Circular 82

The development of improved plant cultivars is accomplished through comprehensive plant breeding programs. Such programs: 1) evaluate genetically-diverse germplasm in order to identify superior-performing genotypes; 2) create new genetic recombinations from crosses or other means using selected parental genotypes; 3) evaluate segregating progeny from these families while exerting selection pressure for desirable characteristics; and 4) identify superior-performing genotypes in yield trials conducted in multiple environments. This circular documents the current status of research in cultivar development associated with the Alaska barley breeding program

Circular 85

The development of improved plant cultivars is accomplished through comprehensive plant breeding programs. Such programs: 1) evaluate promising germplasm to identify superior-performing genotypes for use as parents; 2) create new genetic recombinations from these selected parental genotypes using crossing or other means; 3) evaluate segregating progeny from the resulting families while exerting selection pressure for desirable characteristics; and 4) identify superior-performing cultivars in yield trials conducted across multiple environments. This circular documents the current status of research in cultivar development associated with the Alaska barley breeding program

Circular 92

The development of improved plant cultivars is accomplished through comprehensive plant breeding programs. Such programs: 1) evaluate promising germplasm to identify superior-performing genotypes for use as parents; 2) create new genetic recombinations from these selected parental genotypes using crossing or other means; 3) evaluate segregating progeny from the resulting families while exerting selection pressure for desirable characteristics; and 4) identify superior-performing cultivars in yield trials conducted across multiple environments. This circular documents the current status of research in cultivar development associated with the Alaska barley breeding program

Average features of the muon component of EAS or = 10(17) eV

Three 10 sq m liquid scintillators were situated at approximately 0 m, 150 m and 250 m from the center of the Haverah Park array. The detectors were shielded by lead/barytes giving muon detection thresholds of 317 MeV, 431 MeV and 488 MeV respectively. During part of the operational period the 431 MeV threshold was lowered to 313 MeV for comparison purposes. For risetime measurement fast phototubes were used and the 10% to 70% amplitude time interval was parameterized by T sub 70. A muon lateral density distribution of the form rho mu (R theta) = krho(500)0.94 1/R(1 + R/490)-eta has been fitted to the data for 120 m R 600 m and 0.27 (500) 2.55. The shower size parameter (500) is the water Cerenkov response at 500 m from the core of the extensive air showers (EAS) and is relatable to the primary energy. The results show general consistency

Muon fluctuation studies of EAS 10(17) eV

Fluctuation studies need to compare a parameter which is sensitive to longitudinal fluctuations against a parameter which is insensitive. Cascade calculations indicate that the shower size parameter at Haverah Park, rho (500), and the muon density are insensitive while parameters that significantly reflect the longitudinal development of a particular extensive air shower (EAS) include the muon/water Cerenkov response ratio and the muon arrival time dispersion. This paper presents conclusions based on muon fluctuation studies of EAS measured between 1976 and 1981 at Haverah Park

The muon content of EAS as a function of primary energy

The muon content of extensive air showers (EAS) was measured over the wide primary energy range 10 to the 16th power to 10 to the 20th power eV. It is reported that the relative muon content of EAS decreases smoothly over the energy range 10 to the 17th power to 10 to the 19th power eV and concluded that the primary cosmic ray flux has a constant mass composition over this range. It is also reported that an apparent significant change in the power index occurs below 10 to the 17th power eV rho sub c (250 m) sup 0.78. Such a change indicates a significant change in primary mass composition in this range. The earlier conclusions concerning EAS of energy 10 to the 17th power eV are confirmed. Analysis of data in the 10 to the 16th power - 10 to the 17th power eV range revealed a previously overlooked selection bias in the data set. The full analysis of the complete data set in the energy range 10 to the 16th power - 10 to the 17th power ev with the selection bias eliminated is presented

Distribution of hydrogen peroxide and methylhydroperoxide over the Pacific and South Atlantic Oceans

The gas phase hydrogen peroxide and methylhydroperoxide concentrations were measured in the troposphere over the tropical Pacific Ocean as a component of NASA's Global Tropospheric Experiment/Pacific Exploratory Mission-Tropics A field campaign. Flights on two aircraft covered the Pacific from 70°S to 60°N and 110°E to 80°W and South Atlantic from 40°S to 15°N and 45°W to 70°E, and extending from 76 to 13,000 m altitude. H2O2 and CH3OOH have the highest concentrations at a given altitude at the equator and decrease with increasing latitude in both the northern and southern hemispheres. Above 4 km the gradient is substantially reduced for both H2O2 and CH3OOH with latitude, and at altitudes in excess of 8 km there is no latitudinal dependence. H2O2 and CH3OOH exhibit maximum mixing ratios between 1 and 2 km at all latitudes. The mean mixing ratio of H2O2 at the equator was 1600 ± 600 parts per trillion by volume (pptv) decreasing to 500 ± 250 pptv at latitudes greater than 55° north and south between 1 and 2 km altitude. CH3OOH at the equator was 1400 ± 250 pptv, decreasing to 330 ± 200 pptv at high latitudes at altitudes between 1 and 2 km. The concentration of peroxides at high latitudes in the northern hemisphere was generally a factor of 2 higher than at corresponding latitudes in the southern hemisphere. The ratio of H2O2 to CH3OOH was between 1 and 2 from 45°S to 35°N at altitudes below 4 km. Between 5° to 15°N the ratio is less than 1, due to preferential removal of H2O2 in the Intertropical Convergence Zone. Copyright 1999 by the American Geophysical Union

Economic impacts on New Zealand of GM crops : result from partial equilibrium modelling

This paper reports findings from economic modelling of the impacts on New Zealand agricultural producer returns from the commercial use of genetically modified (GM) food crops. Several possibilities are considered in the modelling, including different rates of GM crop adoption, positive and negative consumer responses, increases in productivity, first- and second-generation GM crops, and control of the intellectual property. The results are consistent with theory, other studies and expectations. Thus, it is not surprising that New Zealand producers increase their revenue the most when they concentrate on products that have a premium in the market because consumers prefer them. If consumers prefer non-GM products, New Zealand producers gain by focusing on those crops. If consumers prefer second-generation GM products, producers increase their returns by growing those crops. Secondly, it is also consistent with theory, experience and expectations that an increase in productivity does not necessarily lead to increased returns. This is dependent upon whether NZ has sole access to the technology or whether it is or becomes available overseas. Thus, even where a product has a consumer preference, producer returns can fall when productivity increases. Thirdly, controlling access to preferred products or to productive technology has beneficial effects for New Zealand commodity producers

Resource Selection by Cougars: Influence of Behavioral State and Season

An understanding of how a predator uses the landscape can assist in developing management plans. We modeled resource selection by cougars (Puma concolor) during 2 behavioral states (moving and killing) and 2 seasons (summer and winter) with respect to landscape characteristics using locations from global positioning system (GPS)-collared cougars in the Pryor Mountains, Montana and Wyoming, USA. Furthermore, we examined predation-specific resource selection at 2 scales (fine and coarse). When possible, we backtracked from cache sites to kill sites and used a fine-scale analysis to examine landscape characteristics of confirmed kills. At this fine scale, kill sites had less horizontal visibility, were more likely to be in juniper (Juniperus spp.)-mountain mahogany (Cercocarpus ledifolius), and were less likely to be in grassland vegetation. For the coarse-scale analysis of predation risk, we used the entire dataset of kills by buffering each cache site by 94.9 m, which was the 95% upper cut-off point of the known distances dragged from kill sites to cache sites, thereby creating buffered cache sites that had a high probability of containing the kill site. We modeled seasonal cougar predation site selection by constructing resource selection functions for these buffered cache sites. The top model for summer predation risk consisted of vegetation class, distance to water, and slope. The top model for winter predation risk included vegetation class and elevation. These predation risk models were similar to but simpler than the resource selection models developed from the moving locations. Essentially, the behavioral state (i.e., killing vs. moving) of the cougar had little influence on resource selection, indicating that cougars are generally in hunting mode while moving through their landscape. To potentially reduce cougar predation on mule deer (Odocoileus hemionus) and bighorn sheep (Ovis canadensis) in our study area, managers can intersect the cougar predation-risk resource selection functions with deer and sheep habitat to guide habitat modification efforts aimed at increasing horizontal visibility in risky vegetation classes

Resource Selection by Cougars: Influence of Behavioral State and Season

An understanding of how a predator uses the landscape can assist in developing management plans. We modeled resource selection by cougars (Puma concolor) during 2 behavioral states (moving and killing) and 2 seasons (summer and winter) with respect to landscape characteristics using locations from global positioning system (GPS)-collared cougars in the Pryor Mountains, Montana and Wyoming, USA. Furthermore, we examined predation-specific resource selection at 2 scales (fine and coarse). When possible, we backtracked from cache sites to kill sites and used a fine-scale analysis to examine landscape characteristics of confirmed kills. At this fine scale, kill sites had less horizontal visibility, were more likely to be in juniper (Juniperus spp.)-mountain mahogany (Cercocarpus ledifolius), and were less likely to be in grassland vegetation. For the coarse-scale analysis of predation risk, we used the entire dataset of kills by buffering each cache site by 94.9 m, which was the 95% upper cut-off point of the known distances dragged from kill sites to cache sites, thereby creating buffered cache sites that had a high probability of containing the kill site. We modeled seasonal cougar predation site selection by constructing resource selection functions for these buffered cache sites. The top model for summer predation risk consisted of vegetation class, distance to water, and slope. The top model for winter predation risk included vegetation class and elevation. These predation risk models were similar to but simpler than the resource selection models developed from the moving locations. Essentially, the behavioral state (i.e., killing vs. moving) of the cougar had little influence on resource selection, indicating that cougars are generally in hunting mode while moving through their landscape. To potentially reduce cougar predation on mule deer (Odocoileus hemionus) and bighorn sheep (Ovis canadensis) in our study area, managers can intersect the cougar predation-risk resource selection functions with deer and sheep habitat to guide habitat modification efforts aimed at increasing horizontal visibility in risky vegetation classes