Interview: Jim Garrison

Interview: Jim GarrisonEntrevista com Jim Garriso

Evaluation of statistical and process-based models as nitrogen recommendation tools in maize production systems

Optimizing nitrogen (N) management in maize (Zea mays L.) production systems is critical and essential to ensure profitability, productivity, and environmental sustainability. However, it represents a challenge because N is highly mobile within the soil-plant-atmospheric system. Therefore finding the optimum N rate for maize is a difficult task. The overall goal of this research was to evaluate crop model and statistical -based approaches to making N recommendations for maize and quantify prediction accuracy in two major maize production regions: Iowa, USA and Buenos Aires, Argentina. I addressed three questions: 1) how accurately process-based modeling and statistical based approaches can simulate yields and optimal N rates, 2) how does the accuracy change when models are used as a forecasting tools (with limited input data), and 3) which soil, crop, and atmospheric variables are most important to improve understanding of optimum N rate variability from year-to-year and from field-to-field? Data to test crop model predictions included yield response to N from a 16-year field experiment conducted in central Iowa, USA with two crop rotations totaling 31 N-trials. Data to test statistical models included a 5-year yield response to N from central-west Buenos Aires, Argentina with different rotations, soil properties, and landscape positions totaling 51 trials. The statistical-based approach predicted optimal N rates with higher accuracy than process-based models (root mean square error, RMSE of 42 vs 62 kg N ha-1, respectively). Yields that were predicted at the end of the season had a RMSE that ranged from 1 to 1.3 Mg ha-1. The accuracy of yield predictions at planting decreased more for optimal N rates when using process-based models. Optimal N rate at planting was predicted with similar accuracy to that predicted at the end-of-season (RMSE 60 and 47 kg N ha-1 for process- and statistical-based approach, respectively). Lastly, I found that the spring precipitation (April to June) and the precipitation events greater than 20 mm accumulated from planting to silking highly explained the variability in optimal N rates in both central Iowa and in central-west Buenos Aires

Pause distribution and working memory capacity in L2 speech production

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro de Comunicação e Expressão. Programa de Pós-Graduação em Letras/Inglês e Literatura CorrespondenteDeparting from a cognitive account of oral speech production, the present study aimed at (1) identifying the role silent pause distribution has in defining fluency, and (2) disentangling the relationship between working memory capacity (WMC) and second language (L2) fluency. Data was gathered at the Universidade Federal de Santa Catarina, from 12 Brazilians (native speakers of Brazilian Portuguese - BP - and L2 speakers of English) and 9 Americans (native speakers of American English - AE). All participants carried out picture description and narrative tasks, orally and spontaneously, in their first languages (L1s). The Brazilian participants also performed these oral tasks in their L2 (English) and a WMC test - the L2 Speaking Span Test (L2 SST). Participants' fluency was assessed through frequency of pauses at and within clause boundaries and mean length of run (MLR). The a level was set at .05. The statistical analyses employed indicated that while the two first languages under scrutinity (AE and BP) did not differ regarding pause distribution or MLR, the L2 (English) speech of the Brazilians presented more pauses (especially within boundaries) and shorter MLRs than both their own L1 (BP) speech and the L1 (AE) speech of the Americans. Moreover, significant correlations were found between individuals' L2 SST scores and frequency of within boundary pauses and MLR. Concerning fluency, the results support the role MLR has in defining fluency and demonstrate the importance of frequency of pauses within rather than at boundaries in distinguishing less and more fluent speakers. As regards the relation between L2 fluency and L2 WMC, it seems that due to being more controlled than L1, L2 oral speech is at least in part constrained by individuals' limited attentional resources, with larger-capacity speakers being better able to sustain L2 fluency (with fewer pauses within boundaries and longer speech runs) than those speakers with fewer resources

An Elementary Model of Focal Adhesion Detachment and Reattachment During Cell Reorientation Using Ideas from the Kinetics of Wiggly Energies

