A conditional linear Gaussian network to assess the impact of several agronomic settings on the quality of Tuscan Sangiovese grapes

• Autorzy: Alessandro Magrini , Stefano Di Blasi , Federico Mattia Stefanini
• Języki publikacji: EN

Abstrakty

EN

In this paper, a Conditional Linear Gaussian Network (CLGN) model is built for a two-year experiment on Tuscan Sangiovese grapes involving canopy management techniques (number of buds, defoliation and bunch thinning) and harvest time (technological and late harvest). We found that the impact of the considered treatments on the color of wine can be predicted still in the vegetative season of the grapevine; the best treatments to obtain wines with good structure are those with a low number of buds; the best treatments to obtain fresh wines suitable for young consumers are those with technological rather than late harvest, preferably with a high number of buds, and anyway with both defoliation and bunch thinning not performed.

Słowa kluczowe

EN

Canopy management; Conditional independence; Directed acyclic graphs; Late grape harvest; Polyphenolic content; Potential alcohol

• Wydawca: De Gruyter Open
• Czasopismo: Biometrical Letters
• Rocznik: 2017
• Tom: 54
• Numer: 1
• Strony: 25-42

Daty

wydano

2017-06-27

online

2017-06-13

Twórcy

autor

Alessandro Magrini

• ,

autor

Stefano Di Blasi

• ,

autor

Federico Mattia Stefanini

• ,

Identyfikatory

DOI

10.1515/bile-2017-0002