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Part 1: Document Description
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Citation |
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Title: |
Replication Data for "A conditional linear Gaussian network to assess the impact of several agronomic settings on the quality of Tuscan Sangiovese grapes" |
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Identification Number: |
doi:10.13130/RD_UNIMI/BMCJ8L |
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Distributor: |
UNIMI Dataverse |
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Date of Distribution: |
2021-10-04 |
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Version: |
1 |
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Bibliographic Citation: |
Stefanini, Federico M.; Alessandro Magrini; Stefano Di Blasi, 2021, "Replication Data for "A conditional linear Gaussian network to assess the impact of several agronomic settings on the quality of Tuscan Sangiovese grapes"", https://doi.org/10.13130/RD_UNIMI/BMCJ8L, UNIMI Dataverse, V1, UNF:6:zl6bIXUg5THPPd+pQwFMWw== [fileUNF] |
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Citation |
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Title: |
Replication Data for "A conditional linear Gaussian network to assess the impact of several agronomic settings on the quality of Tuscan Sangiovese grapes" |
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Identification Number: |
doi:10.13130/RD_UNIMI/BMCJ8L |
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Authoring Entity: |
Stefanini, Federico M. (University of Milan) |
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Alessandro Magrini (University of Florence) |
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Stefano Di Blasi (Marchesi Antinori, Florence, Italy) |
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Distributor: |
UNIMI Dataverse |
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Access Authority: |
Stefanini, Federico M. |
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Depositor: |
Stefanini, Federico M. |
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Date of Deposit: |
2021-09-29 |
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Holdings Information: |
https://doi.org/10.13130/RD_UNIMI/BMCJ8L |
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Study Scope |
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Keywords: |
Agricultural Sciences, Earth and Environmental Sciences, Other, Canopy management; Conditional independence; Directed acyclic graphs; Late grape harvest; Polyphenolic content; Potential alcohol. |
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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. |
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Notes: |
Notes: - regression models use the baseline treatment parameterization, where the reference treatment is 'T1a'; - parameter names 'sigmaSq' is the variance of random errors; - data are log transformed and centered by year, this implies that they represent proportional change with respect to the yearly average (e.g., +0.015 means an increase by 1.5%, while -0.003 means a decrease by 0.3%). Also, regression coefficients can be interpreted as expected percentage change (w.r.t. yearly average) in the value of the response due to a 1% increase (w.r.t. yearly average) in the value of the parent, at constant values of all the other parents. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Identification Number: |
https://doi.org/10.1515/bile-2017-0002 |
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Bibliographic Citation: |
Magrini, Alessandro, Di Blasi, Stefano and 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, vol.54, no.1, 2017, pp.25-42. |
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File Description--f6593 |
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File: sangioData.tab |
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Notes: |
UNF:6:zl6bIXUg5THPPd+pQwFMWw== |
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List of Variables: |
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Variables |
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Year;Vineyard;Block;Treatment;SproutN;BunchN;GrapeW;WoodW;SPAD06;NDVI06;SPAD08;NDVI08;Acid;Potass;Brix;pH;Anthoc;Polyph |
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f6593 Location: |
Variable Format: character Notes: UNF:6:zl6bIXUg5THPPd+pQwFMWw== |