Repositori URV

Identificador: imarina:9244717

Handle: https://hdl.handle.net/20.500.11797/imarina9244717

Autores:
Sanchez-Ortiz, AntoniMateo-Sanz, Josep M.Nadal, MontserratLampreave, Miriam

Resumen:
Multiple factors, such as the vineyard environment and winemaking practices, are known to affect the development of vines as well as the final composition of grapes. Water stress promotes the synthesis of phenols and is associated with grape quality as long as it does not inhibit production. To identify the key parameters for managing water stress and grape quality, multivariate statistical analysis is essential. Classification and regression trees are methods for constructing prediction models from data, especially when data are complex and when constructing a single global model is difficult and models are challenging to interpret. The models were obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. The partitioning can be represented graphically as a decision tree. This approach permitted the most decisive variables for predicting the most vulnerable vineyards and wine quality parameters associated with water stress. In Priorat AOC, Carignan grapevines had the highest water potential and abscisic acid concentration in the early growth plant stages and permitted vineyards to be classified by mesoclimate. This information is useful for identifying which measurements could most easily differentiate between early and late-ripening vineyards. LWP and T-s during an early physiological stage (pea size) permitted warm and cold areas to be differentiated.