Abstract
This paper investigates the cyclical behaviour of the wine production in Douro region during the period 1932-2008. In general, wine production is characterised by large fluctuations which are composed of short-term and/or long-term cycles. The aim of this paper is twofold: firstly, we decompose the wine production's variance in order to find the dominating production cycles, i.e we try to explain whether wine production follows more long-term or short-term cycles. In the next step, we try to explain those cycles using a dependent variable, namely the medium spring temperature (Tm_Sp) for the period 1967-2008. We estimated a Time-Varying Autoregressive Model, which could explain 75% of the production that is characterised by 4.8- and 2.5-year cycles. We use the Short Time Fourier Transform to decompose the link between wine production and temperature. When the temperature was incorporated, the R (2) increased and the Akaike criterion value was lower. Hence, Tm_Sp causes a large amount of these cycles and the wine production variation reflects this relationship. In addition to an upward trend, there is a clearly identifiable cycle around the long-term trend in production. We also show how much of the production cycle and what cycle in particular is explained by the Tm_Sp. There is a stable but not constant link between production and the Tm_Sp. In particular, the temperature is responsible for 5.2- and 2.4-year cycles which has been happening since the 1980s. The Tm_Sp can also be used as an indicator for the 4.8- and 2.5-year cycles of production. The developed model suggests that stationarity is a questionable assumption, and this means that historical distributions of wine production are going to need dynamic updating.

Abstract
A forecast model for estimating the annual variation in regional wine production has been developed over the past decade for the main wine regions of northern Portugal. Annual wine production has been forecasted based on a hierarchical analysis, including a determination of potential production by measuring airborne pollen concentrations at flowering with Cour traps followed by an evaluation of the possible impact of postflowering conditions on limited crop production. For each region, airborne pollen concentrations, in conjunction with wine yield, account for up to 90 to 95% of the interannual variations in wine production. Ninety-seven to 99% of the annual variability in regional wine production can be explained by agropalynoclimatological forecast models that take into account a number of agronomic and weather conditions after flowering. The reliability and early-indication ability of these forecast models justify their use by institutions and economists in decision making, adoption of technical improvements, and fraud detection.

Abstract
In a general screening of some mining areas in SE Spain for wild and autochthonous plant species with the potential to accumulate zinc and lead, of which some classified as accumulators were able to accumulate large concentrations of metal ( > 1000 mg kg(-1)) dry matter. Several Mediterranean taxa showed moderately enhanced heavy-metal accumulation and tolerance, e.g. Piptathertan miliacetall (L.) Cosson, or Smilo grass. This could be considered a Pb hyperaccumulator, being most efficient at removing and accumulating Ph from contaminated soil, and also efficient in Zn extraction. When soils were polluted with both metals, accumulation of Ph was greater. Results indicated that concentration of metal in the plant was directly correlated with that of the soil, although the transfer factors for Ph and Zn were not high. Most plants remained healthy for 3 weeks after introduction of pollutants.

Abstract
The application of organic wastes to agricultural soils is not risk-free and can affect soil invertebrates. Ecotoxicological tests based on the behavioral avoidance of earthworms and springtails were performed to evaluate effects of different fertilization strategies on soil quality and habitat function for soil organisms. These tests were performed in soils treated with: i) slurry and chemical fertilizers, according to the conventional fertilization management of the region, ii) conventional fertilization + sludge and iii) unfertilized reference soil. Both fertilization strategies contributed to soil acidity mitigation and caused no increase in soil heavy metal content. Avoidance test results showed no negative effects of these strategies on soil organisms, compared with the reference soil. However, results of the two fertilization managements differed: Springtails did not avoid soils fertilized with dairy sludge in any of the tested combinations. Earthworms avoided soils treated with sludge as of May 2004 (DS1), when compared with conventional fertilization. Possibly, the behavioral avoidance of earthworms is more sensitive to soil properties (other than texture, organic matter and heavy metal content) than springtails

Abstract
The aim of the present study was to develop a hierarchical bioclimatic model for forecasting olive crop yields in the Alentejo region of south-eastern Portugal. The model was estimated for three different developmental stages: (1) at flowering, using only the regional pollen index (RPI); (2) at fruit growth using RPI and a plant water requirements index (PWRI) and (3) at fruit maturing using RPI plus a water requirements index plus a phytopathological index (PPI). Olive airborne pollen was sampled from 1999 to 2007, using a Cour trap installed in Reguengos de Monsaraz. The meteorological parameters used in the calculation of the post-flowering indices corresponded to data from a meteorological station located near the airborne sampling point. At the flowering stage, 0.66 of the regional olive yield can be explained by the RPI with an average deviation between observed and predicted production of 0.15 for the forecast model internal validation and of 0.19 for the cross-validation. The addition of the variable PWRI to the forecasting model explained an additional 026 of the variation, while the PPI explained an additional 0.05. The final bioclimatic model, with all the three variables tested, explained 0.97 of the regional olive fruit yield being the average deviation between observed and predicted production of 0.04 for the internal validation of the model and of 0.07 for the external validation. The hierarchical nature of this bioclimatic model, along three different development stages, enabled the prediction to be updated as the growing season progressed.

Abstract