Grapevine productivity modelling in the Portuguese Douro Region

A survey has been conducted on 32 students and 25 former students of 28 nationalities of an international master course training executives of the international Wine sector.

Within-field variability can be managed through Precision Viticulture (PV) protocols aiming at identifying homogeneous zones and addressing site-specific operations including selective harvesting (SH). Several authors demonstrated SH profitability in extensive viticulture while few information is available within the Italian context.

The effects of temperature on cv Piedirosso, indigenous of the Campania region (South of Italy), were tested in order to study its possible influence on grapevine and to discover how to optimize the qualitative expression

Landscapes such as its representation express values, beliefs and intentions of the individuals and the communities that produce them.

In viticulture, the challenges of local climate modelling are multiple: taking into account the local environment, fine temporal and spatial scales, reliable time series of climate data, ease of implementation and reproducibility of the method. At the local scale, recent studies have demonstrated the contribution of spatialization methods for ground-based climate observation data considering topographic factors such as altitude, slope, aspect, and geographic coordinates (Le Roux et al, 2017; De Rességuier et al, 2020). However, these studies have shown questions in terms of the reproducibility and sustainability of this type of climate study. In this context, we evaluated the potential of MODIS thermal satellite images validated with ground-based climate data (Morin et al, 2020). Previous studies have been encouraging, but questions remain to be explored at the regional scale, particularly in the dynamics of the massive use of bioclimatic indices to classify the climate of wine regions. The results at the local scale were encouraging, but this approach was tested in the current study at the regional scale. Several objectives were set: 1) to evaluate the downscaling method for land surface temperature time series, 2) to identify regional thermal structure variations. We used weekly minimum and maximum surface temperature time series acquired by MODIS satellites at a spatial resolution of 1000 m and downscaled at 500 m using topographical variables. Two types of analyses were performed: