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

Alessandro Magrini, Stefano Di Blasi, and Federico Mattia Stefanini. "A conditional linear Gaussian network to assess the impact of several agronomic settings on the quality of Tuscan Sangiovese grapes." Biometrical Letters 54.1 (2017): 25-42. <http://eudml.org/doc/288489>.

@article{AlessandroMagrini2017,
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.},
author = {Alessandro Magrini, Stefano Di Blasi, Federico Mattia Stefanini},
journal = {Biometrical Letters},
keywords = {Canopy management; Conditional independence; Directed acyclic graphs; Late grape harvest; Polyphenolic content; Potential alcohol},
language = {eng},
number = {1},
pages = {25-42},
title = {A conditional linear Gaussian network to assess the impact of several agronomic settings on the quality of Tuscan Sangiovese grapes},
url = {http://eudml.org/doc/288489},
volume = {54},
year = {2017},
}

TY - JOUR
AU - Alessandro Magrini
AU - Stefano Di Blasi
AU - Federico Mattia Stefanini
TI - A conditional linear Gaussian network to assess the impact of several agronomic settings on the quality of Tuscan Sangiovese grapes
JO - Biometrical Letters
PY - 2017
VL - 54
IS - 1
SP - 25
EP - 42
AB - 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.
LA - eng
KW - Canopy management; Conditional independence; Directed acyclic graphs; Late grape harvest; Polyphenolic content; Potential alcohol
UR - http://eudml.org/doc/288489
ER -