Arsenic in berries and its correlation with natural soil content: experience in Trentino (Italy)

Estudio comparativo del comportamiento de ocho variedades de viníferas blancas en el Penedés, injertadas sobre los portainjertos 41-B y 110-R.
Se describen los comportamientos

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Aim: Climate change is altering some aspects of winegrape production with an advancement of phenological stages which may endanger viticultural areas in the event of a late frost. This study aims to evaluate the potential of satellite-based remote sensing to assess the damage and the recovery time after late frost events.

For a long time environment was known as one of the most important factors to characterize the quality of wines but at the same time it appears very difficult to distinguish inside the “terroir” the role of the single factor. These remarks partially explain why methods for viticultural evaluation are often quite different (Amerine et al., 1944; Antoniazzi et al., 1986; Asselin et al., 1987; Astruc et al., 1980; Bonfils, 1977; Boselli, 1991; Colugnati, 1990; Costantinescu, 1967; Costantini et al., 1987; Dutt et al., 1981; Falcetti et al., 1992; Fregoni et al., 1992; Hidalgo, 1980; Intrieri et al., 1988; Laville, 1990; Morlat et al., 1991; Scienza et al., 1990; Shubert et al., 1987; Turri et al., 1991).

Barolo DOCG is an economically important wine producing region in Northwest Italy. It is a small region of approximately 70 km2 gross area. The topography is very complex with steep sloped hills ranging in elevation from below 200 m to 550 m. Barolo DOCG wine is made exclusively from the Nebbiolo grape. Bioclimatic indexes are often used in viticulture to gain a better understanding of broader climate trends which can be compared temporally and geographically. These indexes are also used for identifying potential phenological timing, growing region suitability, and potential risks associated with expected climatic changes. Understanding how topography influences bioclimatic indexes can help with understanding of mesoscale climate behaviour leading to improved decision making and risk management strategies. The average monthly maximum and minimum temperatures, the Cool Night Index, the Huglin Index, and the monthly diurnal range (from July to October) were calculated using data from 45 weather stations within a 40 km radius of the Barolo DOCG growing area between the years 1996 and 2019. Linear and multiple regression models were developed using independent variables (elevation, aspect, slope) extracted from a digital elevation model to identify significant relationships. Bioclimatic indexes were then kriged with external drift using independent variables that showed significant relationships with the bioclimatic index using a 100 m resolution grid. The maximum monthly temperatures and the Huglin Index showed consistent significant negative relationships with elevation in all years. The minimum monthly temperatures showed no relationship with elevation but in some months a small but significant relationship was observed with aspect. Due to the lack of a relationship between minimum monthly temperatures and elevation compared to the significant relationship between maximum monthly temperatures and elevation, monthly diurnal range had a negative relationship with elevation.