Actual and potential distribution of an invasive canola pest, Meligethes viridescens (Coleoptera: Nitidulidae), in Canada

Meligethes viridescens (Fabricius), bronzed or rape blossom beetle, is a widespread and common pest of oilseed rape [Brassica napus L. and Brassica rapa L. (Brassicaceae)] in the western Palaearctic subregion. The establishment of M. viridescens in eastern North America has raised concern that its presence is a potential risk to the Canadian canola industry, especially to the prairie ecozone of western Canada where up to 4 million ha of summer canola (B. napus and B. rapa) are grown annually. Study of museum specimens indicated that M. viridescens was first recorded in Nova Scotia in 1947. Field surveys indicated that, as of 2001, M. viridescens was established as far west as Saint-Hyacinthe, Quebec. A CLIMEXTM model for M. viridescens in Europe was developed and validated with actual distribution records. In Canada the model predicted that once introduced, M. viridescens would readily survive in the canola-growing areas. The actual distribution of M. viridescens in eastern Canada matched the predicted distribution well. The westward dispersal to and establishment of M. viridescens in canola-growing areas of Ontario and western Canada, particularly southern Manitoba, appear to be inevitable. Establishment in these areas presents the risk of substantial production losses to canola producer

Evaluation of remote sensing approaches to monitor crop conditions under specific input levels and cropping diversity

Non-Peer ReviewedThis study was conducted as part of the Alternative Cropping Systems (ACS) study at Scott, Saskatchewan. The 18 year study was initiated in 1995 to evaluate the sustainability of nine arable crop production systems. The nine cropping systems, derived from combinations of three input levels (organic, reduced, and high) and three cropping diversity levels (low, diversified annual grains, and diversified annual perennials), were designed to monitor and assess alternative input use and cropping strategies for arable crop production on the Canadian Prairies. Field data including leaf area index (LAI) and spectral reflectance were collected three times during the growing season of 2003: early growing season (June), mid growing season (July) and late growing season (August). LAI was measured with an LAI-2000 plant canopy analyzer. The spectral measurements were made with a handheld ADS spectroradiometer, which covers wavelengths from 350 nm to 2500 nm with 2151 bands. Results showed that remote sensing can be used to indicate different crop conditions. The spectral and LAI differences among input levels were significant at early to mid growing seasons. Mid July was the best season and the red over near infrared spectral ratio as well as the normalized difference vegetation index based on these two bands were the best vegetation indices to use for crop vigor monitoring

Becoming The Boss: Discretion And Postsuccession Success In Family Firms

Family firms can enjoy substantial longevity. Ironically, however, they are often imperiled by the very process that is essential to this longevity. Using the concept of managerial discretion as a starting point, we use a human agency lens to introduce the construct of successor discretion as a factor that affects the family business succession process. While important in general, successor discretion is positioned as a particularly relevant factor for productively managing organizational renewal in family businesses. This study represents a foundation for future empirical research investigating the role of agency in entrepreneurial action in the family business context, which consequently can contribute to the larger research literature on succession and change

Summer CO2 evasion from streams and rivers in the Kolyma River basin, north-east Siberia

Inland water systems are generally supersaturated in carbon dioxide (CO2) and are increasingly recognized as playing an important role in the global carbon cycle. The Arctic may be particularly important in this respect, given the abundance of inland waters and carbon contained in Arctic soils; however, a lack of trace gas measurements from small streams in the Arctic currently limits this understanding.We investigated the spatial variability of CO2 evasion during the summer low-flow period from streams and rivers in the northern portion of the Kolyma River basin in north-eastern Siberia. To this end, partial pressure of carbon dioxide (pCO2) and gas exchange velocities (k) were measured at a diverse set of streams and rivers to calculate CO2 evasion fluxes. We combined these CO2 evasion estimates with satellite remote sensing and geographic information system techniques to calculate total areal CO2 emissions. Our results show that small streams are substantial sources of atmospheric CO2 owing to high pCO2 and k, despite being a small portion of total inland water surface area. In contrast, large rivers were generally near equilibrium with atmospheric CO2. Extrapolating our findings across the Panteleikha-Ambolikha sub-watersheds demonstrated that small streams play a major role in CO2 evasion, accounting for 86% of the total summer CO2 emissions from inland waters within these two sub-watersheds. Further expansion of these regional CO2 emission estimates across time and space will be critical to accurately quantify and understand the role of Arctic streams and rivers in the global carbon budget

