr absorption by 3.7 to 4.3% over the herbicide alone. However, none of the adjuvants increased dithiopyr translocation out of the treated leaf. Treatment of a single large crabgrass leaf indicated that enhancement of dithiopyr activity by adjuvants involved an effect on translocation, on spray retention, and on the external movement of the spray solution to the apical meristem, with the relative importance of any 1 factor depending on the particular adjuvant used. Because of the large losses of dithiopyr during absorption/translocation studies, a separate volatilization experiment determined that 70% of the applied dithiopyr volatilized within 24 h at 24 C. Dithiopyr absorption decreased and volatilization increased with each 10 C increase in temperature. Nonrecoverable dithiopyr ranged from 46 to 55% at 5 C to 92 to 95% at 35 C. Volatility losses of this magnitude may mitigate adjuvant enhancement of postemergence grass activity of dithiopyr.

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.

Clovis sites occur throughout the southwestern United States and northwestern Mexico, but are poorly documented in the central Rio Grande rift region. Here, we present data from two relatively unknown Clovis projectile point assemblages from this region: the first is from the Mockingbird Gap Clovis site and the second is from a survey of the surrounding region. Our goals are to reconstruct general features of the paleoecological adaptation of Clovis populations in the region using raw material sourcing and then to compare the point technology in the region to other Clovis assemblages in the Southwest and across the continent. Our results show that both assemblages were manufactured from similar suites of raw materials that come almost exclusively from the central Rio Grande rift region and the adjacent mountains of New Mexico. Additionally, we show that Clovis projectile points in the study region are significantly smaller than the continental average. Our results suggest that Clovis populations in this region operated within a large, well-known, and relatively high-elevation territory encompassing much of northern and western New Mexico.

The Australian Square Kilometre Array Pathfinder (ASKAP) will give us an unprecedented opportunity to investigate the transient sky at radio wavelengths. In this paper we present VAST, an ASKAP survey for Variables and Slow Transients. VAST will exploit the wide-field survey capabilities of ASKAP to enable the discovery and investigation of variable and transient phenomena from the local to the cosmological, including flare stars, intermittent pulsars, X-ray binaries, magnetars, extreme scattering events, interstellar scintillation, radio supernovae, and orphan afterglows of gamma-ray bursts. In addition, it will allow us to probe unexplored regions of parameter space where new classes of transient sources may be detected. In this paper we review the known radio transient and variable populations and the current results from blind radio surveys. We outline a comprehensive program based on a multi-tiered survey strategy to characterise the radio transient sky through detection and monitoring of transient and variable sources on the ASKAP imaging timescales of 5 s and greater. We also present an analysis of the expected source populations that we will be able to detect with VAST.

We report the results of a successful 7-hour 1.4 GHz Very Long Baseline Interferometry (VLBI) experiment using two new stations, ASKAP-29 located in Western Australia and WARK12M located on the North Island of New Zealand. This was the first geodetic VLBI observing session with the participation of these new stations. We have determined the positions of ASKAP-29 and WARK12M. Random errors on position estimates are 150–200 mm for the vertical component and 40–50 mm for the horizontal component. Systematic errors caused by the unmodeled ionosphere path delay may reach 1.3 m for the vertical component.