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.
@article{bwmeta1.element.doi-10_1515_bile-2017-0002,
author = {Alessandro Magrini and Stefano Di Blasi and Federico Mattia Stefanini},
title = {A conditional linear Gaussian network to assess the impact of several agronomic settings on the quality of Tuscan Sangiovese grapes},
journal = {Biometrical Letters},
volume = {54},
year = {2017},
pages = {25-42},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.doi-10_1515_bile-2017-0002}
}
Alessandro Magrini; Stefano Di Blasi; 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, Tome 54 (2017) pp. 25-42. http://gdmltest.u-ga.fr/item/bwmeta1.element.doi-10_1515_bile-2017-0002/