Can varying the number of teams in a shift schedule constitute a preventive strategy?

OBJECTIVE: The study examines the implications for shiftworkers of applying different numbers of teams in the organization of shiftwork. METHODS: The participating operators came from five different companies applying continuous shift rotation systems. The companies shared the same product organization and a common corporate culture belonging to the same multinational company. Each company had a shift system consisting of four, five or six teams, with the proportion of shifts outside day work decreasing as the number of teams increased. Questionnaire and documentary data were used as data sources. RESULTS: Operators in systems with additional teams had more daywork but also more irregular working hours due to both overtime and schedule changes. Operators using six teams used fewer social compensation strategies. Operators in four teams were most satisfied with their work hours. Satisfaction with the time available for various social activities outside work varied inconsistently between the groups. CONCLUSIONS: In rotating systems the application of more teams reduces the number of shifts outside day work. This apparent improvement for shiftworkers was counteracted by a concomitant irregularity produced by greater organizational requirements for flexibility. The balance of this interaction was found to have a critical impact on employees.

Applicability of a distributed watershed pollution model in a data-poor environment in Santa Catarina State, Brazil

Intensification of agricultural production without a sound management and regulations can lead to severe environmental problems, as in Western Santa Catarina State, Brazil, where intensive swine production has caused large accumulations of manure and consequently water pollution. Natural resource scientists are asked by decision-makers for advice on management and regulatory decisions. Distributed environmental models are useful tools, since they can be used to explore consequences of various management practices. However, in many areas of the world, quantitative data for model calibration and validation are lacking. The data-intensive distributed environmental model AgNPS was applied in a data-poor environment, the upper catchment (2,520 ha) of the Ariranhazinho River, near the city of Seara, in Santa Catarina State. Steps included data preparation, cell size selection, sensitivity analysis, model calibration and application to different management scenarios. The model was calibrated based on a best guess for model parameters and on a pragmatic sensitivity analysis. The parameters were adjusted to match model outputs (runoff volume, peak runoff rate and sediment concentration) closely with the sparse observed data. A modelling grid cell resolution of 150 m adduced appropriate and computer-fit results. The rainfall runoff response of the AgNPS model was calibrated using three separate rainfall ranges (< 25, 25-60, > 60 mm). Predicted sediment concentrations were consistently six to ten times higher than observed, probably due to sediment trapping along vegetated channel banks. Predicted N and P concentrations in stream water ranged from just below to well above regulatory norms. Expert knowledge of the area, in addition to experience reported in the literature, was able to compensate in part for limited calibration data. Several scenarios (actual, recommended and excessive manure applications, and point source pollution from swine operations) could be compared by the model, using a relative ranking rather than quantitative predictions.