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Feb 1, 2024 - Federico M. Stefanini Dataverse
Stefanini, Federico M., 2023, "Replication Data for "A Bayesian Causal Model to Support Decisions on Treating of a Vineyard"", https://doi.org/10.13130/RD_UNIMI/LGP5MW, UNIMI Dataverse, V2
Data from Monte Simulations to estimate incidence
Aug 9, 2023 - Federico M. Stefanini Dataverse
Stefanini, Federico M., 2023, "Replication Data for "A Bayesian model for control strategy selection against Plasmopara viticola infections"", https://doi.org/10.13130/RD_UNIMI/AYUPDC, UNIMI Dataverse, V1, UNF:6:UaDMc6Awo2TcGK4Et0janQ== [fileUNF]
Plant pathogens pose a persistent threat to grape production, causing significant economic losses if disease management strategies are not carefully planned and implemented. Simulation models are one approach to address this challenge because they provide short-term and field-sca...
Federico M. Stefanini Dataverse(University of Milan)
Apr 20, 2023
A Bayesian Causal Model to Support Decisions on Treating of a Vineyard
Oct 4, 2021 - A conditional linear Gaussian network - Sangiovese grapes
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]
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 th...
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