A simple, transparent, two-dimensional, nonlinear model of cell reorientation is constructed in this paper. The cells are attached to a substrate by “focal adhesions” that transmit the deformation of the substrate to the “stress fibers” in the cell. When the substrate is subjected to a deformation, say an in-plane bi-axial deformation with stretches λ1 and λ2, the stress fibers deform with it and change their length and orientation. In addition, the focal adhesions can detach from the substrate and reattach to it at new nearby locations, and this process of detachment and reattachment can happen many times. In this scenario the (varying) fiber angle Θ in the reference configuration plays the role of an internal variable. In addition to the elastic energy of the stress fibers, the energy associated with the focal adhesions is accounted for by a wiggly energy ϵacos Θ / ϵ, 0 < ϵ≪ 1. Each local minimum of this energy corresponds to a particular configuration of the focal adhesions. The small amplitude ϵa indicates that the energy barrier between two neighboring configurations is relatively small, and the small distance 2 πϵ between the local minima indicates that a focal adhesion does not have to move very far before it reattaches. The evolution of this system is studied using a gradient flow kinetic law, which is homogenized for ϵ→ 0 using results from weak convergence. The results determine (a) a region of the λ1, λ2-plane in which the (referential) fiber orientation remains stuck at the angle Θ and does not evolve, and (b) the evolution of the orientation when the stretches move out of this region as the fibers seek to minimize energy

Development of a nitrogen recommendation tool for corn considering static and dynamic variables

Many soil and weather variables can affect the economical optimum nitrogen (N) rate (EONR) for maize. We classified 54 potential factors as dynamic (change rapidly over time, e.g. soil water) and static (change slowly over time, e.g. soil organic matter) and explored their relative importance on EONR and yield prediction by analyzing a dataset with 51 N trials from Central-West region of Argentina. Across trials, the average EONR was 113 ± 83 kg N ha−1 and the average optimum yield was 12.3 ± 2.2 Mg ha−1, which is roughly 50% higher than the current N rates used and yields obtained by maize producers in that region. Dynamic factors alone explained 50% of the variability in the EONR whereas static factors explained only 20%. Best EONR predictions resulted by combining one static variable (soil depth) together with four dynamic variables (number of days with precipitation\u3e20 mm, residue amount, soil nitrate at planting, and heat stress around silking). The resulting EONR model had a mean absolute error of 39 kg N ha−1 and an adjusted R2 of 0.61. Interestingly, the yield of the previous crop was not an important factor explaining EONR variability. Regression models for yield at optimum and at zero N fertilization rate as well as regression models to be used as forecasting tools at maize planting time were developed and discussed. The proposed regression models are driven by few easy to measure variables filling the gap between simple (minimum to no inputs) and complex EONR prediction tools such as simulation models. In view of increasing data availability, our proposed models can be further improved and deployed across environments. Includes supplemental figures and table. Excel model attached below as additional file

Growth of the neo-aortic valve after the Ross procedure

Rovira Riera, AntoniPla general d'edifici, en un solar amb tres façanes. Fou construït al segle segle XIX, abans de 1842. Tot i que la seva autoria és encara dubtosa, ha estat atribuït a Antoni Rovira Riera

Acesso universal à saúde : um comparativo entre as cinco grandes regiões brasileiras

A legislação brasileira vigente afirma que é assegurado o acesso universal à prevenção, promoção e recuperação a todo cidadão do Brasil, indiscriminadamente. No entanto, as realidades regionais brasileiras evidenciam divergências neste quesito que deveria ser equânime. Dessa forma, esse trabalho tem como objetivo examinar o acesso ao Sistema Único de Saúde nas grandes regiões brasileiras, no período de 2005 ao ano de 2014. Para isso, foi realizada uma pesquisa de levantamento, utilizando o comparativo deste período de 10 anos, quando possível, ou os dados mais recentes disponibilizados. Como resultado, foi constatado divergências no acesso aos cuidados em saúde entre as 05 (cinco) grandes regiões do país: Norte, Nordeste, Sudeste, Centro-Oeste e Sul, em que as duas primeiras, respectivamente, obtiveram os piores indicativos na maioria dos aspectos analisados. Assim, flutuações verificadas por meio dos indicadores utilizados demonstram diversas disparidades regionais, que não são desejáveis do ponto de vista de um sistema unificado em todo o território nacional.The Brazilian legislation in force states that universal access to prevention, promotion and recovery is guaranteed to every citizen of Brazil, indiscriminately. Nevertheless, the Brazilian regional realities show divergences in this question that should be equanimous. Thus, this study aims to examine the access to the Unified Health System in the great Brazilian regions, from 2005 to 2014. To do so, a survey was carried out using the comparative of this period of 10 years, when the latest available data. As a result, there were differences in access to health care among the country's 05 (five) major regions: North, Northeast, Southeast, Center-West and South, where the first two, respectively, obtained the worst indicatives in most analyzed. Thus, fluctuations verified through the indicators used demonstrate several regional disparities, which are not desirable from the point of view of a unified system throughout the national territory