In the context of global warming, the general trend towards earlier flowering dates of many temperate tree species is likely to result in an increased risk of damage from exposure to frost. To test this hypothesis, a phenological model of apple flowering was applied to a temperature series from two locations in an important area for apple production in Europe (Trentino, Italy). Two simulated 50-year climatic projections (A2 and B2 of the Intergovernmental Panel on Climate Change--Special Report on Emission Scenarios) from the HadCM3 general circulation model were statistically downscaled to the two sites. Hourly temperature records over a 40-year period were used as the reference for past climate. In the phenological model, the heat requirement (degree hours) for flowering was parameterized using two approaches; static (constant over time) and dynamic (climate dependent). Parameterisation took into account the trees' adaptation to changing temperatures based on either past instrumental records or the downscaled outputs from the climatic simulations. Flowering dates for the past 40 years and simulated flowering dates for the next 50 years were used in the model. A significant trend towards earlier flowering was clearly detected in the past. This negative trend was also apparent in the simulated data. However, the significance was less apparent when the "dynamic" setting for the degree hours requirement was used in the model. The number of frost episodes and flowering dates, on an annual basis, were graphed to assess the risk of spring frost. Risk analysis confirmed a lower risk of exposure to frost at present than in the past, and probably either constant or a slightly lower risk in future, especially given that physiological processes are expected to acclimate to higher temperatures.

Accurate and nondestructive methods to determine individual leaf areas of plants are a useful tool in physiological and agronomic research. Determining the individual leaf area (LA) of small fruit like raspberry (Rubus idaeus L.), redcurrant (Ribes rubrum L.), blackberry (Rubus fruticosus L.), gooseberry (Ribes grossularia L.), and highbush blueberry (Vaccinium corymbosum L.) involves measurements of leaf parameters such as length (L) and width (W) or some combinations of these parameters. A 2-year investigation was carried out during 2006 (on seven raspberry, seven redcurrant, six blackberry, five gooseberry, and two highbush blueberry cultivars) and 2007 (on one cultivar per species) under open field conditions to test whether a model could be developed to estimate LA of small fruits across cultivars. Regression analysis of LA versus L and W revealed several models that could be used for estimating the area of individual small fruit leaves. A linear model having LW as the independent variable provided the most accurate estimate (highest R 2, smallest mean square error, and the smallest predicted residual error sum of squares) of LA in all small fruit berries. Validation of the model having LW of leaves measured in the 2007 experiment coming from other cultivars of small fruit berries showed that the correlation between calculated and measured small fruit berries LAs was very high. Therefore, these models can estimate accurately and in large quantities the LA of small fruit plants in many experimental comparisons without the use of any expensive instruments.

Six phenological series were available for 'Golden Delicious' apple blooming at six sites in Trentino, an alpine fruit-growing region. Several models were tested to predict flowering dates, all involving a "chilling and forcing" approach. In many cases, application of the models to different climatic conditions results in low accuracy of prediction of flowering date. The aim of this work is to develop a model with more general validity, starting from the six available series, and to test it against five other phenological series outside the original area of model development. A modified version of the "Utah" model was the approach that performed best. In fact, an algorithm using "chill units" for rest completion and a thermal sum for growing-degree-hours (GDH), whose efficiency changes over time depending on the fraction of forcing attained, yielded a very good prediction of flowering. Results were good even if hourly temperatures were reconstructed from daily minimum and maximum values. Errors resulting from prediction of flowering data were relatively small, and root mean square errors were in the range of 1-6 days, being <2 days for the longest phenological series. In the most general form of the model, the summation of GDH required for flowering is not a fixed value, but a function of topoclimatic variables for a particular site: slope, aspect and spring mean temperature. This approach allows extension of application of the model to sites with different climatic features outside the test area.

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Abstract. This work is a pilot study that used neurometric indexes during the listening of selected pieces of Dante's "Divina Commedia" in 20 participants. Half of them had a literary formation (Humanist; university students of literature) while the other half of is attending other university courses (Not Humanist). The study applied the electroencephalographic (EEG) rhythms variations, the heart rate (HR) and galvanic skin response (GSR) during the listening of the excerpts. The neurometric indexes here employed were the Approach Withdrawal (AW), the Cerebral Effort (CE) and the Emotional indexes (EI). Results for the comparisons of the estimated AW, CE and EI related to the perception of the canticas showed as the Humanist group reported higher AW and EI values when compared to the Not Humanist sample (p < 0.03 and p < 0.01, respectively). Results suggest that the perception of the aesthetic experience is significantly modulated by the previous specific knowledge experienced by the participants. Finally, results of this kind of research could find application in the implementation of software and devices based on symbiotic relation with the perspective reader or listener of a literature opera, in order to personalize and maximize the fruition of them.