Rationalization of pesticide treatments against powdery mildew of grape.

The powdery mildew represents one of the diseases which affect the grape, it is diffused in all agricultural regions with variable intensity and epidemic course in operation of many microclimatic factors. The powdery mildew of grape is caused from Uncinala necator (Schw.) Burr. (nowadays named Erysiphe necator Schwein.); it is controlled with systemic therapy and contact chemicaL products. In some vineyards located in Latium (central Italy), different field trials have been carried out purposely to rationalize the treatments against E. necator. We have studied the powdery mildew infections through monitoring a set of environmental parameters, the evaluation of cultivar sensibility, the agricultural production method and the area characteristics. We have analysed the following environmental parameters monitoring every 15 minutes: precipitation, soil temperature, solar radiation, wind direction, wind speed, atmospheric relative humidity, atmospheric temperature, leaf wetness, soil humidity to cm 20 and soil humidity to cm 40. Besides, we have used Artificial Intelligence analysis techniques to try to forecast U. necator infections. Guideline EPPO/OEPP PP 1/4 (4) has been used. The trials were conducted in conventional and organic farms. In 2 conventional farms and in organic farm we have considered 1 untreated control thesis, in order to follow the course of infection, 1 standard farm reference thesis (standard), where the treatments were carried out according to the usual farm procedures and 1 thesis where the treatments were carried out according to examining the environmental data. In another conventional vineyard, we have considered only 1 untreated control thesis and 1 standard farm reference thesis (standard) to study disease trend. The achieved results have underlined the possibility (through the knowledge of data pedoclimatic and cultural) to position the treatments against the powdery mildew so that to reduce their number. The lower number of treatments that could follow as a result of environmental data to analyse could bring a series of evident economic and ecologic advantages for the farms.

A two years study results in the use of artificial neural networks to forecast Plasmopara viticola infection in viticulture.

This paper describes the further results of the study that has been described in session 5 of the 58th International Symposium on Crop Protection (Ghent 2006). Since then our attention has been focused on verifying the previous communication results working on a two years basis data set belonging to a specific farm. The choice of using data from a single farm derives from the considerations that have been explained in the previous study in which it was clear that an efficient forecasting Artificial Neural Network (ANN) model can be created only in restricted (or at least comparable) pedoclimatic areas. On the basis of the matured experience, at the moment we have realized an ANN which, being trained on 2005 year data, elaborating the following year data is capable of correctly predicting the real Plasmopara viticola (Berk. et Curt.) Berl. et De Toni outbreak, never giving false negative signals (no alarm in presence of infection on the field) and, finally, giving few other alarms which are totally comparable with the ones given by the most common statistical instrument used in this field trials. We confirm the advantages of this approach in terms of: (a) Management and optimization improvement of agricultural activities. (b) Reduction of plant protection products use. (c) Quality improvement of the final product for a real lowering of plant protection products use. (d) Reduction of environmental impact. (e) A more efficient management of the climate changes.

Artificial intelligence approach with the use of artificial neural networks for the creation of a forecasting model of Plasmopara viticola infection.

Most of the forecasting models of Plasmopara viticola infections are based upon empiric correlations between meteorological/environmental data and pathogen outbreak. These models generally overestimate the risk of infections and induce to treat the vineyard even if it should be not necessary. In rare cases they underrate the risk of infection leaving the pathogen to breakout. Starting from these considerations we have decided to approach the problem from another point of view utilizing Artificial Intelligence techniques for data elaboration and analysis. Meanwhile the same data have been studied with a more classic approach with statistical tools to verify the impact of a large data collection on the standard data analysis methods. A network of RTUs (Remote Terminal Units) distributed all over the Italian national territory transmits 12 environmental parameters every 15 minutes via radio or via GPRS to a centralized Data Base. Other pedologic data is collected directly from the field and sent via Internet to the centralized data base utilizing Personal Digital Assistants (PDAs) running a specific software. Data is stored after having been preprocessed, to guarantee the quality of the information. The subsequent analysis has been realized mostly with Artificial Neural Networks (ANNs). Collecting and analizing data in this way will probably bring us to the possibility of preventing Plasmospara viticola infection starting from the environmental conditions in this very complex context. The aim of this work is to forecast the infection avoiding the ineffective use of the plant protection products in agriculture. Applying different analysis models we will try to find the best ANN capable of forecasting with an high level of affordability.

Hyperlipidemia, hypertension and atherosclerosis in women. Reality and perspectives.

The prevalence of ischemic cardiopathy is different from country to country, race to race, although there is a constant greater prevalence among males. After a certain age there is a modification of the ratio between males and females; but this ratio, before and after menopause, is varying in time and to a certain extent is due to the intervention of new risk factors or the greater influence of the traditional factors which are not age related. in this review are analyzed, with greater emphasis on the female sex, the major risk factors for the ischemic cardiopathy, their dependence on hormonal status before and after menopause, and the different life styles and behaviours that are probably influenced by ethnologic factors, in the so called advanced societies.