Environmental cues affecting horseweed (Conyza canadensis) growth type and their sensitivity to glyphosate

Horseweed [Conyza canadensis (L.) Cronquist] is a facultative winter annual weed that can emerge from March to November in Michigan. Fall emerging C. canadensis overwinters as a rosette, while spring emerging C. canadensis skips the rosette stage and immediately grows upright upon emergence. In Michigan, primary emergence recently shifted from fall to spring/summer and therefore from a rosette to an upright growth type. Growth chamber experiments were conducted to determine 1) whether both C. canadensis growth types could originate from a single parent and 2) if common environmental cues can influence growth type. Variations in temperature, photoperiod, competition, shading, and soil moisture only resulted in the rosette growth type in four C. canadensis populations originating from seed collected from a single parent of the upright growth type. However, a vernalization period of four weeks following water imbibition, but prior to germination, resulted in the upright growth type. Dose-response experiments were conducted to determine whether glyphosate sensitivity differed between C. canadensis growth types generated from a single parent of the upright growth type. Upright type C. canadensis from known glyphosate-resistant populations ISB-18 and MSU-18 were four and three-fold less sensitive to glyphosate than their rosette siblings, respectively. Interestingly, differences in glyphosate sensitivity was not observed between growth types from the susceptible population. These results suggest that while C. canadensis populations shift from winter to summer annual lifecycles, concurrent increases in glyphosate resistance could occur.

«Time preference and investment expenditure»: comment on Hülsmann

Hülsmann (2008) argues that the neglect of time preference changes on the demand side of the time market renders Rothbard’s (1993) analysis incomplete in that it unduly portrays a rise in the volume of investment as a necessary counterpart to a fall in the pure interest rate. Focusing on the determinants of the demand for present goods, this paper shows that though Hülsmann’s strictures are essentially valid, Rothbard has actually explained why the direct impact of time preference changes should display themselves mostly on the supply side. However, the implications have been neglected both by Hülsmann and Rothbard. We explore these implications, demonstrating that the present demand schedule and the volume of investment should be considered as mostly independent from present time preferences and determined instead by past production decisions. We show how this approach allows for a more «dynamic» understanding of the time market and growth processes. Key words: Time Preference, Investment Expenditure, Time Market, Structure of Production, Interest Rate, Growth Types, Austrian Macroeconomics. JEL Classification: B53, E22, E23, E43, O11, O16, O40. Resumen: Hülsmann (2008) argumenta que pasar por alto los cambios en la preferencia temporal del lado de la demanda en el mercado de tiempo hace incompleto el análisis de Rothbard (1993) al representar indebidamente un incremento en el volumen de inversión como contrapartida necesaria a una caída en la tasa de interés pura. Centrándose en los determinantes de la demanda para bienes presentes, este paper muestra que, siendo las críticas de Hülsmann esencialmente válidas, Rothbard realmente explicó por qué el impacto directo de los cambios en la preferencia temporal debería reflejarse fundamentalmente en el lado de la oferta. Sin embargo, las implicaciones derivadas de ello fueron ignoradas por Hülsmann y Rothbard. Las exploraremos, demostrando que la curva de demanda presente y el volumen de inversión deberían considerarse esencialmente independientes de las preferencias temporales presentes, estando determinadas realmente por decisiones pro-ductivas pasadas. Esta aproximación permite una comprensión más «diná-mica» del mercado de tiempo y los procesos de crecimiento. Palabras clave: Preferencia temporal, gasto de inversión, mercado del tiempo, estructura de la producción, tipo de interés, tipos de crecimiento, macroeconomía austriaca. Clasificación JEL: B53, E22, E23, E43, O11, O16, O40.

Spatial and Temporal Human Settlement Growth Differentiation with Symbolic Machine Learning for Verifying Spatial Policy Targets: Assiut Governorate, Egypt as a Case Study

