Abstract
This paper shows how bibliometric models can be used to assist peers in selecting candidates for academic openings. Several studies have demonstrated that a relationship exists between results from peer-review evaluations and results obtained with certain bibliometric indicators. However, very little has been done to analyse the predictive power of models based on bibliometric indicators. Indicators with high predictive power will be seen as good instruments to support peer evaluations. The goal of this study is to assess the predictive power of a model based on bibliometric indicators for the results of academic openings at the level of Associado and Catedratico at Portuguese universities. Our results suggest that the model can predict the results of peer-review at this level with a reasonable degree of accuracy. This predictive power is better when only the scientific performance is assessed by peers.

Abstract
The Douro Valley of Portugal is a well-known wine region producing Port wine since the end of the 18th century, with quality table wines becoming increasingly important over the last 20 years. Port wine production is the most important economic sector of the region and Vintage Port is the top quality Port wine type, produced only from the best vintages. The purpose of this research was to examine how the variability of annual weather influences the quality of Vintage Port. A weather and climate data set for the period 1980-2009 and a consensus ranking that combined a collection of vintage chart scores into a ranking were used to characterize both the weather and the vintage quality. In order to more precisely model the weather influences on the quality of the vintages it was necessary to partition the growing season into smaller growth intervals in which several heat and precipitation variables were evaluated. The heat-related variables were defined according to the phenology of grapevines, using a partition of the growing season based on accumulated temperature, rather than on calendar dates. Precipitation variables were calculated using broad periods corresponding to the dormant, vegetative and maturation stages of the grapevines. A logistic regression model was used as a tool to identify the weather variables that help to explain the relationships between yearly weather characteristics and vintage quality. The results show that several weather characteristics are strongly associated with better quality vintages: growing season mean temperatures above the region's average, warm winters, cool July through veraison and cool temperatures during ripening. In summary, although the weather is not solely responsible for determining a vintage quality, it plays an important role on it; therefore, its understanding can provide invaluable management insights to growers and producers.

Abstract
A model based on a set of bibliometric indicators is proposed for the prediction of the ranking of applicants to an academic position as produced by a committee of peers. The results show that a very small number of indicators may lead to a robust prediction of about 75% of the cases. We start with 12 indicators to build a few composite indicators by factor analysis. Following a discrete choice model, we arrive at 3 comparatively good predicative models. We conclude that these models have a surprisingly good predictive power and may help peers in their selection process.

Abstract
The United States, Brazil, and Argentina are responsible for 83% of world’s soybean production. Together, they respond to more than 80% of soybean grains and soybean meal exported and for more than 60% of soybean oil exportation. This paper studies the soybean trade of these three major exporters with the top ten commercial partners of each one in order to examine the main factors that influence this relationship. We follow a network analysis approach to evaluate the level of interdependence between exporters and importers. Our research studies the three main soybean products: grain, meal, and oil. The findings seem to indicate that countries prefer importing soybean grains to process inside their borders due to commodity prices and logistics costs.

Abstract

Abstract
Temperature and water status profiles during the growing season are the most important factors influencing the ripening of wine grapes. To model weather influences on the quality and productivity of the vintages, it is necessary to partition the growing season into smaller growth intervals in which weather variables are evaluated. A significant part of past and ongoing research on the relationships between weather and wine quality uses calendar-defined intervals to partition the growing season. The phenology of grapevines is not determined by calendar dates but by several factors such as accumulated heat. To examine the accuracy of different approaches, this work analyzed the difference in average temperature and accumulated precipitation using growth intervals with boundaries defined by means of estimated historical phenological dates and intervals defined by means of accumulated heat or average calendar dates of the Douro Valley of Portugal. The results show that in situations where there is an absence of historical phenological dates and/or no available data that makes the estimation of those dates possible, it is more accurate to use grapevine heat requirements than calendar dates to define growth interval boundaries. Additionally, we analyzed the ability of the length of growth intervals with boundaries based on grapevine heat requirements to differentiate the best from the worst vintage years with the results showing that vintage quality is strongly related to the phenological events. Finally, we analyzed the variability of growth interval lengths in the Douro Valley during 1980-2009 with the results showing a tendency for earlier grapevine physiology.