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Volume (54) Number 1 pp. 25-42
Stefano Di Blasi 2, Federico Mattia Stefanini 1
1Department of Statistics, Computer Science, Applications – University of Florence, , Florence, Italy
2R&D wine and sensory consultant – Marchesi Antinori, , Florence, Italy
2R&D wine and sensory consultant – Marchesi Antinori, , Florence, Italy
A conditional linear Gaussian network to assess the impact of several agronomic settings on the quality of Tuscan Sangiovese grapes
Summary
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.
Keywords: Canopy management, Conditional independence, Directed acyclic graphs, Late grape harvest, Polyphenolic content, Potential alcohol
DOI: 10.1515/bile-2017-0002
For citation:
| MLA | Blasi, Stefano Di, 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. DOI: 10.1515/bile-2017-0002 |
| APA | Blasi, S. D., & Stefanini, F. M. (2017). A conditional linear Gaussian network to assess the impact of several agronomic settings on the quality of Tuscan Sangiovese grapes. Biometrical Letters 54(1), 25-42 DOI: 10.1515/bile-2017-0002 |
| ISO 690 | BLASI, Stefano Di, STEFANINI, Federico Mattia. A conditional linear Gaussian network to assess the impact of several agronomic settings on the quality of Tuscan Sangiovese grapes. Biometrical Letters, 2017, 54.1: 25-42. DOI: 10.1515/bile-2017-0002 |