Robustness of a Cellular Automata Model for the HIV Infection

An investigation was conducted to study the robustness of the results obtained from the cellular automata model which describes the spread of the HIV infection within lymphoid tissues [R. M. Zorzenon dos Santos and S. Coutinho, Phys. Rev. Lett. 87, 168102 (2001)]. The analysis focussed on the dynamic behavior of the model when defined in lattices with different symmetries and dimensionalities. The results illustrated that the three-phase dynamics of the planar models suffered minor changes in relation to lattice symmetry variations and, while differences were observed regarding dimensionality changes, qualitative behavior was preserved. A further investigation was conducted into primary infection and sensitiveness of the latency period to variations of the model's stochastic parameters over wide ranging values. The variables characterizing primary infection and the latency period exhibited power-law behavior when the stochastic parameters varied over a few orders of magnitude. The power-law exponents were approximately the same when lattice symmetry varied, but there was a significant variation when dimensionality changed from two to three. The dynamics of the three-dimensional model was also shown to be insensitive to variations of the deterministic parameters related to cell resistance to the infection, and the necessary time lag to mount the specific immune response to HIV variants. The robustness of the model demonstrated in this work reinforce that its basic hypothesis are consistent with the three-stage dynamic of the HIV infection observed in patients.Comment: 14 pages, 6 figures, 21 references, Latex style Elsar

The Effect of Hot Deformation Parameters on Microstructure Evolution of the α-Phase in Ti-6Al-4V

The effect of high-temperature deformation and the influence of hot working parameters on microstructure evolution during isothermal hot forging of Ti-6Al-4V in the alpha phase field were investigated. A series of hot isothermal axis-symmetric compression tests were carried out at temperatures both low and high in the alpha stability field [(1153 K and 1223 K (880 °C and 950 °C), respectively], using three strain rates (0.01, 0.1 and 1.0/s) relevant to industrial press forging. The microstructures and orientation of the alpha laths were determined using optical microscopy and electron backscatter diffraction techniques. The experimental results show that there is a change in lath morphology of the secondary α phase under the influence of the deformation parameters, and that α lath thickness appears to have little influence on flow behavior

Optimal designs for rational function regression

We consider optimal non-sequential designs for a large class of (linear and nonlinear) regression models involving polynomials and rational functions with heteroscedastic noise also given by a polynomial or rational weight function. The proposed method treats D-, E-, A-, and $\Phi_p$-optimal designs in a unified manner, and generates a polynomial whose zeros are the support points of the optimal approximate design, generalizing a number of previously known results of the same flavor. The method is based on a mathematical optimization model that can incorporate various criteria of optimality and can be solved efficiently by well established numerical optimization methods. In contrast to previous optimization-based methods proposed for similar design problems, it also has theoretical guarantee of its algorithmic efficiency; in fact, the running times of all numerical examples considered in the paper are negligible. The stability of the method is demonstrated in an example involving high degree polynomials. After discussing linear models, applications for finding locally optimal designs for nonlinear regression models involving rational functions are presented, then extensions to robust regression designs, and trigonometric regression are shown. As a corollary, an upper bound on the size of the support set of the minimally-supported optimal designs is also found. The method is of considerable practical importance, with the potential for instance to impact design software development. Further study of the optimality conditions of the main optimization model might also yield new theoretical insights.Comment: 25 pages. Previous version updated with more details in the theory and additional example