Abstract
This paper develops a decision support tool that can help fishery authorities to forecast bivalve landings for the dredge fleet accounting for several contextual conditions. These include weather conditions, phytotoxins episodes, stock-biomass indicators per species and tourism levels. Vessel characteristics and fishing effort are also taken into account for the estimation of landings. The relationship between these factors and monthly quantities landed per vessel is explored using multiple linear regression models and data mining techniques (random forests, support vector machines and neural networks). The models are specified for different regions in the Portugal mainland (Northwest, Southwest and South) using six years of data 2010-2015). Results showed that the impact of the contextual factors varies between regions and also depends on the vessels target species. The data mining techniques, namely the random forests, proved to be a robust decision support tool in this context, outperforming the predictive performance of the most popular technique used in this context, i.e. linear regression.

Abstract
It is widely recognized that skippers can have a significant role in their vessel performance levels. However, in many studies that seek to address differences in performance of vessels, the contribution of the skipper is often not quantified, or the influencing factors are not explained. This study examines the effect of social factors, such as family heritage, education and professional expertise, on skipper skill and the economic performance of the Portuguese artisanal dredge fleet. This is done using a stochastic production frontier model and data on the weekly activity of 54 vessels operating during 2013 and 2014. The results suggest that in this fishery age and education levels of skippers are important determinants of efficiency. Experience as a skipper was no determinant of performance, possibly due to the simplicity of the technical equipment onboard. From a managerial perspective, this indicates that local authorities should consider initiatives to enhance the education levels and professional training of skippers, if they aim to improve the effectiveness of artisanal fleets.

Abstract
This study aimed to assess the technical efficiency (TE) of individual companies and their respective sectors that are traded on the Portuguese stock market. We accomplished this by combining the internal input variables (e.g., market value and return') with exogenous variables (e.g., interest income', depreciation', cost of goods', employees' and net sales') into a Stochastic Frontier Analysis (SFA) model. The TE of the PSI-20 (Portuguese Stock Index) was estimated using factors that affect efficiency variability. The main advantage of using the SFA approach is its potential to discriminate between measurement error and systematic inefficiencies in the estimation process. The results demonstrated that TE is higher for enterprises in the industrial, construction and distribution sectors, whereas the commercial banking sector has the lowest TE scores. The employees' and depreciation' are the variables which most contribute to stock market inefficiency.

Abstract
This paper presents a management model developed to promote the sustainability of artisanal fisheries, considering socioeconomic and environmental dimensions. The management of artisanal fisheries faces many challenges, including the lack of appropriate tools to support policy makers and stakeholders in the decision-making process. The model presented in this study is based on system dynamics and allows the simulation of the behavior of the artisanal dredge fishery on the south coast of Portugal, including four main species and two fleets. Two scenarios were simulated to assess the impact of regulatory measures on the system sustainability: scenario 1 simulated a permanent reduction of fishing effort, returning an improvement of biologic and economic sustainability; scenario 2 simulated the closure of one species for a period of one year to allow its recovery. It was found that although the revenue of fisheries decreases in that year, the system is able to return to the average levels of revenue three years after the closure. The study aimed to guide decision makers in the selection of the most appropriate measures toward the preservation of marine ecosystems and socioeconomic stability of fishing communities. The developed system dynamics model, combining available data with knowledge of fisheries experts, proved to be a useful scientific tool to identify management policies and organizational structures leading to greater success. This technique can be applied to the study of other fishery systems worldwide.

Abstract
The bivalve dredge fleet is by far the most extensively studied fleet among the Portuguese artisanal segment. It is considered one of the most important artisanal fisheries, essentially due to the number of fishermen and vessels involved and to the high volume and value of the catches. The present study aimed to explore the efficiency of the dredge fleets that operated in the west coast of Portugal between 2006 and 2012. The methodology was based on the use of data envelopment analysis to assess vessels' efficiency. The inputs considered included the number of days at sea, a biomass stock indicator, and the characteristics of the vessels (power, length and tonnage). The annual fishing quota per vessel was also included in the model as a contextual factor. In the technical efficiency analysis, the outputs were defined by the weight of captures for three different bivalve species. Using data on the prices of each species in the wholesale market, revenue efficiency was also estimated to complement the technical efficiency analysis. The results allowed to gain insights concerning the performance of both Northwest and Southwest fleets, considering both technical and economic aspects of the fishery. It was also possible to identify the benchmark vessels, whose practices should be followed by the other vessels of the fleet.