<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></abstract>

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.

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

Biometrical Letters

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

In this paper, a Conditional Linear Gaussian Network (CLGN) model is built for a two-year experiment on Tuscan Sangiovese grapes involving canopy management...

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

Pubblicato online: 13 giu 2017

Pagine: 25 - 42

DOI: https://doi.org/10.1515/bile-2017-0002

Parole chiave
Canopy management, Conditional independence, Directed acyclic graphs, Late grape harvest, Polyphenolic content, Potential alcohol

© by Federico Mattia Stefanini

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Pubblicato online: 13 giu 2017

Pagine: 25 - 42

DOI: https://doi.org/10.1515/bile-2017-0002

Parole chiaveCanopy management, Conditional independence, Directed acyclic graphs, Late grape harvest, Polyphenolic content, Potential alcohol

© by Federico Mattia Stefanini

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
Canopy management, Conditional independence, Directed acyclic graphs, Late grape harvest, Polyphenolic content, Potential alcohol