Abstract
The application of organic wastes to agricultural soils may have adverse effects on soil quality. Different end-points are proposed to be analysed and included in organic waste management programmes. The aim of this laboratory study was to determine how the application of increasing concentrations of broiler chicken litter, dairy sludge and cattle slurry can affect survival, body mass change and metal accumulation in Eisenia fetida (Savigny, 1826). Generally, no mortality occurred for the different organic wastes concentrations at the end of the study. In addition, significant increases of body mass were observed on earthworms exposed to the highest concentrations of the organic wastes. Organic wastes increased concentration of some metals in soil (mainly, Cu and Zn in broiler litter and dairy sludge mixtures) and in E. fetida tissue (mainly, Cu in earthworms from broiler litter containers), although metal contents in soil-waste mixtures were always below legal limits. The negligible toxic effect of organic wastes on earthworms was due to the low heavy metal levels of those organic wastes and due to some nutritional or habitat advantages promoted by them. Results obtained demonstrated that body mass change was the most sensitive end-point and, therefore, should be integrated on a battery of ecotoxicological assays to evaluate the ecotoxicity of this type of organic wastes.

Abstract
A forecast model for estimating the annual variation in regional olive crop yield has been developed during the years 1998 to 2006 for the Alentejo region (south Portugal). This model was estimated hierarchically along three different in-season stages: i) flowering, considering only the regional airborne pollen index; ii) fruit growth with the addition of a plant water-stress index; iii) fruit maturing with the addition of a phytopathological index. Appropriated statistical tests indicated that the regional airborne pollen index accounted for 64% of the inter-annual olive yield variability with an average deviation between observed and predicted production of 16%. The addition of the variable plant water-stress index to the forecasting model allowed an increase in its accuracy of 27% while the phytopathological index allowed an increase of 6%. The final bioclimatic model, with all the three variables tested, explained 97% of the regional olive fruit yield being the average deviation between observed and predicted production of 4% for the internal validation of the model and of 9% for the external validation. The hierarchical structure of this bioclimatic model, along three different development stages, enabled an update along the growing season.

Abstract
Pollen morphology and quality of 20 olive cultivars grown in Portugal were analysed and multivariate statistical analysis was performed in order to group the cultivars with similar characteristics. The morphology was analysed by scanning electron microscopy and the quality was evaluated through pollen viability and in vitro germination. Pollen grains from the studied cultivars were elliptical, subprolate or prolate spheroidal with an average size (22x20 mu m), 3- zonocolpate for the majority of cultivars but 3-zonocolporate for three of the cultivars studied. Pollen viability varied between 76% ('Verdeal de Tras-os- Montes') to 6% ('Madural') and the germination varied between 65% ('Verdeal de Tras-os-Montes') and 2% ('Conserva de Elvas'). Hierarchical cluster analysis based on all characteristics analysed, showed an organization into three groups composed by cultivars with similar pollen morphometry characteristics and percentages of pollen viability and in vitro germination. Our results indicate that the highest values of olive pollen viability and germination are associated with highest average values of pollen exine reticulum thickness and diameter.

Abstract
PhenoSat is an experimental software tool that extracts phenological information from satellite vegetation index time-series. Temporal satellite NDVI data provided by VEGETATION sensor from three different vegetation types (Vineyard, Closed Deciduous Forest and Deciduous Shrubland with Sparse Trees) and for different geographical locations were used to test the ability of the software in extracting vegetation dynamics information. Six noise reduction filters were tested: piecewise-logistic, Savitzky-Golay, cubic smoothing splines, Gaussian models, Fourier series and polynomial curve fitting. The results showed that PhenoSat is an useful tool to extract phenological NDVI metrics, providing similar results to those obtained from field measurements. The best results presented correlations of 0.89 (n=6; p<0.01) and 0.71 (n=6; p<0.06) for the green-up and maximum stages, respectively. In the fitting process, the polynomial and Gaussian algorithms over smoothed the peak related with a double-growth season, the opposite to the other methods that could detect more accurately this peak.

Abstract
The performance of several commercial and experimental software packages (Gotas, StainMaster, ImageTool, StainAnalysis, AgroScan, DropletScan and Spray_imageI and II) that produce indicators of crop spraying quality based on the image processing of water-sensitive papers used as artificial targets were compared against known coverage, droplet size spectra and class size distribution verified through manual counting. A number of artificial targets used to test the software were obtained by controlled spray applications and given droplet density between 14 and 108 drops cm(-2) and a wide range of droplet size spectra. The results showed that artificial targets coupled with an appropriate image system can be an accurate technique to compute spray parameters. The between-methods differences were 6.7% for droplet density, 11.5% for volume median diameter, <3% for coverage (%) and <3% coverage density. For the 16 droplet class size distribution tested the between-methods differences were all <15%. However, most of the image analysis systems were not effective in accurately measuring coverage density when coverage rate is greater than about 17%. The Spray_imageII software estimated the coverage density with a mean absolute error of 2% and the absolute error is below 10%, even with about 43% of coverage rate. This software, when compared to the other programmes tested, provided the best accuracy for coverage and droplet size spectrum as well as for droplet class size distribution.

Abstract
In recent years there has been a growing interest by the sport's experts (teachers and coaches) in developing automatic systems for detecting, tracking and identifying player's movements with the purpose of improving the players' performance and accomplishing a consistent and standard analysis of the game metrics. A challenge like this requires sophisticated techniques from the areas of image processing and artificial intelligence. The objective of our work is to study and develop hardware and software techniques in order to build an automatic visual system for detecting and tracking players in indoor sports games that can aid coaches to analyse and improve the players' perfoiniance. Our methodology is based on colour features and therefore several colour image processing techniques such as background subtraction, blob colour definition (RGB and HSL colour spaces) and colour blob manipulation are employed in order to detect the players. Past information and players' velocity allow the tracking algorithm to define probable areas. Tests conducted with a single IP surveillance camera on the sports hall of the Faculty of Sports from the University of Porto showed detection rates from 72.2% to 93.3%.