Since 2005, Egypt has a new land-use development policy to control unplanned human settlement growth and prevent outlying growth. This study assesses the impact of this policy shift on settlement growth in Assiut Governorate, Egypt, between 1999 and 2020. With symbolic machine learning, we extract built-up areas from Landsat images of 2005, 2010, 2015, and 2020 and a Landscape Expansion Index with a new QGIS plugin tool (Growth Classifier) developed to classify settlement growth types. The base year, 1999, was produced by the national remote sensing agency. After extracting the built-up areas from the Landsat images, eight settlement growth types (infill, expansion, edge-ribbon, linear branch, isolated cluster, proximate cluster, isolated scattered, and proximate scattered) were identified for four periods (1999:2005, 2005:2010, 2010:2015, and 2015:2020). The results show that prior to the policy shift of 2005, the growth rate for 1999–2005 was 11% p.a. In all subsequent periods, the growth rate exceeded the target rate of 1% p.a., though by varying amounts. The observed settlement growth rates were 5% (2005:2010), 7.4% (2010:2015), and 5.3% (2015:2020). Although the settlements in Assiut grew primarily through expansion and infill, with the latter growing in importance during the last two later periods, outlying growth is also evident. Using four class metrics (number of patches, patch density, mean patch area, and largest patch index) for the eight growth types, all types showed a fluctuated trend between all periods, except for expansion, which always tends to increase. To date, the policy to control human settlement expansion and outlying growth has been unsuccessful.

Effects of a spatially heterogeneous nutrient distribution on the growth of clonal wetland plants

Background Clonal plants are important in maintaining wetland ecosystems. The main growth types of clonal plants are the guerrilla and phalanx types. However, little is known about the effects of these different clonal growth types on plant plasticity in response to heterogeneous resource distribution. We compared the growth performance of clonal wetland plants exhibiting the two growth forms (guerrilla growth form: Scirpus yagara, Typha orientalis, Phragmites australis and Sparganium stoloniferum; phalanx growth form: Acorus calamus, Schoenoplectus tabernaemontani and Butomus umbellatus) grown in soil substrates that were either homogeneous or heterogeneous but had the same total amount of nutrients. Results We found that the morphological traits (plant height, ramet number, spacer diameter and length) and biomass accumulation of the guerrilla clonal plants (T. orientalis) were significantly enhanced by heterogeneity, but those of the phalanx clonal plants (A. calamus, S. tabernaemontani and B. umbellatus) were not. The results showed that the benefits of environmental heterogeneity to clonal plants may be correlated with the type of clonal structure. Conclusions Guerrilla clonal plants, which have a dispersed, flexible linear structure, are better suited to habitats with heterogeneous resources. Phalanx clonal plants, which form compact structures, are better suited to habitats with homogeneous resources. Thus, wetland clonal species with the guerrilla clonal structure benefit more from soil nutrient heterogeneity.

Posttraumatic Growth Impacts Views of Others’ Trauma: The Roles of Shared Experience and Gender

Posttraumatic growth (PTG) may foster skills that allow one to understand traumatic experiences in others. The primary aim of this study is to determine the relationship between an individual’s PTG reports and their ability to relate to two growth types, PTG and illusory growth. The purpose of this study is to elucidate perceptions of illusory growth and PTG so clinicians may more readily identify catalysts for growth and customize treatments. Participants were read two vignettes that detailed the experience of PTG and illusory growth as a result of a specific traumatic event (i.e., car accident). They completed a questionnaire regarding their perceptions of the vignettes. Results revealed that participants with higher PTG were more likely to relate to the PTG vignette than the illusory growth vignette, and when participants had experienced the same event in the vignette (i.e., car accident), this relationship was stronger. Last, participants mostly perceived the PTG vignette as female, and the illusory growth vignette as male. Our results suggest that interventions following traumatic experiences could be best facilitated/supplemented by reflective interactions with others who have experienced similar events. Considering one’s gender may be important in order to ensure that gender stereotypes regarding emotional expression are mitigated.

Virus Graph and COVID-19 Pandemic: A Graph Theory Approach

Graph theory plays significant role in every field of science as well as technology. Every situation can be articulated in terms of suitable graphs by using various approaches of graph theory. Considering the recent pandemic in the world and the precautions taken for prevention of the COVID 19, it is the most appropriate way to exercise the graph models with theoretical as well as practical aspects to control this epidemic. This paper defines the variable set, variable graphs and their types with respect to variable vertex sets and variable edge sets. Depending upon the nature of the pandemic, there are four types of Virus graphs. Virus graph I and III are not so perilous for all living beings. Although, Virus graphs II and IV are extremely hazardous for the harmony of the world. In view of different aspects for expand of pandemic, growth types of virus graphs are divided in 1-1, 1-P and 1-all growth types. The COVID19 initially was in Virus graph-I type, but presently it is in Virus graph-II types. We present the table involving the number of infected people after n days with respect to different values of P and growth rates with I0 = 100. Moreover, the country wise starting dates of stages of the virus graph-I and II are specified. The concept of cut sets is applicable for prevention of COVID19 and the whole world is using the same analogy.